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| import cv2 | |
| import numpy as np | |
| import random | |
| def extract_video_frames(video_path, n_frames=30, frame_size=(96, 96)): | |
| """ | |
| Simplified robust frame extractor for short videos (2-10 sec) | |
| - Automatically handles varying video lengths | |
| - Ensures consistent output shape | |
| - Optimized for MP4/MPEG | |
| """ | |
| # Open video | |
| cap = cv2.VideoCapture(video_path) | |
| if not cap.isOpened(): | |
| print(f"Error: Could not open video {video_path}") | |
| return None | |
| total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) | |
| fps = cap.get(cv2.CAP_PROP_FPS) | |
| # Basic video validation | |
| if total_frames < 1 or fps < 1: | |
| print(f"Error: Invalid video (frames:{total_frames}, fps:{fps})") | |
| cap.release() | |
| return None | |
| # Calculate how many frames to skip (adaptive based on video length) | |
| video_length = total_frames / fps | |
| frame_step = max(1, int(total_frames / n_frames)) | |
| frames = [] | |
| last_good_frame = None | |
| for i in range(n_frames): | |
| # Calculate position to read (spread evenly across video) | |
| pos = min(int(i * (total_frames / n_frames)), total_frames - 1) | |
| cap.set(cv2.CAP_PROP_POS_FRAMES, pos) | |
| ret, frame = cap.read() | |
| # Fallback strategies if read fails | |
| if not ret or frame is None: | |
| if last_good_frame is not None: | |
| frame = last_good_frame.copy() | |
| else: | |
| # Generate placeholder frame (light gray) | |
| frame = np.full((*frame_size[::-1], 3), 0.8, dtype=np.float32) | |
| else: | |
| # Process valid frame | |
| frame = cv2.resize(frame, frame_size) | |
| frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) | |
| frame = frame.astype(np.float32) / 255.0 | |
| last_good_frame = frame | |
| frames.append(frame) | |
| cap.release() | |
| return np.array(frames) |