Restores the time_lapse_video function

docs/updating.md

@@ -1625,7 +1625,7 @@ pages for more details on the input and output variable types.
 
 * pre v4.0: NA
 * post v4.0: frame_size = **pcv.visualize.time_lapse_video**(*img_list, out_filename='./time_lapse_video.mp4', fps=29.97, display=True*)
-* post v5.0: deprecated.
+* post v5.0: frame_size = **pcv.visualize.time_lapse_video**(*source, out_filename='./time_lapse_video.mp4', fps=29.97*)
 
 #### plantcv.watershed_segmentation
 

docs/visualize_time_lapse_video.md

@@ -0,0 +1,36 @@
+## Automatically Generate a Time-Lapse Video given A Directory of Images
+
+This function generates and saves the time-lapse video based on a list of paths to the images.
+
+**plantcv.visualize.time_lapse_video**(*source, out_filename='./time_lapse_video.mp4', fps=29.97*)
+
+**returns** frame_size
+
+- **Parameters:**
+    - list_img       - List of paths to the images to create the video or file path to a directory of images to use.
+    - out_filename   - Name of file to save the generated video to.
+    - fps            - Frame rate (frames per second) By default fps=29.97. Commonly used values: 23.98, 24, 25, 29.97, 30, 50, 59.94, 60
+
+- **Context:**
+    - Used to generate time-lapse video given a list of images.
+
+- **Example Use:**
+    - Below
+
+
+```python
+from plantcv import plantcv as pcv
+# Note you will have to change this part on your own path
+img_directory = './path_to_images_directory/'
+
+frame_size = pcv.visualize.time_lapse_video(source=img_directory,
+                                            out_filename='./eg_time_lapse.mp4')
+```
+
+**Video generated**
+
+The generated video should look similar to the one below:
+<iframe src="https://player.vimeo.com/video/436453444" width="640" height="640" frameborder="0" allow="autoplay; fullscreen" allowfullscreen></iframe>
+
+
+**Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/master/plantcv/plantcv/visualize/time_lapse_video.py)

environment.yml

@@ -29,6 +29,8 @@ dependencies:
   - flyr
   - nbconvert
   - nbformat
+  - imageio >= 2.28
+  - imageio-ffmpeg
 
 channels:
   - conda-forge

mkdocs.yml

@@ -225,6 +225,7 @@ nav:
           - 'Pixel Scatter Plot': 'visualize_pixel_scatter_vis.md'
           - 'Pseudocolor': visualize_pseudocolor.md
           - 'Tile': visualize_tile.md
+          - 'Time Lapse Video': visualize_time_lapse_video.md
       - 'Watershed Segmentation': watershed.md
       - 'White balance': white_balance.md
       - 'Within Frame': within_frame.md

plantcv/plantcv/visualize/__init__.py

@@ -11,7 +11,8 @@
 from plantcv.plantcv.visualize.pixel_scatter_vis import pixel_scatter_plot
 from plantcv.plantcv.visualize.chlorophyll_fluorescence import chlorophyll_fluorescence
 from plantcv.plantcv.visualize.tile import tile
+from plantcv.plantcv.visualize.time_lapse_video import time_lapse_video
 
 __all__ = ["pseudocolor", "colorize_masks", "histogram", "colorspaces", "auto_threshold_methods",
            "overlay_two_imgs", "colorize_label_img", "obj_size_ecdf", "obj_sizes", "hyper_histogram",
-           "pixel_scatter_plot", "chlorophyll_fluorescence", "tile"]
+           "pixel_scatter_plot", "chlorophyll_fluorescence", "tile", "time_lapse_video"]

