91 lines
3.3 KiB
Python
91 lines
3.3 KiB
Python
import tensorflow as tf
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import numpy as np
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import cv2
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from video_compression_model import NUM_FRAMES, PRESET_SPEED_CATEGORIES, VideoCompressionModel
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# Constants
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MAX_FRAMES = 24
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CHUNK_SIZE = 24 # Adjust based on available memory and video resolution
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COMPRESSED_VIDEO_FILE = 'compressed_video.mkv'
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# Step 2: Load the trained model
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model = tf.keras.models.load_model('models/model_differencing.keras', custom_objects={'VideoCompressionModel': VideoCompressionModel})
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# Step 3: Load the uncompressed video
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UNCOMPRESSED_VIDEO_FILE = 'test_data/training_video.mkv'
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def load_frames_from_video(video_file, start_frame=0, num_frames=CHUNK_SIZE):
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cap = cv2.VideoCapture(video_file)
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frames = []
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cap.set(cv2.CAP_PROP_POS_FRAMES, start_frame)
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for _ in range(num_frames):
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ret, frame = cap.read()
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if not ret:
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break
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frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB).astype(np.float32) / 255.0 # Normalize and convert to float32
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frames.append(frame)
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cap.release()
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return frames
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def predict_in_chunks(uncompressed_frames, model, crf_values, preset_speed_values):
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num_sequences = len(uncompressed_frames) - NUM_FRAMES + 1
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compressed_frames = []
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#for frame in uncompressed_frames:
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# cv2.imshow("frame", frame)
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# cv2.waitKey(50)
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for start in range(0, num_sequences, CHUNK_SIZE):
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end = min(start + CHUNK_SIZE, num_sequences)
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frame_chunk = uncompressed_frames[start:end + NUM_FRAMES - 1]
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crf_chunk = crf_values[start:end]
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speed_chunk = preset_speed_values[start:end]
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frame_sequences = []
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for i in range(len(frame_chunk) - NUM_FRAMES + 1):
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sequence = frame_chunk[i:i + NUM_FRAMES]
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frame_sequences.append(sequence)
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frame_sequences = np.array(frame_sequences)
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compressed_chunk = model.predict({"frames": frame_sequences, "crf": crf_chunk, "preset_speed": speed_chunk})
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compressed_frames.extend(compressed_chunk)
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return compressed_frames
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def save_frames_chunk(frames, video_writer):
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for frame in frames:
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frame = np.clip(frame * 255.0, 0, 255).astype(np.uint8)
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frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
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video_writer.write(frame)
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cap = cv2.VideoCapture(UNCOMPRESSED_VIDEO_FILE)
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total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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if MAX_FRAMES != 0 and total_frames > MAX_FRAMES:
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total_frames = MAX_FRAMES
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cap.release()
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crf_values = np.full((CHUNK_SIZE + NUM_FRAMES - 1, 1), 25, dtype=np.float32) # Chunk size + look-ahead frames
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preset_speed_index = PRESET_SPEED_CATEGORIES.index("fast")
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preset_speed_values = np.full((CHUNK_SIZE + NUM_FRAMES - 1, 1), preset_speed_index, dtype=np.float32)
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out = None # Video writer instance
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for i in range(0, total_frames, CHUNK_SIZE):
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uncompressed_frames_chunk = load_frames_from_video(UNCOMPRESSED_VIDEO_FILE, start_frame=i)
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compressed_frames_chunk = predict_in_chunks(uncompressed_frames_chunk, model, crf_values, preset_speed_values)
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# Initialize video writer if it's the first chunk
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if out is None:
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height, width = compressed_frames_chunk[0].shape[:2]
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fourcc = cv2.VideoWriter_fourcc(*'XVID')
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out = cv2.VideoWriter(COMPRESSED_VIDEO_FILE, fourcc, 24.0, (width, height))
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save_frames_chunk(compressed_frames_chunk, out)
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out.release()
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print("Compression completed.")
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