From 5ec73089056854f48abb61a5a51bde014f599b8f Mon Sep 17 00:00:00 2001
From: plohray <123500717+plohray@users.noreply.github.com>
Date: Sat, 18 Jul 2026 00:52:53 -0600
Subject: [PATCH] [Feature consistency]: Simple portable features to all
notebooks
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
### Notebook features — all sessions
- **Gradient header** — title, subtitle, and topic tags at the top of every notebook
- **Objectives box** — bullet list of what students will learn
- **Step headers** — teal gradient bars marking each section; green for interactive or bonus steps
- **Teal callout boxes** — one-line plain-English description before every action code cell (run, display, load)
- **Purple callout boxes** — one-line description before every setup code cell (define, import, configure)
### Notebook features — DPU sessions only (201, 301, 401, 402, 403, 503)
- **Presenter tips** — yellow bars with suggested questions and demo ideas for instructors
- **Student tips** — green bars with guided exploration tasks for students
- **Challenge section** — levelled challenges at the end (🏫 Middle School / 🎓 High School)
- **Cleanup box** — reminder to release DPU and FPGA overlay resources when done
---
README.md | 4 +
.../PYNQ101_intro_to_pynq.ipynb | 109 +-
.../PYNQ 201 - MNIST/PYNQ 201 - MNIST.ipynb | 230 ++-
.../PYNQ 301 - Object Detection.ipynb | 1510 +++++++++++++----
.../class_explorer.py | 66 +
.../confidence_explorer.py | 99 ++
.../yolo_helpers.py | 188 ++
.../PYNQ 503 - Crowd Estimation.ipynb | 135 +-
.../PYNQ 403 - Face Mask Detection.ipynb | 208 ++-
.../PYNQ 402 - Automotive.ipynb | 148 +-
.../PYNQ 501 - Computer Vision.ipynb | 117 +-
.../PYNQ 502 - Intermediate Python.ipynb | 170 +-
.../PYNQ 503 - Pose Detection.ipynb | 258 ++-
bootcamp_utils/__init__.py | 1 +
bootcamp_utils/color_utils.py | 29 +
bootcamp_utils/dpu_utils.py | 48 +
bootcamp_utils/live_webcam.py | 76 +
17 files changed, 2595 insertions(+), 801 deletions(-)
create mode 100644 bootcamp_sessions/PYNQ 301 - Object Detection/class_explorer.py
create mode 100644 bootcamp_sessions/PYNQ 301 - Object Detection/confidence_explorer.py
create mode 100644 bootcamp_sessions/PYNQ 301 - Object Detection/yolo_helpers.py
create mode 100644 bootcamp_utils/__init__.py
create mode 100644 bootcamp_utils/color_utils.py
create mode 100644 bootcamp_utils/dpu_utils.py
create mode 100644 bootcamp_utils/live_webcam.py
diff --git a/README.md b/README.md
index dcdd746..3748595 100644
--- a/README.md
+++ b/README.md
@@ -21,3 +21,7 @@ models:
get_latest_training_material.ipynb
Contains a sumple bas git pull command to update the repository and pull all of the new materials onto the board.
+bootcamp_utils:
+
+ Shared Python helper modules that are imported by the DPU-based session notebooks. Place this folder at the root of the repo so notebooks can import.
+
diff --git a/bootcamp_sessions/PYNQ 101 - Intro to PYNQ/PYNQ101_intro_to_pynq.ipynb b/bootcamp_sessions/PYNQ 101 - Intro to PYNQ/PYNQ101_intro_to_pynq.ipynb
index 3bcf5d7..a102dc3 100644
--- a/bootcamp_sessions/PYNQ 101 - Intro to PYNQ/PYNQ101_intro_to_pynq.ipynb
+++ b/bootcamp_sessions/PYNQ 101 - Intro to PYNQ/PYNQ101_intro_to_pynq.ipynb
@@ -3,11 +3,7 @@
{
"cell_type": "markdown",
"metadata": {},
- "source": [
- "# Getting started with JupyterLab\n",
- "\n",
- "This notebook will introduce you to the JupyterLab interface. The goal of this session is to get you comfortable with talking to your new SOM board. You can get started by just reading the text here — but there are plenty of friendly helpers ready to help out too!"
- ]
+ "source": "\n
🐍 PYNQ 101: Intro to Python & JupyterLab
\n
Get started with Python programming and the JupyterLab environment
\n
🐍 Python Basics📓 JupyterLab📊 Matplotlib🎮 Interactive
\n
\n
📋 In this notebook you will:\n
- Navigate the JupyterLab interface and run your first Python code
- Work with variables, lists, loops, and if statements
- Import and use Python libraries
- Create matplotlib plots and display media in a notebook
\n
\n
📓 Part 1 — JupyterLab
\n
Get familiar with the notebook interface
\n
\n
✏️ Part 2 — Markdown
\n
Add formatted text and notes to your notebook
\n
\n
🐍 Part 3 — Python Basics
\n
Variables, lists, loops, and if statements
\n
\nRun your first Python print statement to say hello.\n
"
},
{
"cell_type": "code",
@@ -117,6 +102,11 @@
"We can write any Python code at all here. Let's try using a variable to store some data."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nCreate a counter variable and print its starting value.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -134,6 +124,11 @@
"Now we can use this `counter` value in future cells. Let's increase the counter value and then print it."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nIncrease the counter by one and print the new value.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -155,6 +150,11 @@
"When working with more complicated data than single numbers, we can start to use Python structures like [lists](https://www.programiz.com/python-programming/list) and [dictionaries](https://www.programiz.com/python-programming/dictionary). Let's make a list of adjectives and a list of nouns."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nCreate two lists of adjectives and nouns to use in a sentence.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -172,6 +172,11 @@
"Now we can select words from each list by position. Be careful here — Python counts positions starting from 0! The first element is at position 0, the second is at position 1, etc."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nPrint specific words from the list using their index positions.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -190,6 +195,11 @@
"Or we can even select multiple words:"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nPrint a slice of the adjectives list to get multiple items at once.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -213,6 +223,11 @@
"\n"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nUse an if/else statement to check whether a condition is true or false.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -236,6 +251,11 @@
"We can use a `for` loop to iterate over all of the elements in a list. In simpler terms, our temporary variable `number` takes on each value in the list, executing the statements within the loop once as each value. "
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nUse a for loop to repeat an action for each number in a list.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -258,6 +278,11 @@
"Let's try it out."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nUse range() to loop through a sequence of numbers automatically.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -272,11 +297,12 @@
{
"cell_type": "markdown",
"metadata": {},
- "source": [
- "### Using libraries\n",
- "\n",
- "Just for fun, let's combine a random pair of adjectives and nouns. For this, we need to import a Python library that can generate [random](https://docs.python.org/3/library/random.html) numbers. There are lots of existing Python modules like this available online — remember that you can import and use these for your own projects!"
- ]
+ "source": "\n
📦 Part 4 — Using Libraries
\n
Import and use pre-built Python tools
\n
\nCreate two lists of adjectives and nouns to use in a sentence.\n
"
},
{
"cell_type": "code",
@@ -299,6 +325,11 @@
"For any new library, we'll need to find out how to use it! We can look at the documentation from the notebook too. If you want to find out more about anything, just type `?` after it. For example, we used something called `random.randint` in the cell above. Let's find out what this does exactly."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nLook up the documentation for the random.randint function.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -320,13 +351,12 @@
{
"cell_type": "markdown",
"metadata": {},
- "source": [
- "### Displaying media\n",
- "\n",
- "So far we've only made Python output text. This is OK, but we can output much more interesting things in a notebook!\n",
- "\n",
- "For example, we can output YouTube videos!"
- ]
+ "source": "\n
🎬 Part 5 — Displaying Media
\n
Show videos, images, and graphs in a notebook
\n
\nDisplay an embedded YouTube video inside the notebook.\n
"
},
{
"cell_type": "code",
@@ -345,6 +375,11 @@
"Finally, we can show off some fancy plotting features using [`matplotlib`](https://matplotlib.org/gallery/index.html). This is quite advanced, so don't worry if the code is a little bit cryptic for now."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nUse numpy and matplotlib to plot a sine wave graph.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -439,4 +474,4 @@
},
"nbformat": 4,
"nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/bootcamp_sessions/PYNQ 201 - MNIST/PYNQ 201 - MNIST.ipynb b/bootcamp_sessions/PYNQ 201 - MNIST/PYNQ 201 - MNIST.ipynb
index 442c683..a6f7aaa 100644
--- a/bootcamp_sessions/PYNQ 201 - MNIST/PYNQ 201 - MNIST.ipynb
+++ b/bootcamp_sessions/PYNQ 201 - MNIST/PYNQ 201 - MNIST.ipynb
@@ -9,27 +9,7 @@
"cell_type": "markdown",
"id": "e2a7d8ec",
"metadata": {},
- "source": [
- "# PYNQ 201 - MNIST\n",
- "----\n",
- "This notebook will introduce you to the MNIST database and its accompanying MNIST Classifier model. The goal of this notebook is to: \n",
- "* understand what the MNIST database is,\n",
- "* learn how to use the MNIST Classifier Model, \n",
- "* program the grove RGB LED stick, and\n",
- "* explore how we can use the MNIST database and model in different ways! \n",
- "\n",
- "### What is the MNIST Database❓ ###\n",
- "MNIST stands for the \"Modified National Institute of Standards and Technology\" database. It can be said that this database is the \"Hello World!\" implementation to machine learning, since it is a great starting point for people interested in learning about image classification and pattern recognition. \n",
- "\n",
- "The MNIST Database consists of 70,000 small, square 28x28 pixel grayscale images of handwritten single digits between 0 and 9. That's a lot! These images all have labels with the respective digits they represent. \n",
- "\n",
- "But why is this MNIST Database so important? While this famous dataset contains handwritten digits, it has begun to inspire similar datasets that contain images and data in different areas such as fashion, medical images, sign language, and skin cancers. These datasets are used to train models that can help to predict and detect outcomes from inputs. For example, there is another dataset called the Skin Cancer MNIST that has over 10,000 images of 7 different classes of skin cancer. This has enabled engineers and scientists to create machine learning models to classify skin cancer from images taken from their patients. \n",
- "\n",
- "This is only the beginning to what machine learning databases and models can accomplish! \n",
- "\n",
- "\n",
- "Here are 128 out of the 70,000 images in the database!\n"
- ]
+ "source": "\n
🔢 PYNQ 201: MNIST Digit Classifier
\n
Handwritten digit recognition on the KRIA KV260 — powered by a CNN & the DPU
\n
🧠 Deep Learning🔢 Digit Recognition⚡ FPGA Acceleration💡 RGB LED
\n
\n
📋 In this notebook you will:\n
- Understand how a convolutional neural network classifies handwritten digits
- Load and run a pre-trained MNIST model on the FPGA DPU
- Test accuracy on 10,000 real MNIST images
- Use a live webcam feed to classify your own handwriting in real time
- Display results on an RGB LED stick
\n
\n
📖 Useful Term Cheat Sheet
\n
| DPU | Data Processing Unit — specialized FPGA hardware for running AI models fast |
| CNN | Convolutional Neural Network — the type of model used here |
| MNIST | A dataset of 70,000 handwritten digit images (0–9) |
| Overlay | A pre-built design loaded onto the FPGA to give it a specific capability |
| Softmax | Converts raw model scores into probabilities that sum to 1 |
| Tensor | A multi-dimensional array — the data format models work with |
\n
⚙️ Step 1 — Load the Overlay
\n
Download the DPU design onto the FPGA and set up the RGB LED stick
\n
\nImport the DPU overlay library and load the FPGA design onto the board.\n
"
},
{
"cell_type": "code",
@@ -179,6 +140,11 @@
"Next let's write some code to prepare the RGB LED stick!"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nSet up the PMOD adapter and initialize the RGB LED stick.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -206,10 +172,12 @@
"cell_type": "markdown",
"id": "e1d5b71e",
"metadata": {},
- "source": [
- "### Step 2: Load the MNIST Classifier Model ###\n",
- "This line loads a model that has already been compiled for you! Remember this line in case you want to load future models onto your board! 😉"
- ]
+ "source": "\n
📦 Step 2 — Load the AI Model
\n
Load the pre-trained digit classifier onto the DPU
\n
\nLoad the pre-trained MNIST digit classifier onto the DPU.\n
"
},
{
"cell_type": "code",
@@ -225,10 +193,12 @@
"cell_type": "markdown",
"id": "01502b31",
"metadata": {},
- "source": [
- "### 📚 Step 3: Import Libraries 📚 ###\n",
- "Let's import some libraries we will need later in this notebook as well. A library is a collection of pre-written code that provides you with different types of functionality so you don't have to write it yourself from scratch! We can thank other developers who shared these libraries for their time and effort!\n"
- ]
+ "source": "\n
🛠️ Step 3 — Import Libraries
\n
Load the Python tools we need for image processing and display
\n
\nImport all the Python libraries needed for image processing and display.\n
"
},
{
"cell_type": "code",
@@ -262,17 +232,12 @@
"cell_type": "markdown",
"id": "2a869c72",
"metadata": {},
- "source": [
- "### Step 4: Load test data ### \n",
- "Let's load up some of the images from the MNIST dataset! Some models require specific types of images as input, and the MNIST classifier model is one of them! \n",
- "\n",
- "Did you notice how most of the images in the database have a black background with a white digit?\n",
- "This means that the images will require some pre-processing. For the NMIST database, there are two pre-processing steps we need to do to the test images before we can use it: \n",
- "1. Normalize the elements to floating-point or decimal numbers ranging from 0 to 1\n",
- "2. Convert each input to an array of 3 dimensions \n",
- "\n",
- "It's ok if there is something you don't understand! Please make sure to ask questions!"
- ]
+ "source": "\n
🖼️ Step 4 — Load Test Images
\n
Load 10,000 MNIST handwritten digit images to test the model
\n
\nLoad 10,000 MNIST handwritten digit images and normalize them for the model.\n
"
},
{
"cell_type": "code",
@@ -298,6 +263,11 @@
" test_data.shape[2]))"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nDisplay a sample digit from the test dataset with its correct label.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -319,10 +289,12 @@
"cell_type": "markdown",
"id": "e3ea5e11",
"metadata": {},
- "source": [
- "### Step 5: More set up\n",
- "The next few cells of code will be seen in a bit throughout future notebooks and help models store and process data. "
- ]
+ "source": "\n
🔧 Step 5 — Set Up the DPU
\n
Wire up the DPU input/output tensors and buffers
\n
\nSet up the DPU runner and define the input and output tensor shapes.\n
"
},
{
"cell_type": "code",
@@ -348,6 +320,11 @@
"softmax = np.empty(outputSize)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nAllocate memory arrays for storing the model's input and output data.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -365,6 +342,11 @@
"image = input_data[0]"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nDefine the softmax helper function that converts model scores into probabilities.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -384,12 +366,22 @@
"cell_type": "markdown",
"id": "88945c94",
"metadata": {},
- "source": [
- "### Step 6: Run DPU to make predictions ### \n",
- "We can now classify a couple of digit pictures. For each picture, \n",
- "the classification result (shown as 'Prediction') is displayed on top of \n",
- "the picture. "
- ]
+ "source": "\n
🚀 Step 6 — Run the Model
\n
Send images through the DPU and see what digit it predicts
\n
\n🎤 Presenter Tip
\nPoint to a digit on screen and ask: 'What do YOU think this is? Let’s see if the AI agrees!' Run the cell and watch the prediction appear.\n
"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\n👩💻 Students
\nTry changing the image index number (0–9999) in the cell below. Can you find a digit the model gets wrong?\n
"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nRun the digit classifier on a set of test images and display each prediction.\n
"
},
{
"cell_type": "code",
@@ -436,6 +428,11 @@
"We can also evaluate on the entire test dataset."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nRun the model on all 10,000 test images and measure accuracy and speed.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -497,14 +494,7 @@
"cell_type": "markdown",
"id": "bcdd772c",
"metadata": {},
- "source": [
- "## 🤔 How good is your handwriting? 🤔 ##\n",
- "\n",
- "### Step 7: Using the USB Camera 📷\n",
- "Next, we will connect the USB Camera and capture a few frames of your handwritten digits to see how accurately the model can classify your numbers! \n",
- "\n",
- "But let's add a twist! When we print out the prediction, let's also display the prediction on the RGB LED Stick in red! "
- ]
+ "source": "\n
📷 Step 7 — Live Webcam Digit Recognition
\n
Hold up a handwritten digit to the camera — can the AI read your writing?
\n
\nDefine an RGB helper and light up the LED stick in a rainbow gradient.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -582,6 +577,11 @@
""
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nDefine a helper function that sets a number of LEDs to a chosen color.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -598,6 +598,11 @@
" led_stick.show()"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nTest the LED helper by lighting up all 10 LEDs in purple.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -630,6 +635,11 @@
""
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nTurn off all the LEDs on the stick.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -670,6 +680,11 @@
"#### Feeding inputs to our models for predicitions #### "
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nImport the libraries needed to show a live webcam feed in the notebook.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -683,6 +698,11 @@
"import threading"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nCreate a Stop button widget so you can halt the webcam feed.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -701,6 +721,11 @@
")"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nDefine the image pre-processing function that resizes and thresholds webcam frames.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -735,6 +760,21 @@
" return (test_data)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\n🎤 Presenter Tip
\nAsk a student to write a digit on paper and hold it to the webcam. Let the class vote on what digit they wrote before running — then check if the model agrees!\n
"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\n👩💻 Students
\nWrite each digit 0–9 on separate pieces of paper and test them one by one. Which digit does the model find hardest to recognize?\n
"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nDefine the webcam loop function that captures frames, runs the model, and lights the LED.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -779,6 +819,11 @@
" break"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nStart the live webcam feed and run digit recognition on each frame.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -796,10 +841,12 @@
"cell_type": "markdown",
"id": "fe04d865",
"metadata": {},
- "source": [
- "### 🧼 Step 7: Clean up 🧼 ### \n",
- "We can let Python garbage-collect the unused objects and stuff from all this code we just ran. This will make sure we can run other notebooks without any issues! "
- ]
+ "source": "\n🧹 Cleanup
\nRun this cell when you are completely done to release the DPU and FPGA overlay so other notebooks can use them.\n
"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nRelease the DPU and FPGA overlay to free up resources.\n
"
},
{
"cell_type": "code",
@@ -838,6 +885,11 @@
" * Is there a way to differentiate how confident the model is in its predictions using this function?\n",
" * Try changing the color of the RGB LED bar to correspond to how confident the model is in its prediction. "
]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\n
🏁 Challenges — Your Turn!
\n
Try these challenges to push the model further!
\n
\n
\n🏫 Middle School\n
Challenge 1 — Change the LED Color
\n\n
Modify the `setRGBLedStick` call so the LED shows a different color for each digit (0=red, 1=orange, 2=yellow…).
\n
\n💡 Hint: Use a list of colors indexed by the predicted digit.\n
\n
\n
\n🏫 Middle School\n
Challenge 2 — Count Correct Predictions
\n\n
Add a counter that tracks how many of the first 20 images the model classifies correctly.
\n
\n💡 Hint: Compare `prediction` to the true label from `test_data`.\n
\n
\n
\n🎓 High School\n
Challenge 3 — Find the Hardest Digit
\n\n
Run all 10,000 test images and find which digit (0–9) has the lowest accuracy.
\n
\n💡 Hint: Use a dict `{digit: [correct, total]}` and loop through all images.\n
\n
\n
\n🎓 High School\n
Challenge 4 — Confusion Matrix
\n\n
Build a 10×10 grid showing how often the model confuses digit X for digit Y.
\n
\n💡 Hint: Use `numpy` to create a 10×10 array and increment `matrix[true][predicted]` for each image.\n
\n
\n",
+ "
🎯 PYNQ 301: Object Detection
\n",
+ "
Real-time AI on the KRIA KV260 — powered by YOLOv3 & the DPU
\n",
+ "
\n",
+ " 🧠 Deep Learning\n",
+ " 📷 Computer Vision\n",
+ " ⚡ FPGA Acceleration\n",
+ " 🎮 Interactive\n",
+ "
\n",
+ "
\n",
+ "
📋 In this notebook you will:\n",
+ "
\n",
+ "- Understand what object detection is and how it has evolved over time
\n",
+ "- Use the YOLOv3 model to detect objects in images and a live webcam feed
\n",
+ "- Interactively explore how confidence thresholds and class filtering affect results
\n",
+ "- Display live detection results on an OLED display
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "📖 Useful Term Cheat Sheet\n",
+ "
\n",
+ "
\n",
+ "\n",
+ " | DPU | \n",
+ " Data Processing Unit — specialized hardware on the FPGA for running AI models fast | \n",
+ "
\n",
+ "\n",
+ " | YOLO | \n",
+ " \"You Only Look Once\" — the object detection algorithm used in this notebook | \n",
+ "
\n",
+ "\n",
+ " | Overlay | \n",
+ " A pre-built design loaded onto the FPGA to give it a specific capability | \n",
+ "
\n",
+ "\n",
+ " | FPGA | \n",
+ " Field Programmable Gate Array — a chip we can reprogram to do many tasks efficiently | \n",
+ "
\n",
+ "\n",
+ " | Anchor Box | \n",
+ " A pre-defined box shape the model uses as a starting guess for where objects are | \n",
+ "
\n",
+ "\n",
+ " | Bounding Box | \n",
+ " The rectangle drawn around a detected object in the image | \n",
+ "
\n",
+ "\n",
+ " | Confidence Score | \n",
+ " A number from 0 to 1 showing how certain the model is about a detection | \n",
+ "
\n",
+ "\n",
+ " | NMS | \n",
+ " Non-Maximum Suppression — removes duplicate overlapping boxes, keeping only the best | \n",
+ "
\n",
+ "\n",
+ " | VOC | \n",
+ " Visual Object Classes — the dataset YOLOv3 was trained on (20 object categories) | \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
🔍 How Does Object Detection Work?
\n",
"\n",
- "## What is YOLO?\n",
- "----\n",
+ "Object detection answers
two questions at once:\n",
+ "
\n",
+ "- What is in the image? (classification)
\n",
+ "- Where is it? (localization — drawing a bounding box)
\n",
+ "
\n",
"\n",
- "\n",
+ "Early approaches like
R-CNN (2014) ran a classifier on hundreds of separate image patches — accurate but very slow.
YOLO changed everything in 2016 by doing it all in a single pass through the network.\n",
+ "
\n",
+ "\n",
+ "
\n",
+ "🚀 The YOLO Family — How It Has Evolved\n",
+ "
\n",
+ "
\n",
+ "\n",
+ " | 2016 | \n",
+ " YOLOv1 | \n",
+ " First single-pass detector — fast but lower accuracy | \n",
+ "
\n",
+ "\n",
+ " | 2018 | \n",
+ " YOLOv3 ⭐ | \n",
+ " Multi-scale detection using 3 grids — what we use today! | \n",
+ "
\n",
+ "\n",
+ " | 2020 | \n",
+ " YOLOv5 | \n",
+ " Easier training, better accuracy, Python-native | \n",
+ "
\n",
+ "\n",
+ " | 2022 | \n",
+ " YOLOv8 | \n",
+ " Anchor-free detection heads, state-of-the-art speed/accuracy | \n",
+ "
\n",
+ "\n",
+ " | 2023 | \n",
+ " RT-DETR | \n",
+ " Transformer-based real-time detector — no anchor boxes at all | \n",
+ "
\n",
+ "
\n",
+ "
\n",
- "
\n",
+ "\n",
+ "💡 How Do Anchor Boxes Work?
\n",
+ "Think of anchor boxes like a fishing net with holes of different sizes and shapes. Before the model even looks at the image, it lays this net over a grid. Each \"hole\" is a pre-defined box shape (tall, wide, square…). The model then asks: \"For each grid cell and each anchor shape, is there an object here, and how much do I need to resize the anchor to fit it?\"
\n",
+ "YOLOv3 uses 9 anchor shapes across 3 grid scales: 13×13 for big objects · 26×26 for medium · 52×52 for small.\n",
+ "
\n",
+ "\n",
+ "\n",
+ "🗂️ What is VOC?
\n",
+ "VOC is a dataset from the PASCAL Visual Object Classes Challenge. It contains 11,540 images with 27,450 annotated objects across 20 categories.\n",
+ "
\n",
+ "\n",
+ "\n",
""
]
},
@@ -95,10 +177,17 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 1. Prepare the overlay\n",
- "We will download the overlay onto the board. "
+ "\n",
+ "
⚙️ Step 1 — Prepare the Overlay
\n",
+ "
Load the DPU design onto the FPGA and connect the OLED display
\n",
+ "
\nLoad the DPU overlay onto the FPGA and connect the OLED display.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -112,6 +201,11 @@
"overlay = DpuOverlay(\"dpu.bit\")"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nSet up the OLED display with default configuration settings.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -119,46 +213,76 @@
"outputs": [],
"source": [
"# Initiate pmod adapter. G4 indicates that the OLED is connected to the G4 slot on the PYNQ Grove Adapter\n",
- "adapter = PmodGroveAdapter(overlay.PMODA, G4='grove_oled')"
+ "adapter = PmodGroveAdapter(overlay.PMODA, G4='grove_oled')\n",
+ "oled = adapter.G4\n",
+ "oled.set_default_config()\n",
+ "oled.set_normal_display()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2. Utility functions\n",
- "\n",
- "In this section, we will prepare a few functions for later use."
+ "\n",
+ "
🛠️ Step 2 — Import Libraries & Define Utility Functions
\n",
+ "
Set up the pre-processing pipeline, bounding box decoder, and drawing helpers
\n",
+ "
\n",
+ "📦 What gets imported (click to see) — click to peek inside
\n",
+ "import numpy as np\n",
+ "import cv2\n",
+ "import matplotlib.pyplot as plt\n",
+ "from IPython.display import display, Image\n",
+ "from yolo_helpers import (\n",
+ " load_classes, make_colors, load_anchors,\n",
+ " setup_dpu_buffers, list_images,\n",
+ " pre_process, evaluate, draw_boxes\n",
+ ")\n",
+ "%matplotlib inline
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nImport the Python libraries needed for image processing and display.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "# Lets import some libraries we will need later on:\n",
- "import os # The os library provides some functions for interacting with your operation system\n",
- "import time # The time library provides some time related functions \n",
- "import numpy as np # numpy or np is a library for processing numerical data.\n",
- "import cv2 # cv2 is a library for image processing which we need later\n",
- "import random # This module help in generating pseudo-random number generators\n",
- "import colorsys # This module helps in creating bidrectional conversions of color values\n",
- "from matplotlib.patches import Rectangle # This module is used to plot rectangles\n",
- "import matplotlib.pyplot as plt # Used to create a figure/ plot areas in a figure, plot lines in a figure\n",
- "from IPython.display import display, Image # Imports public APIs for display tools in IPython\n",
- "\n",
- "# This line enables matplotlib graphs to be included in the notebook, next to the code\n",
- "%matplotlib inline "
+ "import numpy as np\n",
+ "import cv2\n",
+ "import matplotlib.pyplot as plt\n",
+ "from IPython.display import display, Image\n",
+ "from bootcamp_utils.color_utils import load_classes, make_colors\n",
+ "from bootcamp_utils.dpu_utils import setup_dpu_buffers, list_images\n",
+ "from yolo_helpers import load_anchors, pre_process, evaluate, draw_boxes\n",
+ "%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Now we'll load the yolov3 xmodel which is trained on the voc dataset."
+ "\n",
+ "Now we'll load the YOLOv3 xmodel trained on the VOC dataset onto the DPU.\n",
+ "
"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nLoad the YOLOv3 object detection model onto the DPU.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -172,370 +296,496 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "The YOLOv3 model predicts offsets from a predetermined set of boxes with particular height-width ratios; those predetermined set of boxes are the anchor boxes. Let's define those.."
