diff --git a/README.md b/README.md index 7d21bb6..f470b7e 100644 --- a/README.md +++ b/README.md @@ -1,17 +1,62 @@ -NE15-MNIST Database -=================== -NE15-MNIST contains four sub datasets: -**Poissonian:** -The code and example for generating Poissonian spikes from MNIST is located in the folder *Poissonian*. -**Focal Rank Code Order:** -The code and example for generating spikes from MNIST using Focol is located in the folder *Focol*. -**DVS recorded flashing MNIST digits:** -download from: https://goo.gl/ru0fXP -**DVS recorded moving MNIST digits:** -download from: http://www2.imse-cnm.csic.es/caviar/MNISTDVS.html - -You are welcome to cite the paper if you use the database. -"Benchmarking Spike-Based Visual Recognition: a Dataset and Evaluation", -Qian Liu, Garibaldi Pineda Garca, Evangelos Stromatias,Teresa Gotarredona, and Steve Furber - -We invite you to **visit the [Wiki page](https://github.com/NEvision/NE15/wiki)** to find further information. +# Convert Images to Poissonian Spikes + +Convert your image data to a Poisson spike source to be able to use with Spiking Neural Networks. + +
+ + + + + + + + +
Pumpkins-RGB → Pumpkins-GrayScale → Pumpkins-SpikesPlot
+
+ + + The parameters below are used when running convert_image_to_spike_array.py in order to turn pumpkins above into a spike array. +
max_freq = 60000 (Hz) +
on_duration = 10000 (ms) +
off_duration = 5000 (ms) + + +## Requirements +I use Python 3.5.2 on Linux, necessary packages are listed below along with their versions for reference. +* matplotlib (3.0.3) +* numpy (1.17.3) +* opencv-python (4.1.1.26) + +Run `pip install -r requirements.txt` to install them all. + +## Project Files and Their Usage +``` +images-to-spikes/ +├── convert_image_to_spike_array.py +├── draw_image.py +├── images +│   ├── cross.png +│   ├── horizontal_line_10x.png +│   ├── horizontal_lines.png +│   └── t10k-images-idx3-ubyte__idx_000__lbl_7_.png +├── poisson_tools.py +└── util_functions.py +``` +**[convert_image_to_spike_array.py](convert_image_to_spike_array.py)** is the main file. + - Please see its usage by running it: `python convert_image_to_spike_array.py` + - The program will store the output spike array as a _pickle_ under _pickles/_ folder in the same directory after the run. + - If you do not want a _pickle_ at the end, change the parameter inside the file, i.e. `save_as_pickle=False`. + - You may use a single image file (extension could be anything _OpenCV_ accepts) or a folder which contains multiple images (extensions need to be _.png_) as input. + +**[draw_image.py](draw_image.py)** enables you to draw your own images by adding simple shapes into it via _OpenCV_. For more information please see the file. + +**[images](images/)** folder contains three of the images that I generated by using _draw_image.py_, and one example from MNIST dataset (t10k-images-idx3-ubyte__idx_000__lbl_7_.png). + +**[poisson_tools.py](poisson_tools.py)** is where the Poisson distribution modelling takes place. + +**[util_functions.py](util_functions.py)** includes utility functions of files and images. + +## References and Citation +I only used the Poissonian spikes approach to obtain spike arrays from images in this project. The original project also contains _Focal Rank Code Order_ approach in this sense. + +Please refer to the original project's [Wiki page](https://github.com/NEvision/NE15/wiki) for further information. diff --git a/convert_image_to_spike_array.py b/convert_image_to_spike_array.py new file mode 100644 index 0000000..2839194 --- /dev/null +++ b/convert_image_to_spike_array.py @@ -0,0 +1,64 @@ +import numpy as np +import cv2 +import sys +import os + +import pylab + +from poisson_tools import image_to_poisson_trains +from util_functions import * + + + +def img_to_spike_array( img_file_name, save_as_pickle=True ): + img = cv2.imread( img_file_name, cv2.IMREAD_GRAYSCALE ) + if img is not None: + height, width = img.shape + + spikes = image_to_poisson_trains( np.array( [img.reshape(height*width)] ), # notice reshape + height, width, + max_freq, on_duration, off_duration ) + pylab.figure() + raster_plot_spike( spikes ) + pylab.show() + + #--- Pickle the spike array for further use -------------------------------------------# + if save_as_pickle: + img_file_name = img_file_name[ img_file_name.rfind('/')+1 : img_file_name.rfind('.') ] + pickle_file = "spike_array_{}".format( img_file_name ) + pickle_it( spikes, pickle_file ) + else: + print( "Image couldn't be read! -> from file ({}) to ({})".format( img_file_name, img ) ) + + + +if __name__ == '__main__': + if len( sys.argv ) != 2 and len( sys.argv ) != 5: + print( "Usage:" ) + print( "\t python convert_image_to_spike_array.py " ) + print( "or (with the default values for up to a 32x32 image {max_freq=1000} {on_duration=200} {off_duration=100}):" ) + print( "\t python convert_image_to_spike_array.py " ) + else: + img_file_name = sys.argv[1] + + if len( sys.argv ) > 2: + max_freq = int(sys.argv[2]) # Hz + on_duration = int(sys.argv[3]) # ms + off_duration = int(sys.argv[4]) # ms + else: + max_freq = 1000 # Hz + on_duration = 200 # ms + off_duration = 100 # ms + + print( "max_freq: {}".format( max_freq ) ) + print( "on_duration: {}".format( on_duration ) ) + print( "off_duration: {}".format( off_duration ) ) + + if os.path.isdir( img_file_name ): + import glob2 + image_list = glob2.glob( os.path.join( img_file_name, "**/*.png" ) ) + for img in image_list: + if os.path.isfile( img ): + img_to_spike_array( img ) + elif os.path.isfile( img_file_name ): + img_to_spike_array( img_file_name ) diff --git a/draw_image.py b/draw_image.py new file mode 100644 index 0000000..1e9bf4b --- /dev/null +++ b/draw_image.py @@ -0,0 +1,150 @@ +""" Draw (onto)black(canvas) & white images with basic shapes (i.e. lines, circles, rectangles) + via the functions below. + + Functions: + - draw_horizontal_lines + - draw_vertical_lines + - draw_line_with_angle + - draw_a_rectangle + - draw_a_circle + + Feel free to mix and match them to have multiple shapes in an image + by passing the same "img" parameter into the functions. +""" + +import numpy as np +import cv2 + +from util_functions import * + + + +""" @Params: + +* width: Width of the image +* height: Height of the image +* thickness: Thickness of the line +* key: Sets where the line would stand in the image. +** key options: start | middle | end | random +* line_count: Only matters if the key equals to 'random', otherwise there would + always be 1 line in the image. +* img: When not supplied, a new image is created, otherwise the passed image is used. +""" +def draw_horizontal_lines( width, height, thickness, line_count=1, key='middle', img=None ): + if thickness < 1 or thickness >= height: + print( "Thickness value is invalid for this image. -> {}".format( thickness ) ) + else: + img = np.zeros( (height, width) ) if img is None else img + + if key == 'random': + random_idx = np.random.random_integers( 0, height-thickness, line_count ) + for i in range( line_count ): + cv2.line( img, (0, random_idx[i]), (width-1, random_idx[i]), 255, thickness ) + else: + if key == 'middle': + idx = height//2 + elif key == 'start': + idx = thickness//2 + elif key == 'end': + idx = height-thickness//2 + cv2.line( img, (0, idx), (width-1, idx), 255, thickness ) + + return img + + +""" For @Params see above. +""" +def draw_vertical_lines( width, height, thickness=1, line_count=1, key='middle', img=None ): + if thickness < 1 or thickness >= width: + print( "Thickness value is invalid for this image. -> {}".format( thickness ) ) + else: + img = np.zeros( (height, width) ) if img is None else img + + if key == 'random': + random_idx = np.random.random_integers( 0, width-thickness, line_count ) + for i in range( line_count ): + cv2.line( img, (random_idx[i], 0), (random_idx[i], height-1), 255, thickness ) + else: + if key == 'middle': + idx = width//2 + elif key == 'start': + idx = thickness//2 + elif key == 'end': + idx = width-thickness//2 + cv2.line( img, (idx, 0), (idx, height-1), 255, thickness ) + + return img + + +""" @Params: + +* angle: Determines the angle of the line to be drawn. + Might be either 45 or 135 degrees. + +For the rest of the @Params see above. +""" +def draw_line_with_angle( width, height, angle, thickness=1, img=None ): + if thickness < 1: + print( "Thickness value is invalid for this image. -> {}".format( thickness ) ) + elif angle not in (45, 135): + print( "Angle is not right. {} is not in [45, 135]".format( angle ) ) + else: + img = np.zeros( (height, width) ) if img is None else img + + if angle == 45: + cv2.line( img, (0, height-1), (width-1, 0), 255, thickness ) + elif angle == 135: + cv2.line( img, (0, 0), (width-1, height-1), 255, thickness ) + + return img + + +""" @Params: + +* top_left_pt: Top left point tuple of the rectangle to be drawn, e.g. (10, 12) +* bottom_right_pt: Bottom right point tuple of the rectangle to be drawn, e.g. (29, 31) +* thickness: -1 to fill inside the rectangle, otherwise determines the thickness + of the rectangle's outer lines. + +For the rest of the @Params see above. +""" +def draw_a_rectangle( width, height, top_left_pt, bottom_right_pt, thickness=1, img=None ): + img = np.zeros( (height, width) ) if img is None else img + cv2.rectangle( img, top_left_pt, bottom_right_pt, 255, thickness ) # 255 is the colour, default is white obviously + return img + + +""" @Params: + +* center_pt: Center point tuple of the circle to be drawn, e.g. (14, 20) +* radius: Radius of the circle, e.g. 6. + +For the rest of the @Params see function draw_a_rectangle. +""" +def draw_a_circle( width, height, center_pt, radius, thickness=1, img=None ): + img = np.zeros( (height, width) ) if img is None else img + cv2.circle( img, center_pt, radius, 255, thickness ) + return img + + + +if __name__ == '__main__': + # Some usage scenarios + + width = height = 32 + thickness = 3 + # img = draw_a_rectangle( width, height, (10, 12), (29, 31), -1 ) + # img = draw_a_circle( width, height, (14, 20), 6, 2 ) + + img = draw_line_with_angle( width, height, 45, thickness ) + # img = draw_line_with_angle( width, height, 135, thickness, img ) + + # img = draw_horizontal_lines( width, height, thickness ) + # img = draw_horizontal_lines( width, height, 1, 5, 'random' ) + + # img = draw_vertical_lines( width, height, thickness, img=img ) + # img = draw_vertical_lines( width, height, 1, 5, 'random' ) + + imshow_opencv( img ) + # img_name = "new_image_etc.png" + # save_img( img_name, img ) diff --git a/focal/__init__.py b/focal/__init__.py deleted file mode 100644 index 1fcaea8..0000000 --- a/focal/__init__.py +++ /dev/null @@ -1,199 +0,0 @@ -from focal import * -from convolution import * -from correlation import * -from dog import * - -import pylab as plt - -idx_to_name = ["midget_off", "midget_on", "parasol_off", "parasol_on"] - - -def rgb2gray(rgb): - '''Convert an RGB array into grayscale - ''' - #return numpy.int16(numpy.dot(rgb[:,:,:3], [0.299, 0.587, 0.144])) - #return numpy.floor(numpy.dot(rgb[:,:,:3], [0.299, 0.587, 0.144])) - return rgb[:,:,0]*0.299 + rgb[:,:,1]*0.587 + rgb[:,:,2]*0.144 - - -def idx2coord(idx, width): - '''Convert a 1D index into a 2D coordinate''' - return (int(idx/width), idx%width) - - -def spike_trains_to_images_g(spike_trains, base_img, num_kernels=4): - '''Transform a FoCal spike set into images - :param spike_trains: Focal encoded image - :param base_img: Original image, used to get the shape - :num_kernels: How many of the representations to convert - ''' - imgs ={} - for cell_type in range(num_kernels): - imgs[cell_type] = numpy.zeros_like(base_img, dtype=numpy.float32) - - - for idx, val, cell_type in spike_trains: - adjusted_idx = idx - coords = idx2coord(adjusted_idx, base_img.shape[1]) - imgs[cell_type][coords] = val - if val == numpy.nan: - print "spike is nan" - if val == numpy.inf: - print "spike is inf" - if val == -numpy.inf: - print "spike is -inf" - - return imgs - - -def plot_images(images, original_img=None, use_abs_vals=False): - '''Create a figure with: - - If images is a dictionary: all images in it - - If original_img is not None: Two pictures (original and images) - - Finally just plot one image (images) - ''' - plt.close("all") - if type(images) == dict: - #num_kernels = numpy.int(numpy.sqrt(len(images.keys()))) - num_kernels = len(images.keys()) - fig = plt.figure()#figsize=(num_kernels, 2), dpi=300) - fig.clear() - ax= plt.subplot(2, num_kernels + 1, 1) - ax.imshow(original_img, cmap=plt.cm.Greys_r) - ax.get_axes().axis('off') - - for cell_type in images: - idx = cell_type - slot = 2 + cell_type - slot += 1 if cell_type >= num_kernels else 0 - ax= plt.subplot(2, num_kernels + 1, slot) - if use_abs_vals: - img = numpy.abs(images[idx].copy()) - else: - img = images[idx].copy() - ax.imshow(img, cmap=plt.cm.Greys_r) - ax.get_axes().axis('off') - elif original_img is not None: - fig = plt.figure()#figsize=(4, 2), dpi=300) - fig.clear() - ax= plt.subplot(1, 2, 1) - ax.imshow(original_img, cmap=plt.cm.Greys_r) - ax.get_axes().axis('off') - - ax= plt.subplot(1, 2, 2) - ax.imshow(images, cmap=plt.cm.Greys_r) - ax.get_axes().axis('off') - else: - fig = plt.figure()#figsize=(4, 2), dpi=300) - fig.clear() - plt.imshow(images, cmap=plt.cm.Greys_r) - plt.axis('off') - - # thismanager = plt.get_current_fig_manager() - # thismanager.window.SetPosition((1920, 0)) - # thismanager.window.wm_geometry("+1921+0") - plt.show() - plt.close("all") - - -def save_images(images, prefix, cmap=plt.cm.Greys_r, title_source=idx_to_name): #plt.cm.Paired - '''Save figures with filename = prefix-cell_type if images is a dictionary - filename = prefix otherwise - ''' - if type(images) == dict: - num_kernels = len(images.keys()) - - for cell_type in images: - - img = images[cell_type] - img_min = numpy.min(img) - img_max = numpy.max(img) - negatives = numpy.sum(img<0) - positives = numpy.sum(img>0) - - # define the colormap - - fig = plt.figure(figsize=(3,3), dpi=300) - - fig.clear() - - - im = plt.imshow(img, interpolation='none', cmap=cmap) - plt.tight_layout() - plt.colorbar(im, use_gridspec=True) - plt.title("%s \n min = %10.4f, max = %10.4f\n num pos = %s, num neg = %s"%\ - (title_source[cell_type], img_min, img_max, positives, negatives)) - - plt.axis('off') - plt.savefig("%s-%s"%(prefix, cell_type), dpi=300) - else: - fig = plt.figure(figsize=(3,3), dpi=300) - fig.clear() - plt.imshow(images, cmap=plt.cm.Greys_r) - plt.axis('off') - plt.savefig(prefix, dpi=300) - - plt.close("all") - - -def count_non_zero(img_set): - '''Count non-zero pixels in an image set - ''' - non_zero = 0 - for i in img_set: - non_zero += numpy.sum(img_set[i] != 0) - return non_zero - - -def focal_to_spike(spikes, img_shape, spikes_per_time_block=10, start_time=0., time_step=1.): - '''Convert FoCal-coded spikes into a SpikeSourceArray - :param spikes: FoCal, rank order-coded spikes - :param img_shape: (Height, Width) of image - :param spikes_per_time_block: How many spikes will go into each time bin - :param start_time: When did the spikes start appearing (milliseconds) - :param time_step: How much time between time bins - ''' - neurons_per_layer = img_shape[0]*img_shape[1] - width = img_shape[1] - height = img_shape[0] - total_width = 2*width - total_height = 2*height - spike_array = [[] for i in range(total_height*total_width)] - pack_time = start_time - spikes_per_block_count = 0 - for spike in spikes: - layer = spike[2] - pad_x = width if layer == 1 or layer == 3 else 0 - pad_y = height if layer == 2 or layer == 3 else 0 - - loc_idx = spike[0] - loc_x = loc_idx%width - loc_y = loc_idx/width - glb_x = pad_x + loc_x - glb_y = pad_y + loc_y - glb_idx = glb_y*total_width + glb_x - - spike_array[glb_idx].append(pack_time) - - spikes_per_block_count += 1 - if spikes_per_block_count == spikes_per_time_block: - spikes_per_block_count = 0 - pack_time += time_step - - return spike_array - - -def raster_plot_spike(spikes, marker='|', markersize=2): - '''Plot PyNN SpikeSourceArrays - :param spikes: The array containing spikes - ''' - x = [] - y = [] - - for neuron_id in range(len(spikes)): - for t in spikes[neuron_id]: - x.append(t) - y.append(neuron_id) - - plt.plot(x, y, marker, markersize=markersize) - diff --git a/focal/convolution.py b/focal/convolution.py deleted file mode 100644 index 3144fbe..0000000 --- a/focal/convolution.py +++ /dev/null @@ -1,148 +0,0 @@ -import numpy -from scipy.signal import sepfir2d - -class Convolution(): - ''' - Utility class to wrap around different functions for convolution - (i.e. separable convolution) - ''' - def __init__(self): - pass - - def sep_convolution(self, img, horz_k, vert_k, col_keep=1, row_keep=1, mode="full"): - ''' Separated convolution - - img => image to convolve - horiz_k => first convolution kernel vector (horizontal) - vert_k => second convolution kernel vector (horizontal) - col_keep => which columns are we supposed to calculate - row_keep => which rows are we supposed to calculate - mode => if "full": convolve all the image otherwise just valid pixels - ''' - width = img.shape[1] - height = img.shape[0] - half_k_width = horz_k.size/2 - half_img_width = width/2 - half_img_height = height/2 - - tmp = numpy.zeros_like(img, dtype=numpy.float32) - - if mode == "full": - horizontal_range = xrange(width) - vertical_range = xrange(height) - else: - horizontal_range = xrange(half_k_width, width - half_k_width + 1) - vertical_range = xrange(half_k_width, height - half_k_width + 1) - - for y in xrange(height): - for x in horizontal_range: - if (x - half_img_width)%col_keep != 0: - continue - - k_sum = 0. - k = 0 - - for i in xrange(-half_k_width, half_k_width + 1): - img_idx = x + i - if img_idx >= 0 and img_idx < img.shape[1]: - k_sum += img[y,img_idx]*horz_k[k] - k += 1 - - tmp[y,x] = k_sum - - tmp2 = numpy.zeros_like(img, dtype=numpy.float32) - for y in vertical_range: - if (y - half_img_height)%row_keep != 0: - continue - - for x in horizontal_range: - if (x - half_img_width)%col_keep != 0: - continue - - k_sum = 0. - k = 0 - for i in xrange(-half_k_width, half_k_width + 1): - img_idx = y + i - if img_idx >= 0 and img_idx < img.shape[0]: - k_sum += tmp[img_idx, x]*vert_k[k] - - k += 1 - - tmp2[y,x] = k_sum - - return tmp2 - - - def dog_sep_convolution(self, img, k, cell_type, originating_function="filter", - force_homebrew = False, mode="full"): - ''' Wrapper for separated convolution for DoG kernels in FoCal, - enables use of NumPy based sepfir2d. - - img => the image to convolve - k => 1D kernels to use - cell_type => ganglion cell type, useful for sampling - resolution numbers - originating_function => if "filter": use special sampling resolution, - else: use every pixel - force_hombrew => if True: use my code, else: NumPy's - mode => "full" all image convolution, else only valid - ''' - - if originating_function == "filter": - row_keep, col_keep = self.get_subsample_keepers(cell_type) - else: - row_keep, col_keep = 1, 1 - - if not force_homebrew: - # has a problem with images smaller than kernel - right_img = sepfir2d(img.copy(), k[0], k[1]) - left_img = sepfir2d(img.copy(), k[2], k[3]) - else: - right_img = self.sep_convolution(img, k[0], k[1], col_keep=col_keep, - row_keep=row_keep, mode=mode) - left_img = self.sep_convolution(img, k[2], k[3], col_keep=col_keep, - row_keep=row_keep, mode=mode ) - - conv_img = left_img + right_img - - if not force_homebrew and originating_function == "filter": - conv_img = self.subsample(conv_img, cell_type) - - return conv_img - - - def get_subsample_keepers(self, cell_type): - ''' return which (modulo) columns and rows to keep for cell_type - ''' - if cell_type > 1: - #~ col_keep = 7 - #~ row_keep = 7 - col_keep = 5 - row_keep = 3 - else: - col_keep = 1 - row_keep = 1 - - return row_keep, col_keep - - - def subsample(self, img, cell_type): - ''' remove unwanted rows/columns ''' - row_keep, col_keep = self.get_subsample_keepers(cell_type) - - if col_keep < img.shape[1] and row_keep < img.shape[0]: - width = img.shape[1] - height = img.shape[0] - half_img_width = width/2 - half_img_height = height/2 - - col_range = numpy.arange(width) - row_range = numpy.arange(height) - - img[:, [x for x in col_range if (x - half_img_width)%(col_keep)!= 0]] = 0 - img[[x for x in row_range if (x - half_img_height)%(row_keep)!= 0], :] = 0 - #~ img[:, [x for x in col_range if (x)%(col_keep)!= 0]] = 0 - #~ img[[x for x in row_range if (x)%(row_keep)!= 0], :] = 0 - else: - img[:,:] = 0 - - return img diff --git a/focal/correlation.py b/focal/correlation.py deleted file mode 100644 index da9834a..0000000 --- a/focal/correlation.py +++ /dev/null @@ -1,73 +0,0 @@ -from scipy.signal import sepfir2d, convolve2d -import numpy -import pickle -from os import listdir -from os.path import isfile, join -import sys -import md5 - -class Correlation(): - ''' - Class to compute the correlations between Difference of Gaussian - kernels. - ''' - - def __init__(self, full_kernels): - ''' :params: full_kernels - Dictionary that contains 2D DoG kernels - for each simulated (ganglion cell) layer - ''' - - self.full_kernels = full_kernels - self.data = self.create_all_correlations() - - - def kernels_to_string(self, kernels): - '''helper for hashing''' - - my_string = "" - for k in kernels: - my_string = "%s%s"%(my_string, kernels[k]) - return my_string - - - def create_all_correlations(self): - - mode = "full" #same, full, valid - seed = self.kernels_to_string(self.full_kernels) - seed += mode - - filename = "correlation-cache-%s.p"%( md5.new(seed).hexdigest() ) - - if isfile(filename): - correlations = pickle.load( open( filename, "rb" ) ) - print("Loaded correlations from file") - return correlations - - correlations = {} - - num_kernels = len(self.full_kernels.keys()) - - for cell_type in range(num_kernels): - correlations[cell_type] = {} - for overlap_cell_type in range(num_kernels): - percent = ((cell_type*num_kernels+overlap_cell_type)*100. + 1.)