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| import cv2 | |
| from PIL import Image | |
| import numpy as np | |
| from datetime import datetime | |
| class VideoService: | |
| def load_video(self, video_path): | |
| """Load video and return frames.""" | |
| cap = cv2.VideoCapture(video_path) | |
| frames = [] | |
| while cap.isOpened(): | |
| ret, frame = cap.read() | |
| if not ret: | |
| break | |
| frames.append(frame) | |
| cap.release() | |
| return frames | |
| def frame_to_pil(self, frame): | |
| """Convert OpenCV frame to PIL Image.""" | |
| frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) | |
| return Image.fromarray(frame_rgb) | |
| def draw_detections(self, frame, detections): | |
| """Draw bounding boxes, labels, and timestamp on frame.""" | |
| frame_copy = frame.copy() | |
| # Add timestamp overlay | |
| timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") | |
| cv2.putText( | |
| frame_copy, | |
| timestamp, | |
| (10, 30), | |
| cv2.FONT_HERSHEY_SIMPLEX, | |
| 0.7, | |
| (255, 255, 255), | |
| 2 | |
| ) | |
| # Draw bounding boxes and labels for detections | |
| for detection in detections: | |
| score = detection["score"] | |
| label = detection["label"] | |
| box = detection["box"] | |
| x1, y1, x2, y2 = box["xmin"], box["ymin"], box["xmax"], box["ymax"] | |
| cv2.rectangle(frame_copy, (x1, y1), (x2, y2), (0, 255, 0), 2) | |
| cv2.putText( | |
| frame_copy, | |
| f"{label}: {score:.2f}", | |
| (x1, y1 - 10), | |
| cv2.FONT_HERSHEY_SIMPLEX, | |
| 0.5, | |
| (0, 255, 0), | |
| 2 | |
| ) | |
| return frame_copy | |
| def save_video(self, frames, output_path, fps=30): | |
| """Save frames as a video.""" | |
| if not frames: | |
| return | |
| height, width, _ = frames[0].shape | |
| fourcc = cv2.VideoWriter_fourcc(*"mp4v") | |
| out = cv2.VideoWriter(output_path, fourcc, fps, (width, height)) | |
| for frame in frames: | |
| out.write(frame) | |
| out.release() |