+ "\n",
+ "YOLOv3 uses 9 anchor box shapes — pre-defined width/height pairs the model was trained with. The helper loads them for you. Expand the toggle below if you want to see the values.\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "⚙️ load_anchors() — anchor box sizes used by YOLOv3 — click to peek inside
\n",
+ "def load_anchors():\n",
+ " # These 9 pairs (w,h) are the anchor box sizes YOLOv3 was trained with\n",
+ " anchor_list = [10,13,16,30,33,23,30,61,62,45,59,119,116,90,156,198,373,326]\n",
+ " return np.array([float(x) for x in anchor_list]).reshape(-1, 2)
"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nLoad the 9 anchor box shapes that YOLOv3 uses to find objects.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "anchor_list = [10,13,16,30,33,23,30,61,62,45,59,119,116,90,156,198,373,326]\n",
- "anchor_float = [float(x) for x in anchor_list]\n",
- "anchors = np.array(anchor_float).reshape(-1, 2)"
+ "anchors = load_anchors()"
]
},
{
- "attachments": {
- "image.png": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAABHYAAAHICAYAAAAryW0iAAAgAElEQVR4nOzdf3SU9Z3//dcVYkWFFWuiEzYok4aVIKggrsygtE6g3qKlMhE0Scl+NS57W6hVoJzahFjDpOsq0L0r6Z47it7GZkLFhLVVPK3JdKXLTFoCKAKhLc2wkpJRYmEFK8qP6/5jfmQmmYQEJiSjz8c5OSfXj8+PaxLpyauf9+cyTNM0BQAAAAAAgKSTMtgTAAAAAAAAwNkh2AEAAAAAAEhSBDsAAAAAAABJimAHAAAAAAAgSaUO9gQAAAAAAADOxt69e/Xxxx8P9jQG3CWXXKLx48fHvTagwU5bW5tee+11paen6e6779awYcMGcjgAAAAAAPAF8vHHH+vGG28c7GkMuG3btvV4bUBLsTZtekN+v1+///1WvfvuuwM5FAAAAAAAwBdOjyt2Tp8+rQ//+lelp6XFuXhSp/6yR6fbdsn8+ENJhoyRl2tY5iSlZORIKcGVOV/+8pf15z//WYZh6Mtf/vJAPQMAAAAAAMAXUo/Bzs9fflm/+93vdO/8+bLZbJIk8/hRndi8Tid+v0HmsY647VIuvVIX/OO9uuDW+3X33d9UVlaWvvzly3TVVVcNzBMMQSdOnNAFF1ww2NMAAAAAAACfcz0GO6mpwUunTVOSdOoPb+nTV36g00fjBzphp//3fX365k/02e9+ruHzn9SUKfYETndoe+utt1RbW6sZM2aooKBgsKcDAAAAAAA+53rcY+eevDyVrVih6Xa7Tv5uvT558f+OCXWMS76sYZmTNOwr05T6lWkaljlRxiWXRa6bH72v4y/8s05s2ziwTzCEtLa2qr29fbCn8QXjU4VhyFjpG+yJAAAAAABw3vW4Yie8L86pFo8+ffUJKSVVqRNzlTrxdqVm3SSNiLP3jiTz6CGdav29Tu76lU7t8ejTuhKl/N0VGjZu+oA9xNk4ffq0UlIGdO/oMzJNU4ZhDOockGSO+LXpWbeO55fImdnPtgGf1j21XVPWLNLkAZkcAAAAAOB86/115387ok/rVyj15vv0Jce3ZYxMP2OHxsh0pV5/p1Kvv1PmR+/rs8ZKffrKD3TRo6/JGD6yT5P64Q9/qNOnT2vp0qW69NJLJQVDkJKSEknSY489ppEjY/vauXOnfv7znysrK0vFxcWR89u2bdOGDRvkcDiUm5urV155RQ0NDTp06JAuvPBC3XjjjSoqKpLFYom0ef755+X3+/XVr35VM2fO7Da/HTt2qL6+Xmlpafrud78bGTsQCEiSPB6Pdu/eLUn62te+plmzZkXanjx5Ur/85S/l8Xj0l7/8RRdeeKGuueYa3XvvvcrJyYkZZ9u2bdq4caNuv/12XXfddaqqqlJzc7MeeOAB3X777X36LPH5Enhjme5cLtXk97+t71m7HvyxS941iZ8XAAAAAGBw9BrsnGh+RcML/x+ljJ16Vp0bf3elLpxbrtN/9ulkc70uuOWf+tTunXfe0enTp/XZZ5/FnA+HJSdPnuzW5siRI9q1a1e383/729/U0tKirKws/fCHP9S7776rrKwsjR49Wnv37tWWLVv0zjvvqKKiQmPHjpUkjR49Wq+++qr++te/xg12GhoatHPnTt17772SpOPHj+uDDz7Qxx9/HBnzgw8+kKTIufB9TzzxhPbs2aPx48frG9/4hv7617/K5/Np586deuyxx3TTTTfFzP3dd9/VpEmTtHHjRv35z3+WFFxtBAAAAAAA0GstUuoN3zjrUCdmkK/YlHrd/3XO/ZyN8NupGhoadODAAT399NNas2aNnnjiCT333HOaOHGijh07ph//+McyQxtFf/WrX9Xw4cPV1tamffv2xfT32Wefqbm5WYZhyOFwSJL+8R//Uc8++2xkFc1dd92lZ599Vs8++6zuvvvuSNsXX3xRe/bs0fz58/Vv//Zvuv/++7V06VJVVFRIkiorK2PCrPDcPR6P3n//fS1btky1tbUxK4DOlv/ZXBlGntxt3a/teHqKDGOxNh0Jnzmilo0VetCWJcMwZGRM0J3frpQnEKfjgEeV375TEzIMGYahLNuDqtjYoiNxbu2TkwF5nlmsOydlBMceZ9eDK+vVcoYOA7+p1OL5uZF5GOPsyvt2pXxd9v4+ssutZfPtyjIMGUaGJjge1OrfdHmwkwF5nn5QuVFzyFvujjOH490/p7j3SYHfrNaDjgnKMAwZRpbs85fJvav3h/KtNJRRUC+pXoVjDBnz3QrIr3WzDRkZhaqP+VmGzy+T51hA7vmG7GWSVCr7AO5JFKjNCz576Kuiqfsz9HQtqhe55xvKq433CwYAAAAAZ2/nT6Zq6k92Du4kPnhV353670rULHoNdoy/uzJBwyS2r/4YNmyYJOnTTz/Vww8/rHHjxkWujRw5Uo8++qhSU1O1f/9+7dwZ/Fgvuugi3XLLLZKk//qv/4rpr7m5WcePH1dOTk5M+daZHDlyRL/+9a+VkZERWekTds011+imm27S4cOH1dzcHDkf3n8nEAjoX/7lX3Trrbfq4osvjryx7FxYbyvQbNWr5k1/lys75HHvkOUxpxyjJMmv+gemaIJzrfyTlqrO06jX1xRq+JuLlTv5TlW+fbyz6X63CifnavGbw1W45nU1euq0dJJfa50TNPtfd+i4+ssv9wOTlfvwJg2fv1qvexpV9/AE+X+apwl3VGhHDx0GNhRqssOlllGzVfp8oxo9dXpuboZ2/Mdi2b9RqZbwjW+v1uxJhfKOyJNrU6MaPZVaNHaPljkma/EvwyHLEW16eLJy17Rr8pJKNXoa9Xq5Q8dfKtSEO1ZrR2TU4/KtdGiCs1SeMcWq2RT6nDYWasIdFfId65zfkV8u1mTHKrXfsFSVnkY1bnLJcbxGhZNma/XbPX8aOfMaVbfCIcmhkpcb1bjcoVGyqvinNXIG3Fr0g3oFQnPZ8a+FevANm1wbXXKMGCXH8kat/WdJKtZaT6Ma5+X0PFC0Dp9WL6/UjmNnvlVNFcoomCKvaco0TZk+l0ptUeFhU4XsZa7gdZ9LpbYKseU1AAAAAJybc08IksSoUaM0eXL3LWPT0tJ00003yefzadeuXbr++uslSV//+tfV0NCgt956S/fff38kIPJ6vZKk3Nzcfo2/Y8cOnTx5UjfffLOGDRsWWR0UNm7cODU1NemPf/yj7PbgK+LDwc7FF18cOZcw2XeqcJ5UuMEj//3FsobPN23SqrctKl5j13BJx3+zTote8MtZ3aq6BeG7HJr9DYdKb7Vr8Q9qNHtTsaw6ovryQrm1VI07VskxInTnbbM1xeJQ8Zt18n5ncuR8XxzZWKHCl6Slv96jVbOGB0/e5tDsyRY5HvKo7rdLNbnb4qUWvb5hh3R/peqqnBoVPn2bU1PSp2jKco92tC1STqa0441V8qlE3ueXyhZ+sttypLY7VfNbr458Y7ZGBTyq+Y+AnNVrtSrq+R2jjsi6fJO8by/V5BskvV2pRWU+TV7hlbfcpuHhz8lmVaGtUM4f2eT/kUPDFZBnQ6UCc2u0dk1B5HN33DpKR8Yt0ybfDi29If7WxqPGO2TPqZQkTbA55Ahvnjy2QKvcr8tesEiueQ6tSq/Uoh/4ZPvRdi2dFpyJZapDU96QJKum3OaIPG+vOjwq/UauKpokS7O0yXOGTZenlSjm13qaQy6Vyt8mKVPyvVkqlXuDY08rVs28DHmaSmSb1pfJAAAAAMAA++BVfXf2Sm2RJH1Lzzc/outClz78xXd1e/kWSdP1rSLpZypW88PXSfpQrz58u1YGowJ967lmPXJDuC+/isN9vP3vmvq8Vb/6iVUvhcbYMlUxY5ytwX0t1HmUnp7e4xuoMjODfyF/+OGHkXPXXHONrr76an300Ufavn27pGAZ1tatW3XhhRdq+vT+veWrrS24bOE///M/dffdd3f7eumllyRJx451XxqRkZGRkFU6sSyanV8sveGWJ1JtdlyeX6xV4IZlyrttuKTj8r5RoYCWatE8a2zzETYVf9vR2f6IV54XpMnLC7uEN8NlK/dqj8fVr1BHOiLvm+ukG5apMBzqhHu8xSXvu41ydTkflKPil/eo/fmoUCckIzP2GUZZpkhaJ9e/blJLx/FI+0W/bpX3qdnB9qNGyWqR6n+6Wuua/Doe2t5p+B1r1f5uoxbdEDze8cYq7ZBDi4rCoU7I2AIt/rZFgX/dJO9xSRqlUZkWaeNarX7BJ3/4xz1itta271HjQ2f3viprvkurF0iVC/N056Ol8t3xnGoem6x4n1CfRIU6kqTMy7p9nv0TkP9dyZkd+zPY7u+t3MoTfJW9YcgwYlf3xJR8zXcr0ktTRey9bW7lhUsOmypkzK9QxXyjswyxqSKqdCx+aWJ4/u75RtS9sfOJKTFbGW8OfRkDAAAAwODZqX+fvVIq+5Wam5v1fNHP9MDDr+pDSfrgVZWXSys2Nau5uViq3tLZ6ie3a6VW6FfNzWp+7lv62YPf1asf9DbOdXpk0wpN7xIcnYukDHa6rnY5VxdddJEkdQt+vv71r0vqLMfatm2bjh8/runTp0fa9NWnn34qSZo8ebLuueeeHr9uvvnmbm3De+0k2qg7CrRUHlX+MlRQdNwrzwsBTS5whFZmHFFgv6R5UzQhTkJgneSQ5FGgQ9KxI2qXZLVkJGh2x3WkQ9K4DJ1tj8eP+LXjNx5tqq1U6bfz5Cyrj7lunefS2nkXadMP7tSE9IuC+/csX6f6t6PChuEOFVctlWN/pR60ZemiCzI0Yc5ira71yB/ZEicgf0tAklXHD3jk+U3sV/uJCZL8Cr40bbgcC9dp6W1+VT5gV9ZIQxmT7tTip93y7DvrnYgkWVXwo9VyBjzyNDn13E+jVmH19hk1Vchu2LX4l1EleV1CHcuCGnmfL+hTf9ECtatUKpccUStypljD5YsWWSf13r6+wC+Haco021Uzr1T28L5AMSVf7apRoTL6umfQhlJpiSnTrFNBpk8Vtu2qORAuHZuiwiVRIVHMsyxS4SRv8L7QfFaF9wCKLjEz22V9tzSqpU8VYwo1xRcco90tFY6hBA0AAAAYcj7wy69vqXjO5ZKk6+5boelej/77A+nDJo+2FBXrm1dI0nVaUBZe6PGh/Pukbz3wTV0uSTcs0Ar7FnmaPow/xgBJmmDHMIxIOdTf/va3btdPnDjRa/verr///vuSpMsvvzzm/Ne+9jVdcMEF+v3vf69PPvlEPl/wz7HbbrutX3OXFHlte3Z2thYsWNDjV/RbsQbccLtmP2bRjmqPdkg6/ttNqgjM1qK5fVw1cvKTAZ3e2TrSXKm8cYYuuixLUxy5Kv5RvVqUoztnOWJvHDFZi15u1eGWRtX8ZKmKxxzR608/qLzJGcp6oD7yB771G6vU6G/X9vrnVPKQXaNaKrWsIFdZObla3Rxc6XP8uCSt02JHrnK7fOWt9EiqV2RxytjZWuXxq31HnZ5bsUj2ES2qXF6o3HE5yn36bPYiCj33Dq+Cq//qtW7jjjPcLenYJi2zlconnyrnFAbDnZ5Cnf4uGGuqUEZBvVy+kr6VfcXhdBeH2saGQMGSLkfkWsESl1Tm6WNYEhs0SfUqfCHUclqJzJcLFG/nLEt+ncwV4Sfxy7/hDPMJa/LEhFuW/GVyqVSeHjeOBgAAADAoDvq1Jfr4Cmvk/9z+y/4tcRpI0l/k90YfXy5r9kBMrndJE+xIwX1ypM6ypmj79+/vte3Bgwf10UcfdTt/8uRJbdu2TZI0YcKEmGsjRoyQ3W6PlGA1Nzfriiuu0KRJ8ZcapKSkRPrsavz48ZKkrVu3JnzF0dkbLsecxbK8XSPP20fk2bhausMpR+QXcZQsYyVt2K49cdIGf4tPkkOWNEkjRilDkj/Q3u2+428uU8akPK3b27+5jUqT9Kd2devxuEfLMiYo74WW7s06Nqn0G4vltT2n7e2fBFdJvNuoup+6VHxr/GKiUeMdKvjOKj3n2aP2T9r1+vcmy/9CpV6PfiHacIsmzy2W66d18v7J1OEda+UMeLTsZa+Oy6KcyRZJJZ0bB8f5KokJFIbLcoNTxeVrVedrlXlou9bOC8izvCZUstVPHZtUurBSWvCc6n5kk2/Jol43YpYkjZitpe5wkOFT5ZxcZdkSE+oYtlK5fF2fObr0Klia1X/xS7rOjk0lpleuMntniVVPK39iSrZWafu8vs0n4N8uzbP2e7UTAAAAgPNstFUxG6584Fe4ruHvx/a0FcvfyxqzHW5wBU88H77X9cVFiZNUwU44HHnjjTdiwpE//elP+vWvfy2p8y1YXX322WeqqqrqFqq43W51dHQoMzNT113XvbotXI5VW1urjz/+WLm5uT3u1TNy5EhJ8UOma6+9VmPGjNH+/fu1fv36Mz3q+TNttpbdsEM1bpc2/YdU/M95UX+EDpf9jhJZtFqVG7r8Eh7zad1PPdI0p+zZkkbZ5bhf2lFdF/MGKMmv+mdXK5Bq15Tx/ZnYKNlnFUtv16juv2OTDv+GSq0ODJd9cpw3O+3bocqAZJ/l0GRLVP3YSb9e31gvya/2Dkk6ok3LczVhdtRbsiRpuEU54y6TNEoXpUp6u1J32rK0+I3YOYwanxP8nIZfpOGScqYVy6J1Wlvb9T9Wv9xFGcqYVCrPMUnaoco5dmU9vCl2ZU5ajnLGStJwXdTbxjipoYsx2WFA9UuKVakCVf6oWM5HK+Wa5tOyh2LfHDY81SIpdpWVNb9G3ki444/8I3RuoU6wvCk21IlfetVZmtVXwX7q9yXqH0WbSsLh24EaOcvscV7DHpB7Tamc7vZQSFepvD7Ox2KdIm3o/B8EAAAAAEPUFVZZ9TOt+0WwjGrn+pXaYnfoliuky6c5NL16XWjvnJ16qTy8gie4Qudnz4f24nn7Ja30TpdjWrga6GfyvC1JH+q/G3pa9XPuev2z7dQ+n4Zln20hRde+vBqWfW5vdrrrrrvk9Xr19ttv67HHHtP111+v9vZ2eb1ezZgxQ42NjTp16lTctmlpadq2bZseeeQR3XLLLbrwwgu1detW7dy5UxdeeKEefvjhyIqbaBMnTtTo0aN18OBBpaSk9Po2rGuvvVZScC+e1atXKz09XdOnT9dXvvIVpaSk6NFHH1VpaanWr1+v7du36+abb9bIkSN17Ngx+f1+paam6pFHHjmnz6j/Jsv5bYeWLVytHSpWXZdVLcNvK1bl/W7lFdmV+9tSLcrP0fCAT+vKSlV/zCHXxmIF45VRcpbVqMBWKPvkFrnKi2UbdUS+F0pVusGqRZuKe3+jUhyj5paoZoFdhbdOUEu5S8W3jNKR/16n0rJ6Wb/zuopviNPoBptKLFLF8sVadrJYs8eO0pH9m1S3pkaeDoukHaGyqVGy35qjw08v1p3z/SrJny3rqOMKNNdr7XKPLA+9rtljJckuhxZr2QN36viSYjmnWjT8iF+bXijVatm0ymkLfU6lqi/3yF5gV+A3wc9p1BG/NtVWaPUGqcBdHNo8erLst0mLlxTrzuPLVDx3sizDj8j/y3Uq/bFkW5MXKV0K1OYpo6BeTne76vKDAYhlzARJpap5qV6WW3I05bYcHa5dqkUvSQUvr5YzMzjG0h+79LqtVIuedsizIrihc8ZYu6RNqnnWrk8mT5FjarBPa36NvJLsBcH9Zc461GlzKy+0Uqcgs/tl2yyX6m3r5Msvka1pnQo3uOR9uZ9jhPqRzSPfCptsodAl8ratTKucKoy8bSvw2zrVS1FBTJf5jqlT3oG64HwzrZoip6xx5i6FwxuL1LROhRsk59xe5qNQOdY0h1yyd84ntO+QlzeBAQAAAIOn+gFNrY46LnpezQ8HNzX+7uzbNbVcCr4VK7R3zhXfVFmZR7fPnqqV+pZWlE2XQus5rnv4V1rx8O26fepKScG3YgX34gm1eXCqfqbpWlH2LakhNN4VVlm1Ug8k6K1YMntxYttG89P//KFpfvq33m7r3fFj5vG6EvPEO6/36fbTp0+b3/zmN805c+aYhw8f7nb917/+tXnvvfeac+bMMefMmWPee++95ssvv2z+7W9/M+fMmWM++uijMff//ve/N+fMmWN+73vfM//whz+YDz30UKTtnDlzzKVLl5p//OMfe53TCy+8YM6ZM8d0uVxnnP+LL74Ymf+cOXPMhoaGmOvvvfee+cMf/tC8++67Y+Zx7733mmvXro2593e/+505Z84cc/ny5Wcc95z86TlztmTqe43mJ3FvOGzuqXeZxdOspiRTlhxz9kNrzcb2OLe2N5prH5pt5lhkShYz5xuLzOe2dv859tmJdrPxJ4vM2RMtpiTTMnG2uej57WZnj17TJZkq93a28b9uuu63mVaF5nBbsblqU6t52F9jOiVz9vOtnU/227Xmonnhe2VapznNRT/xmjEzPrrHrCsvNh2hOciSYzrud5l1LV0/rdDndFuOaZFMyWra5i0y13q6flCfdLkvOEdX/Z6Yz7/d7TQlmU53dPtW8/XvzQ7Nd6nZuLfGLLDItCyoM9u7jOFdMdmUbKbLF+r16HZzbX5ozFnPdfuoW19eZDoeqjFbT/T0w+hdeL5dv6Ln7y3vPO/y9diTWTMvTruon3HMWPNqYp49egynu8Z0yWnWHDBN0+cyJZcZ9ZsSOhd/rmZP95V7u82nc0ynWePuMs6B4O9d5PqBM3+WAAAAAPqmubn5PI/YYf7nd240H36147yO2ttzGqbZy4YvpqlPqr4l869t+tLX/lmpNzqlL13ct8To02M6sbVOn731rIZdOU7DH3yhT80++ugjLViwQJK0YcMGfelLX+p2z/Hjx9Xa2qqUlBRdffXVkTdUnThxQoZhxLwafOvWrXK5XLrmmmv01FNPyTRNvffee/roo490xRVX6Morr+x1PqZp6tFHH5Xf79ePfvSjyKqc3hw6dEjvv/++RowYoauuuiruSqBjx47p4MGDOnnypC699FJdeeWV3V5pfvr0aZ06dUopKSk9lpgBGGKaKmSssaq9h42YAQAAACTOtm3bdOONNw7sIG//u6Y++LPO49AKn/Opt+fsvdjCMHThfWv0yU/n6/irK6VNTyl13C0alvWPSrlynIzLRsu4cKQkU/r0Y50+3KbT7X/QKf9WnfyTVzr5qVK+nKkL73u6z5P985//LEmyWCxxQx1JGj58eLeNjqW+vRbcMAxdffXVfZ7PL37xC/n9fl1//fV9CnUkKT09Xenp6b3eM2LECP3DP/xDr/ekpKTEDYUADB2B2jxl7FsWemtWqBRrkpdQBwAAAPi8uOERNTef721T+u6Mu2ikXHqlLvrnF3X8/1uo0x3/o5N7GnVyT2OfOk+54isa/n/+Xxkj0vp0///+7/9GNhaePr2nXafPn1/96ld64YUXdMEFF+jBBx8c7OkAGIIs+cvkMuwyykIn5tWofUVi9iYDAAAAgDPp0/aoKWljddGiDfrsjad1YmudZJ7u9X4jJUWpN92rL92xVMbwkWfs/9SpU3rmmWe0detWHTt2TKNHj9Y999zTtycYAEeOHNGPf/xjvf322zIMQ4888oiuuuqqQZvP50V4M+A+KfeGVkAAQ13wzVolgz0NAAAAAF9Ivb8V69Qp/emPf5IMady4cbrQ6dIF0/+PTvx+vU79YbPMD9/rfH24YWhY2lgNu+arSr35PqWkW3vrOsawYcO0detWGYahu+66S/n5+br44j7u5XMGF110ka6++mpZLH0vjLj44ovV0tKiyy67TA8//LCmTJmSkLl80VnmrtPhw+v6dnPqqDPfAwAAAADAF1yvmyc3vNkQ2fPmqquu0h2z74i94fhRnT4WfMd7ysg06cIRZz2RY8eO6ZJLLpFhGGfdRyLt3btX2dnZ3TY0BgAAAAAAQ8N52Tx5CDjrzZP3798f+f69997TqVOnYt/ONHykUvpQatUXI0acfSg0EMaPHz/YUwAAAAAAAOhVr69cii5fSk9P55XbAAAAAAAAQ0ivK3ZmfX2W3t35rk6fPq2JkyaerzkBAAAAAACgD3rdYwcAAAAAAGCo2rt3rz7++OPBnsaAu+SSS3rcMoZgBwAAAAAAIEn1uscOAAAAAAAAhi6CHQAAAAAAgCRFsAMAAAAAAJCkCHYAAAAAAACSFMEOAAAAAABAkiLYAQAAAAAASFIEOwAAAAAAAEmKYAcAAAAAACBJpQYCgcGeAwAAAAAAAM6CYZqmGQgEdNlllw32XAAAAAAAANAHhw8flsVioRQLAAAAAAAgWRHsAAAAAAAAJCmCHQAAAAAAgCRFsAMAAAAAAJCkCHYAAAAAAACSFMEOAAAAAABAkiLYAQAAAAAASFIEOwAAAAAAAEkqNTHd7FF10ZNqiHNl5vJqFVkaVL7EK/uaMs0MVKvoKen71UWakJjBE6znZ1F2kVaXzVR61/O7op5p1xB7vl3VKnqqIfhzmBh7ac+LRXryf4q02nlQS4fSnM/SnheL9KS+r+p/SuanAAAAAAD0qmOz1j7TqENdz197n56YnyNJann5ca1X53GPWtbr8fXSfU/cpzPc2aVNSw8Xc/rXVwIkKNgJihceSJI6EjnK+dHjs/RHR1SglZaQafXfxKmaqQbtbz8kTYyOpPaouVHKXnC90nVwkCYHAAAAAMDZSFfudxZrRuRv7Ratf3y91r61WIu/2m05RmLl3KcnnggPexbBUBx9DqLiSGiw06O0mSqrnhn8PnBeRhw8E4tUXT3Yk4g2QVNzpQbfOzo0K2q10a5mNWimvj8rXdJQmzMAAAAAAP2Ro1xHutbubNGh/gQ70SFNkjo/wU70ypW416q1T5KUraLBXN3SD3teLNKTjcHvZ+ZGPVWkFGuqmkPPtW9JuTSIzzXhxplSo1fvdMyMzGHPtgYp9/vB0quu5WNxfyaH1FC+VAed4ZVMwZI1LY8+fkWjh/DPL/pnFvO7Fnr+mbkNaghdz16wWmWzQv8YRH8e2TODK6Bsq1U2S2ooXyqvrfPeruVgPY4pRcrkJEm5MzWzsaHL5wdVRzAAACAASURBVNlZEpiQFWQAAAAA8AXV8vLjWr87fBS12idqxY1eflzrlaOc3S1q6Xpff8SUioX7OKTNz6xVo3K1+DszlC7p0FtrtdaTptmODm3aLUnr9fjL/V+1k9Bgp+Gpopi9aWL+OI5rj6qXVGvs8mqVTVTwD90l1Ro9BPZ66fosUtQf17uq9WTjzFAQEgw81C2ymqCiNUXaP9ilWFKkHMu745BmzkpXuAxr5vJ4n3LPP5Prbdmq3rZHRRMnSLuatT87W/uijhuy7Vo9REMd7aoO7idUHVy1tOfFIj35+h7NjOzH06D9o1erujo9FLj8hxomhwKtn1ZLC1arelZ6JATKtp3rmHtUHbX30aE3y9X5W3RIDeVPan94zI4GlS8pV8Ng/x4BAAAAwJDVokbPIaU7cpSuLjvCtKzX+vdztfiJYKDS8vLjWu9p0Yx4AcruDo3+zhO6Ly10X+1m5YSCmL7OY/0zjUq77wktzgmO/fgz65X+xH2a8Z3F0jNr9fO3crT42hb93CPlfuc+3Zwm/d37Q6QUq9+rCnY1qyG7SKvDbSbeoaLspWreVaQJg7w6obdniVntonTNdM5U9VPncXL9FirHOnhIUnpnGVa85+vlZ1KUMVbyHdQhTZDa92us8x6NrQ8eH9rWoGzb6n78sp9nE4tUHXneQzr4P5Kujr5hpu4Jh5CW0crW/uD3He/Iuy9b9m+Hrk28Q0XZDfKe65hdfgbps+7RzJeejBpzpu4pC42ZNlP35FbrlUgwBwAAAABfdIfU+Mzjaow+de19eiJeGVbOfXoip7PdofclXdlDt9fmRlbopF2ZLr3fz2m1vKOWtNxgqCNJObnKTVurd1qknJx0zcjP1c5n1upxj5TuOIvVQHGcn1KsHhxq3y/ta9DSotgNXrK7bfQ7lAT/QM+2Rc3PMlrZQ3wD4gk3zpSeataef5ogxQRTsXr9mcyaqplPvaJ3Oq6XfNLob6dLCh3/T7bsdw7Vn5m6lZdlZ/exXeCg9mms7jmb/9h6GfNQ+34p2x4/CAsc1D416MmiLmvGckPBHAAAAAB84fWjTKpLaVT6AFZCHPqgQ+po0drHYyInpX9wSMpJl9JmKPfaRq1/P1f3JmiT50ENdtIzxkrZ9vivEB+y0jX6amnfwag/sgMHQ3+8D2ETp2qmnlTzrqlSj2VYZ/qZpGtq7j41Bw5JsuuOtHTJJr2x