/float(num_kernels**2) - sys.stdout.write("\rCorrelations cell(%s, %s) %03d%%"%(cell_type, overlap_cell_type, percent)) - sys.stdout.flush() - - # using the correlate2d method threw different matrices than - # the ones Basab gets - correlation = convolve2d(self.full_kernels[cell_type], - self.full_kernels[overlap_cell_type], - boundary='fill', fillvalue=0, mode=mode) - - correlations[cell_type][overlap_cell_type] = correlation - - pickle.dump( correlations, open( filename, "wb" ) ) - - print("All correlations calculated\n") - - return correlations - - - def __getitem__(self, index): - return self.data[index] diff --git a/focal/dog.py b/focal/dog.py deleted file mode 100644 index ae00f6e..0000000 --- a/focal/dog.py +++ /dev/null @@ -1,174 +0,0 @@ -import numpy - -MIDGET_OFF, MIDGET_ON, PARASOL_OFF, PARASOL_ON = range(4) - -KERNEL_TYPES = [MIDGET_OFF, MIDGET_ON, PARASOL_OFF, PARASOL_ON] - -KERNEL_STR = {"midget_off": MIDGET_OFF, MIDGET_OFF: "midget_off", - "midget_on": MIDGET_ON, MIDGET_ON: "midget_on", - "parasol_off": PARASOL_OFF, PARASOL_OFF: "parasol_off", - "parasol_on": PARASOL_ON, PARASOL_ON: "parasol_on"} - - -class DifferenceOfGaussians(): - '''Difference of Gaussian kernel generator. Developed to implement FoCal - ''' - - def __init__(self): - '''We simulate four layers of Ganglion cells, so we create their - kernels here - ''' - self.max_num_kernels = 4 - self.kernels = None - self.full_kernels = None - self.kernels, self.full_kernels = self.create_all_kernels() - - - def __getitem__(self, index): - '''utility to access the kernels variable directly''' - return self.kernels[index] - - - @staticmethod - def kernel_width(self, cell_type): - '''Get the kernel width for a particular layer - :param cell_type: Index for the cell type in a layer [0 -> 3] - :returns: Width for the layer's kernel - ''' - is_off_centre, width, sigma, sigma_mult = self.get_params(cell_type) - return width - - - def create_single_kernel(self, cell_type): - '''Generate separable and 2D kernels for a particula layer - :param cell_type: Index for the cell type in a layer [0 -> 3] - :returns: Separable kernels (two 1D kernel pairs are needed) and - 2D kernel - - ''' - is_off_centre, width, sigma, sigma_mult = self.get_params(cell_type) - - kernels = self.diff_of_gauss(is_off_centre, width, sigma, sigma_mult) - - gauss1 = numpy.outer(kernels[0], kernels[1]) - gauss2 = numpy.outer(kernels[2], kernels[3]) - full_kernel = gauss1 + gauss2 - - return kernels, full_kernel - - - def create_all_kernels(self): - '''Generate separable and 2D kernels for each layer - :returns: Dictionaries for separable and 2D kernels, indexed by - integers representing each layer [0 -> 3] - ''' - kernels = {} - full_kernels = {} - - for cell_type in range(self.max_num_kernels): - kernels[cell_type], full_kernels[cell_type] = self.create_single_kernel(cell_type) - - return kernels, full_kernels - - - def diff_of_gauss(self, is_off_centre, width, sigma, sigma_mult): - '''Compute separated kernels for Difference of Gaussians 2D kernels - :param is_off_centre: Whether the simulated ganglion cell has an - OFF-centre (True) or ON-centre (False) behaviour - :param width: Width of the kernel - :param sigma: `Width` of centre Gaussian - :param sigma_mult: `Width` of surround Gaussian = sigma*sigma_mult - :returns: Separated kernels for centre (vertical_c, horizontal_c) - and surround (vertical_s, horizontal_s) components of the - difference of Gaussians - ''' - half_width = width/2 - x, y = numpy.meshgrid(numpy.arange(-half_width, half_width + 1), - numpy.arange(-half_width, half_width + 1)) - y = -y - - coord_range = numpy.arange(-half_width, half_width + 1) - - sigma_c = sigma - sigma_s = sigma_mult*sigma_c - sigma_c2 = (sigma_c**2) - sigma_s2 = (sigma_s**2) - x2_plus_y2 = x**2 + y**2 - - #get signs for selected centre-surround behaviour - sign_sigma_c, sign_sigma_s = (-1., 1.) if is_off_centre == True else (1., -1.) - - #calculate 2D centre kernel, just to get normalizing weight - kernel_c = sign_sigma_c*(1./(2.*numpy.pi*sigma_c2))*numpy.exp((-x2_plus_y2)/(2.*sigma_c2)) - mat_norm_weight = 1./numpy.sum(numpy.abs(kernel_c)) - #normalize it to sum to 1 - kernel_c *= mat_norm_weight - - #calculate 1D centre kernels and normalize them - vec_norm_weight = sign_sigma_c*numpy.sqrt(mat_norm_weight)*numpy.sqrt(1./(2.*numpy.pi*sigma_c2)) - vertical_c = (numpy.exp((-coord_range**2)/(2.*sigma_c2))*vec_norm_weight).astype(numpy.float32) - horizontal_c = (sign_sigma_c*vertical_c).astype(numpy.float32) - - #calculate 2D surround kernel, just to get normalizing weight - kernel_s = sign_sigma_s*(1./(2.*numpy.pi*sigma_s2))*numpy.exp((-x2_plus_y2)/(2.*sigma_s2)) - mat_norm_weight = 1./numpy.sum(numpy.abs(kernel_s)) - #normalize it to sum to 1 - kernel_s *= mat_norm_weight - - #calculate 1D surround kernels and normalize them - vec_norm_weight = sign_sigma_s*numpy.sqrt(mat_norm_weight)*numpy.sqrt(1./(2.*numpy.pi*sigma_s2)) - vertical_s = (numpy.exp((-coord_range**2)/(2.*sigma_s2))*vec_norm_weight).astype(numpy.float32) - horizontal_s = (sign_sigma_s*vertical_s).astype(numpy.float32) - - #calculate difference of gaussians (sums to 0) - kernel = kernel_s - kernel += kernel_c - - #get auto-convolution to 1 normalization - final_weight = 1.0/numpy.sqrt(numpy.sqrt(numpy.sum(kernel*kernel))) - kernel /= numpy.sqrt(numpy.sum(kernel*kernel)) - - vertical_c *= final_weight - horizontal_c *= final_weight - vertical_s *= final_weight - horizontal_s *= final_weight - - return vertical_c, horizontal_c, vertical_s, horizontal_s - - - def get_params(self, cell_centre_type): - '''PARAMETERS FROM: - Filter Overlap Correction ALgorithm, simulates the foveal pit - region of the human retina. - Created by Basabdatta Sen Bhattacharya. - See DOI: 10.1109/TNN.2010.2048339 - ''' - - if cell_centre_type == MIDGET_OFF: - off_centre = True - #width = 5 - width = 3 - sigma = 0.8 - #sigma_mult = 6.5 - sigma_mult = 6.7 - elif cell_centre_type == MIDGET_ON: - off_centre = False - width = 11 - sigma = 1.04 - #sigma_mult = 6.5 - sigma_mult = 6.7 - elif cell_centre_type == PARASOL_OFF: - off_centre = True - width = 61 - sigma = 8 - sigma_mult = 4.8 - elif cell_centre_type == PARASOL_ON: - off_centre = False - width = 243 - sigma = 10.4 - sigma_mult = 4.8 - - return off_centre, width, sigma, sigma_mult - - - diff --git a/focal/focal.py b/focal/focal.py deleted file mode 100644 index 409ad75..0000000 --- a/focal/focal.py +++ /dev/null @@ -1,279 +0,0 @@ -# coding: