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| import gradio as gr | |
| import torch | |
| import cv2 | |
| import numpy as np | |
| import time | |
| from ultralytics import YOLO | |
| import spaces | |
| import os | |
| import logging | |
| class CrowdDetection: | |
| def __init__(self, model_path="yolov8n.pt"): | |
| logger.info(f"Initializing CrowdDetection with model: {model_path}") | |
| self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| try: | |
| if not os.path.exists(model_path): | |
| logger.info(f"Model {model_path} not found, downloading...") | |
| self.model = YOLO("yolov8n.pt") # Downloads if not present | |
| self.model.save(model_path) | |
| else: | |
| self.model = YOLO(model_path) | |
| self.model.to(self.device) | |
| logger.info("CrowdDetection model loaded successfully") | |
| except Exception as e: | |
| logger.error(f"Failed to initialize model: {str(e)}") | |
| raise | |
| def detect_crowd(self, video_path): | |
| logger.info(f"Processing video for crowd detection: {video_path}") | |
| try: | |
| cap = cv2.VideoCapture(video_path) | |
| if not cap.isOpened(): | |
| logger.error(f"Failed to open video: {video_path}") | |
| raise ValueError(f"❌ Failed to open video: {video_path}") | |
| fps = int(cap.get(cv2.CAP_PROP_FPS)) | |
| width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) | |
| height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) | |
| logger.debug(f"Video specs - FPS: {fps}, Width: {width}, Height: {height}") | |
| output_path = "output_crowd.mp4" | |
| fourcc = cv2.VideoWriter_fourcc(*"mp4v") | |
| out = cv2.VideoWriter(output_path, fourcc, fps, (width, height)) | |
| if not out.isOpened(): | |
| cap.release() | |
| logger.error(f"Failed to initialize video writer for {output_path}") | |
| raise ValueError(f"❌ Failed to initialize video writer") | |
| CROWD_THRESHOLD = 10 | |
| frame_count = 0 | |
| while cap.isOpened(): | |
| ret, frame = cap.read() | |
| if not ret: | |
| break | |
| frame_count += 1 | |
| results = self.model(frame) | |
| person_count = sum(1 for result in results for cls in result.boxes.cls.cpu().numpy() if int(cls) == 0) | |
| logger.debug(f"Frame {frame_count}: Detected {person_count} people") | |
| for result in results: | |
| boxes = result.boxes.xyxy.cpu().numpy() | |
| classes = result.boxes.cls.cpu().numpy() | |
| for box, cls in zip(boxes, classes): | |
| if int(cls) == 0: | |
| x1, y1, x2, y2 = map(int, box) | |
| cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2) | |
| cv2.putText(frame, "Person", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) | |
| alert_text = "Crowd Alert!" if person_count > CROWD_THRESHOLD else f"People: {person_count}" | |
| cv2.putText(frame, alert_text, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, | |
| (0, 0, 255) if person_count > CROWD_THRESHOLD else (0, 255, 0), 2) | |
| out.write(frame) | |
| cap.release() | |
| out.release() | |
| if frame_count == 0 or not os.path.exists(output_path): | |
| logger.error(f"Processing failed: Frames processed: {frame_count}, Output exists: {os.path.exists(output_path)}") | |
| raise ValueError("❌ Processing failed: No frames processed or output not created") | |
| logger.info(f"Crowd detection completed, output saved to: {output_path}") | |
| return output_path | |
| except Exception as e: | |
| logger.error(f"Error in detect_crowd: {str(e)}") | |
| raise | |
| class PeopleTracking: | |
| def __init__(self, yolo_model_path="yolov8n.pt"): | |
| logger.info(f"Initializing PeopleTracking with model: {yolo_model_path}") | |
| self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| if not os.path.exists(yolo_model_path): | |
| self.model = YOLO("yolov8n.pt") | |
| self.model.save(yolo_model_path) | |
| else: | |
