import argparse
import time

from models import *
from utils.datasets import *
from utils.utils import *

from utils import torch_utils


def detect(
        cfg,
        weights,
        images_path,
        output='output',
        img_size=416,
        conf_thres=0.3,
        nms_thres=0.45,
        save_txt=False,
        save_images=True,
):
    device = torch_utils.select_device()

    os.system('rm -rf ' + output)
    os.makedirs(output, exist_ok=True)

    # Load model
    model = Darknet(cfg, img_size)

    if weights.endswith('.pt'):  # pytorch format
        if weights.endswith('weights/yolov3.pt') and not os.path.isfile(weights):
            os.system('wget https://storage.googleapis.com/ultralytics/yolov3.pt -O ' + weights)
        checkpoint = torch.load(weights, map_location='cpu')
        model.load_state_dict(checkpoint['model'])
        del checkpoint
    else:  # darknet format
        load_darknet_weights(model, weights)

    model.to(device).eval()

    # Set Dataloader
    dataloader = load_images(images_path, img_size=img_size)

    # Classes and colors
    classes = load_classes(parse_data_cfg('cfg/coco.data')['names'])  # Extracts class labels from file
    color_list = [[random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)] for _ in range(len(classes))]

    for i, (path, img, img0) in enumerate(dataloader):
        print('image %g/%g: %s' % (i + 1, len(dataloader), path))
        t = time.time()

        # Get detections
        with torch.no_grad():
            img = torch.from_numpy(img).unsqueeze(0).to(device)
            if ONNX_EXPORT:
                pred = torch.onnx._export(model, img, 'weights/model.onnx', verbose=True)
                return  # ONNX export
            pred = model(img)
            pred = pred[pred[:, :, 4] > conf_thres]

            if len(pred) > 0:
                detections = non_max_suppression(pred.unsqueeze(0), conf_thres, nms_thres)[0]

            # Draw bounding boxes and labels of detections
            if detections is not None:
                img = img0

                # The amount of padding that was added
                pad_x = max(img.shape[0] - img.shape[1], 0) * (img_size / max(img.shape))
                pad_y = max(img.shape[1] - img.shape[0], 0) * (img_size / max(img.shape))
                # Image height and width after padding is removed
                unpad_h = img_size - pad_y
                unpad_w = img_size - pad_x

                unique_classes = detections[:, -1].cpu().unique()
                bbox_colors = random.sample(color_list, len(unique_classes))

                # write results to .txt file
                results_img_path = os.path.join(output, path.split('/')[-1])
                results_txt_path = results_img_path + '.txt'
                if os.path.isfile(results_txt_path):
                    os.remove(results_txt_path)

                for i in unique_classes:
                    n = (detections[:, -1].cpu() == i).sum()
                    print('%g %ss' % (n, classes[int(i)]))

                for x1, y1, x2, y2, conf, cls_conf, cls_pred in detections:
                    # Rescale coordinates to original dimensions
                    box_h = ((y2 - y1) / unpad_h) * img.shape[0]
                    box_w = ((x2 - x1) / unpad_w) * img.shape[1]
                    y1 = (((y1 - pad_y // 2) / unpad_h) * img.shape[0]).round().item()
                    x1 = (((x1 - pad_x // 2) / unpad_w) * img.shape[1]).round().item()
                    x2 = (x1 + box_w).round().item()
                    y2 = (y1 + box_h).round().item()
                    x1, y1, x2, y2 = max(x1, 0), max(y1, 0), max(x2, 0), max(y2, 0)

                    # write to file
                    if save_txt:
                        with open(results_txt_path, 'a') as file:
                            file.write(('%g %g %g %g %g %g \n') % (x1, y1, x2, y2, cls_pred, cls_conf * conf))

                    if save_images:
                        # Add the bbox to the plot
                        label = '%s %.2f' % (classes[int(cls_pred)], conf)
                        color = bbox_colors[int(np.where(unique_classes == int(cls_pred))[0])]
                        plot_one_box([x1, y1, x2, y2], img, label=label, color=color)

                if save_images:
                    # Save generated image with detections
                    cv2.imwrite(results_img_path.replace('.bmp', '.jpg').replace('.tif', '.jpg'), img)

        print('Done. (%.3fs)\n' % (time.time() - t))

    if platform == 'darwin':  # MacOS (local)
        os.system('open ' + output)


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--image-folder', type=str, default='data/samples', help='path to images')
    parser.add_argument('--output-folder', type=str, default='output', help='path to outputs')
    parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
    parser.add_argument('--weights', type=str, default='weights/yolov3.pt', help='path to weights file')
    parser.add_argument('--conf-thres', type=float, default=0.50, help='object confidence threshold')
    parser.add_argument('--nms-thres', type=float, default=0.45, help='iou threshold for non-maximum suppression')
    parser.add_argument('--img-size', type=int, default=32 * 13, help='size of each image dimension')
    opt = parser.parse_args()
    print(opt)

    detect(
        opt.cfg,
        opt.weights,
        opt.image_folder,
        output=opt.output_folder,
        img_size=opt.img_size,
        conf_thres=opt.conf_thres,
        nms_thres=opt.nms_thres,
    )