app.py

import gradio as gr
import torch
import cv2
import numpy as np
import time
from ultralytics import YOLO
import spaces
import os
import logging
@spaces.GPU

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)