4x3tl10PDeEyoT2vV2tfbpe9b/rS0DJa2fLqYIc0oZ/P19uY6RljpX0HFTeqsYxWtmbqniFQkggAAAAAya7F06hD3V6FPjDSr0iT0q6L7KPTfTLrtX53utLTGoMlWQkId1LOuYdzMXGqZu6r1hu7QscdDSovKlL1rl5bDbrgZsSvqCFUtLdnW9fdeIaiUDnWU0+qQTM1tadStzP8TNJHZ6uh/hXtv3q00hUMKPb7vJLt+qEfzv1PMEhRR4NeaTzTzSFp18uevU/eHcFsV7veUHUkxQuFfL53QslvcO+iPo0Z2veoOfS5Hnrzlc49ndKulz27Qa+8GRpTe1RdVKTyyDEAAAAAoF/ePxT622yzGnef6eZzkHO9cjoa1dgSOu7YrLWPP671LZLUovXrW5Rz32Itzs+VPD/X5o5e+uqjQV2xE95guHxJkYpCZ7IXrB4Sb/+Jt3myFNoweWKRVi8o19IlRaqWlJ07U3Ere9Kulz27WtVLinRwCLzVKBhINUi5U3tZCdL7zyR9sl3ZL1VLts79aLRvf+ceNIOt8UkVxYQroTdR3Vmk7CXVwRKz7CJ9f0G2ngztF9T7zNM189tF8i5ZqqKXJOUWqShbkT12JkT3q5kqWpCtcFXehF7HnKCi5TNVFPo9y15QpJmRdV/pmln2fR0sCo0pSbnfD26kDAAAAADolxxHrtKfaQyWR6Xl6j5HutbvPKRDyhmABQo5uu87uVr7zON6PHQm3bFY9+W0aP3j69VybXi/nxm617FTa0MbK+dMypHWr9fjz+T2vNqnB4ZpmmYgENBll12W8McBPn+6vvY9UYb+6+IBAAAAAEPH4cOHZbFYBrkUCxjy9qi6qDxSdhcsxcrWaMs5dtvRoPKiau0JHQZLscZqNKEOAAAAAKAfBrkUCxjqJuiOBYqU3UnSzOXV576qJm2m7sktinrzVbaK1rBZMgAAAACgfyjFAgAAAAAASDKUYgEAAAAAACQ5gh0AAAAAAIAkRbADAAAAAACQpAh2AAAAAAAAkhTBDgAAAAAAQJIi2AEAAAAAAEhSBDsAAAAAAABJKkWSUlJSdOLEicGeCwAAAAAAAM7gxIkTSkkJrtUxWltbzQsuuECpqamDPC0AAAAAAAD0xenTp/Xpp58qNSUlRadOndKpU6cGe04AAAAAAADoo5SUlGCwAwAAAAAAgORDsAMAAAAAAJCkUg3DGOw5AAAAAAAA4CywYgcAAAAAACBJ9RjsmKZ5nqcCAAAAAACAeHqquIqUYnUNcijRAgAAAAAAGJrCuU1q1xMAAAAAAABIDr1unkw5FgAAAAAAwODqLbuJrNghxAEAAAAAABh64mU2vZZiEfIAAAAAAAAMLfFW7qRK3YMcgh0AAAAAAIChzTAMpYZDnOgwh2AHAAAAAABg6DIMQ6ZpdpZi9YagBwAAAAAA4PzqyxvMu63Y6c/KHQIfAAAAAACAc9NTgBPOXcLXTdPsdm/cYIeyLAAAAAAAgPOjt+wlXHIV/r6r1K6BDnvuAAAAAAAADL6uoU54xU50wJPaNcwh3AEAAAAAABhcPYU6YeHj1OgwJ17IE90AAAAAAAAAAy86xAmv0olesRO+nmqapv7+7/9+sOYZ0draqqysrMGeBgAAAAAAQFL4y1/+ohRW4gAAAAAAACQf0zQJdgAAAAAAAJIRwQ4AAAAAAECSMk1TKYM9CQAAAAAAAJwdVuwAAAAAAAAkIUqxAAAAAAAAkhSlWAAAAAAAAEmMYOes+VRhGDIMQ4aRJ3fbYM8HAAAAAAB80QxIKdbeTVWqqqrVlkMJ73oIsanENGWaXrkGeyoAAAAAAOALZwBKsfbqtaoq7Rt1rUYmtmMAAAAAAAB0kdBgZ++mHbps7kLdNS6RvQbknh8ueTJkzHcrEHM9uiTKUF5t1NU2t/KMCvmi74lpH9u2+7UK+drcyutx7DPMvDavs++Ycq2A3PPz5G6LHr9CvujGTRU9tAUAAAAAAAhKaLAzfna+pqcnskcpULtOWmPKNE2ZZrtqVKhFkfAmIPd8u7a720PXvZpSkKGKpugeSmU3Vsl6IFQ2taFQ65ritQ32nbHSF9t2jF/L4o59Bm1urVNlqG9T7W6pcEl0MFSvwjHhebWrZl6pVoX7bnMrz7ZdNQdCz+2bosIxXYIfAAAAAADwhTfkN0+25JeoIDNyJMdcp+r3+YOHbR7VqUaV+ZbQdZuK3U6Vvhkbgbh8daE+bHKUS9v9AalpnQo3uLQs0taigjU1cpZ5ogIUp2oOlMgWb+wzySxQSaRvyXJrnpwb/Ipu3Tmv2L4Dv62T3JWdzz2tWDXzSuWJCawAAAAAAMAX3ZAPdhRdCmUYyiioj7rmV/2GQmX0dF2S5JJjWueRbYWpunDgMs8qa3/n866/j+VYXUrIxhSq68x64t9Xr/qCjKhSrAwVbujvRAEAAAAAwOfdEA92fKoYUyhFyqVMtbudsbfMq1G7aUaum6Ypc4Wtb913WUGjNv+Zw5dJVlnOdI8k38oMFSpqbgdq5Dxzswhn1DOHv0qmnbkdAAAAAAD44hjiwU7QFGsoSmlza1H0ipxpDrk29GPfm2jTHHIpal8bBeReUyqnu1hxY6HQ2K5ZfQyNpKgQKCD3kr6v2LHNcqm+YBEbJgMAAAAAgF4lNNjp8NaqqqpKVRt366iOavfGKlVV1WrLobPtMbRnji1czuRXXsyKHZtKQhsmG1HlWBV92ouma9sMFU7ydpZpSQpucNxZSjXFF7VqJvLWKrtKI/d1vr3Kdn+NnGX2SN/+uf1YsTOtJLRhclQpV9e3ZgEAAAAAgC88o7W11bRa+73TTMK1trYqKytrsKcRxacKY5WsB+qiNm8GAAAAAAAYGvx+f3KUYgEAAAAAAKA7gh0AAAAAAIAklTrYExi6bCox6wZ7EgAAAAAAAD1ixQ4AAAAAAECSItgBAAAAAABIUgQ7AAAAAAAASWqIBzsBuecbqmg6w21tbuUZhoyVvvMyqyCfKgxDhmHIMPLkbjuPQwMAAAAAACjhmyd3aEttvXYfDR1mztDC2eMTO8SQYVOJaapEPlUYqwZ7MgAAAAAA4AsoocFOh/dNvXe1UwvtaZL26rWqzar1pinfnpbIYbrLLFCdWTCwYwAAAAAAAAwxCS3FSrPnR4U445WdKR090pGAnqPLnioUXXDlWxk+byivNhCnbbCcy+javqmiW1/xzgVq86La9qEsrMe2XcYCAAAAAAA4R0N8j52gUtsqWQ+YMk1T3vJS2aP20rGtCJ+P1zIg9/wMFU7yyjTN0FeJbJI0zSGXSuWJCmp8b5ZK5Y7gdQWDmYyCKfJG2poqmdbHSTdVxLRtd2+Xfb5b8aInAAAAAACAszFwwc7e17S5baSuvenc99hx+epUkBn83jbLJZV5+rb6pWmdCje45F1hi3PRpmK3U6VvhnvyyVPmVM39tsjxuoJ6uXwlitf6THxvlsa0teQvk2tDnTxssgwAAAAAABJkYIKdQ1tUu/mgRs/I1/T0BPedaZWzj7cG/NuleVZZe7huuTVPznBI1ORRafmySICkNr+2yylrZg+Nex9Z/nelUlt0CZhdpWfTFQAAAAAAQA8S/FYsBUOdjbuliU7dNRAvxGrzq36eVZV9uNVindL7DZkO5c3LkKepRHqzVK5ZZtQ1q87Q+oxcvn6UbgEAAAAAAPRTYlfsRIU6A/MmrIDca0rlnOuQpS+3T3PItaFQi+JuqixJFhUscal0TZ5WvVuj4pgQxiZHeb0Kl5zNvjgWOeY6VWpjw2QAAAAAADBwEhrs7N26W0clHd1Vr6qqqtBXrbYcOrd+O0uaMlQ3t111+eFYp/NtWfYyqb4go8vbsWwqMb2aEjof9+1U0xxybaiX4oRFthWmvJMKlRHvrVhNFVElVvUqHGPIMPLkDu2hY8mvC26YHNXWYPNkAAAAAACQQEZra6tptfa0C83509raqqysrEEY2acKY5WsBzo3aAYAAAAAABjq/H7/AOyxk2R8K+0qLffKJNQBAAAAAABJZuBedz7E+VaGSrjerVF73NehAwAAAAAADG1f2BU7thWmzBWDPQsAAAAAAICz94VdsQMAAAAAAJDsCHYAAAAAAACSFMEOAAAAAABAkhriwU5A7vmGKprOcFubW3mGIWOl75xG8600lFcbOKc+AAAAAAAAzpfEbp689zVVbT7YeZw5Qwtnj0/oEAAAAAAAAAhKbLAz/i4tjOQ4HdpSW69ab5ry7WkJHaabzALVmQUDOwYAAAAAAMAQM4ClWGm6/NJE9eVThWHIMAwZRoWiC658K8Pn45RRtbmVZ1TIF91+vluddwVLvcLt7WWJmi8AAAAAAMDAG8BgZ6/2tY3UVePOfbVOqW2VrAdMmaYpb3mp7FF76dhWhM/32Fp2I9zeK9eGQq0L7dnjW5mhwklemeaZ+gAAAAAAABh6EluKJanDW6v6XUeDB5kzdFf6uffp8tWpIDP4vW2WS7J55Fthk63f7W1ylEur/AEp06NVZS55zb72AgAAAAAAMLQkPNhJs+droT34fYe3VlW1H8qZP10J22Un0yqn/P1o4JJjWueRbYWpOklqS9SEAAAAAAAABseAvu48bdxVGnn0sDoS2WmbX/XzrLImsk8AAAAAAIAkNKDBzt6tu3U0M1uJe+F5QO41pXLOdchyrl1lWjVFpfKE9tsJ1OaxeTIAAAAAAEgqCS3FitlfR5IyZ2jh7HOPdUpthkpD3zvd7arLD8c6PlUY9sg1KUNGQdd7emJTic8lI9x3uVftbmnROc8WAAAAAADg/DBaW1tNq3XwC5taW1uVlZU12NMAAAAAAABICn6/f2BLsQAAAAAAADBwCHYAAAAAAACSFMEOAAAAAABAkiLYAQAAAAAASFIEOwAAAAAAAEmKYAcAAAAAACBJDfFgJyD3fEMVTYM9DwAAAAAAgKFngIKdDm2prVJVVa22HBqYERLFt9JQXm1gsKcBAAAAAADQbwMS7HR439TuS0dr9EB0DgAAAAAAAEkDEewc2qI3d0nX3pSdwE59qjAMGYYhw6iQL/pSm1t5kWuGjJW+2KZNFZ3XjM6yrkBtngzDkL1Mqi/I6Lk9AAAAAADAEJXgYKdDWxp2SxNnaXp64notta2S9YAp0zTlLS+VPRK++FQxplBTfMFrpumVq8weVVrlU8WbjtA1U6bPpVJbMBiy5NeF+pOc7vbOe1bYEjdxAAAAAACAAZTYYGdvk3brWs2ypyW0W5evTgWZwe9ts1xSmUc+SYHaVSqdV6PiaeE7bSrxuVS/0aNA+Dg6qJnmkEvb5W9L6PQAAAAAAAAGRQKDnb16bfNRXTtzuhIb63SRaZUz+niSVZZebvetjCrTMuwqHci5AQAAAAAAnEeJC3b27tNBHdXujVWqqqpSVdXmzuNNexM2jNr8qp9nlTV8/K5f0e+0Cvi3d35fmyd7mUteM6pUK3EzAQAAAAAAGFSpCetp/F1aOD76xF69VrVDl83NT+B+OwG515TKObc9uErn1jw5Cwq1rqlAJdMkyad1BfVy+epkkYKBT1QI5FtpV6mcqonq0ZrtDJZu5Rf0uvIHAAAAAABgqBmQ150nWqktXEqVobq57arLD0UwmQWqO1Cj7bbOUqvt7vZQyCNZ8pfJtaFQGaFSrFXZNd1W7FjyK1Wjznt4KxYAAAAAAEgWRmtrq2m1Ws985wBrbW1VVlbWYE8DAAAAAAAgKfj9/uRYsQMAAAAAAIDuCHYAAAAAAACSFMEOAAAAAABAkiLYAQAAAAAASFIEOwAAAAAAAEmKYAcAAAAAACBJDfFgJyD3fEMVTYMzum+lIcMIfuXVBgZnEgAAAAAAAD1ITWhvh7aoduNuHY0+lzlDC2ePT+gw/dLmVt4Yv5aZJbLFuexbaWhVdrvq8i3drtlWmDJXhO4Z+JkCAAAAAAD0S2KDHUkaea2c+dOVlvCOAQAAAAAAEG2Il2KF+VRhhMuiKuSLvtTmVp7RWTJlrPTFthlTqHqVyh65J0/uNilQmyfDMGQvk+oLMuK074Omis52oX4BAAAAAADOl6QIdkptq2Q9YMo0TXnLS2WPDm/GFGqKL3jNNL1yldlD++HYVGKaMg/UyCmXvGb4njoVZEqW/LpQf5LT3R66ZspcEa9gK442t/Js21UTmpfpm6LCMV1CJwAAAAAAgAGU+FKso7tVX7U7dDBaMxbepXPdYcflC4YxkmSb5ZJsHvlW2GStXaXSeTVqnxa+06YSn0vGGo8C+QXqvmtO4gR+Wye5KyPz0rRi1czLkKepRLZpvTYFAAAAAABIiMQGO+nTlb9weuSww1ur+qrXpASEOxGZVjnl7zyeZB3QAKcn/n31qi+rl1EQe961ZBAmAwAAAAAAvpAGtBQrbdxVGpnoTtv8qp9nlTV8/K5f0S8iD/i3J3rEHsWUcIW+SlitAwAAAAAAzpMBDHY6tKVht45mZidutY4Ccq8plXOuQxZJllvz5NxQqHVN4es+rSuol2tJVBlWplVTVCpPU9wOZc12qn6jJyYc6gvbLJfqCxaxYTIAAAAAABg0CS3F6vDWqn7X0cjxyIlOLbSf+4vPS22GSkPfO93tqssPxTaZBao7IOWNMWREX49ZNRPadyfSh1M1Bzr37LHkV6pmY4YyjMLgiXJvaANlnyoMe2RcKUNGQdT400pk+ipkjDFUGLnHJa9Zoj5uvwwAAAAAAHBOjNbWVtNqtZ75zgHW2tqqrKyswZ4GAAAAAABAUvD7/cnxunMAAAAAAAB0R7ADAAAAAACQpAh2AAAAAAAAkhTBDgAAAAAAQJIi2AEAAAAAAEhSBDsAAAAAAABJaogHOwG55xuqaBrseQAAAAAAAAw9qQPR6d5NVdrcFjrInKGFs8cPxDAAAAAAAABfaAkOdjq0pbZeuy+doYULCXMAAAAAAAAGUmJLsfY2BUOdhK/Q8anCMGQYhgyjQr6Y83lyt0XdudJQXm2g80RTRahd8CvmGgAAAAAAQBJL6Iqdva0HNXLUZXqtqkoHQ+dGz1iou84x5ym1rVLNAVNmZjC4sa90yFxh60NLnyps21VzwFRB5rnNAQAAAAAAYKhJ4IqdDn34v9LRXYeVvXChFi5cqIUzRuvg5lptOXRuPbt8dZFgxjbLJZV5olbtnEm96n7LKh0AAAAAAPD5k/C3Yo2ecZciC3TGT9O1I4/q8IcJHCDTKmefb7ap5ECNVJARp4wLAAAAAAAguSUw2EnT5ZdKR//akbgu42nzq36eVda+3p9ZoDrTlGmaandvl51wBwAAAAAAfE4kdMXO+KzROrqrSXvDJ/Y2affR0cpO2F7KAbnXlMo51yFL5Fy9/KHNkwO1ebKX9dzaYp2SqIkAAAAAAAAMusSWYo2/S86JR7W5qkpVVVWq2izNWBhVmnWWSm3ht1plqG5uu+ryw7GOTSU+V+R6xr5l8pZHNezyRizDtl01B0rUl22XAQAAAAAAhjqjtbXVtFr7XNg0YFpbW5WVlTXY0wAAAAAAAEgKfr8/8ZsnAwAAAAAA4Pwg2AEAAAAAAEhSBDsAAAAAAABJimAHAAAAAAAgSRHsAAAAAAAAJCmCHQAAAAAAgCRFsAMAAAAAAJCkUhPXVYe21NZr99Gu50fq2rn5mp6euJEAAAAAAACQ0GAnTdPzF2p69Km9r6lqs3Q5oQ4AAAAAAEDCDWgp1t7Wgxo5cZrGn2tHTRUyDCPylVcbiLroU0XUNWOlr/NSm1t5RoV80ffMdyvQbQAAAAAAAIDkM3DBzqEt2tE2WpPtaefWT1OFDNt21RwwZZrBr7p8S+Syb6VHDjN8zStXmV0VTdEdlMpurJL1QOj6hkKta+o2CgAAAAAAQNIZsGBn79bd0jmv1gnIvaZUTnelCjLj32FbUSJb55Ec5dJ2f+yaHJevLtQ+/nUAAAAAAIBkNDDBzqEt2tE2UleNO8fVOvLLv0GaYrX0fEuXMi17WdcbXHJM6zyyrYhd8QMAAAAAAJCsBiTY2bt1t45mTk7Am7Csss7r5XKbW3m2Url8nWVa3vJzHRMAAAAAACA5JD7YCa3Wufamc94yWZJFjrlOldoq5OvxHqes4TKtpoo4K3YAAAAAAAA+nxIc7HRoS0OiVusEWfLr1O7eLnu8t2JlFmhZeb0Kx4SurbGqhhU7AAAAAADgC8JobW01rVbrYM9Dra2tysrKGuxpAAAAAAAAJAW/3z+ArzsHAAAAAADAgCLYAQAAAAAASFIEOwAAAAAAAEmKYAcAAAAAACBJEewAAAAAAAAkKYIdAAAAAACAJEWwAwAAAAAAkKRSE93h3k1V2tzWeTx6xkLdNT7Ro/RDm1t5Y/xaZpbINojTAAAAAAAASLSEBjsd3lpt1gwtXBhKcva+pqrNr2nv+Ls0mNkOAAAAAADA51FCS7E6jhzVyFFpnScuv0wjE9FxU4UMw4h85dUGoi76VBF1zVjpiz0/plD1KpU9ck+e3G3xBgEAAAAAAEguCQ12xmeN1tFd9XptryR1aEvDbh3NzD631TpNFTJs21VzwJRpBr/q8i2Ry76VHjnM8DWvXGV2VTRJkk0lpinzQI2ccskbuadOBZnn9JgAAAAAAABDQmL32Bl/lxaO36vXqqpUtVkaOdGphfa0M7frUUDuNaVyutt7DGNsK0qij+Qol1b5A9I0S/wGAAAAAAAAnxOJfSvW3tdUVbVDl81dqIULZ2jkrnpV1W5Rx1l36Jd/gzTF2ktI06VMy1521oMBAAAAAAAklQQGOx3asuOgRk6cpenpkjRedy2codFHd6tp79n2aZV1Xi+X29zKs5XK5ess0/KWn+1YAAAAAAAAySWxK3YkHT0StT5n7z4d1EhddvnZ9maRY65TpbYK+Xq8xylruEyrqaL7ip1Mq6aoVJ6ms50DAAAAAADA0JTAYCdN0/NnaHTbZlVV/f/t3b9rG/m6x/HPwLbm3CUOyEbeRSbLMXhTxLBgKawLc9MsbiJhg6TqEFCTbklnxYUtdWa7bQbCqTSCNRo3Jk0OLrRYYzjgLRKDLhgNxMISxHtzD/oD5hb6rdiOZck/lH2/QJCZ7495nPLh+zxfs/7L1zT7NNo4wXM1vmhOFeug41arjlux/DG9WLcVn2qM/RJQ5pMTO0GtOiklg9yKBQAAAAAAvixGqVTyAoHAbcehUqmk6enp2w4DAAAAAABgJLiuO/xSLAAAAAAAANwMEjsAAAAAAAAjisQOAAAAAADAiCKxAwAAAAAAMKJI7AAAAAAAAIwoEjsAAAAAAAAjasQTO47SRkRWedA9DBkrlqrDCgsAAAAAAOAGfDXsDYuvTeWbiZaxWYWjjzU+7I8AAAAAAABguImd00JW+f/MKpyoJ3OKr03Zr+8p8dPMMD8zZEGtep5WbzsMAAAAAACAPg2xFKuo/Xc1TT5qn9CZ+WFWY+UjFQfdej8twzBav0i2p2iqbCnSHO8tqepZm95vD1WzkfbYhtPz0aqslfp8Z6O5ftCyLwAAAAAAgOEZco+dMX19r+Px/j2NqaY/Pwyw5X5aRvBAmWNPnlf/5aK+jgm24kFXLzxPnldQaiuuV63kjaP0m8XWOs9JKRlMq5nC8UVz8jxPFSt87ueTQUObDyryPE+FdVvxf/YmgAAAAAAAAG7HEBM7M3rgr+nw3+3zOcXXeZ0MtGdV1i9Jha1fFfOfNyeszPGqgpKkoBbXpQO3eWYnqNWXwfbU+UWldCC3n1M364VWIin4JCW9dWmyDAAAAAAAZuK01QAAIABJREFU7oSh9tiZ+WlBR2ZeppmXJE0uLGiyfKR796+6oyt3S5r72ff5qedwNgyF1jrfhJXpY33qSWdiaFXeb1cOBQAAAAAAYKiGfCvWjJYSHY2SP+wpO/b1ALdiBRRYltwrrq5mIwqtpVTwmid6HKWNzStHAwAAAAAAcJcMucdOhw97ym6/1zf/Pch15z4tPg139cXp23JAgcY/nY2QkleOBQAAAAAA4G4Z+nXn9rta42lSC4moBr3o3BfNqaKIJgyj9S5sVXoaKJ+39oVSsZAmjHhjXUYp5RqjVVkrE4pvNWfbMtYkrRfkdfblAQAAAAAAuKOMUqnkBQKBz8+8ZqVSSdPT07cdBgAAAAAAwEhwXfcaS7EAAAAAAABwrUjsAAAAAAAAjCgSOwAAAAAAACOKxA4AAAAAAMCIIrEDAAAAAAAwokjsAAAAAAAAjCgSOwAAAAAAACPqq6ssOi1kZb+raXIhoaWZs8fqJrWQWNLMJzsAAAAAAABgUH2e2DnVXtbUG32jybOGizuy341pIZFQIpHQgv9E+eyeTocRKQAAAAAAALr0ldg5LbzRx0cJRUP3zhrV3h8nGvt+vnVCZ+aHWY3V3ut/PgwY5X5ahmG0fpFstT1WthTpGDM2nI73aTln7bViqdr7HgAAAAAAYMT0ldgZD0U/Kb1qO9XH2pi++W688VzUzvahaqrp458DRLiflhE8UObYk+fVf7morzHoKD0V15zTHCsotRaqJ378Ac3pQG55gG8DAAAAAADcYdfQPLlermWaeWkhoQW/VPvfqxZjVWX9klTY+lUx/xmj2U0llzN6Nt98E9Sqk5K9vauqAgosN153nN6pugfSw4B8n24HAAAAAAAwUq7UPPl8NR1u25pcSCgxI9XLs6Sx6fHPLTyHK3dLmvv5gjTMuUkanwIPbe2Wparram5dcstSQFL4QeCK8QAAAAAAANwdQzyxM66vx6Sx78Md5Vr18qyvz2rJcykdp27O89bt6pdTdQ/aqx+EdeA62t2WFv8RkPt7Ve6RrbkA53UAAAAAAMDoG2pi5+/fjqn27o32Gs2STwt/6GTsG/39/lX39GnxaVjJ4BlNkCX5fowovBXXq/3mG0evYrZSP8c6TvG4ch8uKugPSNuvtPs2rMAZZV0AAAAAAACjpr9SrOKOzPxJ+zlvysxLkwsJLc3UmyuHlZW9bepQksZmFY4+1lULsSTJF82poogmDKP1LmxV6g2U/THljqXIlCGjc6zRc8cXmJMdrDdXlqTFhyGF1lIq/DZAQAAAAAAAAHeEUSqVvEDg9nvOlEolTU9P33YYAAAAAAAAI8F13eu4FQsAAAAAAAA3gcQOAAAAAADAiCKxAwAAAAAAMKJI7AAAAAAAAIwoEjsAAAAAAAAjisQOAAAAAADAiBq5xI6zYSiSrXa/LFuKGIaMDecqOyptGDJWLFU/P/lm7Kcvjqf59xqGDCOtq/zVAAAAAABg9F0psXNayMo0Te0Uzx4vvjZlmlntfRgktC/A5xI0V+WPKed58o4zCg97bwAAAAAAMDK+6m/6qfaytt5/O6tJHZ4xXtSOmZe+n9VY+f1QArwUf0w5L3bFxUGtep5WhxoQAAAAAADA9evrxM5p4Y0+PkooGrp35njx9R/6+mlCS98NJbaGqqyVZtmRodBa96iz0R77pERrPy1jw1E1GzlzTuf7T8u4HKWNtJzOsqee0zdd6zv3aa4JJqWtuCbOLJvq/rvOOtnjnhN3v/9nVytRAwAAAAAAd11fiZ3xUFRLM+ePz/wU1eP7g4bUzdmYUPxhQZ7nyfM8Fda7x4Mvz37fshbSxNGL+nonJTv2qpVc8UVz8jxPFeu8gqakQlOuXniePK+g1FZcr/YbQ2VLz2NS5rjj+8sZVV4G26VSTqr+rhG7560q2Fhezb6Sfmm+ryijuJ53Jm+24go14z7OSLHnsspX+T+rKPM21GdiCAAAAAAAjIK73Ty5bGlzLaXCy+Dn556nmWyRpPlFpXQg95IJEimszHEzGRPU4rp04NYTJNXfc7KXI1r012cGn6SkLVfuJXf2RVcV87eetPg0LPuoY3Vn3P5FRZZt5X6/THLG0W7X/5lPsZ9Tsrd3705zaAAAAAAAMBR3O7Fzh/kCc9JWTruNJJHzJimtL+rSKaium60MTcTsi76mwMPL7uvqQEmFOsvDgsnLRgUAAAAAAEZIn82T0eIPKKyk4lOG4pKklAreZdM6jtJTccmqyIv6JNX79UwcnTe/KvetNPfEd8n9Uyp0lH0BAAAAAIAv090+seMPaE5J7Tb62lSzkU+aJ98W55+NxMwZ/XNa/AGFO0719JoLNBI1ZUvPLzqxs/9K8a2UFucvEZh/UZHlpEI0TAYAAAAA4IvXX2KnuCPTNGWaeZ1IOsmbMk1TO8X68GkhWx/fPlRNNR1umzLNrPY+XDW8oFadlJLBRrnS0YueRseO0h23ZdmxiT5ukGrfHDURs6W1UF83SAX/kZEa3zPOvPVKkj+mXy0pPtU7HtQzK9z6u4wpV5HeBs6dt2kF1XUCp3Ub11Rcdqvsqrm3T7Hf6g2TO2OjeTIAAAAAAF8eo1QqeYFA4LbjUKlU0vT09G2HcWnOhqHNBxXlor6udyEV5A3S7BkAAAAAAOASXNe946VYd1a95003R7trUvjB7SfJAAAAAADAXwOJnSvxKfZLbylWSAdW9wkeAAAAAACA68StWFfljynnxW47CgAAAAAA8BfGiR0AAAAAAIARRWIHAAAAAABgRJHYAQAAAAAAGFEjl9hxNgxFstXbDgMAAAAAAODWXal58mkhK/tdTZMLCS3NdI1oL2vrsNZ49C8o8dPMGTsAAAAAAABgUH0mduqJm/ffzmpSh5+OFt7o/bdhJULjkoraMfPKFsYVDY0PJ1oAAAAAAAC09FWKdVp4o4+PEoqG7p05Ph6KdiRxZvTAL9X+73TAEKuyVgwZRv0XWusZLluKGO1xY8PpXp2NtMfOmQMAAAAAADCK+krsjIeiPaVX18/ZmFD8YUGe58nzPBXWu0aVnoprzvEa4wWl1kLtHjxlS89jUua4Y+1yRpWXwZv9IwAAAAAAAK7B9TVPLu4oXx7T7A8DZILKljbXUiqck4ipZjeVXM7o2XzzTVCrTkr29q6qkqq/52QvR7Tob4w+SUlbrtyrRwQAAAAAAHBnXE9i58OesvkTTS5E9fj+tXyh7WFAvnOGfIE5aSun3XL92XmTlNYXxXkdAAAAAADwJbjSrVgX+rCn7Pah9H34Zsq23rqqKthK7lTdA0mB+oM/oLCSik8ZikuSUip4pHUAAAAAAMCXYbgndjqSOkO5Ccsf0JyS2t2vP1azka7myb4fIwpvxfVqv/nG0auYrdTPMfkkOf+MS1al1Z/H81Y5rQMAAAAAAL4Y/Z3YKe7IzJ+0n/OmzLw0uZDQ0oxU/PehapL0zpb5rjlpTLNPr1qSVe+ZYwQNJSVpvaCKJT1vDvtjyh1LkSlDRuNV2Koo1+i5E/xHRpqakBHr3DOlAgkeAAAAAADwBTBKpZIXCARuOw6VSiVNT08PdU9nw9Dmg4pyUV/Xu5AK8rgZCwAAAAAAjDDXda/xVqxbV5X7tvedo901Kfzg9hNZAAAAAAAAg/qCEzs+xX7JSLEJGYbR+IV0YHWf4AEAAAAAABhVw78V6y7xx5TzYp+fBwAAAAAAMIK+4BM7AAAAAAAAXzYSOwAAAAAAACOKxA4AAAAAAMCIuuOJnaqsFUPp/duOAwAAAAAA4O65UvPk00JW9ruaJhcSWprpGCjuyMyftJ/9C0r8NPPJegAAAAAAAAyuz8TOqfaytt5/O6tJHX46PLOkxEz33GxhXNHQ+KBxAgAAAAAAoEdfpVinhTf6+CihaOjeJWaP697frhjVJxylDUOGYcgw0nI6h8qWIq0xQ8aG0zOWltO5fsVStTWhXuplnLd313cNRbJVAQAAAAAA3BV9JXbGQ9Hu0qsLFXVUHtM33w1+WicZ3FTg2JPnVZRZTmqzlWBxlJ6Ka87x5HmePK+g1FqoJwGTVMhori8otRXXq0bPnmr2ueIPC421njxvVcHWuqqslZAOrEpr77nYBP1+AAAAAADAnTH05smnhaxM05Rp5nXif6TH9wffM+XkFPNLkk+LT8Oyj1xJUjW7qeRyRs/mmzODWnVSsrd3VT1zfVCL69KB2zG6tttzSqehvKucMvo16mvt/cwKK/nmzNkAAAAAAAA37krNky8yHooqEar/+7SQlZn9U+HoY11bl52HAfkunJDS4nz7KfjSU67xb180p8KRoZCRlCSFrYpyzURO2ZW9lZRtxLu3W38xpMABAAAAAAAGc63XnY9/943Gah91ep0feet2nc6pugd9LQ++9LpKrbrKuJYzqrTKtBq/l8HzNwMAAAAAALhB15rYKf77UDX/A13Xhee+HyMKd/TMkRy9itlK/Rz7zCmeswQUWO54nF9Uaiuu5zRMBgAAAAAAd1R/pVjFHZn5k/Zz3pSZlyYXElqaqZde2e9q7XH/ghI/XVdaR5I/ptyxFJkyZDReha2KcvMXrmqoylqZUHyr49V6QV5HT51Vr6C0MSEj1p6ScjytXmp/AAAAAACA62WUSiUvEAjcdhwqlUqanp6+7TAAAAAAAABGguu611uKBQAAAAAAgOtDYgcAAAAAAGBEkdgBAAAAAAAYUSR2AAAAAAAARhSJHQAAAAAAgBFFYgcAAAAAAGBEkdi5qv20jBVL1c9MczYMGUZEVrnzbVXWiqFI9nOrAQAAAAAAznelxM5pISvTNLVTPHeG9rKmTDOrvQ9XD24oypYiRlrOLYcBAAAAAAAwbF/1N/1Ue1lb77+d1aQOz59VeKPDv01qslYbMLzRF3zpyXt521EAAAAAAIAvUV8ndk4Lb/TxUULR0L3zJ33Y05t30uwPDwaNrV7utGHJWmmUM+1bihhGdwlUufGu+dtons1xlDYMGVNx2Uoq1JrTWRbVmNP8dZVW1cul0vvNcqqzSqokNxtpre8qreqK63MnhppxdMzbT7fjOuO7AAAAAAAAfSV2xkNRLc1cNONUe/86lL5/osf3BwusZS0u92dPhXVb8aCrF15Bqa2cdsuS5Cg9Fdec48nzPHleQam1UCPBEtSq58k7ziislApec05OMb9UT9yEdGBVGu8ryiiuiY3uFEwyaGjzQX1OYd1W/J8d41txhY5e1NcfZ6TY83YCxh9TrvX9izhKG804VhWU6kmh4IEyx42YnTnFpygnAwAAAAAA3YbbPLm4r0PN6klofHh7Lmf0bL7+z7D1rJ74aKhmN5XsGJeCWnVSsrd3P9vUWPuvFN9K6UXU13jhU+yXjMJru90JlPWCco05wScp6a3b3ns5o8rLRkT+RUWWbeV+76ch8m4rqZNrxSFVf89J1q+NBJSk+WfKLCe1u9/H1gAAAAAA4Is3xMROUTv5mmb/+7GGmNb5vIcB+T4/62zLAQU+MyX1pCOVNL8q77fYOd/zKfCwv8/bsbiSCivyY/eO7pEtOzbRUYo1ofhWf3sDAAAAAIAv3/ASO8Ujnaimw21TpmnKNPPt59fnXp81uM4TNJKq7sHl1265cjufy67sKwdSlftWmgtcPs0UtiqqWDqzzCrcKhFr/1bnz9wGAAAAAAD8RfV5K9YFZpaU6Oq/U9SO+Ye+fhodXr+dHr4fIwrH4nq1H2skPRy9itlKObn2qRp/QHOKa3d/VcHOxMj8olIKaTP7rFEGVZX1S1Jhq9JV7nVpjdKuwm99/g3RnApHhkIrAVUap4GCT1Kyg89l/Zhrl2MBAAAAAAD06O/ETnGn4zSOdJKvn87ZucYDORfyx5Q7zugg2CxZqver6T7ZUu+7kwz23jAV1KpX0Fyr5GlC8YeFrl43n7UV10SzXCooFZrNjyVVm7dldd3KdXYD5ODLRuPmZmzzq42GyR03dn32Zi0AAAAAAPBXY5RKJS8Q+FynmetXKpU0PT1922EAAAAAAACMBNd1h3wrFgAAAAAAAG4MiR0AAAAAAIARRWIHAAAAAABgRJHYAQAAAAAAGFEkdgAAAAAAAEYUiR0AAAAAAIARRWIHAAAAAABgRH11lUWnhazsdzVNLiS0NNMx8GFP2e1D1Ton+xeU+GlGAAAAAAAAGK4+Ezun2svaev/trCZ1ePaUsVmFo481PnhsAAAAAAAAuEBfpVinhTf6+CihaOjedcXToyprJSKr7ChtGDIMQ4aRltM1p3PMUCRbbQ+VLUWMtJzOOSuWql37t9d+snfZUqRjb2OjY3Q/LWPDUTUbOfvbAAAAAAAA16yvxM54KNpdenUjbMWnNhU49uR5ngrrSYVaCZaqrJWQDqyKPM+T5xU0F5tQer9zfVIho7m+oNRWXK8a49Xsc8UfFhprPXneqoKtdY7SU3HNOV5r79RaqDt5sxbSxNGL+riTkh171ZN0AgAAAAAAuD7Db55cO5RtmjJNU6a5o+IQtkw5OcX89X8Hn6Skt2791E15Vzll9GvU15gZ1DMrrOQb55z1QS2uSwduZ3Jm98xkTDW7qeRyRs/mm2+CWnVSsrd32yd+ljOqvGykguYXldKB3PLAfy4AAAAAAMClXKl58rnuP1Y08bj1eFrIyjZ3pMSShnrQZ8uVK8lXdmVvJWUb8e7x9RcdDyktzrefgi895Rr/9kVzKhwZChlJSVLYqijXShJJehhQxxMAAAAAAMCdcq3XnY9/943GrmPj5YACrX9nVGmVUjV+L4MXre4SfOl1lXF1lVo1TwY1VN2DYUQPAAAAAAAwFNeY2DnV3r8OVfM/GOJpHUfpYFLhp4v1kzTzi0ptxfV8KE2LAwost598P0YU7ujHIzl6FbOV+jnGKR4AAAAAAHAn9FeKVdyRmT9pP+dNmXlpciGhpZlG6dW7Wmt47PuwEqHBLz5PBg0lG//uLpcKatUrKG1MyIi156ccT6vzvbv0qspamVB8q+PVekFec29/TLljKTJlyOj89mf3BQAAAAAAuBlGqVTyAoHA52des1KppOnp6Z639eSL+/NlEjUAAAAAAAB/Ha7rXm+PHQAAAAAAAFwfEjsAAAAAAAAjarjXnQ+dT7HfvNsOAgAAAAAA4E7ixA4AAAAAAMCIIrEDAAAAAAAwokjsAAAAAAAAjCgSO8NSthQx0nIuOb2ajcgwjPpv47KrAAAAAAAA2q6U2DktZGWapnaKZ48XX5syzcbv9TmTbspnEi7OhqFItnqjIUmSL5qT53mqWOFr2f+2/i4AAAAAAHBz+rwV61R7WVvvv53VpA7PHT/824ISiZmhBAgAAAAAAICz9XVi57TwRh8fJRQN3Tt7QnG/ntT5aVhJnaqslYissqN0s2yp9/RN2VKkNdZZ1tRYMxWXraRCrTkRWeV2KVRoTbJjE+eURXV+94wTMPvp9rqpuOzOyLMRRbJVORvt9en9Pv70zr171l609+X+LgAAAAAA8CXoK7EzHopq6YKcTbF0orH/+lM7ZrsU67xyrcuzFZ/aVODYk+d5KqwnFepM3kzFNefUxzyvoNRaqJGACWrV8+QdZxRWSgWvOSenmL9dClVYl8JWpTHmyXsZbOxdlbUS0kFrrKC52EQ7wVK2FAkeKNOIq/6dnshjE9p80FjvpJQMXrYHj6P0m8V2TGes7dy7YoWV/MVSVZf5uwAAAAAAwJdiiM2TT/Xnf6Tau496kEgokUgosTCpk3xWex8G2znl1JMxkhR8kpLeuqpKqmY3lVzO6Nl8c2ZQq05K9vauBu4uU95VThn9GvW19n5mhZV8U0+vOP+MS9avrbjOtF5Qrrl+flEpHcgtX+bjQa12JmLOWtuxt+/HiMJbrtxL/mkAAAAAAODLMPRbsSYXltQ61DMzr9mxmj7+OeSPdCYxHgbku2juVZVd2VtxTXSUQ03E7M+vu5B9ycSOusqsDCOk5IBfBgAAAAAAX54hJnbGde9vUu1/T4e35XmWAwo0/904vdNUdQ+G+J2MKq0SrmGUNIUVuOiET0M1G1ForbN8rKDUAF8FAAAAAABfpqGe2JmZnlTt3b5abXWK+zqsTerB0C7IcpQOJhV+uiifmiVIcb3ab4+/itlK/Rxrn+LxBzSnpHbPaVwceBA+u3RrflGprbien3NlePe6eq+fi87zOBshJZcjWrxEYkdSV/LK2ej/xM65fxcAAAAAAPhi9JfYKe40miLndSLpJN/TIHlmSeHva8o3myfnpYVER2nWFSWD7ZKkA6vS7lvjjyl3nNFBz/jqfOfqet+d9h71W7GafNFflVFHyVWrMXNQq42GyWfdTtW9blOB4zNO1ayFWutCbzOq/NZMOFVlrXSUdzXnNb7ti75QqqMMbPNBpu8TO+f/XQAAAAAA4EthlEolLxAIfH7mNSuVSpqenu55W5W1MiH3Z68nWXP3VbMRTRy94DYqAAAAAABwLVzXHX7zZAAAAAAAANwMEjsAAAAAAAAj6qvbDuBiPsV+8247iCvxRXMazcgBAAAAAMCo4MQOAAAAAADAiCKxAwAAAAAAMKJI7AAAAAAAAIyoEU/sOEobEVnl244DAAAAAADg5l2pefJpISv7XU2TCwktzbTeai9r67DWO3tMs0+jenx/kDABAAAAAADQq8/ETj158/7bWU3qsGdsXI+jCT3ufFXckZmX7pHUAQAAAAAAGLq+SrFOC2/08VFC0dC9S80vlk409v28Zj4/9QJVWSuGDKP5S8vpnVK2FGmOr1iqdo7tpzvW9pZt9ey90bmzo7SRlnPR3gAAAAAAALeor8TOeCjaUXr1GR/29Ed5Uo9C41cIq62afa74w4I8z2v8VhXsmmErHnT1wvPkeQWltuJ6td8YKluKBA+UOW6sdeYUn2onhpyNiY69K8q8DSmS7UzdJBWaau5dUUZxPc+S2gEAAAAAAHfDtTVPLv77UBr4tE7D2u6np3RawsocN5M9QS2uSwduPflS/T0nWb8q5m9MnX+mzHJSu/uS5Gh3LaXCy2aayKfYzynZ27sdp3I69/Zp8WlY9pE7jL8IAAAAAABgYNeT2Pmwpz/KY/rmu8FO60iSL5pTYT2pUKMcKtLHiRn3yJYdm+goxZpQfKsxWHZ1oPa+hmHICCY/v+lbl3IsAAAAAABwJ1xLYqf470PV/I+GdhNW8GWzDKugudhEX8mdsFXpKOOq/1bnm6MpFXrGvN9i8l204cPAxeMAAAAAAAA3ZPiJncZpndkfhlKE1SOgwPLlZwefpGTHnvc0TG7wLyqynFRo4/wiry5lS89jtlJPgp+fCwAAAAAAcAP6S+wUd2SapkwzrxNJJ3lTpmlqp9iccKq9fw3ztE7vjVj1Zse56CXPzMyvNhomn3Wrlk+x3+oNk42Ocqzu00B2e+1UXHNO52kfAAAAAACA22WUSiUvEAjcdhwqlUqanp6+7TA6OEobmwoc59rNlwEAAAAAAO4I13Wv71YsAAAAAAAAXC8SOwAAAAAAACPqq9sO4O4KatXL3XYQAAAAAAAA5+LEDgAAAAAAwIgisQMAAAAAADCiSOwAAAAAAACMqL9IYqcqa8WQYTR+G85tBwQAAAAAADCwKzVPPi1kZb+raXIhoaWZ7rHia1P5cvv5rDk3rZp9rrgyqngx+W43FAAAAAAAgKHpM7Fzqr2srfffzmpSh5+OFrLKa0GJRCOTU9yRmd9RcWZJt5nbcY9shZ/+SlIHAAAAAAB8UfoqxTotvNHHRwlFQ/fOHv+/msb+a7z94t7XGhsoPOmTMiojra5CqrKliHFRmVVV7ttL7r1iqTpwvAAAAAAAADejr8TOeCh6YVnVzPSkau9s7RQl6VR7/zpUzf9goNM61exzxR8W5Hle47eqYGvUUXoqrjmnOVZQai2kSLZaHzMMGcaE4luSHZtoJHAissrNvV9JvzTXVpRRXM+zpHYAAAAAAMBoGG7z5JklJRILUt6Uadp6/21YiZ+GUIS1tquz2h1Xs5tKLmf0bL75JqhVJyV7e1dVBbXaTNgsS2Gr0kjg5BTz12f7oqutf0s+LT4Nyz5yB48XAAAAAADgBgw3sVPckWn+oa+fJpRILGjsnS0zu6fTAbb0RXMqrCcVapRLRXpP1DwMXL13Tk8Z10TMHiBSAAAAAACAmzXExM6p9v440dj3T/T4viTNaCmxoMnaofaLg+0cfNkutZqLTXQnd966XX1xqu7BJXetl3GpdZLHU8UKDxYoAAAAAADADRruiR1Jtf/rOJ9TPNKJxvT12b2WryCgwHL7yfdjROGtuF7tN984ehWzlfr58teazwUaM8uWnnNiBwAAAAAAjJD+rjsv7sjMn7Sf86bMvDS5kNDSzLgeRxf00czLNPONCWOafRptnOC5iqqslXrz45b1grxoIxnjjyl3LEWmDBmN4bBVUW6+d5+zBPXMCmsiaCgpSUopY4VlH101VgAAAAAAgJtllEolLxAI3HYcKpVKmp6evu0wAAAAAAAARoLrusMvxQIAAAAAAMDNILEDAAAAAAAwokjsAAAAAAAAjCgSOwAAAAAAACOKxA4AAAAAAMCIIrEDAAAAAAAwokjsyFHaMGSsWKredigAAAAAAAB9+Ooqi04LWdnvappcSGhppnus+NpUvtx4GJtVOPpY4wMG6WwY2nxQUS7qG3AnAAAAAACAL0efiZ1T7WVtvf92VpM6/HS0kFX+P7MKJ+rJnOJrU/bre0r8NPPpVndGUKuep9XbDgMAAAAAAKBPfZVinRbe6OOjhKKhe2eMFrX/rqbJR+0TOjM/zGqsfKTiFYOrZiMyDEOhNcmOTcgwjPpvw2nOkLViKL1fP9VTH4/IKndssp9urzPqc3v3796zyVHaSMspW4o051CuBQAAAAAA7pC+EjvjoegnpVfdxvR1Z87n/j2NqaY/P1wpNvmiOXmep8K6FLYq8jyv/nsZ7JqXDNZLtepzbcX/2UzSOEq/WWyvc1IDvkPEAAAG+ElEQVRKBtNyevavWOFzIkgqNOXqhefJ8wpKbcX1av+cqQAAAAAAADdsiM2TZ/TAX9Phv9vnc4qv8zoZ3gfOt15o9d8JPklJb93GyZqgVjuTQPOLSulAbvmsTc4SVuZ4VcHGXovr0oHLmR0AAAAAAHA3XKl58nlmflrQkZmXaeYlSZMLC5osH+ne/WF+5VOpJ53Jm1V5v7UfnY16KVdbWJnrDQcAAAAAAOBGDDWxI81oKdFRq/VhT9mxrwe+FeuqqtmIQmspFbzmqRtHaWPzlqIBAAAAAAAYriGWYvX4sKfs9nt989+DX3ceeBCWvb17tcbFywEFGv90NkJKDhgLAAAAAADAXdHfiZ3ijsx8R9ecvCkzL00uJLQ0U7/u3H5XawxOaiER1TAuOvdFf1Vme0ITRrz+Yr3wSQPls9e9UCoWaq0LWxmllGuMVmWtTCi+1Zxty1i7/N4AAAAAAAC3zSiVSl4gEPj8zGtWKpU0PT1922EAAAAAAACMBNd1r7EUCwAAAAAAANeKxA4AAAAAAMCIIrEDAAAAAAAwokjsAAAAAAAAjCgSOwAAAAAAACOKxA4AAAAAAMCIIrEzoGo2IsMwlN6/7UgAAAAAAMBfzVf9Lii+NpUvNx7GZhWOPtZ4x/hpISv7Xa3xNKmFxJJmBgzS2TC0+aCiXNQ34E43bD8t45eAKr/FNGKRAwAAAACAEdBfYqe4o7wWlEjMSDrVXtaW/fqeEj/NtMbtd2NaSEQ1o0YSKLun8Z7kz5fEF83Ji952FAAAAAAA4K+ov1KsmaV2Ekfj+vu3Y9J//tSpJOlUe3+caOz7+dYJnZkfZjVWe6//+XC14JplTqE1yY5NyDCM+m/DaY+vWKr2rlmxVFVV1kpEVjbdWuNsGD1lU47SzT0No2cvR2kjLadsKXLueHNtRFa5I4jmmmBS2oprojUvLefM9T17ly1FjLSczjk9fycAAAAAAMAQe+yc6mNtTN981zybU9TO9qFqqunjn1fb0RfNyfM8FdalsFWR53n138tgffzHiMJbOe12JFXcI1vhp4uN0idb8e2AKscZhddC2nxQUcUKK/nGkVSVtRLSQWvfijKKa2LD6YggqdCUqxeeJ88rKLUV16tWUiio1eb73sD9MeU8T56TkpYzqjTj9lZVj/yS3zY2FTg+69sAAAAAAACDJHY+7OnNu5omH/WWWZ1qL2vKNPPSQkILfqn2v6eDxnk2f0wv1m3lfm+eZXG0u5bSi45ePKmfm/1tut9r/5XiW53vfIr9klF4bbfjVE1YmeNmMiaoxXXpwB3CuZlLfVtKOTnF/EP+NgAAAAAA+GL03Ty5rn4aR9+HtdTVGbmmw21bkwsJJWakenmWNDZ9fR12gk9Ssn/ZVTUak29/V8n1RXmXXbwcUODaIhv02yktzrefgi895a45JAAAAAAAMFqucGKnqB0zrxP/gqKhzoTNuL4ek8a6kj318qyv7w0h0vPMLyrVKMdy3iSVehK8/NotV27nc9mVPez47uK3AQAAAADAF6HPxE47qdNuotxUb6Zce/dGe41myaeFP3Qy9o3+fn+wIAMPwrK3d89pHhzU4rott+xo921Gz+bPnPSp+UWllNRmtrlrVdYvSYWtZ+ojNXQxf+CTHkA39m0AAAAAAPDF66sU67Twh04kqZyv99BpmFxIaGlGGg9FFVZW9rapQ0kam1V4CFed+6K/KrM9oQkjXn+xXmg1UJak4D8y2pwKSVZFq5feNahVr6C0MSEjpva+nX14LrKfrt961TRlKK6wMsfNvjiS/DH9auU0MWWoHnlKBW9VwUG/DQAAAAAAIMkolUpeIHBrnWZaSqWSpqenbzsMAAAAAACAkeC67jCvOwcAAAAAAMBNIrEDAAAAAAAwokjsAAAAAAAAjCgSOwAAAAAAACOKxA4AAAAAAMCIIrEDAAAAAAAwokjsAAAAAAAAjKiv+l1QfG0qX248jM0qHH2s8TPnjGn2aVSP7w8eJAAAAAAAAD7V34md4o7yWlAikVAiEdasDmW/LnZO0I5p6ui/ZjU23DgBAAAAAADQo7/EzsySEj/NNB7G9fdvx6T//KnTxpvi6z/09dOElr4baowAAAAAAAA4Q9+lWBeZ+Sla/8eHYe4KAAAAAACAs1y9efKHPb15V9Pko0977AAAAAAAAOD6XTGxU9TO9qH0fVhLM5+fDQAAAAAAgOG7QmKnqB0zrxP/gqIhzuoAAAAAAADclj4TO+2kTruJMgAAAAAAAG5DX82TTwt/6ESSynmZZr71fnIhoaUZ6bSQlf2u1np/uG3qUGOafRrV4/vDChkAAAAAAACSZJRKJS8QCNx2HCqVSpqenr7tMAAAAAAAAEaC67oD3IoFAAAAAACAW/X/xlIS2HfGNRwAAAAASUVORK5CYII="
- }
- },
"cell_type": "markdown",
"metadata": {},
"source": [
- "The VOC dataset consists of 20 classes which can be detected. The voc_classes.txt has the list of classes. \n",
- "\n",
- "
"
+ "\n",
+ "The VOC dataset has 20 detectable classes (person, dog, car, bicycle…). load_classes() reads them from img/voc_classes.txt. Each class is automatically assigned a unique color for its bounding box.\n",
+ "
"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "📋 load_classes() — reads the 20 VOC class names from a text file — click to peek inside
\n",
+ "def load_classes(classes_path):\n",
+ " with open(classes_path) as f:\n",
+ " class_names = [c.strip() for c in f.readlines()]\n",
+ " return class_names
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nLoad the list of 20 object class names the model can detect.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "# Get Model Classification Information\n",
- "def get_class(classes_path):\n",
- " with open(classes_path) as f:\n",
- " class_names = f.readlines()\n",
- " class_names = [c.strip() for c in class_names]\n",
- " return class_names\n",
- " \n",
- "classes_path = \"img/voc_classes.txt\"\n",
- "class_names = get_class(classes_path)"
+ "class_names = load_classes('img/voc_classes.txt')"
]
},
{
- "attachments": {
- "image.png": {
- "image/png": "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"
- }
- },
"cell_type": "markdown",
"metadata": {},
"source": [
- "### To understand the output of the model in an easier manner we would want to draw a bounding box around the object which is detected and display a score which represents the probability of thhe detected object belonging to a specific class.\n",
- "\n",
- "### We can associate each class with a specific color. The cell below does this for us."
+ "\n",
+ "make_colors() generates a unique color for each of the 20 classes so bounding boxes are easy to tell apart at a glance. The color list is stored in colors and used by draw_boxes() inside yolo_helpers.py.\n",
+ "
"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "🎨 make_colors() — assigns a unique color to every class — click to peek inside
\n",
+ "def make_colors(class_names):\n",
+ " n = len(class_names)\n",
+ " hsv_tuples = [(1.0 * x / n, 1., 1.) for x in range(n)]\n",
+ " colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))\n",
+ " colors = list(map(lambda x: (int(x[0]*255), int(x[1]*255), int(x[2]*255)), colors))\n",
+ " random.seed(0)\n",
+ " random.shuffle(colors) # reproducible but shuffled so nearby classes look different\n",
+ " random.seed(None)\n",
+ " return colors
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nGenerate a unique color for each of the 20 detectable object classes.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "# Define unique colors for each class\n",
- "num_classes = len(class_names)\n",
- "hsv_tuples = [(1.0 * x / num_classes, 1., 1.) for x in range(num_classes)]\n",
- "colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))\n",
- "colors = list(map(lambda x: \n",
- " (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), \n",
- " colors))\n",
- "random.seed(0)\n",
- "random.shuffle(colors)\n",
- "random.seed(None)"
+ "colors = make_colors(class_names)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "\n",
- "### Let's define some useful functions which will be used later in the notebook."
