Added comments explaining all variables and functions

Author: MatthewTafazoli

code/chapter-2/visualization.py

@@ -18,12 +18,16 @@
 import glob
 import os
 
+#Select a model to use, in this case VGG16
 model = VGG16(weights='imagenet', include_top=True, input_tensor=None, input_shape=None, pooling=None, classes=1000)
 #Check with 'print(model.summary())', in this case it is "block5_conv3"
 last_conv_layer_name = "block5_conv3"
-#Include layers between last convolutional layer and prediction layer
+#Must include layers between last convolutional layer and prediction layer
+#Layer names can be found through 'print(model.summary())'
 classifier_layer_names = ["block5_pool", "flatten", "fc1", "fc2", "predictions"]
 
+#This function is called from 'make_gradcam_heatmap'
+#Takes iaage_path from 'get_command_line_arguments', turns into it an array
 def get_img_array(img_path, size):
  img = tensorflow.keras.preprocessing.image.load_img(img_path, target_size=(224, 224))
  # `array` is a float32 Numpy array
@@ -33,12 +37,17 @@ def get_img_array(img_path, size):
  array = np.expand_dims(array, axis=0)
  return array
 
-#This replaces several functions from visualization.py
+#'make_gradcam_heatmap' is main function and ultimately returns heatmap superimposed onto the input image(s)
+
+#inputs are the image path specified in the command line, the last convolutional layer and
+#the classifier layer names of which both are defined above and depend on your model, and the output path
+#for our heatmap superimposed onto original image which are specified in the script's final if statement
 def make_gradcam_heatmap(
  img_path, model, last_conv_layer_name, classifier_layer_names, output_path
 ):
-
+ #pre_processes the array returned from 'get_img_array'
  img_array = preprocess_input(get_img_array(img_path, size= (224, 224)))
+
  # First, we create a model that maps the input image to the activations
  # of the last conv layer
  last_conv_layer = model.get_layer(last_conv_layer_name)
@@ -55,9 +64,11 @@ def make_gradcam_heatmap(
  # Then, we compute the gradient of the top predicted class for our input image
  # with respect to the activations of the last conv layer
  with tensorflow.GradientTape() as tape:
+
  # Compute activations of the last conv layer and make the tape watch it
  last_conv_layer_output = last_conv_layer_model(img_array)
  tape.watch(last_conv_layer_output)
+
  # Compute class predictions
  preds = classifier_model(last_conv_layer_output)
  top_pred_index = tensorflow.argmax(preds[0])
@@ -111,20 +122,27 @@ def make_gradcam_heatmap(
  #Save the the superimposed image to the output path
  superimposed_img.save(output_path)
 
-
+#Runs body of code for entirety of videoframs_path (folder specified in command line)
 def process_video(videoframes_path, output_prefix):
  counter = 0
+ #define output directory
  output_dir = output_prefix + "_output"
+
+ #Creates directory output directoy if it doesn't already exist
  if not os.path.exists(output_dir):
  os.makedirs(output_dir)
  for input_path in sorted(glob.glob(videoframes_path + "/*.jpg")):
- counter += 1
+ counter +=
+
  output_path = output_dir + "/result-" + str(counter).zfill(4) + '.jpg'
 
+ #Runs main function with specified image_path, output_prefix, and layers defined near top of script
  make_gradcam_heatmap(input_path, model, last_conv_layer_name, classifier_layer_names, output_path)
 
+#Function for taking inputs through the command line
 def get_command_line_arguments():
  parser = ArgumentParser()
+ #We specify either image or video to
  parser.add_argument("--process", choices=["image", "video"], required=True,
  dest="process_type", help="Process a single image or video")
  parser.add_argument("--path", required=True, dest="path",
@@ -134,17 +152,25 @@ def get_command_line_arguments():
 
 args = get_command_line_arguments()
 
+#If process is specified as 'image', defines image_path and output_prefix according to command line argument
 if args.process_type == "image":
+ #image path is location of image that we want to generate a heatmap for
  image_path = args.path
  output_prefix = os.path.splitext(os.path.basename(image_path))[0]
+ #Runs main function with specified image_path and output_prefix from command line
+ #layers defined near top of script
  make_gradcam_heatmap(image_path, model, last_conv_layer_name, classifier_layer_names, output_prefix + "_output.jpg")
 
  #Plot the superimposed image
  img = mpimg.imread(output_prefix + "_output.jpg")
  plt.imshow(img)
  plt.show()
 
+#If process is specified as 'image', defines videoframes_path and output_prefix according to command line argument
 elif args.process_type == "video":
+ #videoframes_path is directory with the video frames split by ffmpeg
  videoframes_path = args.path
+ #will be used to specify or create output folder
  output_prefix = os.path.dirname(videoframes_path)
+ #Runs 'process_video' function with inputs taken from command line
  heatmaps = process_video(videoframes_path, output_prefix)