| self.model = YOLO(yolo_model_path) | |
| self.model.to(self.device) | |
| def track_people(self, video_path): | |
| logger.info(f"Tracking people in video: {video_path}") | |
| try: | |
| cap = cv2.VideoCapture(video_path) | |
| if not cap.isOpened(): | |
| raise ValueError(f"❌ Failed to open video: {video_path}") | |
| fps = int(cap.get(cv2.CAP_PROP_FPS)) | |
| width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) | |
| height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) | |
| output_path = "output_tracking.mp4" | |
| out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (width, height)) | |
| if not out.isOpened(): | |
| cap.release() | |
| raise ValueError(f"❌ Failed to initialize video writer") | |
| while cap.isOpened(): | |
| ret, frame = cap.read() | |
| if not ret: | |
| break | |
| results = self.model.track(frame, persist=True) | |
| for result in results: | |
| boxes = result.boxes.xyxy.cpu().numpy() | |
| classes = result.boxes.cls.cpu().numpy() | |
| ids = result.boxes.id.cpu().numpy() if result.boxes.id is not None else np.arange(len(boxes)) | |
| for box, cls, obj_id in zip(boxes, classes, ids): | |
| if int(cls) == 0: | |
| x1, y1, x2, y2 = map(int, box) | |
| cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 2) | |
| cv2.putText(frame, f"ID {int(obj_id)}", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2) | |
| out.write(frame) | |
| cap.release() | |
| out.release() | |
| if not os.path.exists(output_path): | |
| raise ValueError("❌ Processing failed") | |
| return output_path | |
| except Exception as e: | |
| logger.error(f"Error in track_people: {str(e)}") | |
| raise | |
| class FallDetection: | |
| def __init__(self, yolo_model_path="yolov8l.pt"): | |
| logger.info(f"Initializing FallDetection with model: {yolo_model_path}") | |
| self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| if not os.path.exists(yolo_model_path): | |
| self.model = YOLO("yolov8l.pt") | |
| self.model.save(yolo_model_path) | |
| else: | |
| self.model = YOLO(yolo_model_path) | |
| self.model.to(self.device) | |
| def detect_fall(self, video_path): | |
| logger.info(f"Detecting falls in video: {video_path}") | |
| try: | |
| cap = cv2.VideoCapture(video_path) | |
| if not cap.isOpened(): | |
| raise ValueError(f"❌ Failed to open video: {video_path}") | |
| fps = int(cap.get(cv2.CAP_PROP_FPS)) | |
| width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) | |
| height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) | |
| output_path = "output_fall.mp4" | |
| out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (width, height)) | |
| if not out.isOpened(): | |
| cap.release() | |
| raise ValueError(f"❌ Failed to initialize video writer") | |
| while cap.isOpened(): | |
| ret, frame = cap.read() | |
| if not ret: | |
| break | |
| results = self.model(frame) | |
| for result in results: | |
| boxes = result.boxes.xyxy.cpu().numpy() | |
| classes = result.boxes.cls.cpu().numpy() | |
| for box, cls in zip(boxes, classes): | |
| if int(cls) == 0: | |
| x1, y1, x2, y2 = map(int, box) | |
| width = x2 - x1 | |
| height = y2 - y1 | |
| aspect_ratio = width / height if height > 0 else float('inf') | |
| if aspect_ratio > 0.55: | |
| color = (0, 0, 255) | |
| label = "FALL DETECTED" | |
| else: | |
| color = (0, 255, 0) | |
| label = "Standing" | |
| cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2) | |
| cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2) | |
| out.write(frame) | |
| cap.release() | |
| out.release() | |
| if not os.path.exists(output_path): | |
| raise ValueError("❌ Processing failed") | |
| return output_path | |
| except Exception as e: | |
| logger.error(f"Error in detect_fall: {str(e)}") | |
| raise | |
| class FightDetection: | |