plantcv/plantcv/visualize/time_lapse_video.py

@@ -0,0 +1,58 @@
+# Create time-lapse videos with input directory of images
+
+import os
+import imageio.v3 as iio
+import numpy as np
+from plantcv.plantcv._globals import params
+from plantcv.plantcv.transform.resize import resize
+from plantcv.plantcv.io.read_dataset import read_dataset
+from plantcv.plantcv.warn import warn
+
+
+def time_lapse_video(source, out_filename='./time_lapse_video.mp4', fps=29.97):
+    """Generate time-lapse video given a list of paths to the images
+
+    Parameters:
+    -----------
+    source         = string or list,
+        Path to a folder of images to use or list of paths to the images to create the video
+    out_filename   = string,
+        name of file to save the generated video to
+    fps            = float,
+        frame rate (frames per second)
+
+    Returns:
+    --------
+    frame_size     = tuple,
+        the frame size of the generated video
+    """
+    params.debug = None
+    img_list = source
+    if isinstance(source, str):
+        img_list = read_dataset(source, sort=True)
+
+    imgs = []
+    list_r = []
+    list_c = []
+    for file in img_list:
+        img = iio.imread(file)
+        list_r.append(img.shape[0])
+        list_c.append(img.shape[1])
+        imgs.append(img)
+    max_c, max_r = np.max(list_c), np.max(list_r)
+
+    # use the largest size of the images as the frame size
+    # frame_size = frame_size or (max_c, max_r)
+    frame_size = (max_c, max_r)
+
+    if not (len(np.unique(list_r)) == 1 and len(np.unique(list_c)) == 1):
+        warn("The sizes of images are not the same, an image resizing (cropping or zero-padding) will be done "
+             f"to make all images the same size ({frame_size[0]}x{frame_size[1]}) before creating the video! ")
+
+    out_path, _ = os.path.splitext(out_filename)
+    out_filename = out_path + '.mp4'
+
+    frames = [resize(img, frame_size, interpolation=None) for img in imgs]
+    iio.imwrite(out_filename, frames, plugin="FFMPEG", fps=fps, codec="libx264")
+
+    return frame_size

pyproject.toml

@@ -29,7 +29,9 @@ dependencies = [
     "nd2",
     "flyr",
     "nbconvert",
-    "nbformat"
+    "nbformat",
+    "imageio >= 2.28",
+    "imageio-ffmpeg"
 ]
 requires-python = ">=3.8"
 authors = [

tests/plantcv/visualize/test_time_lapse_video.py

@@ -0,0 +1,56 @@
+import os
+import cv2
+import numpy as np
+from plantcv.plantcv.visualize.time_lapse_video import time_lapse_video
+
+
+def test_plantcv_visualize_time_lapse_video_list_input(tmpdir):
+    """Test for PlantCV."""
+    # Generate 3 test images and saved in tmpdir
+    list_im = []
+    for i in range(3):
+        temp_img = np.random.rand(3, 3)
+        min_, max_ = np.nanmin(temp_img), np.nanmax(temp_img)
+        temp_img = np.interp(temp_img, (min_, max_), (0, 255)).astype('uint8')
+        img_i_path = os.path.join(tmpdir, f"img{i}.png")
+        cv2.imwrite(img_i_path, temp_img)
+        list_im.append(img_i_path)
+
+    vid_name = os.path.join(tmpdir, 'test_time_lapse_video.mp4')
+    _ = time_lapse_video(source=list_im, out_filename=vid_name, fps=29.97)
+    assert os.path.exists(vid_name) and os.path.getsize(vid_name) > 100
+
+
+def test_plantcv_visualize_time_lapse_video_str_input(tmpdir):
+    """Test for PlantCV."""
+    # Generate 3 test images and saved in tmpdir
+    for i in range(3):
+        temp_img = np.random.rand(3, 3)
+        min_, max_ = np.nanmin(temp_img), np.nanmax(temp_img)
+        temp_img = np.interp(temp_img, (min_, max_), (0, 255)).astype('uint8')
+        img_i_path = os.path.join(tmpdir, f"img{i}.png")
+        cv2.imwrite(img_i_path, temp_img)
+
+    vid_name = os.path.join(tmpdir, 'test_time_lapse_video.mp4')
+    _ = time_lapse_video(source=str(tmpdir), out_filename=vid_name, fps=29.97)
+    assert os.path.exists(vid_name) and os.path.getsize(vid_name) > 100
+
+
+# not all images have the same size (essential to generate a video)
+def test_plantcv_visualize_time_lapse_video_different_img_sizes_warns(tmpdir, capsys):
+    """Test for PlantCV."""
+    # Generate 3 test images of different size and save in tmpdir
+    list_im = []
+    for i in range(2):
+        temp_img = np.random.rand(i+2, 3)
+        min_, max_ = np.nanmin(temp_img), np.nanmax(temp_img)
+        temp_img = np.interp(temp_img, (min_, max_), (0, 255)).astype('uint8')
+        img_i_path = os.path.join(tmpdir, f"img{i}.png")
+        cv2.imwrite(img_i_path, temp_img)
+        list_im.append(img_i_path)
+
+    vid_name = os.path.join(tmpdir, 'test_time_lapse_video.mp4')
+    _ = time_lapse_video(source=list_im, out_filename=vid_name, fps=29.97)
+    _, err = capsys.readouterr()
+
+    assert "Warning" in err and os.path.exists(vid_name)