backend/tracker.py

import cv2
import numpy as np
from collections import defaultdict

class VideoTracker:
    def __init__(self, video_path, detection_method='background'):
        self.video_path = video_path
        self.detection_method = detection_method
        self.trajectories = defaultdict(list)
        
    def detect_objects_background(self, frame, bg_subtractor):
        """Detect moving objects using background subtraction"""
        fg_mask = bg_subtractor.apply(frame)
        fg_mask = cv2.GaussianBlur(fg_mask, (21, 21), 0)
        _, thresh = cv2.threshold(fg_mask, 25, 255, cv2.THRESH_BINARY)
        
        # Find contours
        contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        
        detections = []
        for contour in contours:
            area = cv2.contourArea(contour)
            if area > 500:  # Filter small objects
                x, y, w, h = cv2.boundingRect(contour)
                center_x = x + w // 2
                center_y = y + h // 2
                detections.append({
                    'bbox': [x, y, w, h],
                    'center': [center_x, center_y],
                    'area': area
                })
        
        return detections
    
    def detect_objects_color(self, frame, color_range):
        """Detect objects by color (example: detecting red objects)"""
        hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
        
        # Default: detect red objects
        lower = np.array([0, 100, 100])
        upper = np.array([10, 255, 255])
        
        mask = cv2.inRange(hsv, lower, upper)
        mask = cv2.GaussianBlur(mask, (21, 21), 0)
        
        contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        
        detections = []
        for contour in contours:
            area = cv2.contourArea(contour)
            if area > 300:
                x, y, w, h = cv2.boundingRect(contour)
                center_x = x + w // 2
                center_y = y + h // 2
                detections.append({
                    'bbox': [x, y, w, h],
                    'center': [center_x, center_y],
                    'area': area
                })
        
        return detections
    
    def match_detections(self, prev_objects, curr_detections, max_distance=50):
        """Match current detections with previous objects"""
        matched = []
        unmatched_detections = list(range(len(curr_detections)))
        
        for obj_id, prev_pos in prev_objects.items():
            if not prev_pos:
                continue
            
            last_pos = prev_pos[-1]['center']
            min_dist = float('inf')
            best_match = None
            
            for idx in unmatched_detections:
                curr_pos = curr_detections[idx]['center']
                dist = np.sqrt((curr_pos[0] - last_pos[0])**2 + 
                             (curr_pos[1] - last_pos[1])**2)
                
                if dist < min_dist and dist < max_distance:
                    min_dist = dist
                    best_match = idx
            
            if best_match is not None:
                matched.append((obj_id, best_match))
                unmatched_detections.remove(best_match)
        
        return matched, unmatched_detections
    
    def process_video(self):
        """Process video and extract trajectories"""
        cap = cv2.VideoCapture(self.video_path)
        
        if not cap.isOpened():
            raise Exception("Could not open video file")
        
        fps = cap.get(cv2.CAP_PROP_FPS)
        frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
        
        bg_subtractor = cv2.createBackgroundSubtractorMOG2(history=500, varThreshold=16)
        
        tracked_objects = {}
        next_id = 0
        frame_idx = 0
        
        while True:
            ret, frame = cap.read()
            if not ret:
                break
            
            # Detect objects
            if self.detection_method == 'background':
                detections = self.detect_objects_background(frame, bg_subtractor)
            else:
                detections = self.detect_objects_color(frame, None)
            
            # Match with existing objects
            matched, unmatched = self.match_detections(tracked_objects, detections)
            
            # Update matched objects
            for obj_id, det_idx in matched:
                detection = detections[det_idx]
                tracked_objects[obj_id].append({
                    'frame': frame_idx,
                    'time': frame_idx / fps,
                    'center': detection['center'],
                    'bbox': detection['bbox']
                })
            
            # Create new objects for unmatched detections
            for det_idx in unmatched:
                detection = detections[det_idx]
                tracked_objects[next_id] = [{
                    'frame': frame_idx,
                    'time': frame_idx / fps,
                    'center': detection['center'],
                    'bbox': detection['bbox']
                }]
                next_id += 1
            
            frame_idx += 1
        
        cap.release()
        
        # Convert to 3D trajectories (add z-axis based on object size/distance)
        trajectories = []
        for obj_id, points in tracked_objects.items():
            if len(points) > 5:  # Filter short trajectories
                trajectory = {
                    'id': obj_id,
                    'points': []
                }
                
                for point in points:
                    # Normalize coordinates to [-1, 1]
                    x = (point['center'][0] / width) * 2 - 1
                    y = -((point['center'][1] / height) * 2 - 1)  # Flip y
                    
                    # Estimate z based on object size (larger = closer)
                    bbox_area = point['bbox'][2] * point['bbox'][3]
                    z = (bbox_area / (width * height)) * 2 - 0.5
                    
                    trajectory['points'].append({
                        'x': x,
                        'y': y,
                        'z': z,
                        'time': point['time'],
                        'frame': point['frame']
                    })
                
                trajectories.append(trajectory)
        
        return {
            'trajectories': trajectories,
            'metadata': {
                'fps': fps,
                'frame_count': frame_count,
                'width': width,
                'height': height,
                'num_objects': len(trajectories)
            }
        }