import gradio as gr
import cv2
import torch
from sahi import AutoDetectionModel
from sahi.predict import get_sliced_prediction
from motpy import Detection as MotpyDetection, MultiObjectTracker
import tempfile

# COCO class names (YOLOv8 default)
COCO_CLASSES = [
    'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
    'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
    'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
    'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
    'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
    'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
    'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
    'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
    'hair drier', 'toothbrush'
]


model_path = "./yolo11n.pt"
detection_model = AutoDetectionModel.from_pretrained(
    model_type='yolov8',
    model_path=model_path,
    confidence_threshold=0.3,
    device='cpu'  # Force CPU usage
)

def track_objects(video_path):
    # Setup video processing
    cap = cv2.VideoCapture(video_path)
    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    fps = cap.get(cv2.CAP_PROP_FPS)
    
    output_file = tempfile.NamedTemporaryFile(suffix='.mp4', delete=False)
    output_path = output_file.name
    
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))

    tracker = MultiObjectTracker(
        dt=0.1,
        model_spec={
            'order_pos': 1, 'dim_pos': 2,
            'order_size': 0, 'dim_size': 2,
            'q_var_pos': 5000., 'r_var_pos': 0.1
        }
    )
    
    frame_count = 0
    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break
        
        rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        result = get_sliced_prediction(
            rgb_frame,
            detection_model,
            slice_height=512,
            slice_width=512,
            overlap_height_ratio=0.2,
            overlap_width_ratio=0.2
        )
        
        detections = [
            MotpyDetection(
                box=[obj.bbox.minx, obj.bbox.miny, obj.bbox.maxx, obj.bbox.maxy],
                score=obj.score.value,
                class_id=obj.category.id
            )
            for obj in result.object_prediction_list
        ]
        
        tracker.step(detections)
        tracks = tracker.active_tracks()
        
        for track in tracks:
            x1, y1, x2, y2 = map(int, track.box)
            track_id = track.id
            class_id = track.class_id if track.class_id is not None else -1
            class_name = COCO_CLASSES[class_id] if 0 <= class_id < len(COCO_CLASSES) else str(class_id)
            
            cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
            cv2.putText(frame, f'{class_name} {track_id}', (x1, y1 - 10),
                       cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
        
        out.write(frame)
    
    cap.release()
    out.release()
    return output_path

def process_video(video):
    output_path = track_objects(video)
    return output_path

interface = gr.Interface(
    fn=process_video,
    inputs=gr.Video(label="Input Video"),
    outputs=[
        gr.Video(label="Processed Video"),
        gr.File(label="Download Processed Video")
    ],
    title="SAHI Video Object Tracker",
    description="Object detection and tracking using SAHI and YOLOv11."
)

if __name__ == "__main__":
    interface.launch()