yolov1-tiny.py

import sys
import time

import cv2

import ailia

# import original modules
sys.path.append('../../util')
from utils import get_base_parser, update_parser  # noqa: E402
from model_utils import check_and_download_models  # noqa: E402
from detector_utils import plot_results, load_image  # noqa: E402
import webcamera_utils  # noqa: E402


# ======================
# Parameters
# ======================
WEIGHT_PATH = 'yolov1-tiny.caffemodel'
MODEL_PATH = 'yolov1-tiny.prototxt'
REMOTE_PATH = 'https://storage.googleapis.com/ailia-models/yolov1-tiny/'

IMAGE_PATH = 'couple.jpg'
SAVE_IMAGE_PATH = 'output.png'
IMAGE_HEIGHT = 448  # for video mode
IMAGE_WIDTH = 448  # for video mode

VOC_CATEGORY = [
    "aeroplane",
    "bicycle",
    "bird",
    "boat",
    "bottle",
    "bus",
    "car",
    "cat",
    "chair",
    "cow",
    "diningtable",
    "dog",
    "horse",
    "motorbike",
    "person",
    "pottedplant",
    "sheep",
    "sofa",
    "train",
    "tvmonitor"
]
THRESHOLD = 0.2
IOU = 0.45


# ======================
# Arguemnt Parser Config
# ======================
parser = get_base_parser('Yolov1 tiny model', IMAGE_PATH, SAVE_IMAGE_PATH)
args = update_parser(parser)


# ======================
# Main functions
# ======================
def recognize_from_image():
    # prepare input data
    img = load_image(args.input)
    print(f'input image shape: {img.shape}')

    # net initialize
    detector = ailia.Detector(
        MODEL_PATH,
        WEIGHT_PATH,
        len(VOC_CATEGORY),
        format=ailia.NETWORK_IMAGE_FORMAT_RGB,
        channel=ailia.NETWORK_IMAGE_CHANNEL_FIRST,
        range=ailia.NETWORK_IMAGE_RANGE_S_FP32,
        algorithm=ailia.DETECTOR_ALGORITHM_YOLOV1,
        env_id=args.env_id,
    )

    # inference
    print('Start inference...')
    if args.benchmark:
        print('BENCHMARK mode')
        for i in range(5):
            start = int(round(time.time() * 1000))
            detector.compute(img, THRESHOLD, IOU)
            end = int(round(time.time() * 1000))
            print(f'\tailia processing time {end - start} ms')
    else:
        detector.compute(img, THRESHOLD, IOU)

    # plot result
    res_img = plot_results(detector, img, VOC_CATEGORY)
    cv2.imwrite(args.savepath, res_img)
    print('Script finished successfully.')


def recognize_from_video():
    # net initialize
    detector = ailia.Detector(
        MODEL_PATH,
        WEIGHT_PATH,
        len(VOC_CATEGORY),
        format=ailia.NETWORK_IMAGE_FORMAT_RGB,
        channel=ailia.NETWORK_IMAGE_CHANNEL_FIRST,
        range=ailia.NETWORK_IMAGE_RANGE_S_FP32,
        algorithm=ailia.DETECTOR_ALGORITHM_YOLOV1,
        env_id=args.env_id,
    )

    capture = webcamera_utils.get_capture(args.video)

    # create video writer if savepath is specified as video format
    if args.savepath != SAVE_IMAGE_PATH:
        writer = webcamera_utils.get_writer(
            args.savepath, IMAGE_HEIGHT, IMAGE_WIDTH
        )
    else:
        writer = None

    while(True):
        ret, frame = capture.read()
        if (cv2.waitKey(1) & 0xFF == ord('q')) or not ret:
            break

        _, resized_img = webcamera_utils.adjust_frame_size(
            frame, IMAGE_HEIGHT, IMAGE_WIDTH
        )

        img = cv2.cvtColor(resized_img, cv2.COLOR_RGB2BGRA)
        detector.compute(img, THRESHOLD, IOU)
        res_img = plot_results(detector, resized_img, VOC_CATEGORY, False)
        cv2.imshow('frame', res_img)

        # save results
        if writer is not None:
            writer.write(res_img)

    capture.release()
    cv2.destroyAllWindows()
    if writer is not None:
        writer.release()
    print('Script finished successfully.')


def main():
    # model files check and download
    check_and_download_models(WEIGHT_PATH, MODEL_PATH, REMOTE_PATH)

    if args.video is not None:
        # video mode
        recognize_from_video()
    else:
        # image mode
        recognize_from_image()


if __name__ == '__main__':
    main()