+ "\n",
+ "All the heavy lifting — image resizing, anchor decoding, NMS box filtering, and drawing — lives in yolo_helpers.py. You do not need to touch that file. The run() function below calls everything automatically.\n",
+ "
"
]
},
{
- "cell_type": "code",
- "execution_count": null,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
"source": [
- "# This function resizes the image with unchanged aspect ratio using padding.\n",
+ "\n",
+ "🔬 Full helper code — pre-processing, NMS, draw_boxes (yolo_helpers.py) — click to peek inside
\n",
+ """"\n",
+ "YOLOv3 helper functions — pre-processing, decoding, NMS, drawing, and setup.\n",
+ "Students don't need to read or edit this file.\n",
+ """"\n",
+ "import os\n",
+ "import time\n",
+ "import random\n",
+ "import colorsys\n",
+ "\n",
+ "import numpy as np\n",
+ "import cv2\n",
+ "import matplotlib.pyplot as plt\n",
+ "from matplotlib.patches import Rectangle\n",
+ "from IPython.display import display, Image\n",
+ "\n",
+ "\n",
+ "# ---------------------------------------------------------------------------\n",
+ "# Class names & colors\n",
+ "# ---------------------------------------------------------------------------\n",
+ "\n",
+ "def load_classes(classes_path="img/voc_classes.txt"):\n",
+ " with open(classes_path) as f:\n",
+ " class_names = [c.strip() for c in f.readlines()]\n",
+ " return class_names\n",
+ "\n",
+ "\n",
+ "def make_colors(class_names):\n",
+ " n = len(class_names)\n",
+ " hsv_tuples = [(1.0 * x / n, 1., 1.) for x in range(n)]\n",
+ " colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))\n",
+ " colors = list(map(lambda x: (int(x[0]*255), int(x[1]*255), int(x[2]*255)), colors))\n",
+ " random.seed(0)\n",
+ " random.shuffle(colors)\n",
+ " random.seed(None)\n",
+ " return colors\n",
+ "\n",
+ "\n",
+ "# ---------------------------------------------------------------------------\n",
+ "# Anchor boxes (YOLOv3 VOC defaults)\n",
+ "# ---------------------------------------------------------------------------\n",
+ "\n",
+ "def load_anchors():\n",
+ " anchor_list = [10,13,16,30,33,23,30,61,62,45,59,119,116,90,156,198,373,326]\n",
+ " return np.array([float(x) for x in anchor_list]).reshape(-1, 2)\n",
+ "\n",
+ "\n",
+ "# ---------------------------------------------------------------------------\n",
+ "# DPU tensor setup\n",
+ "# ---------------------------------------------------------------------------\n",
+ "\n",
+ "def setup_dpu_buffers(overlay):\n",
+ " """Return (dpu, input_data, output_data, image_buf, shape_in, shape_outs)."""\n",
+ " dpu = overlay.runner\n",
+ " inputTensors = dpu.get_input_tensors()\n",
+ " outputTensors = dpu.get_output_tensors()\n",
+ "\n",
+ " shapeIn = tuple(inputTensors[0].dims)\n",
+ " shapeOut0 = tuple(outputTensors[0].dims)\n",
+ " shapeOut1 = tuple(outputTensors[1].dims)\n",
+ " shapeOut2 = tuple(outputTensors[2].dims)\n",
+ "\n",
+ " input_data = [np.empty(shapeIn, dtype=np.float32, order="C")]\n",
+ " output_data = [np.empty(shapeOut0, dtype=np.float32, order="C"),\n",
+ " np.empty(shapeOut1, dtype=np.float32, order="C"),\n",
+ " np.empty(shapeOut2, dtype=np.float32, order="C")]\n",
+ "\n",
+ " return dpu, input_data, output_data, input_data[0], shapeIn, (shapeOut0, shapeOut1, shapeOut2)\n",
+ "\n",
+ "\n",
+ "def list_images(image_folder="img"):\n",
+ " images = [i for i in os.listdir(image_folder) if i.endswith("JPEG")]\n",
+ " return image_folder, images\n",
+ "\n",
+ "\n",
+ "# ---------------------------------------------------------------------------\n",
+ "# Pre-processing\n",
+ "# ---------------------------------------------------------------------------\n",
+ "\n",
"def letterbox_image(image, size):\n",
" ih, iw, _ = image.shape\n",
" w, h = size\n",
" scale = min(w/iw, h/ih)\n",
- " \n",
- " nw = int(iw*scale)\n",
- " nh = int(ih*scale)\n",
- "\n",
- " image = cv2.resize(image, (nw,nh), interpolation=cv2.INTER_LINEAR)\n",
- " new_image = np.ones((h,w,3), np.uint8) * 128\n",
- " h_start = (h-nh)//2\n",
- " w_start = (w-nw)//2\n",
+ " nw, nh = int(iw*scale), int(ih*scale)\n",
+ " image = cv2.resize(image, (nw, nh), interpolation=cv2.INTER_LINEAR)\n",
+ " new_image = np.ones((h, w, 3), np.uint8) * 128\n",
+ " h_start, w_start = (h-nh)//2, (w-nw)//2\n",
" new_image[h_start:h_start+nh, w_start:w_start+nw, :] = image\n",
" return new_image\n",
"\n",
- "# This function performs pre-processing by helping us in converting the image into an array which can be fed for processing.\n",
+ "\n",
"def pre_process(image, model_image_size):\n",
" image = image[...,::-1]\n",
" image_h, image_w, _ = image.shape\n",
- " \n",
" if model_image_size != (None, None):\n",
- " assert model_image_size[0]%32 == 0, 'Multiples of 32 required'\n",
- " assert model_image_size[1]%32 == 0, 'Multiples of 32 required'\n",
+ " assert model_image_size[0] % 32 == 0, 'Multiples of 32 required'\n",
+ " assert model_image_size[1] % 32 == 0, 'Multiples of 32 required'\n",
" boxed_image = letterbox_image(image, tuple(reversed(model_image_size)))\n",
" else:\n",
" new_image_size = (image_w - (image_w % 32), image_h - (image_h % 32))\n",
" boxed_image = letterbox_image(image, new_image_size)\n",
- " image_data = np.array(boxed_image, dtype='float32')\n",
- " image_data /= 255.\n",
- " image_data = np.expand_dims(image_data, 0) \n",
- " return image_data\n",
+ " image_data = np.array(boxed_image, dtype='float32') / 255.\n",
+ " return np.expand_dims(image_data, 0)\n",
+ "\n",
+ "\n",
+ "# ---------------------------------------------------------------------------\n",
+ "# Decoding / NMS\n",
+ "# ---------------------------------------------------------------------------\n",
"\n",
- "# This function gets information on box position, its size along with confidence and box class probabilities\n",
"def _get_feats(feats, anchors, num_classes, input_shape):\n",
" num_anchors = len(anchors)\n",
- " anchors_tensor = np.reshape(np.array(anchors, dtype=np.float32), [1, 1, 1, num_anchors, 2])\n",
+ " anchors_tensor = np.reshape(np.array(anchors, dtype=np.float32), [1,1,1,num_anchors,2])\n",
" grid_size = np.shape(feats)[1:3]\n",
" nu = num_classes + 5\n",
" predictions = np.reshape(feats, [-1, grid_size[0], grid_size[1], num_anchors, nu])\n",
- " grid_y = np.tile(np.reshape(np.arange(grid_size[0]), [-1, 1, 1, 1]), [1, grid_size[1], 1, 1])\n",
- " grid_x = np.tile(np.reshape(np.arange(grid_size[1]), [1, -1, 1, 1]), [grid_size[0], 1, 1, 1])\n",
- " grid = np.concatenate([grid_x, grid_y], axis = -1)\n",
- " grid = np.array(grid, dtype=np.float32)\n",
- "\n",
- " box_xy = (1/(1+np.exp(-predictions[..., :2])) + grid) / np.array(grid_size[::-1], dtype=np.float32)\n",
- " box_wh = np.exp(predictions[..., 2:4]) * anchors_tensor / np.array(input_shape[::-1], dtype=np.float32)\n",
- " box_confidence = 1/(1+np.exp(-predictions[..., 4:5]))\n",
- " box_class_probs = 1/(1+np.exp(-predictions[..., 5:]))\n",
+ " grid_y = np.tile(np.reshape(np.arange(grid_size[0]), [-1,1,1,1]), [1,grid_size[1],1,1])\n",
+ " grid_x = np.tile(np.reshape(np.arange(grid_size[1]), [1,-1,1,1]), [grid_size[0],1,1,1])\n",
+ " grid = np.array(np.concatenate([grid_x, grid_y], axis=-1), dtype=np.float32)\n",
+ " box_xy = (1/(1+np.exp(-predictions[...,:2])) + grid) / np.array(grid_size[::-1], dtype=np.float32)\n",
+ " box_wh = np.exp(predictions[...,2:4]) * anchors_tensor / np.array(input_shape[::-1], dtype=np.float32)\n",
+ " box_confidence = 1/(1+np.exp(-predictions[...,4:5]))\n",
+ " box_class_probs = 1/(1+np.exp(-predictions[...,5:]))\n",
" return box_xy, box_wh, box_confidence, box_class_probs\n",
"\n",
"\n",
- "# This function is used to correct the bounding box position by scaling it\n",
"def correct_boxes(box_xy, box_wh, input_shape, image_shape):\n",
- " box_yx = box_xy[..., ::-1]\n",
- " box_hw = box_wh[..., ::-1]\n",
- " input_shape = np.array(input_shape, dtype = np.float32)\n",
- " image_shape = np.array(image_shape, dtype = np.float32)\n",
- " new_shape = np.around(image_shape * np.min(input_shape / image_shape))\n",
+ " box_yx = box_xy[...,::-1]\n",
+ " box_hw = box_wh[...,::-1]\n",
+ " input_shape = np.array(input_shape, dtype=np.float32)\n",
+ " image_shape = np.array(image_shape, dtype=np.float32)\n",
+ " new_shape = np.around(image_shape * np.min(input_shape/image_shape))\n",
" offset = (input_shape - new_shape) / 2. / input_shape\n",
- " scale = input_shape / new_shape\n",
+ " scale = input_shape / new_shape\n",
" box_yx = (box_yx - offset) * scale\n",
" box_hw *= scale\n",
- "\n",
- " box_mins = box_yx - (box_hw / 2.)\n",
- " box_maxes = box_yx + (box_hw / 2.)\n",
- " boxes = np.concatenate([\n",
- " box_mins[..., 0:1],\n",
- " box_mins[..., 1:2],\n",
- " box_maxes[..., 0:1],\n",
- " box_maxes[..., 1:2]\n",
- " ], axis = -1)\n",
- " boxes *= np.concatenate([image_shape, image_shape], axis = -1)\n",
+ " box_mins = box_yx - (box_hw/2.)\n",
+ " box_maxes = box_yx + (box_hw/2.)\n",
+ " boxes = np.concatenate([box_mins[...,0:1], box_mins[...,1:2],\n",
+ " box_maxes[...,0:1], box_maxes[...,1:2]], axis=-1)\n",
+ " boxes *= np.concatenate([image_shape, image_shape], axis=-1)\n",
" return boxes\n",
"\n",
- "# This function is used to get information on the valid objects detected and their scores\n",
+ "\n",
"def boxes_and_scores(feats, anchors, classes_num, input_shape, image_shape):\n",
" box_xy, box_wh, box_confidence, box_class_probs = _get_feats(feats, anchors, classes_num, input_shape)\n",
- " boxes = correct_boxes(box_xy, box_wh, input_shape, image_shape)\n",
- " boxes = np.reshape(boxes, [-1, 4])\n",
- " box_scores = box_confidence * box_class_probs\n",
- " box_scores = np.reshape(box_scores, [-1, classes_num])\n",
+ " boxes = np.reshape(correct_boxes(box_xy, box_wh, input_shape, image_shape), [-1, 4])\n",
+ " box_scores = np.reshape(box_confidence * box_class_probs, [-1, classes_num])\n",
" return boxes, box_scores\n",
"\n",
"\n",
- "# This function suppresses non-maximal boxes by eliminating boxes which are lower than the threshold\n",
"def nms_boxes(boxes, scores):\n",
- " \"\"\"Suppress non-maximal boxes.\n",
- "\n",
- " # Arguments\n",
- " boxes: ndarray, boxes of objects.\n",
- " scores: ndarray, scores of objects.\n",
- "\n",
- " # Returns\n",
- " keep: ndarray, index of effective boxes.\n",
- " \"\"\"\n",
- " x1 = boxes[:, 0]\n",
- " y1 = boxes[:, 1]\n",
- " x2 = boxes[:, 2]\n",
- " y2 = boxes[:, 3]\n",
- "\n",
+ " x1, y1, x2, y2 = boxes[:,0], boxes[:,1], boxes[:,2], boxes[:,3]\n",
" areas = (x2-x1+1)*(y2-y1+1)\n",
" order = scores.argsort()[::-1]\n",
- "\n",
" keep = []\n",
- " while order.size > 0:\n",
+ " while order.size > 0:\n",
" i = order[0]\n",
" keep.append(i)\n",
- "\n",
" xx1 = np.maximum(x1[i], x1[order[1:]])\n",
" yy1 = np.maximum(y1[i], y1[order[1:]])\n",
" xx2 = np.minimum(x2[i], x2[order[1:]])\n",
" yy2 = np.minimum(y2[i], y2[order[1:]])\n",
- "\n",
- " w1 = np.maximum(0.0, xx2 - xx1 + 1)\n",
- " h1 = np.maximum(0.0, yy2 - yy1 + 1)\n",
- " inter = w1 * h1\n",
- "\n",
- " ovr = inter / (areas[i] + areas[order[1:]] - inter)\n",
- " inds = np.where(ovr <= 0.55)[0] # threshold\n",
- " order = order[inds + 1]\n",
- "\n",
+ " w1 = np.maximum(0.0, xx2-xx1+1)\n",
+ " h1 = np.maximum(0.0, yy2-yy1+1)\n",
+ " inter = w1*h1\n",
+ " ovr = inter / (areas[i] + areas[order[1:]] - inter)\n",
+ " order = order[np.where(ovr <= 0.55)[0]+1]\n",
" return keep\n",
"\n",
- "# This function gives essential information about the objects detected like bounding box information, score of the object \n",
- "# detected and the class associated with it\n",
- "def evaluate(yolo_outputs, image_shape, class_names, anchors):\n",
- " score_thresh = 0.2\n",
- " anchor_mask = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]\n",
- " boxes = []\n",
- " box_scores = []\n",
- " input_shape = np.shape(yolo_outputs[0])[1 : 3]\n",
- " input_shape = np.array(input_shape)*32\n",
"\n",
+ "def evaluate(yolo_outputs, image_shape, class_names, anchors, score_thresh=0.2):\n",
+ " anchor_mask = [[6,7,8],[3,4,5],[0,1,2]]\n",
+ " input_shape = np.array(np.shape(yolo_outputs[0])[1:3]) * 32\n",
+ " boxes, box_scores = [], []\n",
" for i in range(len(yolo_outputs)):\n",
- " _boxes, _box_scores = boxes_and_scores(\n",
- " yolo_outputs[i], anchors[anchor_mask[i]], len(class_names), \n",
- " input_shape, image_shape)\n",
- " boxes.append(_boxes)\n",
- " box_scores.append(_box_scores)\n",
- " boxes = np.concatenate(boxes, axis = 0)\n",
- " box_scores = np.concatenate(box_scores, axis = 0)\n",
- "\n",
- " mask = box_scores >= score_thresh\n",
- " boxes_ = []\n",
- " scores_ = []\n",
- " classes_ = []\n",
+ " b, s = boxes_and_scores(yolo_outputs[i], anchors[anchor_mask[i]],\n",
+ " len(class_names), input_shape, image_shape)\n",
+ " boxes.append(b); box_scores.append(s)\n",
+ " boxes = np.concatenate(boxes, axis=0)\n",
+ " box_scores = np.concatenate(box_scores, axis=0)\n",
+ " mask = box_scores >= score_thresh\n",
+ " boxes_, scores_, classes_ = [], [], []\n",
" for c in range(len(class_names)):\n",
- " class_boxes_np = boxes[mask[:, c]]\n",
- " class_box_scores_np = box_scores[:, c]\n",
- " class_box_scores_np = class_box_scores_np[mask[:, c]]\n",
- " nms_index_np = nms_boxes(class_boxes_np, class_box_scores_np) \n",
- " class_boxes_np = class_boxes_np[nms_index_np]\n",
- " class_box_scores_np = class_box_scores_np[nms_index_np]\n",
- " classes_np = np.ones_like(class_box_scores_np, dtype = np.int32) * c\n",
- " boxes_.append(class_boxes_np)\n",
- " scores_.append(class_box_scores_np)\n",
- " classes_.append(classes_np)\n",
- " boxes_ = np.concatenate(boxes_, axis = 0)\n",
- " scores_ = np.concatenate(scores_, axis = 0)\n",
- " classes_ = np.concatenate(classes_, axis = 0)\n",
- "\n",
- " return boxes_, scores_, classes_\n",
- "\n",
- "# This function is used to draw boxes around objects post prediction.\n",
- "def draw_boxes(image, boxes, scores, classes):\n",
+ " cb = boxes[mask[:,c]]\n",
+ " cs = box_scores[:,c][mask[:,c]]\n",
+ " if len(cs) == 0:\n",
+ " continue\n",
+ " idx = nms_boxes(cb, cs)\n",
+ " boxes_.append(cb[idx])\n",
+ " scores_.append(cs[idx])\n",
+ " classes_.append(np.ones_like(cs[idx], dtype=np.int32) * c)\n",
+ " if not boxes_:\n",
+ " return np.array([]), np.array([]), np.array([], dtype=np.int32)\n",
+ " return (np.concatenate(boxes_),\n",
+ " np.concatenate(scores_),\n",
+ " np.concatenate(classes_))\n",
+ "\n",
+ "\n",
+ "# ---------------------------------------------------------------------------\n",
+ "# Drawing\n",
+ "# ---------------------------------------------------------------------------\n",
+ "\n",
+ "def draw_boxes(image, boxes, scores, classes, class_names, colors):\n",
" _, ax = plt.subplots(1)\n",
" ax.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n",
- " image_h, image_w, _ = image.shape\n",
- "\n",
" for i, bbox in enumerate(boxes):\n",
- " [top, left, bottom, right] = bbox\n",
- " width, height = right - left, bottom - top\n",
- " center_x, center_y = left + width*0.5, top + height*0.5\n",
+ " top, left, bottom, right = bbox\n",
+ " width, height = right-left, bottom-top\n",
" score, class_index = scores[i], classes[i]\n",
- " label = '{}: {:.4f}'.format(class_names[class_index], score) \n",
- " color = tuple([color/255 for color in colors[class_index]])\n",
- " ax.add_patch(Rectangle((left, top), width, height,\n",
- " edgecolor=color, facecolor='none'))\n",
- " ax.annotate(label, (center_x, center_y), color=color, weight='bold', \n",
- " fontsize=12, ha='center', va='center')\n",
- " return ax"
+ " label = '{}: {:.4f}'.format(class_names[class_index], score)\n",
+ " color = tuple([c/255 for c in colors[class_index]])\n",
+ " ax.add_patch(Rectangle((left,top), width, height,\n",
+ " edgecolor=color, facecolor='none'))\n",
+ " ax.annotate(label, (left+width*0.5, top+height*0.5),\n",
+ " color=color, weight='bold', fontsize=12,\n",
+ " ha='center', va='center')\n",
+ " return ax
"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nConfirm that all helper functions from yolo_helpers.py are ready to use.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "# Extract the total number of images ending with \"JPEG\" format present in the 'img' directory\n",
- "image_folder = 'img'\n",
- "original_images = [i for i in os.listdir(image_folder) if i.endswith(\"JPEG\")]\n",
- "total_images = len(original_images)\n"
+ "print('Helper functions loaded from yolo_helpers.py')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3. Object Detection in an image using a filesystem\n",
- "The next few cells help us in processing data for our predictions."
+ "\n",
+ "🖼️ list_images() — scans the img/ folder for JPEG files — click to peek inside
\n",
+ "def list_images(image_folder):\n",
+ " images = [i for i in os.listdir(image_folder) if i.endswith('JPEG')]\n",
+ " return image_folder, images
"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nScan the img/ folder and build a list of JPEG images to run detection on.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "# Setup input and output tensors\n",
- "dpu = overlay.runner\n",
- "inputTensors = dpu.get_input_tensors()\n",
- "outputTensors = dpu.get_output_tensors()\n",
- "\n",
- "shapeIn = tuple(inputTensors[0].dims)\n",
- "\n",
- "shapeOut0 = (tuple(outputTensors[0].dims)) # (1, 13, 13, 75)\n",
- "shapeOut1 = (tuple(outputTensors[1].dims)) # (1, 26, 26, 75)\n",
- "shapeOut2 = (tuple(outputTensors[2].dims)) # (1, 52, 52, 75)\n",
- "\n",
- "outputSize0 = int(outputTensors[0].get_data_size() / shapeIn[0]) # 12675\n",
- "outputSize1 = int(outputTensors[1].get_data_size() / shapeIn[0]) # 50700\n",
- "outputSize2 = int(outputTensors[2].get_data_size() / shapeIn[0]) # 202800\n",
- "\n",
- "# Setup Buffers\n",
- "input_data = [np.empty(shapeIn, dtype=np.float32, order=\"C\")]\n",
- "output_data = [np.empty(shapeOut0, dtype=np.float32, order=\"C\"), \n",
- " np.empty(shapeOut1, dtype=np.float32, order=\"C\"),\n",
- " np.empty(shapeOut2, dtype=np.float32, order=\"C\")]\n",
- "image = input_data[0]"
+ "image_folder, original_images = list_images('img')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "### The function defined below is the main function which performs pre-processing on a frame, makes model predictions and decode the output."
+ "\n",
+ "
🖼️ Step 3 — Detect Objects in a Static Image
\n",
+ "
Run YOLOv3 inference on sample JPEG images from the img/ folder
\n",
+ "
\n",
+ "🔧 setup_dpu_buffers() — wires up the FPGA input/output memory buffers — click to peek inside
\n",
+ "def setup_dpu_buffers(overlay):\n",
+ " dpu = overlay.runner\n",
+ " inputTensors = dpu.get_input_tensors()\n",
+ " outputTensors = dpu.get_output_tensors()\n",
+ " shapeIn = tuple(inputTensors[0].dims)\n",
+ " shapeOut0 = tuple(outputTensors[0].dims)\n",
+ " shapeOut1 = tuple(outputTensors[1].dims)\n",
+ " shapeOut2 = tuple(outputTensors[2].dims)\n",
+ " input_data = [np.empty(shapeIn, dtype=np.float32, order='C')]\n",
+ " output_data = [np.empty(shapeOut0, dtype=np.float32, order='C'),\n",
+ " np.empty(shapeOut1, dtype=np.float32, order='C'),\n",
+ " np.empty(shapeOut2, dtype=np.float32, order='C')]\n",
+ " return dpu, input_data, output_data, input_data[0], shapeIn,\\\n",
+ " (shapeOut0, shapeOut1, shapeOut2)
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nWire up the DPU input and output memory buffers for inference.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "# Function to perform pre-processing, model predictions and decoding output\n",
- "def run(frame, display=False):\n",
- " \n",
- " # Pre-processing\n",
+ "dpu, input_data, output_data, image, shapeIn, (shapeOut0, shapeOut1, shapeOut2) = setup_dpu_buffers(overlay)\n",
+ "_dpu_state = dict(dpu=dpu, input_data=input_data, output_data=output_data,\n",
+ " image=image, shapeIn=shapeIn, shapeOuts=(shapeOut0, shapeOut1, shapeOut2))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "🧩 The run() Function — the engine of this notebook
\n",
+ "This is the only function you need to call to detect objects. It handles the full pipeline: pre-process the frame → send to the DPU → retrieve output tensors → decode results using helpers from yolo_helpers.py → return boxes, scores, classes. Expand the toggle above to see inside it.\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "🚀 The run() function — curious? peek at the detection pipeline inside — click to peek inside
\n",
+ "def run(frame, display=False):\n",
" image_size = frame.shape[:2]\n",
" image_data = np.array(pre_process(frame, (416, 416)), dtype=np.float32)\n",
- " \n",
- " # Fetch data to DPU and trigger it\n",
" image[0,...] = image_data.reshape(shapeIn[1:])\n",
" job_id = dpu.execute_async(input_data, output_data)\n",
" dpu.wait(job_id)\n",
- " \n",
- " # Retrieve output data\n",
- " conv_out0 = np.reshape(output_data[0], shapeOut0)\n",
- " conv_out1 = np.reshape(output_data[1], shapeOut1)\n",
- " conv_out2 = np.reshape(output_data[2], shapeOut2)\n",
- " yolo_outputs = [conv_out0, conv_out1, conv_out2]\n",
- " \n",
- " # Decode output from YOLOv3\n",
+ " yolo_outputs = [\n",
+ " np.reshape(output_data[0], shapeOut0),\n",
+ " np.reshape(output_data[1], shapeOut1),\n",
+ " np.reshape(output_data[2], shapeOut2),\n",
+ " ]\n",
" boxes, scores, classes = evaluate(yolo_outputs, image_size, class_names, anchors)\n",
- " \n",
" if display:\n",
- " _ = draw_boxes(frame, boxes, scores, classes)\n",
- " \n",
- " return boxes, scores, classes"
+ " draw_boxes(frame, boxes, scores, classes, class_names, colors)\n",
+ " return boxes, scores, classes
"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nDefine the run() wrapper function that handles the full detection pipeline.\n
"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from yolo_helpers import run as _run\n",
+ "\n",
+ "def run(frame, display=False):\n",
+ " return _run(frame, dpu, input_data, output_data, image,\n",
+ " shapeIn, shapeOut0, shapeOut1, shapeOut2,\n",
+ " class_names, anchors, colors, display)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nRead one of the sample images from disk to run detection on.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -546,6 +796,11 @@
"input_image = cv2.imread(os.path.join(image_folder, original_images[4]))"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nRun YOLOv3 on the sample image and display the bounding boxes.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -560,68 +815,178 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 4. Object Detection by Using a Webcam"
+ "\n",
+ "
🎮 Step 3.5 — Interactive: Class Explorer
\n",
+ "
Filter detections by class using a live dropdown — no re-inference needed
\n",
+ "
\n",
+ "🎤 Presenter Tip
\n",
+ "Ask the class: \"What objects do you think the model found in this image? Let's pick one from the dropdown and check!\"
Run the cell, then pick different classes to reveal what the model found — and what it missed.\n",
+ "
\n",
+ "\n",
+ "\n",
+ "👩💻 Students
\n",
+ "Use the dropdown to select any of the 20 VOC classes. Classes marked with ✅ were detected in this image. Try selecting a class that wasn't detected — what does the model show?\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "🔍 Class Explorer — full widget code in class_explorer.py — click to peek inside
\n",
+ """"\n",
+ "Class Explorer widget — filter detections by class using a dropdown.\n",
+ "Students don't need to read or edit this file.\n",
+ """"\n",
+ "import cv2\n",
+ "import matplotlib.pyplot as plt\n",
+ "import ipywidgets as widgets\n",
+ "from IPython.display import display as ipy_display\n",
+ "\n",
+ "\n",
+ "def launch_class_explorer(image, boxes, scores, classes, class_names, colors):\n",
+ " """Display a dropdown that filters bounding boxes by detected class."""\n",
+ "\n",
+ " def draw_filtered_boxes(filter_class_idx):\n",
+ " _, ax = plt.subplots(1, figsize=(10, 6))\n",
+ " ax.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n",
+ " found = False\n",
+ " for i, bbox in enumerate(boxes):\n",
+ " if classes[i] != filter_class_idx:\n",
+ " continue\n",
+ " found = True\n",
+ " top, left, bottom, right = bbox\n",
+ " width, height = right-left, bottom-top\n",
+ " label = '{}: {:.0f}%'.format(class_names[filter_class_idx], scores[i]*100)\n",
+ " color = tuple([c/255 for c in colors[filter_class_idx]])\n",
+ " ax.add_patch(plt.Rectangle((left, top), width, height,\n",
+ " edgecolor=color, facecolor='none', linewidth=2))\n",
+ " ax.annotate(label, (left+width*0.5, top+height*0.5),\n",
+ " color=color, weight='bold', fontsize=12,\n",
+ " ha='center', va='center')\n",
+ " if not found:\n",
+ " ax.set_title('No "{}" detected.'.format(class_names[filter_class_idx]),\n",
+ " fontsize=14, color='red')\n",
+ " plt.axis('off')\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n",
+ "\n",
+ " detected_idxs = sorted(set(classes.tolist())) if len(classes) > 0 else []\n",
+ " separator_val = detected_idxs[0] if detected_idxs else 0\n",
+ " dropdown_options = (\n",
+ " [('✅ ' + class_names[i], i) for i in detected_idxs]\n",
+ " + [('─────────────', separator_val)]\n",
+ " + [(' ' + class_names[i], i) for i in range(len(class_names)) if i not in detected_idxs]\n",
+ " )\n",
+ "\n",
+ " class_dropdown = widgets.Dropdown(\n",
+ " options=dropdown_options,\n",
+ " description='Show class:',\n",
+ " style={'description_width': 'initial'},\n",
+ " layout=widgets.Layout(width='300px')\n",
+ " )\n",
+ " explorer_output = widgets.Output()\n",
+ "\n",
+ " def on_change(change):\n",
+ " explorer_output.clear_output(wait=True)\n",
+ " with explorer_output:\n",
+ " draw_filtered_boxes(change['new'])\n",
+ "\n",
+ " class_dropdown.observe(on_change, names='value')\n",
+ "\n",
+ " print("Detected classes:", [class_names[i] for i in detected_idxs] if detected_idxs else "None")\n",
+ " ipy_display(class_dropdown, explorer_output)\n",
+ "\n",
+ " if detected_idxs:\n",
+ " with explorer_output:\n",
+ " draw_filtered_boxes(detected_idxs[0])
"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nLaunch the interactive class explorer widget to filter detections by category.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "# Start capturing a video \n",
- "videoIn = cv2.VideoCapture(0)\n",
- "videoIn.set(cv2.CAP_PROP_BUFFERSIZE, 1) # Disable buffering\n",
- "videoIn.set(cv2.CAP_PROP_FRAME_WIDTH, 640);\n",
- "videoIn.set(cv2.CAP_PROP_FRAME_HEIGHT, 480);\n",
+ "from class_explorer import launch_class_explorer\n",
"\n",
- "print(\"Capture device is open: \" + str(videoIn.isOpened()))"
+ "_boxes, _scores, _classes = run(input_image)\n",
+ "launch_class_explorer(input_image, _boxes, _scores, _classes, class_names, colors)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Running the next cell, would result in capturing a frame"
+ "\n",
+ "
📷 Step 4 — Single-Frame Webcam Detection
\n",
+ "
Capture one frame from the webcam and run detection on it
\n",
+ "
\nOpen the webcam to capture frames for single-frame detection.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "# Extract the frame from the video\n",
- "ret, frame = videoIn.read()"
+ "# Start capturing a video \n",
+ "videoIn = cv2.VideoCapture(0)\n",
+ "videoIn.set(cv2.CAP_PROP_BUFFERSIZE, 1) # Disable buffering\n",
+ "videoIn.set(cv2.CAP_PROP_FRAME_WIDTH, 640);\n",
+ "videoIn.set(cv2.CAP_PROP_FRAME_HEIGHT, 480);\n",
+ "\n",
+ "print(\"Capture device is open: \" + str(videoIn.isOpened()))"
]
},
{
- "cell_type": "code",
- "execution_count": null,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
"source": [
- "# Perform predictions on the frame\n",
- "boxes, scores, classes = run(frame, display=True)"
+ "\n",
+ "Run the next cell to capture a single frame from the webcam. Then the cell after that runs YOLOv3 detection on it.\n",
+ "
"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nCapture a single frame from the webcam.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "# Store information of the detected object which has the highest score. And store the class information of this object\n",
- "best_score = np.argmax(scores)\n",
- "class_names[classes[best_score]]"
+ "# Capture a single frame from the webcam\n",
+ "ret, frame = videoIn.read()\n",
+ "if not ret or frame is None:\n",
+ " print('Camera not ready — run this cell again in a moment.')\n",
+ "else:\n",
+ " print('Frame captured successfully:', frame.shape)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
- "source": [
- "We will now print the object with the highest score on the OLED. "
- ]
+ "source": "\nRun YOLOv3 on the captured webcam frame and show the detected objects.\n
"
},
{
"cell_type": "code",
@@ -629,53 +994,175 @@
"metadata": {},
"outputs": [],
"source": [
- "oled = adapter.G4\n",
- "oled.set_default_config()\n",
- "oled.set_normal_display()\n",
- "oled.put_string(\"Detected\") \n",
- "oled.set_position(2, 0)\n",
- "oled.put_string(f\"{class_names[classes[best_score]]}\")"
+ "# Perform predictions on the frame\n",
+ "boxes, scores, classes = run(frame, display=True)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nPrint the highest-scoring detection from the webcam frame.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "videoIn.release() # Releasing the video capture object"
+ "# Show the highest-scoring detection (guard against empty results)\n",
+ "if len(scores) > 0:\n",
+ " best_score = np.argmax(scores)\n",
+ " print('Top detection:', class_names[classes[best_score]],\n",
+ " f'({scores[best_score]*100:.0f}%)')\n",
+ "else:\n",
+ " print('No objects detected in this frame — try re-capturing.')\n"
]
},
{
- "attachments": {
- "image.png": {
- "image/png": "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"
- }
- },
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Example OLED Output for a monitor would look like:\n",
+ "\n",
+ "
🎚️ Step 4.5 — Interactive: Confidence Threshold Explorer
\n",
+ "
Drag the slider to see how the detection threshold affects results — in real time
\n",
+ "
\n",
+ "
\n",
+ "⬇️ Lower threshold
\n",
+ "More boxes appear — the model shows its uncertain guesses too\n",
+ "
\n",
+ "
\n",
+ "⬆️ Higher threshold
\n",
+ "Fewer boxes — only the most confident detections remain\n",
+ "
\n",
+ "
\n",
+ "🎤 Presenter Tip
\n",
+ "\"Who thinks a higher threshold is always better? Let's test it — drag the slider and watch what happens to the boxes!\"\n",
+ "
\n",
+ "\n",
+ "\n",
+ "👩💻 Student Challenge
\n",
+ "Find the threshold where only the most obvious object in the frame remains. What value did you land on? Compare with a classmate!\n",
+ "
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 5. Real Time Object Detection from webcam"
+ "\n",
+ "🎚️ Confidence Explorer — full widget code in confidence_explorer.py — click to peek inside
\n",
+ """"\n",
+ "Confidence Threshold Explorer widget — drag a slider to filter detections.\n",
+ "Students don't need to read or edit this file.\n",
+ """"\n",
+ "import numpy as np\n",
+ "import cv2\n",
+ "import matplotlib.pyplot as plt\n",
+ "import ipywidgets as widgets\n",
+ "from IPython.display import display as ipy_display\n",
+ "\n",
+ "from yolo_helpers import boxes_and_scores, nms_boxes\n",
+ "\n",
+ "\n",
+ "def launch_confidence_explorer(frame, dpu_state, class_names, colors, anchors):\n",
+ " """\n",
+ " Run inference once at a very low threshold, then let students drag a slider\n",
+ " to see how confidence cutoff affects what gets drawn — no re-inference needed.\n",
+ "\n",
+ " dpu_state must be the dict returned by setup_dpu_buffers(), i.e.:\n",
+ " {'dpu': dpu, 'input_data': input_data, 'output_data': output_data,\n",
+ " 'image': image_buf, 'shapeIn': shapeIn, 'shapeOuts': (s0,s1,s2)}\n",
+ " """\n",
+ " from yolo_helpers import pre_process\n",
+ "\n",
+ " dpu = dpu_state['dpu']\n",
+ " input_data = dpu_state['input_data']\n",
+ " output_data= dpu_state['output_data']\n",
+ " image_buf = dpu_state['image']\n",
+ " shapeIn = dpu_state['shapeIn']\n",
+ " shapeOut0, shapeOut1, shapeOut2 = dpu_state['shapeOuts']\n",
+ "\n",
+ " # --- Run inference once at a very low threshold ---\n",
+ " image_size = frame.shape[:2]\n",
+ " image_data = np.array(pre_process(frame, (416, 416)), dtype=np.float32)\n",
+ " image_buf[0, ...] = image_data.reshape(shapeIn[1:])\n",
+ " job_id = dpu.execute_async(input_data, output_data)\n",
+ " dpu.wait(job_id)\n",
+ "\n",
+ " yolo_outputs = [\n",
+ " np.reshape(output_data[0], shapeOut0),\n",
+ " np.reshape(output_data[1], shapeOut1),\n",
+ " np.reshape(output_data[2], shapeOut2),\n",
+ " ]\n",
+ " anchor_mask = [[6,7,8],[3,4,5],[0,1,2]]\n",
+ " input_shape = np.array(np.shape(yolo_outputs[0])[1:3]) * 32\n",
+ " all_boxes, all_scores = [], []\n",
+ " for i in range(3):\n",
+ " b, s = boxes_and_scores(yolo_outputs[i], anchors[anchor_mask[i]],\n",
+ " len(class_names), input_shape, image_size)\n",
+ " all_boxes.append(b); all_scores.append(s)\n",
+ " raw_boxes = np.concatenate(all_boxes)\n",
+ " raw_scores = np.concatenate(all_scores)\n",
+ " src_frame = frame.copy()\n",
+ "\n",
+ " def draw_at_threshold(threshold):\n",
+ " _, ax = plt.subplots(1, figsize=(10, 6))\n",
+ " ax.imshow(cv2.cvtColor(src_frame, cv2.COLOR_BGR2RGB))\n",
+ " count = 0\n",
+ " mask = raw_scores >= threshold\n",
+ " for c in range(len(class_names)):\n",
+ " cb = raw_boxes[mask[:,c]]\n",
+ " cs = raw_scores[:,c][mask[:,c]]\n",
+ " if len(cs) == 0:\n",
+ " continue\n",
+ " for idx in nms_boxes(cb, cs):\n",
+ " count += 1\n",
+ " top, left, bottom, right = cb[idx]\n",
+ " w, h = right-left, bottom-top\n",
+ " color = tuple([v/255 for v in colors[c]])\n",
+ " ax.add_patch(plt.Rectangle((left, top), w, h,\n",
+ " edgecolor=color, facecolor='none', linewidth=2))\n",
+ " ax.annotate('{}: {:.0f}%'.format(class_names[c], cs[idx]*100),\n",
+ " (left+w*0.5, top+h*0.5), color=color,\n",
+ " weight='bold', fontsize=10, ha='center', va='center')\n",
+ " ax.set_title('Threshold: {:.2f} | Boxes shown: {}'.format(threshold, count), fontsize=13)\n",
+ " plt.axis('off')\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n",
+ "\n",
+ " thresh_slider = widgets.FloatSlider(\n",
+ " value=0.2, min=0.05, max=0.95, step=0.05,\n",
+ " description='Min confidence:',\n",
+ " style={'description_width': 'initial'},\n",
+ " layout=widgets.Layout(width='500px'),\n",
+ " continuous_update=False\n",
+ " )\n",
+ " thresh_output = widgets.Output()\n",
+ "\n",
+ " def on_change(change):\n",
+ " thresh_output.clear_output(wait=True)\n",
+ " with thresh_output:\n",
+ " draw_at_threshold(change['new'])\n",
+ "\n",
+ " thresh_slider.observe(on_change, names='value')\n",
+ " ipy_display(thresh_slider, thresh_output)\n",
+ " with thresh_output:\n",
+ " draw_at_threshold(0.2)\n",
+ "\n",
+ " print("Done — drag the slider to explore!")
