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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 | |
| class CrowdDetection: | |
| def __init__(self, model_path="yolov8n.pt"): | |
| self.model_path = model_path | |
| def crowd_detection(self, video_path): | |
| try: | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| if not os.path.exists(self.model_path): | |
| model = YOLO("yolov8n.pt") | |
| model.save(self.model_path) | |
| else: | |
| model = YOLO(self.model_path) | |
| model.to(device) | |
| 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_crowd.mp4" | |
| fourcc = cv2.VideoWriter_fourcc(*"mp4v") | |
| out = cv2.VideoWriter(output_path, fourcc, fps, (width, height)) | |
| if not out.isOpened(): | |
| cap.release() | |
| 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 = model(frame) | |
| person_count = sum(1 for result in results for cls in result.boxes.cls.cpu().numpy() if int(cls) == 0) | |
| 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): | |
| raise ValueError("❌ Processing failed: No frames processed or output not created") | |
| return output_path | |
| except Exception as e: | |
| raise ValueError(f"Error in crowd_detection: {str(e)}") | |
| class PeopleTracking: | |
| def __init__(self, yolo_model_path="yolov8n.pt"): | |
| self.model_path = yolo_model_path | |
| def people_tracking(self, video_path): | |
| try: | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| if not os.path.exists(self.model_path): | |
| model = YOLO("yolov8n.pt") | |
| model.save(self.model_path) | |
| else: | |
| model = YOLO(self.model_path) | |
| model.to(device) | |
| 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 = 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: | |
| raise ValueError(f"Error in people_tracking: {str(e)}") | |
| class FallDetection: | |
| def __init__(self, yolo_model_path="yolov8l.pt"): | |
| self.model_path = yolo_model_path | |
| def fall_detection(self, video_path): | |
| try: | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| if not os.path.exists(self.model_path): | |
| model = YOLO("yolov8l.pt") | |
| model.save(self.model_path) | |
| else: | |
| model = YOLO(self.model_path) | |
| model.to(device) | |
| 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 = 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: | |
| raise ValueError(f"Error in fall_detection: {str(e)}") | |
| class FightDetection: | |
| def __init__(self, yolo_model_path="yolov8n-pose.pt"): | |
| self.model_path = yolo_model_path | |
| def fight_detection(self, video_path): | |
| try: | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| if not os.path.exists(self.model_path): | |
| model = YOLO("yolov8n-pose.pt") | |
| model.save(self.model_path) | |
| else: | |
| model = YOLO(self.model_path) | |
| model.to(device) | |
| 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 = 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: | |
| raise ValueError(f"Error in fight_detection: {str(e)}") | |
| class IntrusionDetection: | |
| def __init__(self, model_path="yolov8n.pt", max_intrusion_time=300, iou_threshold=0.5, conf_threshold=0.5): | |
| self.model_path = model_path | |
| self.max_intrusion_time = max_intrusion_time | |
| self.iou_threshold = iou_threshold | |
| self.conf_threshold = conf_threshold | |
| def detect_intrusion(self, video_path): | |
| try: | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| if not os.path.exists(self.model_path): | |
| model = YOLO("yolov8n.pt") | |
| model.save(self.model_path) | |
| else: | |
| model = YOLO(self.model_path) | |
| model.to(device) | |
| 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_intrusion.mp4" | |
| fourcc = cv2.VideoWriter_fourcc(*"mp4v") | |
| out = cv2.VideoWriter(output_path, fourcc, fps, (width, height)) | |
| if not out.isOpened(): | |
| cap.release() | |
| raise ValueError(f"❌ Failed to initialize video writer") | |
| frame_count = 0 | |
| while cap.isOpened(): | |
| ret, frame = cap.read() | |
| if not ret: | |
| break | |
| frame_count += 1 | |
| results = model(frame) | |
| for result in results: | |
| boxes = result.boxes.xyxy.cpu().numpy() | |
| classes = result.boxes.cls.cpu().numpy() | |
| confidences = result.boxes.conf.cpu().numpy() | |
