import cv2
import numpy as np
from pathlib import Path
from ultralytics import YOLO
from collections import defaultdict
import time
import json


# Configuration
INPUT_VIDEOS_DIR = "datasets/usplf/tracking/pen2_cam2"  # Directory containing input videos
OUTPUT_DIR = "datasets/usplf/tracking/detected_json/pen2_cam2"
# POLYGON_VERTICES = np.array([[89,139], [325,57], [594, 6], [1128, 29], [1129, 364], [1031, 717],[509, 653],[287, 583], [100, 506],[74, 321]])  # Pen2 Cam1 polygon coordinates
POLYGON_VERTICES = np.array([[179, 3], [844, 8], [1151, 137], [1151, 316], [1135, 486], [995, 531],[801, 592],[278, 711], [167, 325]])  # Pen2 Cam2 polygon coordinates
CONF_THRESH = 0.55  # Confidence threshold
# TRACKER_CONFIG = "custom_bytetrack.yaml"  # Built-in tracker config

# Visualization settings
SHOW_MASK_OVERLAY = True
MASK_ALPHA = 0.3  # Transparency for polygon mask
BOX_COLOR = (0, 255, 0)  # Green
TEXT_COLOR = (255, 255, 255)  # White
FONT_SCALE = 0.8
THICKNESS = 2

def create_mask(frame_shape):
    mask = np.zeros(frame_shape[:2], dtype=np.uint8)
    cv2.fillPoly(mask, [POLYGON_VERTICES], 255)
    return mask

def draw_visuals_detection(frame, mask, detections, frame_count, fps):
    if SHOW_MASK_OVERLAY:
        overlay = frame.copy()
        cv2.fillPoly(overlay, [POLYGON_VERTICES], (0, 100, 0))
        cv2.addWeighted(overlay, MASK_ALPHA, frame, 1 - MASK_ALPHA, 0, frame)

    for det in detections:
        x, y, w, h = det['bbox']
        conf = det['confidence']
        
        # Draw bounding box
        cv2.rectangle(
            frame,
            (int(x - w / 2), int(y - h / 2)),
            (int(x + w / 2), int(y + h / 2)),
            BOX_COLOR, THICKNESS
        )
        
        # Display confidence
        cv2.putText(frame, f"{conf:.2f}", 
                    (int(x - w / 2), int(y - h / 2) - 10),
                    cv2.FONT_HERSHEY_SIMPLEX, FONT_SCALE, TEXT_COLOR, THICKNESS)

    # Overlay info
    cv2.putText(frame, f"Frame: {frame_count}", (10, 30),
                cv2.FONT_HERSHEY_SIMPLEX, FONT_SCALE, TEXT_COLOR, THICKNESS)
    cv2.putText(frame, f"FPS: {fps:.1f}", (10, 60),
                cv2.FONT_HERSHEY_SIMPLEX, FONT_SCALE, TEXT_COLOR, THICKNESS)
    cv2.putText(frame, f"Pigs: {len(detections)}", (10, 90),
                cv2.FONT_HERSHEY_SIMPLEX, FONT_SCALE, TEXT_COLOR, THICKNESS)
    
    return frame


def process_video(video_path, output_path):
    model = YOLO("trained_model_weight/pig_detect/yolo/pig_detect_pen2_best.pt")
    cap = cv2.VideoCapture(str(video_path))
    frame_count = 0
    results_list = []
    fps_history = []
    
    # Video properties
    frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    mask = create_mask((frame_height, frame_width))
    
    win_name = f"Pig Detection - {video_path.name}"
    cv2.namedWindow(win_name, cv2.WINDOW_NORMAL)
    
    while cap.isOpened():
        start_time = time.time()
        success, frame = cap.read()
        if not success:
            break

        masked_frame = cv2.bitwise_and(frame, frame, mask=mask)
        
        # Detection instead of tracking
        results = model.predict(
            masked_frame,
            conf=CONF_THRESH,
            verbose=False
        )

        detections = []
        if results[0].boxes is not None:
            boxes = results[0].boxes.xywh.cpu().numpy()
            scores = results[0].boxes.conf.cpu().numpy()
            
            for box, score in zip(boxes, scores):
                x, y, w, h = box
                detections.append({"confidence": float(score), "bbox": [x, y, w, h]})
                results_list.append({
                    "frame_id": frame_count,
                    "frame_width": frame_width,
                    "frame_height": frame_height,
                    "confidence": float(score),
                    "bbox": [float(x), float(y), float(w), float(h)],
                    "area": float(w * h)
                })

        # FPS calculation
        processing_time = time.time() - start_time
        fps = 1 / processing_time
        fps_history.append(fps)
        if len(fps_history) > 10:
            fps = np.mean(fps_history[-10:])
        
        # Draw visuals
        display_frame = draw_visuals_detection(frame.copy(), mask, detections, frame_count, fps)
        cv2.imshow(win_name, display_frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

        frame_count += 1

    cap.release()
    cv2.destroyWindow(win_name)

    # Save JSON
    output_file = output_path / f"{video_path.stem}_detection.json"
    with open(output_file, 'w') as f:
        json.dump(results_list, f, indent=2)

if __name__ == "__main__":
    input_dir = Path(INPUT_VIDEOS_DIR)
    output_dir = Path(OUTPUT_DIR)
    output_dir.mkdir(exist_ok=True)

    for video_file in input_dir.glob("*.mp4"):
        print(f"Processing {video_file.name}...")
        process_video(video_file, output_dir)
    cv2.destroyAllWindows()