"
]
},
{
- "cell_type": "code",
- "execution_count": null,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
- "source": [
- "cap = cv2.VideoCapture(0) # Start the video capture\n",
- "cap.set(cv2.CAP_PROP_BUFFERSIZE, 1) # Disable buffering"
- ]
+ "source": "\nLaunch the confidence threshold slider to explore how cutoff affects detections.\n
"
},
{
"cell_type": "code",
@@ -683,104 +1170,324 @@
"metadata": {},
"outputs": [],
"source": [
- "display_handle=display(None, display_id=True) # This displays the video once the frame is updated"
+ "from confidence_explorer import launch_confidence_explorer\n",
+ "\n",
+ "launch_confidence_explorer(frame, _dpu_state, class_names, colors, anchors)\n",
+ "videoIn.release()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Keeping track of only best scoring object in the image..."
+ "\n",
+ "
🎥 Step 5 — Live Real-Time Object Detection
\n",
+ "
All objects detected above the threshold are drawn with colored boxes and labels — press Stop when done
\n",
+ "
\n",
+ "
🏆 Class Activity — \"Guess What the Model Will See!\"
\n",
+ "
\n",
+ "- Before running, everyone shouts out one object they think the model will detect
\n",
+ "- Presenter runs the live loop
\n",
+ "- Check the \"Objects seen this session\" label below the video — who was right?
\n",
+ "
\n",
+ "
\n",
+ "💡 Leaderboard idea: Keep a tally on the whiteboard. Each correct guess = 1 point!\n",
+ "
\n",
+ "
\n",
+ "🎤 Presenter Tip
\n",
+ "Try adjusting the Min confidence slider live during the session — students will see boxes appear and disappear as you drag it. Ask: \"Why might we want fewer boxes?\"\n",
+ "
"
]
},
{
- "cell_type": "code",
- "execution_count": null,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
"source": [
- "last_object = \"None\"\n",
+ "\n",
+ "🎥 Live detection loop — full code in live_detection.py
\n",
+ """"\n",
+ "Live real-time object detection loop.\n",
+ "Students don't need to read or edit this file.\n",
+ """"\n",
+ "import threading\n",
+ "import cv2\n",
+ "import ipywidgets as widgets\n",
+ "from IPython.display import display as ipy_display, Image\n",
+ "\n",
+ "\n",
+ "def launch_live_detection(run, class_names, colors, oled=None):\n",
+ " """\n",
+ " Start the live webcam detection loop.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " run : the run() function from the notebook\n",
+ " class_names : list of class name strings\n",
+ " colors : list of (R,G,B) tuples, one per class\n",
+ " oled : optional OLED display object (set None to skip OLED output)\n",
+ " """\n",
+ " stop_button = widgets.ToggleButton(\n",
+ " value=False,\n",
+ " description='Stop Camera',\n",
+ " button_style='danger',\n",
+ " icon='stop',\n",
+ " layout=widgets.Layout(width='150px', height='40px')\n",
+ " )\n",
+ " thresh_slider = widgets.FloatSlider(\n",
+ " value=0.3, min=0.05, max=0.95, step=0.05,\n",
+ " description='Min confidence:',\n",
+ " style={'description_width': 'initial'},\n",
+ " layout=widgets.Layout(width='400px'),\n",
+ " continuous_update=False\n",
+ " )\n",
+ " session_classes_seen = set()\n",
+ " session_label = widgets.Label(value='Objects seen this session: none yet')\n",
+ "\n",
+ " def _loop(stop_btn):\n",
+ " cap = cv2.VideoCapture(0)\n",
+ " cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)\n",
+ " cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)\n",
+ " cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)\n",
+ " display_handle = ipy_display(None, display_id=True)\n",
+ " last_oled_text = ""\n",
+ "\n",
+ " while not stop_btn.value:\n",
+ " ret, live_frame = cap.read()\n",
+ " if not ret:\n",
+ " break\n",
+ "\n",
+ " threshold = thresh_slider.value\n",
+ " boxes, scores, classes = run(live_frame)\n",
+ "\n",
+ " for i, bbox in enumerate(boxes):\n",
+ " if scores[i] < threshold:\n",
+ " continue\n",
+ " y_min, x_min, y_max, x_max = map(int, bbox)\n",
+ " class_idx = classes[i]\n",
+ " color_bgr = colors[class_idx]\n",
+ " label = '{}: {:.0f}%'.format(class_names[class_idx], scores[i] * 100)\n",
+ " cv2.rectangle(live_frame, (x_min, y_min), (x_max, y_max), color_bgr, 2)\n",
+ " cv2.putText(live_frame, label, (x_min, max(y_min - 8, 15)),\n",
+ " cv2.FONT_HERSHEY_SIMPLEX, 0.6, color_bgr, 2, cv2.LINE_AA)\n",
+ " session_classes_seen.add(class_names[class_idx])\n",
"\n",
- "# while True:\n",
- "for i in range(200):\n",
- " _, frame = cap.read()\n",
- " boxes, scores, classes = run(frame)\n",
- " \n",
- " if scores.any():\n",
+ " seen_str = ', '.join(sorted(session_classes_seen)) if session_classes_seen else 'none yet'\n",
+ " session_label.value = 'Objects seen this session: ' + seen_str\n",
"\n",
- " best_score = np.argmax(scores)\n",
+ " if oled is not None and len(scores) > 0:\n",
+ " above = [(scores[i], classes[i]) for i in range(len(scores)) if scores[i] >= threshold]\n",
+ " if above:\n",
+ " top_name = class_names[max(above, key=lambda x: x[0])[1]]\n",
+ " if top_name != last_oled_text:\n",
+ " oled.clear_display()\n",
+ " oled.put_string(top_name)\n",
+ " last_oled_text = top_name\n",
"\n",
- " # Draw bounding box\n",
- " y_min,x_min,y_max,x_max = map(int, boxes[best_score])\n",
- " frame = cv2.rectangle(frame, (x_min,y_min), (x_max, y_max), color=255)\n",
+ " _, encoded = cv2.imencode('.jpeg', live_frame)\n",
+ " display_handle.update(Image(data=encoded.tobytes()))\n",
"\n",
- " # Label\n",
- " text = f\"{class_names[classes[best_score]]}: {scores[best_score]:.2f}\"\n",
- " text_size = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 2)[0]\n",
- " frame = cv2.putText(frame, text, (x_min, y_min-text_size[1]), cv2.FONT_HERSHEY_SIMPLEX, 1, 255, 1, cv2.LINE_AA)\n",
+ " cap.release()\n",
+ " display_handle.update(None)\n",
"\n",
- " _, frame = cv2.imencode('.jpeg', frame)\n",
- " display_handle.update(Image(data=frame.tobytes()))\n",
- " \n",
- " if class_names[classes[best_score]] == last_object:\n",
- " pass\n",
- " else:\n",
- " oled.clear_display()\n",
- " oled.put_string(class_names[classes[best_score]])\n",
- " last_object = class_names[classes[best_score]]"
+ " ipy_display(widgets.HBox([stop_button, thresh_slider]))\n",
+ " ipy_display(session_label)\n",
+ " threading.Thread(target=_loop, args=(stop_button,)).start()
"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nStart the live real-time object detection loop with the webcam.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "cap.release() # Releasing the video capture object"
+ "from live_detection import launch_live_detection\n",
+ "\n",
+ "launch_live_detection(run, class_names, colors, oled=oled)"
]
},
{
+ "attachments": {
+ "image.png": {
+ "image/png": "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"
+ }
+ },
"cell_type": "markdown",
"metadata": {},
"source": [
- "# END OF NOTEBOOK
\n",
- "\n"
+ "## Example OLED Output for a monitor would look like:\n",
+ "\n",
+ "\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "# CHALLENGES:\n",
- "## Modify the code in section 5 above in incremental steps:\n",
- "- Draw a bounding box only when a person is detected.\n",
- "- Draw a bounding box when a person is detected with a score of 0.8 and above.\n",
+ "\n",
+ "
🏁 Challenges — Your Turn!
\n",
+ "
Modify the live detection loop from Step 5. Work through in order — each challenge builds on the last.
\n",
+ "
\n",
+ "
\n",
+ "🏫 Middle School\n",
+ "
Challenge 1 — Person Detector
\n",
+ "\n",
+ "
Modify the live loop so that it only draws a bounding box when a person is detected.
\n",
+ "
\n",
+ "💡 Hint: class_names.index(\"person\") gives you the class number. Check classes[i] inside the loop.\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "🏫 Middle School\n",
+ "
Challenge 2 — High Confidence Only
\n",
+ "\n",
+ "
Update Challenge 1 to only draw the box when a person is detected with a score of 0.8 or higher (80% confident).
\n",
+ "
\n",
+ "💡 Hint: scores[i] gives the confidence for detection i.\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "🎓 High School\n",
+ "
Challenge 3 — Object Counter
\n",
+ "\n",
+ "
Build a session object counter that tracks how many frames each class was detected in, then prints a summary when the camera stops.
\n",
+ "
\n",
+ "Session summary:
\n",
+ " person — detected in 42 frames
\n",
+ " chair — detected in 18 frames
\n",
+ " laptop — detected in 7 frames\n",
+ "
\n",
+ "
\n",
+ "💡 Hint: Use a Python dict to map class names to frame counts. Increment inside the detection loop.\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "🎓 High School\n",
+ "
Challenge 4 — OLED Score Display
\n",
+ "\n",
+ "
Make the OLED show both the class name and its confidence percentage — e.g. person 87% — instead of just the name.
\n",
+ "
\n",
+ "💡 Hint: Use oled.set_position(1, 0) to move to line 2 before calling oled.put_string(...).\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "🌟 Bonus\n",
+ "
Build Your Own Filter
\n",
+ "\n",
+ "
Design a custom detection filter with at least two rules of your choice. Be creative!
\n",
+ "
\n",
+ "- Only detect animals:
dog, cat, bird, horse, sheep, cow \n",
+ "- Flash the frame border red when a car is detected
\n",
+ "- Count people in frame and show the count on the OLED
\n",
+ "
\n",
+ "
The full VOC class list is in img/voc_classes.txt
\n",
+ "
\n",
+ "🏁 Challenge starter code — expand to see the full template, then copy what you need below
\n",
+ "import ipywidgets as widgets\n",
+ "import threading\n",
+ "from IPython.display import display as ipy_display\n",
+ "\n",
+ "# --- Your challenge code goes here ---\n",
+ "# Copy the live_detection function from Section 5 as your starting point,\n",
+ "# then modify it according to whichever challenge you are working on.\n",
+ "\n",
+ "PERSON_CLASS = class_names.index("person") # useful for Challenges 1 & 2\n",
+ "\n",
+ "stopButton_challenge = widgets.ToggleButton(\n",
+ " value=False,\n",
+ " description='Stop Camera',\n",
+ " button_style='danger',\n",
+ " icon='stop',\n",
+ " layout=widgets.Layout(width='150px', height='40px')\n",
+ ")\n",
+ "\n",
+ "def my_detection(stop_button):\n",
+ " cap = cv2.VideoCapture(0)\n",
+ " cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)\n",
+ " cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)\n",
+ " cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)\n",
+ " display_handle = ipy_display(None, display_id=True)\n",
+ "\n",
+ " # Challenge 3: add a dict here to track class frame counts\n",
+ " # frame_counts = {}\n",
+ "\n",
+ " while not stop_button.value:\n",
+ " ret, live_frame = cap.read()\n",
+ " if not ret:\n",
+ " break\n",
+ "\n",
+ " boxes, scores, classes = run(live_frame)\n",
+ "\n",
+ " for i, bbox in enumerate(boxes):\n",
+ " # --- MODIFY BELOW FOR CHALLENGES 1 & 2 ---\n",
+ " y_min, x_min, y_max, x_max = map(int, bbox)\n",
+ " class_idx = classes[i]\n",
+ " color_bgr = colors[class_idx]\n",
+ " label = '{}: {:.0f}%'.format(class_names[class_idx], scores[i] * 100)\n",
+ " cv2.rectangle(live_frame, (x_min, y_min), (x_max, y_max), color_bgr, 2)\n",
+ " cv2.putText(live_frame, label, (x_min, max(y_min - 8, 15)),\n",
+ " cv2.FONT_HERSHEY_SIMPLEX, 0.6, color_bgr, 2, cv2.LINE_AA)\n",
+ " # --- END MODIFY ---\n",
+ "\n",
+ " _, encoded = cv2.imencode('.jpeg', live_frame)\n",
+ " display_handle.update(Image(data=encoded.tobytes()))\n",
+ "\n",
+ " cap.release()\n",
+ " display_handle.update(None)\n",
+ "\n",
+ " # Challenge 3: print summary here\n",
+ " # print("Session summary:")\n",
+ " # for name, count in sorted(frame_counts.items(), key=lambda x: -x[1]):\n",
+ " # print(" {:10s} — detected in {} frames".format(name, count))\n",
+ "\n",
+ "ipy_display(stopButton_challenge)\n",
+ "thread = threading.Thread(target=my_detection, args=(stopButton_challenge,))\n",
+ "thread.start()
"
]
},
{
- "cell_type": "code",
- "execution_count": null,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
- "source": [
- "display_handle=display(None, display_id=True) # This displays the video once the frame is updated"
- ]
+ "source": "\nSet up the challenge detection loop where you can modify the detection filters.\n
"
},
{
"cell_type": "code",
@@ -788,29 +1495,90 @@
"metadata": {},
"outputs": [],
"source": [
- "# Enter Code Here..\n",
+ "import ipywidgets as widgets\n",
+ "import threading\n",
+ "from IPython.display import display as ipy_display\n",
+ "\n",
+ "# --- Your challenge code goes here ---\n",
+ "# Copy the live_detection function from Section 5 as your starting point,\n",
+ "# then modify it according to whichever challenge you are working on.\n",
+ "\n",
+ "PERSON_CLASS = class_names.index(\"person\") # useful for Challenges 1 & 2\n",
"\n",
+ "stopButton_challenge = widgets.ToggleButton(\n",
+ " value=False,\n",
+ " description='Stop Camera',\n",
+ " button_style='danger',\n",
+ " icon='stop',\n",
+ " layout=widgets.Layout(width='150px', height='40px')\n",
+ ")\n",
"\n",
+ "def my_detection(stop_button):\n",
+ " cap = cv2.VideoCapture(0)\n",
+ " cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)\n",
+ " cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)\n",
+ " cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)\n",
+ " display_handle = ipy_display(None, display_id=True)\n",
"\n",
+ " # Challenge 3: add a dict here to track class frame counts\n",
+ " # frame_counts = {}\n",
"\n",
- " \n",
- "cap.release() # Releasing the video capture object"
+ " while not stop_button.value:\n",
+ " ret, live_frame = cap.read()\n",
+ " if not ret:\n",
+ " break\n",
+ "\n",
+ " boxes, scores, classes = run(live_frame)\n",
+ "\n",
+ " for i, bbox in enumerate(boxes):\n",
+ " # --- MODIFY BELOW FOR CHALLENGES 1 & 2 ---\n",
+ " y_min, x_min, y_max, x_max = map(int, bbox)\n",
+ " class_idx = classes[i]\n",
+ " color_bgr = colors[class_idx]\n",
+ " label = '{}: {:.0f}%'.format(class_names[class_idx], scores[i] * 100)\n",
+ " cv2.rectangle(live_frame, (x_min, y_min), (x_max, y_max), color_bgr, 2)\n",
+ " cv2.putText(live_frame, label, (x_min, max(y_min - 8, 15)),\n",
+ " cv2.FONT_HERSHEY_SIMPLEX, 0.6, color_bgr, 2, cv2.LINE_AA)\n",
+ " # --- END MODIFY ---\n",
+ "\n",
+ " _, encoded = cv2.imencode('.jpeg', live_frame)\n",
+ " display_handle.update(Image(data=encoded.tobytes()))\n",
+ "\n",
+ " cap.release()\n",
+ " display_handle.update(None)\n",
+ "\n",
+ " # Challenge 3: print summary here\n",
+ " # print(\"Session summary:\")\n",
+ " # for name, count in sorted(frame_counts.items(), key=lambda x: -x[1]):\n",
+ " # print(\" {:10s} — detected in {} frames\".format(name, count))\n",
+ "\n",
+ "ipy_display(stopButton_challenge)\n",
+ "thread = threading.Thread(target=my_detection, args=(stopButton_challenge,))\n",
+ "thread.start()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Let's clear the the overlay and the dpu specific data:"
+ "\n",
+ "🧹 Cleanup
\n",
+ "Run this cell when you are completely done with the notebook to release the DPU and FPGA overlay resources so other notebooks can use them.\n",
+ "
"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nRelease the DPU and FPGA overlay to free resources when done.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "del overlay # Clean up code"
+ "del overlay # Releases DPU and FPGA overlay resources"
]
}
],
@@ -835,4 +1603,4 @@
},
"nbformat": 4,
"nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/bootcamp_sessions/PYNQ 301 - Object Detection/class_explorer.py b/bootcamp_sessions/PYNQ 301 - Object Detection/class_explorer.py
new file mode 100644
index 0000000..5715757
--- /dev/null
+++ b/bootcamp_sessions/PYNQ 301 - Object Detection/class_explorer.py
@@ -0,0 +1,66 @@
+"""
+Class Explorer widget — filter detections by class using a dropdown.
+Students don't need to read or edit this file.
+"""
+import cv2
+import matplotlib.pyplot as plt
+import ipywidgets as widgets
+from IPython.display import display as ipy_display
+
+
+def launch_class_explorer(image, boxes, scores, classes, class_names, colors):
+ """Display a dropdown that filters bounding boxes by detected class."""
+
+ def draw_filtered_boxes(filter_class_idx):
+ _, ax = plt.subplots(1, figsize=(10, 6))
+ ax.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+ found = False
+ for i, bbox in enumerate(boxes):
+ if classes[i] != filter_class_idx:
+ continue
+ found = True
+ top, left, bottom, right = bbox
+ width, height = right-left, bottom-top
+ label = '{}: {:.0f}%'.format(class_names[filter_class_idx], scores[i]*100)
+ color = tuple([c/255 for c in colors[filter_class_idx]])
+ ax.add_patch(plt.Rectangle((left, top), width, height,
+ edgecolor=color, facecolor='none', linewidth=2))
+ ax.annotate(label, (left+width*0.5, top+height*0.5),
+ color=color, weight='bold', fontsize=12,
+ ha='center', va='center')
+ if not found:
+ ax.set_title('No "{}" detected.'.format(class_names[filter_class_idx]),
+ fontsize=14, color='red')
+ plt.axis('off')
+ plt.tight_layout()
+ plt.show()
+
+ detected_idxs = sorted(set(classes.tolist())) if len(classes) > 0 else []
+ separator_val = detected_idxs[0] if detected_idxs else 0
+ dropdown_options = (
+ [('✅ ' + class_names[i], i) for i in detected_idxs]
+ + [('─────────────', separator_val)]
+ + [(' ' + class_names[i], i) for i in range(len(class_names)) if i not in detected_idxs]
+ )
+
+ class_dropdown = widgets.Dropdown(
+ options=dropdown_options,
+ description='Show class:',
+ style={'description_width': 'initial'},
+ layout=widgets.Layout(width='300px')
+ )
+ explorer_output = widgets.Output()
+
+ def on_change(change):
+ explorer_output.clear_output(wait=True)
+ with explorer_output:
+ draw_filtered_boxes(change['new'])
+
+ class_dropdown.observe(on_change, names='value')
+
+ print("Detected classes:", [class_names[i] for i in detected_idxs] if detected_idxs else "None")
+ ipy_display(class_dropdown, explorer_output)
+
+ if detected_idxs:
+ with explorer_output:
+ draw_filtered_boxes(detected_idxs[0])
diff --git a/bootcamp_sessions/PYNQ 301 - Object Detection/confidence_explorer.py b/bootcamp_sessions/PYNQ 301 - Object Detection/confidence_explorer.py
new file mode 100644
index 0000000..b7a5c95
--- /dev/null
+++ b/bootcamp_sessions/PYNQ 301 - Object Detection/confidence_explorer.py
@@ -0,0 +1,99 @@
+"""
+Confidence Threshold Explorer widget — drag a slider to filter detections.
+Students don't need to read or edit this file.
+"""
+import numpy as np
+import cv2
+import matplotlib.pyplot as plt
+import ipywidgets as widgets
+from IPython.display import display as ipy_display
+
+from yolo_helpers import boxes_and_scores, nms_boxes
+
+
+def launch_confidence_explorer(frame, dpu_state, class_names, colors, anchors):
+ """
+ Run inference once at a very low threshold, then let students drag a slider
+ to see how confidence cutoff affects what gets drawn — no re-inference needed.
+
+ dpu_state must be the dict returned by setup_dpu_buffers(), i.e.:
+ {'dpu': dpu, 'input_data': input_data, 'output_data': output_data,
+ 'image': image_buf, 'shapeIn': shapeIn, 'shapeOuts': (s0,s1,s2)}
+ """
+ from yolo_helpers import pre_process
+
+ dpu = dpu_state['dpu']
+ input_data = dpu_state['input_data']
+ output_data= dpu_state['output_data']
+ image_buf = dpu_state['image']
+ shapeIn = dpu_state['shapeIn']
+ shapeOut0, shapeOut1, shapeOut2 = dpu_state['shapeOuts']
+
+ # --- Run inference once at a very low threshold ---
+ image_size = frame.shape[:2]
+ image_data = np.array(pre_process(frame, (416, 416)), dtype=np.float32)
+ image_buf[0, ...] = image_data.reshape(shapeIn[1:])
+ job_id = dpu.execute_async(input_data, output_data)
+ dpu.wait(job_id)
+
+ yolo_outputs = [
+ np.reshape(output_data[0], shapeOut0),
+ np.reshape(output_data[1], shapeOut1),
+ np.reshape(output_data[2], shapeOut2),
+ ]
+ anchor_mask = [[6,7,8],[3,4,5],[0,1,2]]
+ input_shape = np.array(np.shape(yolo_outputs[0])[1:3]) * 32
+ all_boxes, all_scores = [], []
+ for i in range(3):
+ b, s = boxes_and_scores(yolo_outputs[i], anchors[anchor_mask[i]],
+ len(class_names), input_shape, image_size)
+ all_boxes.append(b); all_scores.append(s)
+ raw_boxes = np.concatenate(all_boxes)
+ raw_scores = np.concatenate(all_scores)
+ src_frame = frame.copy()
+
+ def draw_at_threshold(threshold):
+ _, ax = plt.subplots(1, figsize=(10, 6))
+ ax.imshow(cv2.cvtColor(src_frame, cv2.COLOR_BGR2RGB))
+ count = 0
+ mask = raw_scores >= threshold
+ for c in range(len(class_names)):
+ cb = raw_boxes[mask[:,c]]
+ cs = raw_scores[:,c][mask[:,c]]
+ if len(cs) == 0:
+ continue
+ for idx in nms_boxes(cb, cs):
+ count += 1
+ top, left, bottom, right = cb[idx]
+ w, h = right-left, bottom-top
+ color = tuple([v/255 for v in colors[c]])
+ ax.add_patch(plt.Rectangle((left, top), w, h,
+ edgecolor=color, facecolor='none', linewidth=2))
+ ax.annotate('{}: {:.0f}%'.format(class_names[c], cs[idx]*100),
+ (left+w*0.5, top+h*0.5), color=color,
+ weight='bold', fontsize=10, ha='center', va='center')
+ ax.set_title('Threshold: {:.2f} | Boxes shown: {}'.format(threshold, count), fontsize=13)
+ plt.axis('off')
+ plt.tight_layout()
+ plt.show()
+
+ thresh_slider = widgets.FloatSlider(
+ value=0.2, min=0.05, max=0.95, step=0.05,
+ description='Min confidence:',
+ style={'description_width': 'initial'},
+ layout=widgets.Layout(width='500px'),
+ continuous_update=False
+ )
+ thresh_output = widgets.Output()
+
+ def on_change(change):
+ thresh_output.clear_output(wait=True)
+ with thresh_output:
+ draw_at_threshold(change['new'])
+
+ thresh_slider.observe(on_change, names='value')
+ ipy_display(thresh_slider, thresh_output)
+ with thresh_output:
+ draw_at_threshold(0.2)
+
+ print("Done — drag the slider to explore!")
diff --git a/bootcamp_sessions/PYNQ 301 - Object Detection/yolo_helpers.py b/bootcamp_sessions/PYNQ 301 - Object Detection/yolo_helpers.py
new file mode 100644
index 0000000..2838686
--- /dev/null
+++ b/bootcamp_sessions/PYNQ 301 - Object Detection/yolo_helpers.py
@@ -0,0 +1,188 @@
+"""
+YOLOv3-specific helpers — pre-processing, anchor decoding, NMS, drawing, run().
+Generic utilities (colors, classes, DPU buffers, image listing) live in bootcamp_utils.
+"""
+import sys, os
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', '..', '..'))
+
+import numpy as np
+import cv2
+import matplotlib.pyplot as plt
+from matplotlib.patches import Rectangle
+from IPython.display import display, Image
+
+# Re-export shared utilities so notebook code keeps working unchanged
+from bootcamp_utils.color_utils import load_classes, make_colors
+from bootcamp_utils.dpu_utils import setup_dpu_buffers, list_images
+
+def load_anchors():
+ anchor_list = [10,13,16,30,33,23,30,61,62,45,59,119,116,90,156,198,373,326]
+ return np.array([float(x) for x in anchor_list]).reshape(-1, 2)
+
+
+# ---------------------------------------------------------------------------
+# Pre-processing
+# ---------------------------------------------------------------------------
+
+def letterbox_image(image, size):
+ ih, iw, _ = image.shape
+ w, h = size
+ scale = min(w/iw, h/ih)
+ nw, nh = int(iw*scale), int(ih*scale)
+ image = cv2.resize(image, (nw, nh), interpolation=cv2.INTER_LINEAR)
+ new_image = np.ones((h, w, 3), np.uint8) * 128
+ h_start, w_start = (h-nh)//2, (w-nw)//2
+ new_image[h_start:h_start+nh, w_start:w_start+nw, :] = image
+ return new_image
+
+
+def pre_process(image, model_image_size):
+ image = image[...,::-1]
+ image_h, image_w, _ = image.shape
+ if model_image_size != (None, None):
+ assert model_image_size[0] % 32 == 0, 'Multiples of 32 required'
+ assert model_image_size[1] % 32 == 0, 'Multiples of 32 required'
+ boxed_image = letterbox_image(image, tuple(reversed(model_image_size)))
+ else:
+ new_image_size = (image_w - (image_w % 32), image_h - (image_h % 32))
+ boxed_image = letterbox_image(image, new_image_size)
+ image_data = np.array(boxed_image, dtype='float32') / 255.