utf-8 - -import numpy -from scipy.signal import sepfir2d, convolve2d -from scipy.misc import imresize -import matplotlib.image as mpimg -from matplotlib import cm -import matplotlib.animation as animation -import matplotlib.pyplot as plt -from matplotlib.colors import BoundaryNorm -import pickle -from os import listdir -from os.path import isfile, join -import sys -import md5 - -from dog import DifferenceOfGaussians -from convolution import Convolution -from correlation import Correlation - -from __init__ import idx2coord - -class Focal(): - '''Filter Overlap Correction ALgorithm, simulates the foveal pit - region of the human retina. - Created by Basabdatta Sen Bhattacharya. - See DOI: 10.1109/TNN.2010.2048339 - ''' - - def __init__(self): - '''Get the four layer's kernels, create the correlations and - a convolution wrapper. - :const MIN_IMG_WIDTH: Minimum image width for which to use - NumPy/SciPy for convolution - ''' - self.kernels = DifferenceOfGaussians() - self.correlations = Correlation(self.kernels.full_kernels) - self.convolver = Convolution() - self.MIN_IMG_WIDTH = 256 - - def apply(self, image, spikes_per_unit=0.3): - '''Wrapper function to convert an image into a FoCal representation - :param image: The image to convert - :param spikes_per_unit: How many spikes to return, specified in per-unit - (i.e. multiply by 100 to get percentage) - ''' - spike_images = self.filter_image(image) - focal_spikes = self.focal(spike_images, spikes_per_unit=spikes_per_unit) - return focal_spikes - - - def focal(self, spike_images, spikes_per_unit=0.3): - '''Filter Overlap Correction ALgorithm, simulates the foveal pit - region of the human retina. - Created by Basabdatta Sen Bhattacharya. - See DOI: 10.1109/TNN.2010.2048339 - - spike_images => A list of the values generated by the convolution - procedure, stored as four 2D arrays, each with - the same size/shape of the original image. - spikes_per_unit => Percentage of the total spikes to be processed, - specified in a per unit [0, 1] range. - - returns: an ordered list of - [spike index, sorting value, cell layer/type] tuples. - ''' - ordered_spikes = [] - - img_size = spike_images[0].size - img_shape = spike_images[0].shape - height, width = img_shape - num_images = len(spike_images) - - #how many non-zero spikes are in the images - max_cycles = 0 - for i in range(num_images): - max_cycles += numpy.sum(spike_images[i] != 0) - - total_spikes = max_cycles.copy() - - #reduce to desired number - max_cycles = numpy.int(spikes_per_unit*max_cycles) - - #copy images from list to a large image to make it a single search space - big_shape = (height*2, width*2) - big_image = numpy.zeros(big_shape) - big_coords = [(0, 0), (0, width), (height, 0), (height, width)] - for cell_type in range(num_images): - row, col = big_coords[cell_type] - tmp_img = spike_images[cell_type].copy() - tmp_img[numpy.where(tmp_img == 0)] = -numpy.inf - big_image[row:row+height, col:col+width] = tmp_img - - # Main FoCal loop - for count in xrange(max_cycles): - - # print out completion percentage - percent = (count*100.)/float(total_spikes-1) - sys.stdout.write("\rFocal %d%%"%(percent)) - - # Get maximum value's index, - max_idx = numpy.argmax(big_image) - # its coordinates - max_coords = numpy.unravel_index(max_idx, big_shape) - # and the value - max_val = big_image[max_coords] - - if max_val == numpy.inf or max_val == -numpy.inf or max_val == numpy.nan: - sys.stderr.write("\nWrong max value in FoCal!") - break - - # translate coordinates from the big image's to a single image coordinates - if max_coords[0] < height and max_coords[1] < width: - single_coords = max_coords - else: - single_coords = self.global_to_single_coords(max_coords, img_shape) - - # calculate a local index, to store per ganglion cell layer info - local_idx = single_coords[0]*width + single_coords[1] - # calculate the type of cell from the index - cell_type = self.cell_type_from_global_coords(max_coords, img_shape) - - # append max spike info to return list - ordered_spikes.append([local_idx, max_val, cell_type]) - - # correct surrounding pixels for overlapping kernel influence - for overlap_cell_type in range(len(spike_images)): - # get equivalent coordinates for each layer - overlap_idx = self.local_coords_to_global_idx(single_coords, - overlap_cell_type, - img_shape, big_shape) - - is_max_val_layer = overlap_cell_type == cell_type - # c_i = c_i - c_{max} - self.adjust_with_correlation(big_image, - self.correlations[cell_type]\ - [overlap_cell_type], - overlap_idx, max_val, - is_max_val_layer=is_max_val_layer) - - - return ordered_spikes - - - def filter_image(self, img, force_homebrew=False): - '''Perform convolution with calculated kernels - img => the image to convolve - force_hombrew => if True: use my separated convolution code - else: use SciPy sepfir2d - ''' - num_kernels = len(self.kernels.full_kernels) - img_width, img_height = img.shape - convolved_img = {} - - for cell_type in range(num_kernels): - if img_width < self.MIN_IMG_WIDTH or img_height < self.MIN_IMG_WIDTH: - force_homebrew = True - else: - force_homebrew = False - - c = self.convolver.dog_sep_convolution(img, self.kernels[cell_type], - cell_type, - originating_function="filter", - force_homebrew=force_homebrew) - convolved_img[cell_type] = c - - return convolved_img - - - def adjust_with_correlation(self, img, correlation, max_idx, max_val, - is_max_val_layer=True): - '''Modify surrounding pixels by the correlation of kernels. - Example: - If max_val is in layer 1, we overlap all layers and then multiply - surrounding pixels by the correlation between layer 1 and layerX (1,2,3, or 4). - :param img: An image representing the spikes generated by the previous step - or simulation of a ganglion cell layer - :param max_idx: index of the pixel/neuron that has the maximum value from - the previous step or simulation - :param max_val: value of the max pixel/neuron - :param is_max_val_layer: Required to mark max_idx as visited - - :returns: img with the proper adjustment - ''' - - img_height, img_width = img.shape - correlation_width = correlation.shape[0] - half_correlation_width = correlation_width/2 - half_img_width = img_width/2 - half_img_height = img_height/2 - - # Get max value's coordinates - row, col = idx2coord(max_idx, img_width) - row_idx = row/half_img_height - col_idx = col/half_img_width - - # Calculate the zone to affect with the correlation - up_lim = (row_idx)*half_img_height - left_lim = (col_idx)*half_img_width - - down_lim = (row_idx + 1)*half_img_height - right_lim = (col_idx + 1)*half_img_width - - max_img_row = numpy.min([down_lim - 1, row + half_correlation_width + 1]) - max_img_col = numpy.min([right_lim - 1, col + half_correlation_width + 1]) - min_img_row = numpy.max([up_lim, row - half_correlation_width]) - min_img_col = numpy.max([left_lim, col - half_correlation_width]) - - max_img_row_diff = max_img_row - row - max_img_col_diff = max_img_col - col - min_img_row_diff = row - min_img_row - min_img_col_diff = col - min_img_col - - min_knl_row = half_correlation_width - min_img_row_diff - min_knl_col = half_correlation_width - min_img_col_diff - max_knl_row = half_correlation_width + max_img_row_diff - max_knl_col = half_correlation_width + max_img_col_diff - - # c_i = c_i - c_{max} - img[min_img_row:max_img_row, min_img_col:max_img_col] -= \ - max_val*correlation[min_knl_row:max_knl_row, min_knl_col:max_knl_col] - - # mark any weird pixels as -inf so they don't matter in the search - inf_indices = numpy.where(img[min_img_row:max_img_row, min_img_col:max_img_col] == numpy.inf) - img[inf_indices] = 0 - img[inf_indices] -= numpy.inf - - nan_indices = numpy.where(img[min_img_row:max_img_row, min_img_col:max_img_col] == numpy.nan) - img[nan_indices] = 0 - img[nan_indices] -= numpy.inf - - # mark max value's coordinate to -inf to get it out of the search - if is_max_val_layer: - img[row, col] -= numpy.inf - - # No need to return, variables are passed as reference because we're doing = and -= ops - - - def local_coords_to_global_idx(self, coords, cell_type, - local_img_shape, global_img_shape): - '''Utility to transform from local (width*height) coordinates to - global (2*width, 2*height) coords. --------- - Layers are represented in the global image as: [ 0 | 1 ] - ------- - [ 2 | 3 ] - --------- - ''' - row_add = cell_type/2 - col_add = cell_type%2 - global_coords = (coords[0] + row_add*local_img_shape[0], - coords[1] + col_add*local_img_shape[1]) - global_idx = global_coords[0]*global_img_shape[1] + global_coords[1] - return global_idx - - - def global_to_single_coords(self, coords, single_shape): - '''Utility to transform from global (2*width, 2*height) coordinates to - local (width*height) coords . --------- - Layers are represented in the global image as: [ 0 | 1 ] - ------- - [ 2 | 3 ] - --------- - ''' - row_count = coords[0]/single_shape[0] - col_count = coords[1]/single_shape[1] - new_row = coords[0] - single_shape[0]*row_count - new_col = coords[1] - single_shape[1]*col_count - - return (new_row, new_col) - - - def cell_type_from_global_coords(self, coords, single_shape): - '''Utility to compute which layer does a coordinate belong to''' - row_type = coords[0]/single_shape[0] - col_type = coords[1]/single_shape[1] - cell_type = row_type*2 + col_type - - return cell_type - diff --git a/images/cross.png b/images/cross.png new file mode 100644 index 0000000..2ec1c7e Binary files /dev/null and b/images/cross.png differ diff --git a/images/horizontal_line_10x.png b/images/horizontal_line_10x.png new file mode 100644 index 0000000..f16bbeb Binary files /dev/null and b/images/horizontal_line_10x.png differ diff --git a/images/horizontal_lines.png b/images/horizontal_lines.png new file mode 100644 index 0000000..a4164ed Binary files /dev/null and b/images/horizontal_lines.png differ diff --git a/images/pumpkins.jpeg b/images/pumpkins.jpeg new file mode 100644 index 0000000..c7d0f53 Binary files /dev/null and b/images/pumpkins.jpeg differ diff --git a/images/pumpkins_gray.jpeg b/images/pumpkins_gray.jpeg new file mode 100644 index 0000000..c006afb Binary files /dev/null and b/images/pumpkins_gray.jpeg differ diff --git a/images/spikes_plot_pumpkins.png b/images/spikes_plot_pumpkins.png new file mode 100644 index 0000000..90a25e8 Binary files /dev/null and b/images/spikes_plot_pumpkins.png differ diff --git a/t10k-images-idx3-ubyte__idx_000__lbl_7_.png b/images/t10k-images-idx3-ubyte__idx_000__lbl_7_.png similarity index 100% rename from t10k-images-idx3-ubyte__idx_000__lbl_7_.png rename to images/t10k-images-idx3-ubyte__idx_000__lbl_7_.png diff --git a/poisson/__init__.py b/poisson/__init__.py deleted file mode 100644 index 73be3b9..0000000 --- a/poisson/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -'''Poisson tools init file: - Just import all functions :O -''' -from poisson_tools import * diff --git a/poisson/poisson_demo.ipynb b/poisson/poisson_demo.ipynb deleted file mode 100644 index 83200a0..0000000 --- a/poisson/poisson_demo.ipynb +++ /dev/null @@ -1,2463 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import math\n", - "import random\n", - "import sys\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import os\n", - "#import pyNN.spiNNaker as p\n", - "import pyNN.nest as p #tested on pynn0.75, nest2.2\n", - "import poisson_tools as pstool" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#An example of STDP learned weights for a digit from MNIST" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##Poissonian Presentation of the digit" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Loading MNIST dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false, - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [], - "source": [ - "train_x,train_y = pstool.get_train_data()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot a digit" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAUoAAAEcCAYAAACh/v3vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvW2IZVt63/dbb/vtnFNV/XLvnRnNoEuIRBgwtkkyStCH\n", - "TIJsYgIR/uAIg0lACQmIRMYYEkkksScJIg6JMLFDcCJZWDhSImwsFIKxpZArrA+ybEuyncjGEWiE\n", - "Z6KZ6dvdVXXOfl1v+bD2rrOrurq7+uXePl29f7BYa+86tc+q6j7/ep71POtZsLCwsLCwsLCwsLCw\n", - "sLCwsLCwsLCwsLCwsLCwsLCwsLCwsLCwsLCwsPD6KSDyYu3Rpz1H8Qrf+68DfxZQwI8Df+byl789\n", - "wu+8wuMXFhbeTr4d+J0X0Zb4X73Ai//T1L2Kdr0w+iW/TwF/Hvge4OvA3wF+HvhH+5f8DvCnrnzb\n", - "R8CXX/It3yQf8fbN+yPevjnD2znvj3j75gyf3Ly/8sLfYT6BWbxOXlYovwT8FvDV8fp/Bb6XS0K5\n", - 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"silence = 100 #ms\n", - "aer_file = 'test.aedat'\n", - "jaer_size = 128\n", - "spike_source_data = pstool.mnist_poisson_gen(train_x[21:22], image_size, image_size, max_rate, duration, silence)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "spike_to_aerfile and aerfile_to_spike functions convert spike_source_array to and from jAER file." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "time_stamp, neuron_id, pol = pstool.spike_to_aerfile(spike_source_data, spike_source_data, aer_file, image_size, jaer_size)\n", - "spike_source_array_on, spike_source_array_off = pstool.aerfile_to_spike('test.aedat', 28, 128) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###setup parameters for pyNN simulation" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "teach_rate = 50.