updated

globalVars.py

@@ -32,11 +32,11 @@ def load_video_metadata(list_path):
     - list: List of dictionaries, each containing video details.
     """
     
-    LOGGER.trace(f"Entering: load_video_metadata({list_path})")
+    #LOGGER.trace(f"Entering: load_video_metadata({list_path})")
     try:
         with open(list_path, "r") as json_file:
             file = json.load(json_file)
-            LOGGER.trace(f"load_video_metadata returning: {file}")
+            #LOGGER.trace(f"load_video_metadata returning: {file}")
             return file
     except FileNotFoundError:
         LOGGER.error(f"Metadata file {list_path} not found.")

train_model.py

@@ -66,14 +66,21 @@ class ImageLoggingCallback(Callback):
         return np.stack(converted, axis=0)
 
     def on_epoch_end(self, epoch, logs=None):
-        itter = iter(self.validation_dataset)
-        random_idx = np.random.randint(0, BATCH_SIZE)
+        random_idx = np.random.randint(0, MAX_FRAMES - 1)
+        
+        validation_data = None
+        dataset_size = 0  # to keep track of the dataset size
         
         # Loop through the dataset until the chosen index
         for i, data in enumerate(self.validation_dataset):
             if i == random_idx:
                 validation_data = data
                 break
+            dataset_size += 1
+            
+        if validation_data is None:
+            print(f"Random index exceeds validation dataset size: {dataset_size}. Using last available data.")
+            validation_data = data  # assigning the last data seen in the loop to validation_data
         
         batch_input_images, batch_gt_labels = validation_data
         
@@ -83,6 +90,10 @@ class ImageLoggingCallback(Callback):
         reconstructed_frame = MODEL.predict(validation_data[0])
         reconstructed_frame = np.clip(reconstructed_frame * 255.0, 0, 255).astype(np.uint8)
         
+        # Save the reconstructed frame to the specified folder
+        reconstructed_path = os.path.join(self.log_dir, f"epoch_{epoch}.png")
+        cv2.imwrite(reconstructed_path, reconstructed_frame[0])  # Saving only the first image as an example
+        
         batch_input_images = self.convert_images(batch_input_images)
         batch_gt_labels = self.convert_images(batch_gt_labels)
         reconstructed_frame = self.convert_images(reconstructed_frame)

video_compression_model.py

@@ -7,7 +7,7 @@ import numpy as np
 import tensorflow as tf
 from tensorflow.keras import layers
 from featureExtraction import preprocess_frame, scale_crf, scale_speed_preset
-from globalVars import HEIGHT, LOGGER, NUM_COLOUR_CHANNELS, NUM_PRESET_SPEEDS, PRESET_SPEED_CATEGORIES, WIDTH
+from globalVars import LOGGER, NUM_COLOUR_CHANNELS, PRESET_SPEED_CATEGORIES
 
 
 #from tensorflow.keras.mixed_precision import Policy
@@ -107,13 +107,20 @@ class VideoCompressionModel(tf.keras.Model):
             layers.Conv2DTranspose(64, (3, 3), dilation_rate=2, padding='same'),  # Using Dilated Convolution
             #layers.BatchNormalization(),
             layers.LeakyReLU(),
-            # Use Sub-Pixel Convolutional Layer
-            layers.Conv2DTranspose(NUM_COLOUR_CHANNELS * 16, (3, 3), padding='same'),  # 16 times the number of color channels
-            layers.Lambda(lambda x: tf.nn.depth_to_space(x, block_size=4))  # Sub-Pixel Convolutional Layer with block_size=4
+            # First Sub-Pixel Convolutional Layer
+            layers.Conv2DTranspose(NUM_COLOUR_CHANNELS * 4, (3, 3), padding='same'),  # 4 times the number of color channels for first upscaling by 2
+            layers.Lambda(lambda x: tf.nn.depth_to_space(x, block_size=2)),  # Sub-Pixel Convolutional Layer with block_size=2
+            # Second Sub-Pixel Convolutional Layer
+            layers.Conv2DTranspose(NUM_COLOUR_CHANNELS * 4, (3, 3), padding='same'),  # 4 times the number of color channels for second upscaling by 2
+            layers.Lambda(lambda x: tf.nn.depth_to_space(x, block_size=2)),  # Sub-Pixel Convolutional Layer with block_size=2
+            layers.Activation('sigmoid')
         ])
 
+
     def call(self, inputs):
+        #print(f"Input: {inputs.shape}")
         encoded = self.encoder(inputs)
-        return self.decoder(encoded)
-
-
+        #print(f"encoded: {encoded.shape}")
+        decoded = self.decoder(encoded)
+        #print(f"decoded: {decoded.shape}")
+        return decoded