Added GC
The data set will now process frames from ALL videos
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1 changed files with 37 additions and 44 deletions
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@ -1,5 +1,6 @@
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# video_compression_model.py
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import gc
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import os
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import cv2
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import numpy as np
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@ -18,6 +19,7 @@ def combine_batch(frame, crf, speed, include_controls=True, resize=True):
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height, width, _ = processed_frame.shape
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combined = [processed_frame]
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if include_controls:
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crf_array = np.full((height, width, 1), crf)
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speed_array = np.full((height, width, 1), speed)
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@ -27,56 +29,52 @@ def combine_batch(frame, crf, speed, include_controls=True, resize=True):
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def data_generator(videos, batch_size):
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# Infinite loop to keep generating batches
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while True:
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# Iterate over each video
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for video_details in videos:
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# Get the paths for compressed and original (uncompressed) video files
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base_dir = os.path.dirname("test_data/validation/validation.json")
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video_path = os.path.join(base_dir, video_details["compressed_video_file"])
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uncompressed_video_path = os.path.join(base_dir, video_details["original_video_file"])
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CRF = scale_crf(video_details["crf"])
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SPEED = scale_speed_preset(PRESET_SPEED_CATEGORIES.index(video_details["preset_speed"]))
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# Open the video files
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cap_compressed = cv2.VideoCapture(video_path)
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cap_uncompressed = cv2.VideoCapture(uncompressed_video_path)
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# Lists to store the processed frames
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compressed_frame_batch = [] # Input data (Target)
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uncompressed_frame_batch = [] # Target data (Training)
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base_dir = os.path.dirname("test_data/validation/validation.json")
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# Read and process frames from both videos
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while cap_compressed.isOpened() and cap_uncompressed.isOpened():
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while True:
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# Lists to store the processed frames
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compressed_frame_batch = [] # Input data (Target)
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uncompressed_frame_batch = [] # Target data (Training)
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# Get a list of video capture objects for all videos
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caps_compressed = [cv2.VideoCapture(os.path.join(base_dir, video["compressed_video_file"])) for video in videos]
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caps_uncompressed = [cv2.VideoCapture(os.path.join(base_dir, video["original_video_file"])) for video in videos]
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# As long as any video can provide frames, keep running
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while any(cap.isOpened() for cap in caps_compressed):
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for idx, (cap_compressed, cap_uncompressed) in enumerate(zip(caps_compressed, caps_uncompressed)):
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#print(f"(Video Change) Processing video {idx}") # Print statement to indicate video change
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ret_compressed, compressed_frame = cap_compressed.read()
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ret_uncompressed, uncompressed_frame = cap_uncompressed.read()
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if not ret_compressed or not ret_uncompressed:
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break
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# Target data
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continue
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CRF = scale_crf(videos[idx]["crf"])
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SPEED = scale_speed_preset(PRESET_SPEED_CATEGORIES.index(videos[idx]["preset_speed"]))
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compressed_combined = combine_batch(compressed_frame, CRF, SPEED, include_controls=False)
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# Input data
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uncompressed_combined = combine_batch(uncompressed_frame, 0, scale_speed_preset(PRESET_SPEED_CATEGORIES.index("veryslow")))
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# Append processed frames to batches
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compressed_frame_batch.append(compressed_combined)
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uncompressed_frame_batch.append(uncompressed_combined)
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# If batch is complete, yield it
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if len(compressed_frame_batch) == batch_size:
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yield (np.array(uncompressed_frame_batch), np.array(compressed_frame_batch)) # Yielding Training and Target data
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compressed_frame_batch = []
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uncompressed_frame_batch = []
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yield (np.array(uncompressed_frame_batch), np.array(compressed_frame_batch))
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compressed_frame_batch.clear()
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uncompressed_frame_batch.clear()
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# Release video files
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cap_compressed.release()
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cap_uncompressed.release()
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# Close all video captures at the end
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for cap in caps_compressed + caps_uncompressed:
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cap.release()
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cv2.destroyAllWindows()
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# If there are frames left that don't fill a whole batch, send them anyway
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if len(compressed_frame_batch) > 0:
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yield (np.array(uncompressed_frame_batch), np.array(compressed_frame_batch))
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# If there are frames left that don't fill a whole batch, send them anyway
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if len(compressed_frame_batch) > 0:
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yield (np.array(uncompressed_frame_batch), np.array(compressed_frame_batch))
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class VideoCompressionModel(tf.keras.Model):
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def __init__(self):
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@ -105,10 +103,5 @@ class VideoCompressionModel(tf.keras.Model):
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])
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def call(self, inputs):
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#print("Input shape:", inputs.shape)
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encoded = self.encoder(inputs)
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#print("Encoded shape:", encoded.shape)
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decoded = self.decoder(encoded)
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#print("Decoded shape:", decoded.shape)
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return decoded
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return self.decoder(self.encoder(inputs))
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