+ return np.expand_dims(image_data, 0)
+
+
+# ---------------------------------------------------------------------------
+# Decoding / NMS
+# ---------------------------------------------------------------------------
+
+def _get_feats(feats, anchors, num_classes, input_shape):
+ num_anchors = len(anchors)
+ anchors_tensor = np.reshape(np.array(anchors, dtype=np.float32), [1,1,1,num_anchors,2])
+ grid_size = np.shape(feats)[1:3]
+ nu = num_classes + 5
+ predictions = np.reshape(feats, [-1, grid_size[0], grid_size[1], num_anchors, nu])
+ grid_y = np.tile(np.reshape(np.arange(grid_size[0]), [-1,1,1,1]), [1,grid_size[1],1,1])
+ grid_x = np.tile(np.reshape(np.arange(grid_size[1]), [1,-1,1,1]), [grid_size[0],1,1,1])
+ grid = np.array(np.concatenate([grid_x, grid_y], axis=-1), dtype=np.float32)
+ box_xy = (1/(1+np.exp(-predictions[...,:2])) + grid) / np.array(grid_size[::-1], dtype=np.float32)
+ box_wh = np.exp(predictions[...,2:4]) * anchors_tensor / np.array(input_shape[::-1], dtype=np.float32)
+ box_confidence = 1/(1+np.exp(-predictions[...,4:5]))
+ box_class_probs = 1/(1+np.exp(-predictions[...,5:]))
+ return box_xy, box_wh, box_confidence, box_class_probs
+
+
+def correct_boxes(box_xy, box_wh, input_shape, image_shape):
+ box_yx = box_xy[...,::-1]
+ box_hw = box_wh[...,::-1]
+ input_shape = np.array(input_shape, dtype=np.float32)
+ image_shape = np.array(image_shape, dtype=np.float32)
+ new_shape = np.around(image_shape * np.min(input_shape/image_shape))
+ offset = (input_shape - new_shape) / 2. / input_shape
+ scale = input_shape / new_shape
+ box_yx = (box_yx - offset) * scale
+ box_hw *= scale
+ box_mins = box_yx - (box_hw/2.)
+ box_maxes = box_yx + (box_hw/2.)
+ boxes = np.concatenate([box_mins[...,0:1], box_mins[...,1:2],
+ box_maxes[...,0:1], box_maxes[...,1:2]], axis=-1)
+ boxes *= np.concatenate([image_shape, image_shape], axis=-1)
+ return boxes
+
+
+def boxes_and_scores(feats, anchors, classes_num, input_shape, image_shape):
+ box_xy, box_wh, box_confidence, box_class_probs = _get_feats(feats, anchors, classes_num, input_shape)
+ boxes = np.reshape(correct_boxes(box_xy, box_wh, input_shape, image_shape), [-1, 4])
+ box_scores = np.reshape(box_confidence * box_class_probs, [-1, classes_num])
+ return boxes, box_scores
+
+
+def nms_boxes(boxes, scores):
+ x1, y1, x2, y2 = boxes[:,0], boxes[:,1], boxes[:,2], boxes[:,3]
+ areas = (x2-x1+1)*(y2-y1+1)
+ order = scores.argsort()[::-1]
+ keep = []
+ while order.size > 0:
+ i = order[0]
+ keep.append(i)
+ xx1 = np.maximum(x1[i], x1[order[1:]])
+ yy1 = np.maximum(y1[i], y1[order[1:]])
+ xx2 = np.minimum(x2[i], x2[order[1:]])
+ yy2 = np.minimum(y2[i], y2[order[1:]])
+ w1 = np.maximum(0.0, xx2-xx1+1)
+ h1 = np.maximum(0.0, yy2-yy1+1)
+ inter = w1*h1
+ ovr = inter / (areas[i] + areas[order[1:]] - inter)
+ order = order[np.where(ovr <= 0.55)[0]+1]
+ return keep
+
+
+def evaluate(yolo_outputs, image_shape, class_names, anchors, score_thresh=0.2):
+ anchor_mask = [[6,7,8],[3,4,5],[0,1,2]]
+ input_shape = np.array(np.shape(yolo_outputs[0])[1:3]) * 32
+ boxes, box_scores = [], []
+ for i in range(len(yolo_outputs)):
+ b, s = boxes_and_scores(yolo_outputs[i], anchors[anchor_mask[i]],
+ len(class_names), input_shape, image_shape)
+ boxes.append(b); box_scores.append(s)
+ boxes = np.concatenate(boxes, axis=0)
+ box_scores = np.concatenate(box_scores, axis=0)
+ mask = box_scores >= score_thresh
+ boxes_, scores_, classes_ = [], [], []
+ for c in range(len(class_names)):
+ cb = boxes[mask[:,c]]
+ cs = box_scores[:,c][mask[:,c]]
+ if len(cs) == 0:
+ continue
+ idx = nms_boxes(cb, cs)
+ boxes_.append(cb[idx])
+ scores_.append(cs[idx])
+ classes_.append(np.ones_like(cs[idx], dtype=np.int32) * c)
+ if not boxes_:
+ return np.array([]), np.array([]), np.array([], dtype=np.int32)
+ return (np.concatenate(boxes_),
+ np.concatenate(scores_),
+ np.concatenate(classes_))
+
+
+# ---------------------------------------------------------------------------
+# Run — full inference pipeline
+# ---------------------------------------------------------------------------
+
+def run(frame, dpu, input_data, output_data, image_buf, shapeIn,
+ shapeOut0, shapeOut1, shapeOut2,
+ class_names, anchors, colors, display=False):
+ """Pre-process frame → DPU → decode → (optionally draw) → return boxes, scores, classes."""
+ image_size = frame.shape[:2]
+ image_data = np.array(pre_process(frame, (416, 416)), dtype=np.float32)
+ image_buf[0, ...] = image_data.reshape(shapeIn[1:])
+ job_id = dpu.execute_async(input_data, output_data)
+ dpu.wait(job_id)
+ yolo_outputs = [
+ np.reshape(output_data[0], shapeOut0),
+ np.reshape(output_data[1], shapeOut1),
+ np.reshape(output_data[2], shapeOut2),
+ ]
+ boxes, scores, classes = evaluate(yolo_outputs, image_size, class_names, anchors)
+ if display:
+ draw_boxes(frame, boxes, scores, classes, class_names, colors)
+ return boxes, scores, classes
+
+
+# ---------------------------------------------------------------------------
+# Drawing
+# ---------------------------------------------------------------------------
+
+def draw_boxes(image, boxes, scores, classes, class_names, colors):
+ _, ax = plt.subplots(1)
+ ax.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+ for i, bbox in enumerate(boxes):
+ top, left, bottom, right = bbox
+ width, height = right-left, bottom-top
+ score, class_index = scores[i], classes[i]
+ label = '{}: {:.4f}'.format(class_names[class_index], score)
+ color = tuple([c/255 for c in colors[class_index]])
+ ax.add_patch(Rectangle((left,top), width, height,
+ edgecolor=color, facecolor='none'))
+ ax.annotate(label, (left+width*0.5, top+height*0.5),
+ color=color, weight='bold', fontsize=12,
+ ha='center', va='center')
+ return ax
diff --git a/bootcamp_sessions/PYNQ 401 - Crowd Estimation (Breakout 1)/PYNQ 503 - Crowd Estimation.ipynb b/bootcamp_sessions/PYNQ 401 - Crowd Estimation (Breakout 1)/PYNQ 503 - Crowd Estimation.ipynb
index 7d28ea7..f58524e 100644
--- a/bootcamp_sessions/PYNQ 401 - Crowd Estimation (Breakout 1)/PYNQ 503 - Crowd Estimation.ipynb
+++ b/bootcamp_sessions/PYNQ 401 - Crowd Estimation (Breakout 1)/PYNQ 503 - Crowd Estimation.ipynb
@@ -4,11 +4,7 @@
"cell_type": "markdown",
"id": "5158dd12",
"metadata": {},
- "source": [
- "# Bayesian Crowd Counting\n",
- "\n",
- "In this example we will use a Machine Learning model to count the number of people in an image. \n"
- ]
+ "source": "\n
👥 PYNQ 401: Crowd Estimation
\n
Count people in a crowd using Bayesian deep learning on the KRIA KV260
\n
🧠 Deep Learning👥 Crowd Counting⚡ FPGA Acceleration💡 LED Bar
\n
\n
📋 In this notebook you will:\n
- Understand how density-map models estimate crowd size without bounding boxes
- Load a Bayesian Crowd Counting (BCC) model onto the FPGA DPU
- Run inference on crowd images and read the estimated count
- Display the count visually on a Grove LED bar
\n
\n
📦 Step 1 — Import Libraries
\n
Load the tools needed for image processing and the DPU
\n
\nImport the Python libraries needed for image processing and the FPGA overlay.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -81,12 +80,12 @@
"cell_type": "markdown",
"id": "8c3612f8",
"metadata": {},
- "source": [
- "\n",
- "## FPGA Overlays\n",
- "\n",
- "FPGAs are specialized hardware that can reconfigure their internal architecture to suit the needs of the application.\n"
- ]
+ "source": "\n
⚙️ Step 2 — Load the Overlay & LED Bar
\n
Program the FPGA and connect the Grove LED bar
\n
\nLoad the DPU overlay onto the FPGA to set up the hardware.\n
"
},
{
"cell_type": "code",
@@ -108,6 +107,11 @@
"Make sure your led bar is plugged into the G2 slot of the PYNQ Grove Adapter."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nSet up the PMOD adapter and connect the Grove LED bar.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -124,21 +128,12 @@
"cell_type": "markdown",
"id": "5f7afa88",
"metadata": {},
- "source": [
- "## AI Model\n",
- "\n",
- "In this tutorial we will be using the Bayesian crowd counting (BCC) model which is a Deep Learning model for crowd detection. \n",
- "\n",
- "**Goal**: To detect the number of people in the image of a crowded environment\n",
- "\n",
- "**Input**: An Image of size 1000 x 800 pixels (preprocessed)\n",
- "\n",
- "**Output**: A Tensor (matrix) of the number of people for some \"key\" points in the images. To get final count sum of all the values.\n",
- "\n",
- "The model is trained on the ShanghaiTechA Dataset which contains images of crowded environments and the \"correct\" count of people in that image.\n",
- "\n",
- "It is based on the Resnet model which is a CNN model."
- ]
+ "source": "\n
🧠 Step 3 — Load the AI Model
\n
Load the Bayesian Crowd Counting model onto the DPU
\n
\nLoad the Bayesian Crowd Counting model onto the DPU.\n
"
},
{
"cell_type": "code",
@@ -156,11 +151,12 @@
"cell_type": "markdown",
"id": "5fe01fc0",
"metadata": {},
- "source": [
- "## Load an Image\n",
- "\n",
- "To test the model, let's load an image of a crowd and see if the model can accurately calculate the number of people in it.\n"
- ]
+ "source": "\n
🖼️ Step 4 — Load a Crowd Image
\n
Pick one of the crowd photos to run estimation on
\n
\nFind all crowd photos in the data folder and load the first one.\n
"
},
{
"cell_type": "code",
@@ -196,6 +192,11 @@
"Let's display our image."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nDisplay the loaded crowd image in the notebook.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -215,6 +216,11 @@
"## Get width, height, channel of an image"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nPrint the width, height, and number of color channels of the image.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -253,6 +259,11 @@
"Takes in an RGB image and outputs normalized image. This makes the image the correct size to input into our model.\n"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nDefine the pre-processing function that resizes and normalizes images for the model.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -281,6 +292,11 @@
"Now, let's call the above function passing in the image that we want processed. You will see that the image shape will be the correct size that the model requires."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nPre-process the crowd image to prepare it for the DPU.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -297,9 +313,7 @@
"cell_type": "markdown",
"id": "f3f148ed",
"metadata": {},
- "source": [
- "## Execute Job on the DPU"
- ]
+ "source": "\n
🚀 Step 5 — Run the Model
\n
Send the image through the DPU and get the crowd count
\n
\nSet up the DPU runner and check the input and output tensor shapes.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -338,6 +357,21 @@
"Now, we add set the input data to equal the input img and tell the FPGA to run our model on the image. The program will wait to continue until the dpu has finished running the model."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\n🎤 Presenter Tip
\nBefore running, ask the class to guess how many people are in the photo. Write guesses on the whiteboard, then reveal the model’s count!\n
"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\n👩💻 Students
\nLook at the photo carefully. Make your own guess before running the cell. Is the model’s count higher or lower than you expected?\n
"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nRun the crowd counting model on the image and wait for results.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -366,6 +400,11 @@
"The dpu has now set the output data equal to the array of \"key points.\" Remember that the keypoints are where there is a person in the image. So, if we sum the number of \"key points\" in the output data, we will get the estimated number of people in the image."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nSum the model output to get the estimated number of people in the image.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -392,10 +431,7 @@
"cell_type": "markdown",
"id": "fa244ff2",
"metadata": {},
- "source": [
- "## Using the LED Bar\n",
- "\n"
- ]
+ "source": "\n
💡 Step 6 — Display Count on LED Bar
\n
Light up the LED bar to show the estimated crowd size visually
\n
\nCalculate how many LED bars to light up based on the crowd count.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -450,6 +491,11 @@
""
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nTurn off all the LED bar lights when done.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -471,6 +517,11 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\n
🏁 Challenges — Your Turn!
\n
Extend the crowd counter with these challenges!
\n
\n
\n🏫 Middle School\n
Challenge 1 — Try Different Images
\n\n
Change the image index to test different crowd photos. Which image gives the highest count?
\n
\n💡 Hint: The image list is stored in `image_path_list`. Try index 0, 1, 2…\n
\n
\n
\n🏫 Middle School\n
Challenge 2 — Scale the LED Bar
\n\n
Change `MAX_NUM_PEOPLE` so the LED bar fills up at a lower count (e.g. 50 people = full bar).
\n
\n💡 Hint: Edit the `MAX_NUM_PEOPLE` variable before the LED bar code runs.\n
\n
\n
\n🎓 High School\n
Challenge 3 — Live Webcam Count
\n\n
Connect the webcam and run the crowd counter on live frames. Display the count on the LED bar in real time.
\n
\n💡 Hint: Use `cv2.VideoCapture(0)` to get frames, then call the preprocessing and DPU execute steps in a loop.\n
\n
\n
\n🎓 High School\n
Challenge 4 — Plot a Density Map
\n\n
Display the raw DPU output as a heatmap overlaid on the original image to show WHERE the model thinks the crowd is densest.
\n
\n💡 Hint: Use `plt.imshow` with `alpha=0.5` to overlay the output array on the image.\n
\n
\n
😷 PYNQ 402: Face Mask Detection
\n
Detect mask / no-mask in real time on the KRIA KV260 — powered by YOLO-fastest & the DPU
\n
🧠 Deep Learning😷 Safety AI⚡ FPGA Acceleration📷 Live Webcam
\n
\n
📋 In this notebook you will:\n
- Understand how a fast YOLO model detects faces and classifies mask usage
- Load a face mask detection model onto the FPGA DPU
- Run detection on static images with bounding boxes and confidence scores
- Run live detection from a webcam feed in real time
\n
\nLoad the DPU overlay onto the FPGA board.\n
"
},
{
"cell_type": "code",
@@ -35,9 +26,12 @@
"cell_type": "markdown",
"id": "181a7ff3",
"metadata": {},
- "source": [
- "## 2. Import in-built libraries"
- ]
+ "source": "\n
🛠️ Step 2 — Import Libraries
\n
Load the tools needed for detection and image display
\n
\nImport all the Python libraries needed for detection and image processing.\n
"
},
{
"cell_type": "code",
@@ -61,9 +55,12 @@
"cell_type": "markdown",
"id": "09620a21",
"metadata": {},
- "source": [
- "## 3. Load the pre-trained model on the overlay programmed in step-1"
- ]
+ "source": "\n
📦 Step 3 — Load the AI Model
\n
Load the face mask detection model onto the DPU
\n
\nLoad the face mask detection model onto the DPU.\n
"
},
{
"cell_type": "code",
@@ -79,9 +76,12 @@
"cell_type": "markdown",
"id": "d499834b",
"metadata": {},
- "source": [
- "## 4. Some utility functions"
- ]
+ "source": "\n
🔧 Step 4 — Helper Functions
\n
Define the pre-processing, NMS, and drawing functions
\n
\nSet up the anchor box sizes used by the YOLO detection model.\n
"
},
{
"cell_type": "code",
@@ -95,6 +95,11 @@
"anchors = np.array(anchor_float).reshape(-1, 2)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nLoad the class names (Mask / No Mask) and assign each a unique color.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -124,6 +129,11 @@
"random.seed(None)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nDefine the image resizing and pre-processing functions.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -169,6 +179,11 @@
" return image_data"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nDefine the functions that decode bounding boxes and confidence scores from model output.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -229,6 +244,11 @@
" return boxes, box_scores"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nDefine the Non-Maximum Suppression function to remove duplicate bounding boxes.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -315,6 +335,11 @@
" return boxes_, scores_, classes_"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nDefine the function that draws labeled bounding boxes on an image.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -350,11 +375,12 @@
"cell_type": "markdown",
"id": "a929ad55",
"metadata": {},
- "source": [
- "## Load the images for running mask detection\n",
- "\n",
- "These images will be used to test if the the face-mask detection model is able to detect the mask or not."
- ]
+ "source": "\n
🖼️ Step 5 — Load Images & Set Up DPU
\n
Prepare the image list and wire up DPU input/output buffers
\n
\nBuild a list of all the face mask images in the img/ folder.\n
"
},
{
"cell_type": "code",
@@ -372,11 +398,12 @@
"cell_type": "markdown",
"id": "33548d91",
"metadata": {},
- "source": [
- "## Start the overlay\n",
- "\n",
- "Follow these three steps to run the overlay, get the input tensors, and output tensors.\n"
- ]
+ "source": "\n
⚙️ Step 1 — Load the Overlay
\n
Program the FPGA with the DPU design
\n
\nSet up the DPU runner and retrieve the input and output tensor shapes.\n
"
},
{
"cell_type": "code",
@@ -391,6 +418,11 @@
"outputTensors = dpu.get_output_tensors()"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nPrint the input tensor shape to confirm it matches the expected dimensions.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -402,6 +434,11 @@
"print(shapeIn)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nPrint the output tensor shapes for both detection heads.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -418,6 +455,11 @@
"print(shapeOut1)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nCalculate and print the total output size for each detection head.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -431,6 +473,11 @@
"print(outputSize1)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nAllocate memory buffers for the model input and output data.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -448,11 +495,12 @@
"cell_type": "markdown",
"id": "544bb9e4",
"metadata": {},
- "source": [
- "## Function to run mask detection on KRIA\n",
- "\n",
- "This is the top function that will call other utility functions to run face mask detection on KRIA"
- ]
+ "source": "\n
🚀 Step 6 — Run Detection on Static Images
\n
Send images through the model and draw bounding boxes
\n
\nDefine the run() function that pre-processes an image and gets predictions.\n
"
},
{
"cell_type": "code",
@@ -497,6 +545,21 @@
"## Make a function call to run\n"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\n🎤 Presenter Tip
\nAsk the class: 'Can you tell just by looking which faces have masks? Let’s see if the AI can!' Run the cell and compare the model’s boxes to what students said.\n
"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\n👩💻 Students
\nChange the image index from 8 to a different number (1–8). Does the model do better on some images than others? What makes a detection harder?\n
"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nRun face mask detection on one of the sample images and show the results.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -507,18 +570,21 @@
"run(8, display=True) #change the image index from 1 to 8 to detect face mask on different images"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\n🎤 Presenter Tip
\nAsk a student to put on a mask (or cover their face with paper) and another to leave their face uncovered. Point the webcam at both — can the model tell the difference in real time?\n
"
+ },
{
"cell_type": "markdown",
"id": "14222699",
"metadata": {},
- "source": [
- "## Setting up the webcam\n",
- "\n",
- "Use the following three steps to set up the webcam: \n",
- "1. Capture Video\n",
- "2. Set frame height\n",
- "3. Set frame width\n"
- ]
+ "source": "\n
📷 Step 7 — Live Webcam Detection
\n
Run face mask detection on a live camera feed
\n
\nOpen the webcam to start capturing live video frames.\n
"
},
{
"cell_type": "code",
@@ -543,6 +609,11 @@
"## Read one frame (image) from the webcam\n"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nCapture a single test frame from the webcam.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -565,6 +636,11 @@
"The only difference between run and run_webcam is the function called to draw bounding boxes on the face.\n"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nDefine the webcam version of the run function for live detection.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -597,6 +673,11 @@
" return input_image\n"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nDefine the function that draws bounding boxes directly onto a webcam frame.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -639,6 +720,11 @@
"\n"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nStart the live webcam feed and run face mask detection on each frame.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -670,6 +756,11 @@
"## Always run the below line of code to release the webcam device\n"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nRelease the webcam device when done.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -688,6 +779,11 @@
"## To get the performance of KRIA board\n"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nMeasure the detection speed by timing the model across all test images.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -706,9 +802,12 @@
"cell_type": "markdown",
"id": "650172e6",
"metadata": {},
- "source": [
- "## Always delete the overlay and dpu object deployed on KRIA\n"
- ]
+ "source": "\n🧹 Cleanup
\nRun this cell when you are completely done to release the DPU and FPGA overlay so other notebooks can use them.\n
"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nRelease the DPU and FPGA overlay to free resources.\n
"
},
{
"cell_type": "code",
@@ -725,12 +824,7 @@
"cell_type": "markdown",
"id": "0781b102",
"metadata": {},
- "source": [
- "## Challenge\n",
- "\n",
- "1. Print total objects detected in the image/frame.\n",
- "2. Ring a siren when a person is detected without a face mask\n"
- ]
+ "source": "\n
🏁 Challenges — Your Turn!
\n
Extend the face mask detector with these challenges!
\n
\n
\n🏫 Middle School\n
Challenge 1 — Count Masked vs Unmasked
\n\n
Count how many 'Mask' and 'No Mask' detections appear in each image. Print a summary.
\n
\n💡 Hint: Check the class label string in your detection loop — `'Mask'` or `'No Mask'`.\n
\n
\n
\n🏫 Middle School\n
Challenge 2 — Alert on No Mask
\n\n
Make the live loop print a warning message whenever a 'No Mask' face is detected.
\n
\n💡 Hint: Check the predicted class inside the webcam loop and use `print()` for the alert.\n
\n
\n
\n🎓 High School\n
Challenge 3 — Confidence Filter
\n\n
Add a slider (like the Object Detection notebook) to filter detections by confidence threshold in real time.
\n
\n💡 Hint: Use `ipywidgets.FloatSlider` and only draw boxes where `score >= threshold`.\n
\n
\n
\n🎓 High School\n
Challenge 4 — Track Detection Rate
\n\n
Record what fraction of frames have at least one 'No Mask' detection, and display it as a percentage when the camera stops.
\n
\n💡 Hint: Keep a `no_mask_frames` counter and divide by total frames at the end.\n
\n
\n
🚗 PYNQ 403: Automotive AI
\n
Semantic image segmentation & ultrasonic distance sensing on the KRIA KV260
\n
🧠 Deep Learning🚗 Autonomous Driving⚡ FPGA Acceleration📡 Ultrasonic Sensor
\n
\n
📋 In this notebook you will:\n
- Understand semantic segmentation — labelling every pixel of an image with a class
- Run ENet segmentation on driving-scene images using the FPGA DPU
- Visualize the segmentation output with a color palette
- Measure real distances using a Grove ultrasonic sensor
\n
\n
📖 Useful Term Cheat Sheet
\n
| DPU | Data Processing Unit — specialized FPGA hardware for running AI models fast |
| ENet | Efficient Neural Network — a fast segmentation model used here |
| Semantic Segmentation | Classifying every pixel in an image into a category (road, car, person…) |
| Overlay | A pre-built design loaded onto the FPGA |
| Ultrasonic Sensor | Measures distance by timing a sound pulse echo |
| Tensor | A multi-dimensional array — the data format models work with |
\n
🛠️ Step 1 — Import Libraries
\n
Load the tools needed for image processing and the DPU
\n
\nImport the Python libraries needed for image processing and visualization.\n
"
},
{
"cell_type": "code",
@@ -145,11 +141,12 @@
"cell_type": "markdown",
"id": "c0fe1f61",
"metadata": {},
- "source": [
- "### Overlay Setup:\n",
- "\n",
- "Now lets define our __overlay__ and load the __Enet__ model onto it."
- ]
+ "source": "\n
⚙️ Step 2 — Load the Overlay & Model
\n
Program the FPGA and load the ENet segmentation model
\n
\nLoad the DPU overlay and the ENet segmentation model onto the FPGA.\n
"
},
{
"cell_type": "code",
@@ -166,11 +163,12 @@
"cell_type": "markdown",
"id": "639e557c",
"metadata": {},
- "source": [
- "### Creating an image list:\n",
- "\n",
- "This cell creates a list of images we intend to use as inputs. It then prints how many images it finds."
- ]
+ "source": "\n
🖼️ Step 3 — Load Images
\n
Build a list of driving-scene photos to segment
\n
\nBuild a list of all the driving-scene PNG images to segment.\n
"
},
{
"cell_type": "code",
@@ -203,11 +201,12 @@
"cell_type": "markdown",
"id": "6c6293c6",
"metadata": {},
- "source": [
- "__Preprocessing Setup:__\n",
- "\n",
- "Here we are going to define a few lists and a function that will be needed for preproccessing. The model cannot take an image as an input directly, so these lists and functions are used to turn the photo into inputs the model can understand."
- ]
+ "source": "\n
🔧 Step 4 — Set Up Pre-processing & DPU
\n
Define the color palette and wire up the DPU buffers
\n
\nSet up the color palette and image normalization values for segmentation output.\n
"
},
{
"cell_type": "code",
@@ -259,6 +258,11 @@
"The runner is how we pass __tensors__ through to the model. Lets initialize it here!"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nInitialize the DPU runner to prepare for running the model.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -295,6 +299,11 @@
"Here we are looking to find what the dimensions of the input and output __tensors__ are so we know how to shape our data."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nRetrieve and print the input and output tensor shapes for the model.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -335,6 +344,11 @@
"Here we want to shape our data to the __tensor__ dimentions we found in the last cell. We do this through a library called numpy which is really good for numerical computing."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nAllocate memory buffers shaped to the model's input and output requirements.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -365,11 +379,12 @@
"cell_type": "markdown",
"id": "d48f0b85",
"metadata": {},
- "source": [
- "__Where all the fun happens!__\n",
- "\n",
- "Here we define our run function which takes our image and __segments__ it. This uses all of the bits and pieces we defined earlier!"
- ]
+ "source": "\n
🚀 Step 5 — Run Segmentation
\n
Send an image through ENet and display the color-coded result
\n
\nDefine the run() function that segments an image and optionally displays the result.\n
"
},
{
"cell_type": "code",
@@ -431,6 +446,21 @@
"Lets finally run this thing by passing in one of our photos from the origional_images list!"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\n🎤 Presenter Tip
\nBefore running, point to different parts of the image and ask 'What is this? Road? Building? Person?' Then run the model — the colors reveal how it labelled every single pixel!\n
"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\n👩💻 Students
\nLook at the color-coded output. Can you find where the model made a mistake? What did it label as the wrong thing?\n
"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nRun the ENet segmentation model on the first image and display the color-coded output.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -467,6 +497,11 @@
"We can use a simple for loop to do this for all of the images we found earlier."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nRun segmentation on all the driving-scene images and display each result.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -482,10 +517,12 @@
"cell_type": "markdown",
"id": "f2f9d11d",
"metadata": {},
- "source": [
- "__Cleanup:__\n",
- "Just like in the other notebooks, we have to release the overlay and dpu so we can use it later!"
- ]
+ "source": "\n🧹 Cleanup
\nRun this cell when you are completely done to release the DPU and FPGA overlay so other notebooks can use them.\n
"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nRelease the DPU and FPGA overlay to free resources.\n
"
},
{
"cell_type": "code",
@@ -498,13 +535,16 @@
"del dpu"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\n🎤 Presenter Tip
\nHold an object at different distances from the sensor and ask students to guess the distance before the code reveals it. Great demo of how self-driving cars 'feel' their surroundings!\n
"
+ },
{
"cell_type": "markdown",
"id": "2dcdafa4",
"metadata": {},
- "source": [
- "# Distance Measurement with Ultrasonic Sensors"
- ]
+ "source": "\n
📡 Step 6 — Ultrasonic Distance Sensor
\n
Measure real-world distances with the Grove ultrasonic sensor
\n
\nSet up the PMOD adapter and connect the Grove ultrasonic ranger sensor.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -570,6 +615,11 @@
"Here's a simple loop that prints the measured distance 10 times:"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nTake 10 distance measurements from the ultrasonic sensor and print each one.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -583,6 +633,11 @@
" sleep(1)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nRelease the base overlay when the ultrasonic sensor session is done.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -608,18 +663,7 @@
}
}
},
- "source": [
- "# Extra Challenges:\n",
- "Was this too fast or boring? Here are some challenges for you!\n",
- "\n",
- "__Easy:__ Change the code to add the .jpeg file in the img directory to the original_images list.\n",
- "\n",
- "__Medium:__ Change the run function to accept images instead of photo indexes. (i.e. origional_images[0] instead of original_images.index(img))\n",
- "\n",
- "__Hard:__ Detect and print what the color is at the very center of the photo.\n",
- "\n",
- "__Do you just want my job???:__ Detect the color at the center of the screen and print \"CAR IN FRONT\" on the OLED display if the color is blue."