\n", - "cell_params_lif = {'cm': 0.25,\n", - " 'i_offset': 0.0,\n", - " 'tau_m': 20.0,\n", - " 'tau_refrac': 2.0,\n", - " 'tau_syn_E': 1.0,\n", - " 'tau_syn_I': 1.0,\n", - " 'v_reset': -70.0,\n", - " 'v_rest': -65.0,\n", - " 'v_thresh': -50.0\n", - " }\n", - "p.setup(timestep=1.0, min_delay=1.0, max_delay=3.0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###SNN Populations: poisson -> input -> output <- teach" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pop_poisson = p.Population(image_size*image_size, p.SpikeSourceArray,{'spike_times' : []})\n", - "for j in range(image_size*image_size):\n", - " pop_poisson[j].spike_times = spike_source_array_on[j]\n", - "pop_input = p.Population(image_size*image_size, p.IF_curr_exp, cell_params_lif)\n", - "pop_output = p.Population(1, p.IF_curr_exp, cell_params_lif)\n", - "pop_teach = p.Population(1,p.SpikeSourcePoisson,{'rate' : teach_rate,\n", - " 'start' : 0,\n", - " 'duration' :duration})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###SNN Projections: \n", - "1. poisson->input\n", - "2. teach->output\\n\n", - "3. input--->output: STDP\\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "ee_connector = p.OneToOneConnector(weights=3.0)\n", - "p.Projection(pop_poisson, pop_input, ee_connector, target='excitatory')\n", - "p.Projection(pop_teach, pop_output, ee_connector, target='excitatory')\n", - "weight_max = 1.3\n", - "stdp_model = p.STDPMechanism(\n", - " timing_dependence=p.SpikePairRule(tau_plus=10.0, tau_minus=10.0),\n", - " weight_dependence=p.MultiplicativeWeightDependence(w_min=0.0, w_max=weight_max, A_plus=0.01, A_minus=0.01)\n", - ")\n", - "proj_stdp = p.Projection(\n", - " pop_input, pop_output, p.AllToAllConnector(weights = 0.0),\n", - " synapse_dynamics=p.SynapseDynamics(slow=stdp_model)\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run simulation" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "100101.0" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pop_poisson.record()\n", - "p.run(duration+silence)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "plot trained weights and recorded poission spikes" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAU0AAAEcCAYAAABDIuaWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvXuMbMt+3/Wpx3r1a2b245z7yFUOSRwUIhSIZV8pAeUq\n", - 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" plt.xlabel('Time (ms)')\n", - " plt.ylabel('Neuron ID')\n", - " plt.title(title)\n", - "\n", - " else:\n", - " print \"No spikes received\"\n", - "spikes = pop_poisson.getSpikes(compatible_output=True)\n", - "plot_spikes(spikes,'Raster Plot')\n", - "p.end()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/poisson/t10k-images.idx3-ubyte b/poisson/t10k-images.idx3-ubyte deleted file mode 100644 index 1170b2c..0000000 Binary files a/poisson/t10k-images.idx3-ubyte and /dev/null differ diff --git a/poisson/t10k-labels.idx1-ubyte b/poisson/t10k-labels.idx1-ubyte deleted file mode 100644 index d1c3a97..0000000 Binary files a/poisson/t10k-labels.idx1-ubyte and /dev/null differ diff --git a/poisson/test.aedat b/poisson/test.aedat deleted file mode 100644 index 065e884..0000000 Binary files a/poisson/test.aedat and /dev/null differ diff --git a/poisson/train-images.idx3-ubyte b/poisson/train-images.idx3-ubyte deleted file mode 100644 index bbce276..0000000 Binary files a/poisson/train-images.idx3-ubyte and /dev/null differ diff --git a/poisson/train-labels.idx1-ubyte b/poisson/train-labels.idx1-ubyte deleted file mode 100644 index d6b4c5d..0000000 Binary files a/poisson/train-labels.idx1-ubyte and /dev/null differ diff --git a/poisson/poisson_tools.py b/poisson_tools.py similarity index 95% rename from poisson/poisson_tools.py rename to poisson_tools.py index e28d359..33d06e4 100644 --- a/poisson/poisson_tools.py +++ b/poisson_tools.py @@ -1,6 +1,5 @@ """poisson_tools.py: - Collection of functions to generate Poisson spike trains for the MNIST - dataset. + Collection of functions to generate Poisson spike trains. """ import math import random @@ -12,8 +11,8 @@ def plot_digit(img_raw): '''Generates a matplotlib plot from the raw pixel array. - - :param img_raw: array containing the pixels for an MNIST digit, + + :param img_raw: array containing the pixels for an MNIST digit, should contain 28*28 entries ''' img_raw = np.uint8(img_raw) @@ -24,8 +23,8 @@ def plot_digit(img_raw): def plot_weight(img_raw): '''Generates a matplotlib plot from the raw weights array. - - :param img_raw: array containing the weights for + + :param img_raw: array containing the weights for pixel-to-neuron connections ''' plt.figure(figsize=(5,5)) @@ -36,7 +35,7 @@ def plot_weight(img_raw): def get_train_data(): '''Extracts images and labels from the train files obtained from http://yann.lecun.com/exdb/mnist/ - + :returns: A tuple containing arrays of the images (train_x) and labels (train_y). ''' @@ -46,20 +45,20 @@ def get_train_data(): train_x = np.fromfile(f, dtype='>u1', count=list_size*image_hight*image_width) train_x = np.reshape(train_x, (list_size,image_hight*image_width)) f.close() - + file_name = 'train-labels.idx1-ubyte' f = open(file_name, "rb") magic_number, list_size = np.fromfile(f, dtype='>i4', count=2) train_y = np.fromfile(f, dtype='>u1', count=list_size*image_hight*image_width) f.close() - + return np.double(train_x), np.double(train_y) def get_test_data(): '''Extracts images and labels from the test files obtained from http://yann.lecun.com/exdb/mnist/ - + :returns: A tuple containing arrays of the images (test_x) and labels (test_y). ''' @@ -69,20 +68,20 @@ def get_test_data(): test_x = np.fromfile(f, dtype='>u1', count=list_size*image_hight*image_width) test_x = np.reshape(test_x, (list_size,image_hight*image_width)) f.close() - + file_name = 't10k-labels.idx1-ubyte' f = open(file_name, "rb") magic_number, list_size = np.fromfile(f, dtype='>i4', count=2) test_y = np.fromfile(f, dtype='>u1', count=list_size*image_hight*image_width) f.close() - + return np.double(test_x), np.double(test_y) def nextTime(rateParameter): '''Helper function to Poisson generator :param rateParameter: The rate at which a neuron will fire (Hz) - + :returns: Time at which the neuron should spike next (seconds) ''' return -math.log(1.0 - random.random()) / rateParameter @@ -94,7 +93,7 @@ def poisson_generator(rate, t_start, t_stop): :param rate: The rate at which a neuron will fire (Hz) :param t_start: When should the neuron start to fire (milliseconds) :param t_stop: When should the neuron stop firing (milliseconds) - + :returns: Poisson train firing at rate, from t_start to t_stop (milliseconds) ''' poisson_train = [] @@ -108,7 +107,7 @@ def poisson_generator(rate, t_start, t_stop): return poisson_train -def mnist_poisson_gen(image_list, image_height, image_width, max_freq, duration, silence): +def image_to_poisson_trains(image_list, image_height, image_width, max_freq, duration, silence): '''Generate Poisson trains for images. :param image_list: MNIST image list, numpy array of size (num images, width*height) :param image_height: MNIST digit height @@ -116,16 +115,20 @@ def mnist_poisson_gen(image_list, image_height, image_width, max_freq, duration, :param max_freq: Maximum frequency a neuron representing a pixel can fire (Hz) :param duration: How long should Poisson trains last (milliseconds) :param silence: Time for which no spikes are emmited (milliseconds) - + :returns: A PyNN SpikeSourceArray-formatted representation of a sequence of MNIST digits, interleaved by silence periods ''' if max_freq > 0: for i in range(image_list.shape[0]): + """ NOTE: + If the spike array wasn't produced at the end, max_freq below + is more likely to be insufficient(low) for your data. + """ image_list[i] = image_list[i]/sum(image_list[i])*max_freq - + spike_source_data = [[] for i in range(image_height*image_width)] - + for i in range(image_list.shape[0]): t_start = i*(duration+silence) t_stop = t_start+duration @@ -133,7 +136,7 @@ def mnist_poisson_gen(image_list, image_height, image_width, max_freq, duration, spikes = poisson_generator(image_list[i][j], t_start, t_stop) if spikes != []: spike_source_data[j].extend(spikes) - + return spike_source_data @@ -142,7 +145,7 @@ def aerfile_to_spike(file_name, image_size, jaer_size): :param file_name: Name of the file to open :param image_size: Width and height of the image :param jaer_size: -Not used?