import argparse
import numpy as np
import cv2
from norfair import Detection, Tracker
from detector.utils import detect_plates, detect_chars, imcrop, draw_text
from threading import Thread

DISTANCE_THRESHOLD_BBOX: float = 0.7
DISTANCE_THRESHOLD_CENTROID: int = 50
MAX_DISTANCE: int = 10000
HIT_COUNTER_MAX: int = 30

# SOURCE = 'rtsp://admin:PRE001@10.0.0.194:554/profile1'
# SOURCE = './costarica/videos/video4.mp4'
SOURCE = '/home/prevantec/Downloads/video.mp4'


def yolo_to_norfair(yolo_detections):
    norfair_detections = []
    detections_as_xyxy = yolo_detections.xyxy[0]
    for detection_as_xyxy in detections_as_xyxy:
        bbox = np.array(
            [
                [detection_as_xyxy[0].item(), detection_as_xyxy[1].item()],
                [detection_as_xyxy[2].item(), detection_as_xyxy[3].item()],
            ]
        )
        scores = np.array(
            [detection_as_xyxy[4].item(), detection_as_xyxy[4].item()]
        )
        norfair_detections.append(
            Detection(
                points=bbox, scores=scores, label=int(detection_as_xyxy[-1].item())
            )
        )
    return norfair_detections


def cut_top_bottom(image, top=0.1, bottom=0):
    height = image.shape[0]
    return image[int(height * top):int(height * (1 - bottom)), :]


def run(source=SOURCE):
    tracker = Tracker(
        distance_function="iou_opt",
        distance_threshold=DISTANCE_THRESHOLD_BBOX,
        hit_counter_max=HIT_COUNTER_MAX,
    )
    cap = cv2.VideoCapture(source)
    fps = cap.get(cv2.CAP_PROP_FPS)
    image_size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
    plates = {}
    final_video = cv2.VideoWriter('output.mp4', cv2.VideoWriter_fourcc(*'mp4v'), fps, image_size)
    while cap.isOpened():
        try:
            ret, frame = cap.read()
            if not ret:
                break
            # frame = cut_top_bottom(frame)
        except Exception as e:
            print(e)
            continue
        yolo_detections = detect_plates(frame)
        detections = yolo_to_norfair(yolo_detections)
        tracked_objects = tracker.update(detections=detections)
        for obj in tracked_objects:
            if obj.last_detection is not None and obj.age > 10:
                bbox = obj.last_detection.points
                bbox = int(bbox[0][0]), int(bbox[0][1]), int(bbox[1][0]), int(bbox[1][1])
                area = (bbox[2] - bbox[0]) * (bbox[3] - bbox[1])
                if obj.id not in plates.keys() and area > 1000:
                    crop = imcrop(frame, bbox)
                    text = detect_chars(crop)
                    plates[obj.id] = text
                    # thread = Thread(target=send_request, args=(frame_copy, text, bbox))
                    # thread.start()
                if obj.id in plates.keys():
                    cv2.rectangle(
                        frame,
                        (bbox[0], bbox[1]),
                        (bbox[2], bbox[3]),
                        (0, 255, 0),
                        2,
                    )
                    draw_text(frame, plates[obj.id], (bbox[0], bbox[1]))
                    cv2.putText(
                        frame,
                        plates[obj.id],
                        (bbox[0], bbox[1]),
                        cv2.FONT_HERSHEY_SIMPLEX,
                        1,
                        (0, 255, 0),
                        2,
                    )
        cv2.imshow("frame", frame)
        final_video.write(frame)
        if cv2.waitKey(1) & 0xFF == ord("q"):
            break
    cap.release()
    final_video.release()


def parse_opt():
    parser = argparse.ArgumentParser()
    opt = parser.parse_args()
    return opt


def main(opt):
    run(**vars(opt))


if __name__ == "__main__":
    opt = parse_opt()
    main(opt)