- ]
+ "source": "\n
🏁 Challenges — Your Turn!
\n
Extend the automotive AI with these challenges!
\n
\n
\n🏫 Middle School\n
Challenge 1 — Segment All Images
\n\n
Run the `run()` function on every image in `original_images` using a for loop.
\n
\n💡 Hint: Use `for i in range(len(original_images)): run(i, display=True)`\n
\n
\n
\n🏫 Middle School\n
Challenge 2 — Spot the Road
\n\n
The road class has a specific color in the palette. Identify it and count how many pixels in the output are labelled as road.
\n
\n💡 Hint: Check `palette` index 0 — that is the road color. Count pixels matching that color in the output.\n
\n
\n
\n🎓 High School\n
Challenge 3 — Distance Alert
\n\n
Combine both sections — print a warning when the ultrasonic sensor detects an object closer than 30 cm.
\n
\n💡 Hint: Use a loop with `sleep` and check `us.get_distance()`.\n
\n
\n
\n🎓 High School\n
Challenge 4 — Live Camera Segmentation
\n\n
Connect a webcam and run ENet on live frames. Display the segmentation output in real time.
\n
\n💡 Hint: Use `cv2.VideoCapture(0)` to get frames, preprocess each one, run the DPU, then display the output.\n
\n
\n
👁️ PYNQ 501: Introduction to Computer Vision
\n
Explore image processing with OpenCV — the building blocks of AI vision
\n
👁️ Computer Vision🖼️ Image Processing🐍 Python📊 OpenCV
\n
\n
📋 In this notebook you will:\n
- Read and display images using OpenCV and matplotlib
- Convert images between color spaces (RGB, Grayscale, HSV)
- Detect corners using the Harris Corner Detector
- Normalize, crop, and resize images for model input
\n
\nImport NumPy, OpenCV, and matplotlib — the core libraries for computer vision.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -64,9 +63,12 @@
{
"cell_type": "markdown",
"metadata": {},
- "source": [
- "## How to read an image?"
- ]
+ "source": "\n
🖼️ Step 1 — Read an Image
\n
Load an image file and display it with matplotlib
\n
\nLoad the mosaic image from disk and display its pixel dimensions.\n
"
},
{
"cell_type": "code",
@@ -82,10 +84,12 @@
{
"cell_type": "markdown",
"metadata": {},
- "source": [
- "## How to plot images next to each other?\n",
- " "
- ]
+ "source": "\n
📊 Step 2 — Compare Images Side by Side
\n
Display original and processed images for comparison
\n
\nDefine a helper function to display two images side by side for comparison.\n
"
},
{
"cell_type": "code",
@@ -105,13 +109,12 @@
{
"cell_type": "markdown",
"metadata": {},
- "source": [
- "## How to convert RGB image to Grayscale image?\n",
- "\n",
- "A color image is represented on the [RGB color space](https://en.wikipedia.org/wiki/RGB_color_space), however there are many different color spaces. Each of them have a particular purpose. We will explore some of them\n",
- "\n",
- "A [grayscale image](https://en.wikipedia.org/wiki/Grayscale) has only one channel which represents the amount of light that each pixel contains. One of the main purposes of grayscale images on vision applications is to detect edges on images."
- ]
+ "source": "\n
⚫ Step 3 — Grayscale Conversion
\n
Remove color information — useful for many CV algorithms
\n
\nConvert the image to grayscale and compare it with the original.\n
"
},
{
"cell_type": "code",
@@ -127,11 +130,12 @@
{
"cell_type": "markdown",
"metadata": {},
- "source": [
- "## How to convert RGB image to HSV format?\n",
- "\n",
- "[HSV color space](https://en.wikipedia.org/wiki/HSL_and_HSV) represents an image using the channels hue, saturation and value. This color space aligns a bit better to the way human perceives color-making attributes, and in vision application this color space is useful to detect color more accurately."
- ]
+ "source": "\n
🌈 Step 4 — HSV Color Space
\n
Separate hue, saturation and brightness — great for color filtering
\n
\nConvert the image to HSV color space to separate hue from brightness.\n
"
},
{
"cell_type": "code",
@@ -146,13 +150,12 @@
{
"cell_type": "markdown",
"metadata": {},
- "source": [
- "## Corner Detector \n",
- "\n",
- "### Harris Corner Detector\n",
- "\n",
- "The Harris Corner Detector is primarily designed for detecting corner points or keypoints in images and not specifically meant for detecting edges. While it can potentially detect some edges, its primary focus is on identifying corners in the image."
- ]
+ "source": "\n
📐 Step 5 — Harris Corner Detection
\n
Find corners and edges — building blocks of feature matching
\n
\nRun the Harris Corner Detector to find edges and corners in the image.\n
"
},
{
"cell_type": "code",
@@ -168,11 +171,12 @@
{
"cell_type": "markdown",
"metadata": {},
- "source": [
- "## How to normalize image?\n",
- "\n",
- "Normalize the image by dividing by 256, then subtracting the mean i.e. 0.5, and then amplifying the values by multiplying by 2.\n"
- ]
+ "source": "\n
📏 Step 6 — Normalize & Crop
\n
Scale pixel values and crop to a region of interest
\n
\nDefine the normalize function that scales pixel values to the range -1 to 1.\n
"
},
{
"cell_type": "code",
@@ -188,6 +192,11 @@
" return image_norm"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nApply normalization to the image and display the result.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -206,6 +215,11 @@
"Reduce the size of the image around the center.\n"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nDefine the central_crop function that trims the image around its center.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -223,6 +237,11 @@
" return image_crop"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nCrop the image to a smaller size around the center and print the new dimensions.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -241,6 +260,11 @@
"## How to convert BGR image to RGB image?\n"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nDefine the BGR2RGB function that swaps color channels from OpenCV order to display order.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -254,6 +278,11 @@
" return processed_image"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nConvert the image from BGR to RGB and compare the colors side by side.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -270,6 +299,11 @@
"## How to resize the shortest edge of an image?\n"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nDefine the resize function that scales an image while keeping its aspect ratio.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -290,6 +324,11 @@
" return processed_image"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nResize the image along its shortest edge and print the new dimensions.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -330,4 +369,4 @@
},
"nbformat": 4,
"nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/bootcamp_sessions/PYNQ 502 - Intermediate Python (Breakout 2)/PYNQ 502 - Intermediate Python.ipynb b/bootcamp_sessions/PYNQ 502 - Intermediate Python (Breakout 2)/PYNQ 502 - Intermediate Python.ipynb
index 1969c37..b7ce234 100644
--- a/bootcamp_sessions/PYNQ 502 - Intermediate Python (Breakout 2)/PYNQ 502 - Intermediate Python.ipynb
+++ b/bootcamp_sessions/PYNQ 502 - Intermediate Python (Breakout 2)/PYNQ 502 - Intermediate Python.ipynb
@@ -3,9 +3,7 @@
{
"cell_type": "markdown",
"metadata": {},
- "source": [
- "# Intermediate Python"
- ]
+ "source": "\n
🐍 PYNQ 502: Intermediate Python
\n
Level up your Python — classes, file I/O, data analysis, and matplotlib
\n
🐍 Python📊 Data Analysis📈 Matplotlib🏗️ OOP
\n
\n
📋 In this notebook you will:\n
- Work with Python lists and dictionaries for data storage
- Define and use your own Python classes
- Read and analyse data from CSV files
- Compute statistics and plot results with matplotlib
\n
\nShow syntax examples for functions, classes, if statements, and loops.\n
"
+ },
{
"cell_type": "code",
"execution_count": 1,
@@ -119,9 +122,7 @@
{
"cell_type": "markdown",
"metadata": {},
- "source": [
- "# Non-primitive Objects"
- ]
+ "source": "\n
📋 Step 1 — Lists, Arrays & Dictionaries
\n
Organize and look up data with Python collections
\n
\nCreate a simple list and store three numbers in it.\n
"
+ },
{
"cell_type": "code",
"execution_count": 2,
@@ -153,6 +159,11 @@
"a = [12, 23, 45]"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nAccess and display the second element of the list using its index.\n
"
+ },
{
"cell_type": "code",
"execution_count": 3,
@@ -187,6 +198,11 @@
"Stepping through a list is a common occurance in programming and you will probably have to do it for your hackathon project. Lets look at some strategies below."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nLoop through a list using a range index and print each element.\n
"
+ },
{
"cell_type": "code",
"execution_count": 4,
@@ -223,6 +239,11 @@
"The range function sets a range of values for the loop to iterate through. As you can see from the printed output, the first number is included in the range, and the second number is not. There is also a third argument you can pass into the range function, which dictates the increment taken. Lets try an increment of two."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nLoop through a list with a step of 2 and print every other element.\n
"
+ },
{
"cell_type": "code",
"execution_count": 5,
@@ -253,6 +274,11 @@
"You could also use a for each loop so that the range just adjusts to the size of your list. Example below."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nUse a for-each loop to print every item in two different lists.\n
"
+ },
{
"cell_type": "code",
"execution_count": 6,
@@ -326,6 +352,11 @@
"NumPy arrays act much like lists, but they are fasters and closer to true arrays. Here is how to instantiate them."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nImport NumPy and create both a 1D and a 2D array.\n
"
+ },
{
"cell_type": "code",
"execution_count": 7,
@@ -348,6 +379,11 @@
"Here is how to access the elements of the array"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nAccess and print specific elements from the NumPy arrays.\n
"
+ },
{
"cell_type": "code",
"execution_count": 8,
@@ -394,6 +430,11 @@
"How would you access the value \"a\" in the following dictionary?"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nCreate a dictionary, add an element, and retrieve values using keys.\n
"
+ },
{
"cell_type": "code",
"execution_count": 9,
@@ -420,6 +461,11 @@
"print(d[2])\n"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nPrint all the keys from the dictionary.\n
"
+ },
{
"cell_type": "code",
"execution_count": 10,
@@ -438,6 +484,11 @@
"print(d.keys())"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nPrint all the key-value pairs from the dictionary.\n
"
+ },
{
"cell_type": "code",
"execution_count": 11,
@@ -517,9 +568,7 @@
{
"cell_type": "markdown",
"metadata": {},
- "source": [
- "# Built-In Functions:"
- ]
+ "source": "\n
⚙️ Step 2 — Built-In Functions
\n
Use Python’s built-in tools: input, range, len, and more
\n
\nRun a number-guessing game that takes input from the user.\n
"
+ },
{
"cell_type": "code",
"execution_count": 14,
@@ -615,9 +669,7 @@
{
"cell_type": "markdown",
"metadata": {},
- "source": [
- "# File I/O"
- ]
+ "source": "\n
💾 Step 3 — File Input & Output
\n
Read from and write to files on disk
\n
\nOpen a text file in write mode to prepare for writing.\n
"
+ },
{
"cell_type": "code",
"execution_count": 15,
@@ -649,6 +706,11 @@
"file = open('text.txt', 'w')"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nWrite several lines of text into the open file.\n
"
+ },
{
"cell_type": "code",
"execution_count": 16,
@@ -676,6 +738,11 @@
"# YOUR TURN! Write something to the file"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nClose the file to save all the written content.\n
"
+ },
{
"cell_type": "code",
"execution_count": 17,
@@ -693,6 +760,11 @@
"Now lets read from the file:"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nOpen and read back the text that was written to the file.\n
"
+ },
{
"cell_type": "code",
"execution_count": 18,
@@ -750,9 +822,7 @@
{
"cell_type": "markdown",
"metadata": {},
- "source": [
- "# Classes"
- ]
+ "source": "\n
🏗️ Step 4 — Classes & Objects
\n
Build your own data types with Python classes
\n
\nDefine a simple Dog class with hardcoded name, breed, and other properties.\n
"
+ },
{
"cell_type": "code",
"execution_count": 19,
@@ -791,6 +866,11 @@
"Like I said earlier, not all dogs are labs or 50 pounds, so lets make an \"__init__\" function so people can set the values of their dog instances."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nImprove the Dog class by adding an __init__ method to set custom values.\n
"
+ },
{
"cell_type": "code",
"execution_count": 20,
@@ -814,6 +894,11 @@
"Now that we can initialize any dog we want, lets make one. I'll base it off of my own."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nCreate a Dog instance with real values and print its name.\n
"
+ },
{
"cell_type": "code",
"execution_count": 21,
@@ -840,6 +925,11 @@
"You can also define a \"__str__\" function in your classes so that they print how you want to. Lets try it below."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nAdd a __str__ method so the Dog class prints a friendly description.\n
"
+ },
{
"cell_type": "code",
"execution_count": 22,
@@ -885,6 +975,11 @@
"My dog Riley loves to nap and play tug-o-war. Lets define those methods below to complete our dog class!"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nDefine the complete Dog class with tug_o_war and nap behavior methods.\n
"
+ },
{
"cell_type": "code",
"execution_count": 23,
@@ -913,6 +1008,11 @@
" print(\"I'm off to go take a nap. See you in \" + str(duration) + \" minutes\")"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nCreate a Dog object and call its methods to see it in action.\n
"
+ },
{
"cell_type": "code",
"execution_count": 24,
@@ -977,9 +1077,7 @@
{
"cell_type": "markdown",
"metadata": {},
- "source": [
- "# Data Processing Basics"
- ]
+ "source": "\n
📈 Step 5 — Data Processing
\n
Load and analyze real-world datasets
\n
\nAnalyze basketball points-per-game statistics and print summary numbers.\n
"
+ },
{
"cell_type": "code",
"execution_count": 26,
@@ -1080,6 +1183,11 @@
"This block is just a bunch a functions that we will be using in a bit. Run this code before you run the next code to make sure that all the proper functions are defined. It shouldn't do anything, but it's necessary for the next example to work properly."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nDefine all the helper functions needed to read and analyze CSV data.\n
"
+ },
{
"cell_type": "code",
"execution_count": 27,
@@ -1197,6 +1305,11 @@
"The code below takes data from a csv file (a spreadsheet) and is able to display all the data and calculate some statisitcs like last time, except it is over a much larger data set and it computes more advanced stats like standard deviation. You also select which part of the data you would like to collect stats on."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nLoad the Fitbit CSV data and compute statistics on a chosen column.\n
"
+ },
{
"cell_type": "code",
"execution_count": 28,
@@ -1258,9 +1371,12 @@
{
"cell_type": "markdown",
"metadata": {},
- "source": [
- "# Graphing"
- ]
+ "source": "\n
📊 Step 6 — Graphing with Matplotlib
\n
Create charts and graphs to visualize data
\n
\nCreate a scatter plot of data points using matplotlib.\n
"
},
{
"cell_type": "code",
@@ -1305,6 +1421,11 @@
"## Exercise 8: Add a title, x label, and y label to the following graph"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nPlot a second data set as a scatter plot — add a title and labels.\n
"
+ },
{
"cell_type": "code",
"execution_count": 30,
@@ -1347,6 +1468,11 @@
"Try to find and fix all of the errors in the following code. You will know if you have done it correctly if you can play hangman with a neighbor!"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nFind and fix the syntax errors in this hangman game code.\n
"
+ },
{
"cell_type": "code",
"execution_count": 31,
@@ -1433,4 +1559,4 @@
},
"nbformat": 4,
"nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/bootcamp_sessions/PYNQ 503 - Pose Detection/PYNQ 503 - Pose Detection.ipynb b/bootcamp_sessions/PYNQ 503 - Pose Detection/PYNQ 503 - Pose Detection.ipynb
index b9d79f5..ad0b988 100644
--- a/bootcamp_sessions/PYNQ 503 - Pose Detection/PYNQ 503 - Pose Detection.ipynb
+++ b/bootcamp_sessions/PYNQ 503 - Pose Detection/PYNQ 503 - Pose Detection.ipynb
@@ -4,9 +4,7 @@
"cell_type": "markdown",
"id": "5158dd12",
"metadata": {},
- "source": [
- "# Pose Detection"
- ]
+ "source": "\n
🤸 PYNQ 503: Pose Detection
\n
Detect body keypoints and classify poses on the KRIA KV260 — powered by OpenPose & the DPU
\n
🧠 Deep Learning🤸 Pose Estimation⚡ FPGA Acceleration📷 Webcam
\n
\n
📋 In this notebook you will:\n
- Understand how keypoint heatmaps represent body pose
- Run OpenPose on the FPGA DPU and visualize heatmap outputs
- Capture a live webcam clip and run pose detection frame by frame
- Compare heatmaps to templates to classify a pose (power pose, normal, wacky)
- Generate your own pose templates
\n
\n
📖 Useful Term Cheat Sheet
\n
| DPU | Data Processing Unit — specialized FPGA hardware for running AI models fast |
| OpenPose | An AI model that detects body joint locations (keypoints) |
| Heatmap | A 2D image where bright pixels indicate likely joint positions |
| Keypoint | A detected body joint: shoulder, elbow, wrist, knee, etc. |
| Template | A reference heatmap used for pose comparison |
| Overlay | A pre-built design loaded onto the FPGA |
\n
🛠️ Step 1 — Import Libraries
\n
Load the tools needed for pose detection and heatmap comparison
\n
\nInstall the scikit-image library needed for image similarity comparisons.\n
"
},
{
"cell_type": "code",
@@ -71,6 +57,11 @@
"pip install scikit-image"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nImport all the Python libraries needed for pose detection and visualization.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -93,16 +84,12 @@
"cell_type": "markdown",
"id": "8c3612f8",
"metadata": {},
- "source": [
- "\n",
- "## 3. FPGA Overlays\n",
- "\n",
- "We run the model on specialized hardware that will allow it to execute faster than on a regular processor. The fastest option would be if we could build custom hardware -- a specialized electronic circuit -- that performs exactly the computations we need. However, soldering your own circuits is hard and takes a lot of time. Also, once such a specialized circuit is built, it can only be used for one thing -- the specific thing it was built for.\n",
- "\n",
- "FPGAs are the next best thing. They are like circuits, but instead of having to solder them physically, the different electrical connections can be \"programmed\". FPGAs consist of a large number of gates that we can use to route electricity (which we use to encode data) different ways. This is different from \"normal\" processor programming: Regular processors read a stream of instructions (our program) and execute those instructions. On FPGAs, we configure a circuit once ahead of time, and can then use it to perform the function it was designed to do multiple times very quickly. The KRIA SoM comes with an FPGA. \n",
- "\n",
- "Engineering these FPGA circuits is hard. For this reason, we will use a circuit somebody else already built for us! This is called an \"overlay\". The circuit we will use allows us to do lightning-fast data processing. This is exactly what we need to perform machine learning inference. Let us first load this circuit. By running the following step, you will configure the electronic circuit on your FPGA as a data processing unit (DPU):"
- ]
+ "source": "\n
⚙️ Step 2 — Load the Overlay
\n
Program the FPGA with the DPU design
\n
\nLoad the DPU overlay onto the FPGA board.\n
"
},
{
"cell_type": "code",
@@ -126,19 +113,12 @@
"cell_type": "markdown",
"id": "5f7afa88",
"metadata": {},
- "source": [
- "## 4. AI Model Setup\n",
- "\n",
- "How ML models work internally is not super important to us as users of the models. What we need to know is that these models take an input matrix, perform some calculations, and produce an output matrix of potentially a different dimension.\n",
- "\n",
- "What the meaning of the input and output matrices is depends on the model. In our model, the input matrix represents an image: Each cell in the matrix can be thought of as a pixel -- a red/green/blue color value at that coordinate in the image. The model outputs many matrices, which can be interpreted as probabilities that a limb is present in a certain pixel.\n",
- "\n",
- "When models are built, they are \"trained\" on a large set of input data. This simply means that the coefficients of the calculations are optimized. In order to run the model on our FPGA overlay, some additional modifications are required. Someone has already done this for us, so we only have to download the model.\n",
- "\n",
- "Here, we load the model onto the overlay. There is also some code to download the model, but we won't need that, since we already have the model.\n",
- "\n",
- "This model expects input of dimensions 368x368."
- ]
+ "source": "\n
📦 Step 3 — Load the AI Model
\n
Download and load the OpenPose model onto the DPU
\n
\nLoad the OpenPose model onto the DPU for pose detection.\n
"
},
{
"cell_type": "code",
@@ -160,15 +140,12 @@
"cell_type": "markdown",
"id": "5fe01fc0",
"metadata": {},
- "source": [
- "## 5. Load an Image\n",
- "\n",
- "To test the model, let's load an image of a person and see if the model can detect the pose.\n",
- "\n",
- "The model works on matrices of size 368 x 368. So first, we need to get our input into the right format. We load a JPEG, decompress it into its raw pixel values, remove the color information and put each pixel value on a 0 - 1 scale. Since the input image also does not exactly match the 368 x 368 format expected by the model, we crop it slightly. We use the OpenCV library which provides functions to do this necessary processing on the image.\n",
- "\n",
- "_Image: Sydney International Tennis ATP, licensed CC-BY-SA 2.0._"
- ]
+ "source": "\n
🖼️ Step 4 — Load a Test Image
\n
Load a photo of a person to run pose detection on
\n
\nSet the model filename and path variables for loading OpenPose.\n
"
},
{
"cell_type": "code",
@@ -190,6 +167,11 @@
"The image shows with distorted colors because CV2 reads as BGR but matplotlib expects RGB. We don't convert because the OpenPose model expects BGR too."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nLoad the test photo and display it to check the image dimensions.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -208,11 +190,12 @@
"cell_type": "markdown",
"id": "f3f148ed",
"metadata": {},
- "source": [
- "## 6. Prepare the DPU\n",
- "\n",
- "Different models have different input and output sizes. Let us see what the expected dimensions are for this model."
- ]
+ "source": "\n
🔧 Step 5 — Set Up the DPU
\n
Wire up the input/output tensors for the OpenPose model
\n
\nSet up the DPU runner and retrieve the input/output tensor shapes.\n
"
},
{
"cell_type": "code",
@@ -236,11 +219,12 @@
"cell_type": "markdown",
"id": "efa8675f",
"metadata": {},
- "source": [
- "## 7. Adjust the Image\n",
- "\n",
- "As we can see, we need an image that has different dimensions than our image we read in previously. To address this, let's pad the image on top and bottom a little:"
- ]
+ "source": "\n
✂️ Step 6 — Pre-process the Image
\n
Resize and crop the photo to match the model’s expected input
\n
\nAdd padding around the image so the person stays centered for the model.\n
"
},
{
"cell_type": "code",
@@ -269,6 +253,11 @@
"Now, let's resize and crop it to get the corrected dimensions:"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nResize and crop the image to exactly match the model's required input size.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -308,6 +297,11 @@
"Here is a look at what the crop looks like compared to the original photo."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nDraw a red rectangle on the original photo to show the crop region.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -328,9 +322,7 @@
"cell_type": "markdown",
"id": "25649366",
"metadata": {},
- "source": [
- "## 8. Run the Model"
- ]
+ "source": "\n
🚀 Step 7 — Run the Model
\n
Send the image through the DPU and get the keypoint heatmaps
\n
\nSend the pre-processed image through the DPU and get the pose output.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -376,6 +373,21 @@
"The model will output several matrices. One of those is a heat map that gives the probabilities that a limb is present at a certain coordinate. A value close to 1 will indicate that the model thinks there is a limb in that model. For now, we will just take the max of these matrices to get an iteresting output. This causes us to lose some information, i.e. which limb the model detected, but it gives us something interesting to look at."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\n🎤 Presenter Tip
\nThe heatmap looks abstract — point out the bright spots and explain each one is where the model thinks a joint is. Ask 'Can you find the shoulder? The knee?' This makes the AI visible!\n
"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\n👩💻 Students
\nLook at the heatmap. Can you match each bright spot to a part of the body in the original photo? Count how many joints the model found.\n
"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nDisplay the body keypoint heatmap overlaid on the input image.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -401,9 +413,7 @@
"cell_type": "markdown",
"id": "7403a135",
"metadata": {},
- "source": [
- "## 9. Put it all in functions"
- ]
+ "source": "\n
📦 Step 8 — Wrap in Functions
\n
Package the pipeline into reusable functions
\n
\nDefine the preprocess_image and run_model helper functions for the webcam loop.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -473,11 +488,12 @@
"cell_type": "markdown",
"id": "b71b45d0",
"metadata": {},
- "source": [
- "## 10. Getting a live video feed\n",
- "\n",
- "You may have to re-run the first code block if it can't open the camera."
- ]
+ "source": "\n
📷 Step 9 — Live Video Capture
\n
Capture frames from the webcam and run pose detection
\n
\nOpen the webcam and configure its resolution for pose detection.\n
"
},
{
"cell_type": "code",
@@ -502,6 +518,11 @@
"After initial setup above, let's do the same processing we did above for ten frames in a row. We will show each frame after the model has run."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nCapture frames from the webcam, run pose detection, and display the heatmap.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -539,9 +560,7 @@
"cell_type": "markdown",
"id": "d1f7ff08",
"metadata": {},
- "source": [
- "## 11. Comparing Heatmaps"
- ]
+ "source": "\n
🔍 Step 10 — Compare Heatmaps
\n
Measure how similar two pose heatmaps are using a distance score
\n
\nDefine the compare function that scores how similar two pose heatmaps are.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -613,6 +637,11 @@
"Here is a look at a heatmap we are going to be testing:"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nLoad a saved heatmap image to use as the test pose for comparison.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -634,6 +663,11 @@
"Now lets load the template we will be comparing to:"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nLoad the reference template heatmap to compare against.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -656,6 +690,11 @@
"The comparison function we defined before returns a low value if the two heatmaps are similar. Ideally, it should return 0 when comparing a photo to itself, but a few changes were made when writing the function so that it runs quickly that make it a little less accurate."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nCompare the test heatmap to the template and print the similarity score.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -678,6 +717,11 @@
"Now what if we compare it to a completely different heatmap?"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nLoad a different heatmap to show how the score changes for dissimilar poses.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -691,6 +735,11 @@
"plt.imshow(test_heatmap2)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nCompare the template to a different pose and see the higher score.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -710,13 +759,16 @@
"Notice how the score was higher when we comparred two heatmaps that don't look alike!"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\n🎤 Presenter Tip
\nHave three students each strike one of the three template poses (power pose, normal, wacky). Capture their heatmaps one at a time and let the class vote on which template each matches — then run the code to check!\n
"
+ },
{
"cell_type": "markdown",
"id": "39b45d1b",
"metadata": {},
- "source": [
- "## 12. Detecting a Pose Using Templates"
- ]
+ "source": "\n
🎯 Step 11 — Classify a Pose
\n
Compare the captured heatmap against templates to identify the pose
\n
\nLoad the three pose templates: power pose, normal stance, and wacky.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -761,6 +818,11 @@
"Lets compare our photo from earlier to these three templates and see which it is closest to:"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nDisplay the test heatmap that will be classified against the templates.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -772,6 +834,11 @@
"plt.imshow(test_heatmap)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nCompare the test heatmap to all three templates and find the closest match.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -801,9 +868,7 @@
"cell_type": "markdown",
"id": "2be463fb",
"metadata": {},
- "source": [
- "## 13. Generating Your Own Templates:"
- ]
+ "source": "\n
📸 Step 12 — Create Your Own Templates
\n
Strike a pose and save your own heatmap as a new template
\n
\nCapture your own pose from the webcam and save it as a new template.\n
"
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -848,13 +918,12 @@
"cell_type": "markdown",
"id": "39f7565f",
"metadata": {},
- "source": [
- "## 14. You Made It!\n",
- "\n",
- "Phew that was a long one, wasn't it? This can be a particularly finnickey model to deal with, so don't get too frustrated if you struggle using it. As always, if you get stuck reach out and ask questions!\n",
- "\n",
- "Dont forget to clean up your environment too!"
- ]
+ "source": "\n🧹 Cleanup
\nRun this cell when you are completely done to release the DPU and FPGA overlay so other notebooks can use them.\n
"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\nRelease the DPU and FPGA overlay to free resources when done.\n
"
},
{
"cell_type": "code",
@@ -874,6 +943,11 @@
"metadata": {},
"outputs": [],
"source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "\n
🏁 Challenges — Your Turn!
\n
You built a pose detector — now make it your own!
\n
\n
\n🏫 Middle School\n
Challenge 1 — New Pose Template
\n\n
Strike a completely original pose, capture your heatmap, and save it as a new template. Then test it against the existing ones.
\n
\n💡 Hint: Copy the template generation code from Step 12 and give your template a new variable name.\n
\n
\n
\n🏫 Middle School\n
Challenge 2 — Best-Match Display
\n\n
Print which pose template has the lowest comparison score (best match) for each captured frame.
\n
\n💡 Hint: Put the three `compare()` calls in a list and use `min()` to find the best match.\n
\n
\n
\n🎓 High School\n
Challenge 3 — Live Pose Classification
\n\n
Run the full classify-pose pipeline on every frame of the live webcam loop and print the current pose name on screen.
\n
\n💡 Hint: Call `compare()` inside the webcam loop and use `cv2.putText()` to overlay the pose name.\n
\n
\n
\n🎓 High School\n
Challenge 4 — Pose Game
\n\n
Build a game that shows a target pose name on screen, waits for the student to strike it, and scores them based on how well their heatmap matches the template.
\n
\n💡 Hint: Use `ipywidgets.Label` to display the target pose, and check the compare score every few frames.\n
\n