- - + :returns: A spike array for each polarity ''' if os.path.exists(file_name): @@ -162,7 +165,7 @@ def aerfile_to_spike(file_name, image_size, jaer_size): polmask=1 # polarity bit is LSB - pol= (AllAddr & polmask) # 0 is on, 1(Polirity = -1) is off + pol= (AllAddr & polmask) # 0 is on, 1(Polirity = -1) is off AllAddr = AllAddr + pol x=(AllAddr & xmask) >> xshift y=(AllAddr & ymask) >> yshift @@ -184,19 +187,19 @@ def aerfile_to_spike(file_name, image_size, jaer_size): -def spike_to_aerfile(spike_source_array_on, spike_source_array_off, +def spike_to_aerfile(spike_source_array_on, spike_source_array_off, file_name, image_size, jaer_size): - '''Converts and writes SpikeSourceArrays for ON and OFF polarities + '''Converts and writes SpikeSourceArrays for ON and OFF polarities into an aer-formated file. :param spike_source_array_on: Array containing ON events for pixels :param spike_source_array_off: Array containing OFF events for pixels :param file_name: Name of file to write to :param image_size: Width and height of image :param jaer_size: -Not used?- - + :returns: An AER representation of the arrays (times, ids, polarities) ''' - + time_stamp = [] neuron_id = [] @@ -211,7 +214,7 @@ def spike_to_aerfile(spike_source_array_on, spike_source_array_off, neuron_id.extend([i]*len(spikes)) num_on = len(time_stamp) pol = [0] * num_on - + # OFF events if len(spike_source_array_off) == num_neuron: for i in range(num_neuron): @@ -223,7 +226,7 @@ def spike_to_aerfile(spike_source_array_on, spike_source_array_off, pol.extend([-1] * (len(time_stamp)-num_on)) else: pol = [-1] * len(time_stamp) - + if len(time_stamp)>0: sort_index = sorted(range(len(time_stamp)), key=time_stamp.__getitem__) AllTs = np.uint32(np.ceil(np.array(time_stamp)[sort_index]*1000.)) #in mus @@ -258,8 +261,5 @@ def spike_to_aerfile(spike_source_array_on, spike_source_array_off, f.close() return AllTs, neuron_id, Polarity else: - print 'Output is []' + print('Output is []') return [] - - - diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..baa583a --- /dev/null +++ b/requirements.txt @@ -0,0 +1,3 @@ +matplotlib +numpy +opencv-python diff --git a/sample_focal.py b/sample_focal.py deleted file mode 100644 index a7fe274..0000000 --- a/sample_focal.py +++ /dev/null @@ -1,38 +0,0 @@ -import scipy -import numpy -import pylab -from focal import * - -# load image -filename = "./t10k-images-idx3-ubyte__idx_000__lbl_7_.png" -img = pylab.imread(filename) #grayscale image -pylab.figure() -pylab.imshow(img, cmap="Greys_r") - -fcl = Focal() -num_kernels = len(fcl.kernels.full_kernels) # four simulated layers - -# spikes contains a rank-ordered list of triples with the following -# information: -# [ pixel/neuron index (int), pixel value (float), layer id (int) ] -spikes = fcl.apply(img) - -# we convert the spike list to 4 images, spike_imgs is a dictionary -# containing an image per simulated layer -spike_imgs = spike_trains_to_images_g(spikes, img, num_kernels) - -pylab.figure() -i = 1 -for k in spike_imgs.keys(): - pylab.subplot(2, 2, i) - pylab.imshow(spike_imgs[k], cmap="Greys_r") - i += 1 - -#convert to spike source array -pylab.figure() -spk_src = focal_to_spike(spikes, img.shape, - spikes_per_time_block=10, - start_time=0., time_step=1.) -raster_plot_spike(spk_src) - -pylab.show() diff --git a/sample_poisson.py b/sample_poisson.py deleted file mode 100644 index 71b38d2..0000000 --- a/sample_poisson.py +++ /dev/null @@ -1,29 +0,0 @@ -import scipy -import numpy -import pylab -from poisson.poisson_tools import * -from focal import raster_plot_spike - -# Demonstration file for the Poisson tools module. It also includes functions -# to load all the MNIST images/labels and converting between different formats - -# load image -filename = "./t10k-images-idx3-ubyte__idx_000__lbl_7_.png" -img = pylab.imread(filename) -height, width = img.shape - -max_freq = 1000 #Hz -on_duration = 200 #ms -off_duration = 100 #ms -pylab.figure() -pylab.imshow(img, cmap="Greys_r") - -spikes = mnist_poisson_gen(numpy.array([img.reshape(height*width)]), #notice reshape - height, width, - max_freq, on_duration, off_duration) - -pylab.figure() - -raster_plot_spike(spikes) - -pylab.show() diff --git a/util_functions.py b/util_functions.py new file mode 100644 index 0000000..e078561 --- /dev/null +++ b/util_functions.py @@ -0,0 +1,82 @@ +import pickle +import cv2 +import os +from matplotlib import pyplot as plt + + + +#--- File Operations ----------------------------------------------------------# +def pickle_it( pickle_to_be, pickle_file_name ): + if pickle_to_be is not None: + dir_name = 'pickles' + if not os.path.exists( dir_name ): + os.makedirs( dir_name ) + + pickle_file_name = "{}/{}".format( dir_name, pickle_file_name ) + pickle_file_obj = open( pickle_file_name, 'wb' ) # open the file for writing + pickle.dump( pickle_to_be, pickle_file_obj, protocol=2 ) # so that python2.x can also read it + pickle_file_obj.close() + print( "Pickle is ready! -> {}".format( pickle_file_name ) ) + + """ For more info., about bz2 etc.: + https://www.datacamp.com/community/tutorials/pickle-python-tutorial """ + + +def unpickle( pickle_file_name ): + pickle_file_obj = open( pickle_file_name, 'rb' ) # read-binary + pickled_one = pickle.load( pickle_file_obj ) + pickle_file_obj.close() + return pickled_one + + +def save_img( img_file_name, img_matrix, show_image=True ): + if img_matrix is not None: + dir_name = 'images' + if not os.path.exists( dir_name ): + os.makedirs( dir_name ) + + img_file_name = "{}/{}".format( dir_name, img_file_name ) + cv2.imwrite( img_file_name, img_matrix ) + + if show_image: + imshow_matplot( img_matrix ) + + +#--- Display Images -----------------------------------------------------------# +def raster_plot_spike( spikes, marker='|', markersize=2 ): + '''Plot PyNN SpikeSourceArrays + :param spikes: The array containing spikes + ''' + x = [] + y = [] + + for neuron_id in range( len(spikes) ): + for t in spikes[neuron_id]: + x.append(t) + y.append(neuron_id) + + plt.plot( x, y, marker, markersize=markersize ) + + +def imshow_opencv( img ): + if img is not None: + window_name = 'Your New Image' + height, width = img.shape + window_size = 100 if (height < 50 and width < 50) else cv2.WINDOW_AUTOSIZE + + cv2.namedWindow( window_name, window_size ) + cv2.imshow( window_name, img ) + + key = cv2.waitKey(0) & 0xFF + if key == 27: # ESC key + cv2.destroyAllWindows() + """ Closing the window won't end the program! Press ESC both to close + the window, and also kill the program. """ + + +def imshow_matplot( img, hide_ticks=False ): + if img is not None: + plt.imshow( img, cmap = 'gray' ) + if hide_ticks: + plt.xticks([]), plt.yticks([]) # to hide tick values on X and Y axis + plt.show()