| def __init__(self, yolo_model_path="yolov8n-pose.pt"): | |
| logger.info(f"Initializing FightDetection with model: {yolo_model_path}") | |
| self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| if not os.path.exists(yolo_model_path): | |
| self.model = YOLO("yolov8n-pose.pt") | |
| self.model.save(yolo_model_path) | |
| else: | |
| self.model = YOLO(yolo_model_path) | |
| self.model.to(self.device) | |
| def detect_fight(self, video_path): | |
| logger.info(f"Detecting fights in video: {video_path}") | |
| try: | |
| cap = cv2.VideoCapture(video_path) | |
| if not cap.isOpened(): | |
| raise ValueError(f"❌ Failed to open video: {video_path}") | |
| fps = int(cap.get(cv2.CAP_PROP_FPS)) | |
| width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) | |
| height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) | |
| output_path = "output_fight.mp4" | |
| out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (width, height)) | |
| if not out.isOpened(): | |
| cap.release() | |
| raise ValueError(f"❌ Failed to initialize video writer") | |
| while cap.isOpened(): | |
| ret, frame = cap.read() | |
| if not ret: | |
| break | |
| results = self.model.track(frame, persist=True) | |
| fight_detected = False | |
| person_count = 0 | |
| for result in results: | |
| keypoints = result.keypoints.xy.cpu().numpy() if result.keypoints else [] | |
| boxes = result.boxes.xyxy.cpu().numpy() if result.boxes else [] | |
| classes = result.boxes.cls.cpu().numpy() if result.boxes else [] | |
| for box, kp, cls in zip(boxes, keypoints, classes): | |
| if int(cls) == 0: | |
| person_count += 1 | |
| x1, y1, x2, y2 = map(int, box) | |
| if len(kp) > 7 and (kp[5][1] < y1 + (y2 - y1) * 0.3 or kp[7][1] < y1 + (y2 - y1) * 0.3): | |
| fight_detected = True | |
| cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255) if fight_detected else (0, 255, 0), 2) | |
| label = "FIGHT DETECTED" if fight_detected else "Person" | |
| cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, | |
| (0, 0, 255) if fight_detected else (0, 255, 0), 2) | |
| if fight_detected and person_count > 1: | |
| cv2.putText(frame, "FIGHT ALERT!", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2) | |
| out.write(frame) | |
| cap.release() | |
| out.release() | |
| if not os.path.exists(output_path): | |
| raise ValueError("❌ Processing failed") | |
| return output_path | |
| except Exception as e: | |
| logger.error(f"Error in detect_fight: {str(e)}") | |
| raise | |
| # Unified processing function with status output | |
| def process_video(feature, video): | |
| detectors = { | |
| "Crowd Detection": CrowdDetection, | |
| "People Tracking": PeopleTracking, | |
| "Fall Detection": FallDetection, | |
| "Fight Detection": FightDetection | |
| } | |
| try: | |
| detector = detectors[feature]() | |
| method_name = feature.lower().replace(" ", "_") | |
| output_path = getattr(detector, method_name)(video) | |
| return f"{feature} completed successfully", output_path | |
| except Exception as e: | |
| logger.error(f"Error processing video with {feature}: {str(e)}") | |
| return f"Error: {str(e)}", None | |
| # Gradio Interface with dual outputs | |
| interface = gr.Interface( | |
| fn=process_video, | |
| inputs=[ | |
| gr.Dropdown(choices=["Crowd Detection", "People Tracking", "Fall Detection", "Fight Detection"], label="Select Feature"), | |
| gr.Video(label="Upload Video") | |
| ], | |
| outputs=[ | |
| gr.Textbox(label="Status"), | |
| gr.Video(label="Processed Video") | |
| ], | |
| title="YOLOv8 Multitask Video Processing", | |
| description="Select a feature to process your video: Crowd Detection, People Tracking, Fall Detection, or Fight Detection." | |
| ) | |
| if __name__ == "__main__": | |
| interface.launch(debug=True) |