| for box, cls, conf in zip(boxes, classes, confidences): | |
| if int(cls) == 0 and conf > self.conf_threshold: # Person class with confidence filter | |
| x1, y1, x2, y2 = map(int, box) | |
| label = "Intruder" | |
| color = (0, 0, 255) | |
| 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 frame_count == 0 or not os.path.exists(output_path): | |
| raise ValueError("❌ Processing failed: No frames processed or output not created") | |
| return output_path | |
| except Exception as e: | |
| raise ValueError(f"Error in detect_intrusion: {str(e)}") | |
| class LoiteringDetection: | |
| def __init__(self, model_path="yolov8n.pt", loitering_threshold=10, conf_threshold=0.5): | |
| self.model_path = model_path | |
| self.loitering_threshold = loitering_threshold | |
| self.conf_threshold = conf_threshold | |
| self.entry_time = {} | |
| self.area = [(153, 850), (139, 535), (239, 497), (291, 857)] | |
| def load_model(self): | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model = YOLO(self.model_path).to(device) | |
| return model | |
| def calculate_center(self, box): | |
| x1, y1, x2, y2 = box | |
| return int((x1 + x2) / 2), int((y1 + y2) / 2) | |
| def track_time(self, id, frame_duration): | |
| if id not in self.entry_time: | |
| self.entry_time[id] = {'duration': 0, 'loitering': False} | |
| else: | |
| self.entry_time[id]['duration'] += frame_duration | |
| if self.entry_time[id]['duration'] > self.loitering_threshold: | |
| self.entry_time[id]['loitering'] = True | |
| def loitering_detect(self, video_path): #edited from detect_loitering | |
| try: | |
| model = self.load_model() | |
| 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)) or 30 | |
| width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) | |
| height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) | |
| output_path = "output_loitering.mp4" | |
| out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (width, height)) | |
| frame_duration = 1 / fps | |
| frame_count = 0 | |
| while cap.isOpened(): | |
| ret, frame = cap.read() | |
| if not ret: | |
| break | |
| frame_count += 1 | |
| results = model.track(frame, conf=self.conf_threshold, iou=0.1, classes=[0], persist=True) | |
| boxes = results[0].boxes.xyxy.cpu().tolist() | |
| ids = results[0].boxes.id.cpu().tolist() | |
| ids_in_area = [] | |
| for box, id in zip(boxes, ids): | |
| center = self.calculate_center(box) | |
| if cv2.pointPolygonTest(np.array(self.area, np.int32), center, False) >= 0: | |
| ids_in_area.append(id) | |
| self.track_time(id, frame_duration) | |
| for id in ids_in_area: | |
| color = (0, 0, 255) if self.entry_time.get(id, {}).get('loitering', False) else (0, 255, 0) | |
| cv2.putText(frame, f"ID {id}, Time: {self.entry_time[id]['duration']:.1f}s", (15, 30 + id * 30), | |
| cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2) | |
| pts = np.array(self.area, np.int32).reshape((-1, 1, 2)) | |
| color = (0, 0, 255) if any(self.entry_time.get(id, {}).get('loitering', False) for id in ids_in_area) else (152, 251, 152) | |
| cv2.polylines(frame, [pts], isClosed=True, color=color, thickness=3) | |
| out.write(frame) | |
| cap.release() | |
| out.release() | |
| if frame_count == 0 or not os.path.exists(output_path): | |
| raise ValueError("❌ Processing failed: No frames processed or output not created") | |
| return output_path | |
| except Exception as e: | |
| raise ValueError(f"Error in detect_loitering: {str(e)}") | |
| # Unified processing function with status output | |
| def process_video(feature, video): | |
| detectors = { | |
| "Crowd Detection": CrowdDetection, | |
| "People Tracking": PeopleTracking, | |
| "Fall Detection": FallDetection, | |
| "Fight Detection": FightDetection, | |
| "Intrusion Detection" : IntrusionDetection, | |
| "Loitering Detection" : LoiteringDetection | |
| } | |
| try: | |
| detector = detectors[feature]() | |
| method_name = feature.lower().replace(" ", "_").replace("detection", "detect") # Ensures correct method name | |
| output_path = getattr(detector, method_name)(video) | |
| return f"{feature} completed successfully", output_path | |
| except Exception as 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", "Intrusion Detection", "Loitering 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) |