# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import argparse
import cv2

from maskrcnn_benchmark.config import cfg
from demo.predictor import COCODemo


def img_to_three_color_map(source_path, target_path):
    parser = argparse.ArgumentParser(description="PyTorch Object Detection Webcam Demo")
    parser.add_argument(
        "--config-file",
        default="configs/caffe2/e2e_mask_rcnn_R_50_FPN_1x_caffe2.yaml",
        metavar="FILE",
        help="path to config file",
    )
    parser.add_argument(
        "--confidence-threshold",
        type=float,
        default=0.7,
        help="Minimum score for the prediction to be shown",
    )
    parser.add_argument(
        "--min-image-size",
        type=int,
        default=800,
        help="Smallest size of the image to feed to the model. "
            "Model was trained with 800, which gives best results",
    )
    parser.add_argument(
        "--show-mask-heatmaps",
        dest="show_mask_heatmaps",
        help="Show a heatmap probability for the top masks-per-dim masks",
        action="store_true",
    )
    parser.add_argument(
        "--masks-per-dim",
        type=int,
        default=2,
        help="Number of heatmaps per dimension to show",
    )
    parser.add_argument(
        "opts",
        help="Modify model config options using the command-line",
        default=None,
        nargs=argparse.REMAINDER,
    )

    args = parser.parse_args()

    # load config from file and command-line arguments
    cfg.merge_from_file(args.config_file)
    # cfg.merge_from_list(args.opts)
    cfg.freeze()

    # prepare object that handles inference plus adds predictions on top of image
    coco_demo = COCODemo(
        cfg,
        confidence_threshold=args.confidence_threshold,
        show_mask_heatmaps=args.show_mask_heatmaps,
        masks_per_dim=args.masks_per_dim,
        min_image_size=args.min_image_size,
    )
    source_path = source_path[3:]
    target_path = target_path[3:]
    print(target_path)
    print(source_path)
    img = cv2.imread(source_path)
    composite = coco_demo.run_on_opencv_image(img)
    if composite is not None:
        cv2.imwrite(target_path, composite)
    # cam = cv2.VideoCapture(0)
    # while True:
    #     start_time = time.time()
        # ret_val, img = cam.read()
        # composite = coco_demo.run_on_opencv_image(img)
        # print("Time: {:.2f} s / img".format(time.time() - start_time))
        # cv2.imshow("COCO detections", composite)
        # if cv2.waitKey(1) == 27:
        #     break  # esc to quit
    # cv2.destroyAllWindows()


