src/object_detection.py

from ultralytics import YOLO
from typing import List, Dict
import cv2 

class ObjectDetector:
    def __init__(self, model_name: str = 'yolov8n.pt', confidence_threshold: float = 0.5):
        print(f"Loading YOLO model: {model_name}")
        self.model = YOLO(model_name)
        self.confidence_threshold = confidence_threshold
        print(f"Model Loaded")
    
    def detect_objects(self, image_path: str) -> List[Dict]:
        results = self.model(image_path, conf=self.confidence_threshold, verbose=False)
        detections = []
        for result in results:
            boxes = result.boxes
            for box in boxes:
                detection = {
                    'class': result.names[int(box.cls[0])],
                    'confidence': float(box.conf[0]),
                    'bbox': box.xyxy[0].cpu().numpy().tolist()
                }
                detections.append(detection)
        return detections
    
    def detect_in_frames(self, frame_paths: List[str]) -> Dict[str, List[Dict]]:
        results = {}
        print(f"Running object detection on {len(frame_paths)} frames")
        for i, frame_path in enumerate(frame_paths):
            detections = self.detect_objects(frame_path)
            results[frame_path] = detections
            if (i + 1) % 10 == 0:  # Print every 10 frames to reduce clutter
                print(f"Frame {i+1}/{len(frame_paths)}: {len(detections)} objects detected")
        return results
    


def visualize_detections(image_path: str, detections: List[Dict], output_path: str, show_track_id: bool = True) -> None:
    image = cv2.imread(image_path)
    if image is None:
        print(f"Warning: Could not read image {image_path}")
        return

    for det in detections:
        x1, y1, x2, y2 = [int(coord) for coord in det['bbox']]
        
        # Color: Green for detection, Orange if it has a track ID
        color = (0, 255, 0) # Green
        if 'track_id' in det and det['track_id'] != -1:
            color = (0, 165, 255) # Orange-ish

        cv2.rectangle(image, (x1, y1), (x2, y2), color=color, thickness=3)
        
        if show_track_id and 'track_id' in det and det['track_id'] != -1:
            label = f"ID:{det['track_id']} {det['class']} {det['confidence']:.2f}"
        else:
            label = f"{det['class']} {det['confidence']:.2f}"
            
        (label_width, label_height), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)
        cv2.rectangle(image, (x1, y1 - label_height - 10), (x1 + label_width, y1), color, -1)
        cv2.putText(image, label, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 0), 2)
        
    cv2.imwrite(output_path, image)
    print(f"Saved Visualization to {output_path}")