import random
from PIL import ImageDraw, Image
from huggingface_hub import hf_hub_download
from ultralytics import YOLO


def plot_one_box(x, img, color=None, label=None, line_thickness=None):
    """
    Helper Functions for Plotting BBoxes
    :param x:
    :param img:
    :param color:
    :param label:
    :param line_thickness:
    :return:
    """
    width, height = img.size
    tl = line_thickness or round(0.002 * (width + height) / 2) + 1  # line/font thickness
    color = color or (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
    c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
    img_draw = ImageDraw.Draw(img)
    img_draw.rectangle((c1[0], c1[1], c2[0], c2[1]), outline=color, width=tl)
    if label:
        tf = max(tl - 1, 1)  # font thickness
        x1, y1, x2, y2 = img_draw.textbbox(c1, label, stroke_width=tf)
        img_draw.rectangle((x1, y1, x2, y2), fill=color)
        img_draw.text((x1, y1), label, fill=(255, 255, 255))


def add_bboxes(pil_img, result, confidence=0.6):
    """
    Plotting Bounding Box on img
    :param pil_img:
    :param result:
    :param confidence:
    :return:
    """
    for box in result.boxes:
        [cl] = box.cls.tolist()
        [conf] = box.conf.tolist()
        if conf < confidence:
            continue
        [rect] = box.xyxy.tolist()
        text = f'{result.names[cl]}: {conf: 0.2f}'
        plot_one_box(x=rect, img=pil_img, label=text)

    return pil_img


class YoloModel:
    def __init__(self, repo_name: str, file_name: str):
        weight_file = YoloModel.download_weight_file(repo_name, file_name)
        self.model = YOLO(weight_file)

    @staticmethod
    def download_weight_file(repo_name: str, file_name: str):
        return hf_hub_download(repo_name, file_name)

    def detect(self, im):
        return self.model(source=im)

    def preview_detect(self, im, confidence):
        results = self.model(source=im)
        res_img = im
        for result in results:
            res_img = add_bboxes(res_img, result, confidence)
        return res_img


def test():
    model = YoloModel("SHOU-ISD/fire-and-smoke", "yolov8n.pt")
    im = Image.open("./tests/fire1.jpg")
    results = model.model(source=im)
    for result in results:
        im = add_bboxes(im, result, confidence=0.1)
        print(result.boxes)


def argument_parser():
    """
    Argument Parser
    :return: args
    """
    import argparse
    parser = argparse.ArgumentParser(description='Help for YoloModel')
    parser.add_argument('--test', '-t', action='store_true', help='Run test')
    # list of repo_name&file_name
    parser.add_argument('--weight_files', '-w', nargs='+', help='List of weight files')
    return parser.parse_args()


def pre_cache_weight_files(weight_files: list[str]):
    """
    Pre-cache weight files
    :return: None
    """
    for weight_file in weight_files:
        weight_file = weight_file.split(":")
        YoloModel.download_weight_file(weight_file[0], weight_file[1])


if __name__ == '__main__':
    args = argument_parser()
    if args.test:
        test()
    else:
        pre_cache_weight_files(args.weight_files)