models/object_detection_nanodet/demo.py

import numpy as np
import cv2 as cv
import argparse

# Check OpenCV version
opencv_python_version = lambda str_version: tuple(map(int, (str_version.split("."))))
assert opencv_python_version(cv.__version__) >= opencv_python_version("4.10.0"), \
       "Please install latest opencv-python for benchmark: python3 -m pip install --upgrade opencv-python"

from nanodet import NanoDet

# Valid combinations of backends and targets
backend_target_pairs = [
    [cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_CPU],
    [cv.dnn.DNN_BACKEND_CUDA,   cv.dnn.DNN_TARGET_CUDA],
    [cv.dnn.DNN_BACKEND_CUDA,   cv.dnn.DNN_TARGET_CUDA_FP16],
    [cv.dnn.DNN_BACKEND_TIMVX,  cv.dnn.DNN_TARGET_NPU],
    [cv.dnn.DNN_BACKEND_CANN,   cv.dnn.DNN_TARGET_NPU]
]

classes = ('person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
           'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
           'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
           'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe',
           'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
           'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat',
           'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
           'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
           'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
           'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
           'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop',
           'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',
           'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock',
           'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush')

def letterbox(srcimg, target_size=(416, 416)):
    img = srcimg.copy()

    top, left, newh, neww = 0, 0, target_size[0], target_size[1]
    if img.shape[0] != img.shape[1]:
        hw_scale = img.shape[0] / img.shape[1]
        if hw_scale > 1:
            newh, neww = target_size[0], int(target_size[1] / hw_scale)
            img = cv.resize(img, (neww, newh), interpolation=cv.INTER_AREA)
            left = int((target_size[1] - neww) * 0.5)
            img = cv.copyMakeBorder(img, 0, 0, left, target_size[1] - neww - left, cv.BORDER_CONSTANT, value=0)  # add border
        else:
            newh, neww = int(target_size[0] * hw_scale), target_size[1]
            img = cv.resize(img, (neww, newh), interpolation=cv.INTER_AREA)
            top = int((target_size[0] - newh) * 0.5)
            img = cv.copyMakeBorder(img, top, target_size[0] - newh - top, 0, 0, cv.BORDER_CONSTANT, value=0)
    else:
        img = cv.resize(img, target_size, interpolation=cv.INTER_AREA)

    letterbox_scale = [top, left, newh, neww]
    return img, letterbox_scale

def unletterbox(bbox, original_image_shape, letterbox_scale):
    ret = bbox.copy()

    h, w = original_image_shape
    top, left, newh, neww = letterbox_scale

    if h == w:
        ratio = h / newh
        ret = ret * ratio
        return ret

    ratioh, ratiow = h / newh, w / neww
    ret[0] = max((ret[0] - left) * ratiow, 0)
    ret[1] = max((ret[1] - top) * ratioh, 0)
    ret[2] = min((ret[2] - left) * ratiow, w)
    ret[3] = min((ret[3] - top) * ratioh, h)

    return ret.astype(np.int32)

def vis(preds, res_img, letterbox_scale, fps=None):
    ret = res_img.copy()

    # draw FPS
    if fps is not None:
        fps_label = "FPS: %.2f" % fps
        cv.putText(ret, fps_label, (10, 25), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)

    # draw bboxes and labels
    for pred in preds:
        bbox = pred[:4]
        conf = pred[-2]
        classid = pred[-1].astype(np.int32)

        # bbox
        xmin, ymin, xmax, ymax = unletterbox(bbox, ret.shape[:2], letterbox_scale)
        cv.rectangle(ret, (xmin, ymin), (xmax, ymax), (0, 255, 0), thickness=2)

        # label
        label = "{:s}: {:.2f}".format(classes[classid], conf)
        cv.putText(ret, label, (xmin, ymin - 10), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), thickness=2)

    return ret

if __name__=='__main__':
    parser = argparse.ArgumentParser(description='Nanodet inference using OpenCV an contribution by Sri Siddarth Chakaravarthy part of GSOC_2022')
    parser.add_argument('--input', '-i', type=str,
                        help='Path to the input image. Omit for using default camera.')
    parser.add_argument('--model', '-m', type=str,
                        default='object_detection_nanodet_2022nov.onnx', help="Path to the model")
    parser.add_argument('--backend_target', '-bt', type=int, default=0,
                    help='''Choose one of the backend-target pair to run this demo:
                        {:d}: (default) OpenCV implementation + CPU,
                        {:d}: CUDA + GPU (CUDA),
                        {:d}: CUDA + GPU (CUDA FP16),
                        {:d}: TIM-VX + NPU,
                        {:d}: CANN + NPU
                    '''.format(*[x for x in range(len(backend_target_pairs))]))
    parser.add_argument('--confidence', default=0.35, type=float,
                        help='Class confidence')
    parser.add_argument('--nms', default=0.6, type=float,
                        help='Enter nms IOU threshold')
    parser.add_argument('--save', '-s', action='store_true',
                        help='Specify to save results. This flag is invalid when using camera.')
    parser.add_argument('--vis', '-v', action='store_true',
                        help='Specify to open a window for result visualization. This flag is invalid when using camera.')
    args = parser.parse_args()

    backend_id = backend_target_pairs[args.backend_target][0]
    target_id = backend_target_pairs[args.backend_target][1]

    model = NanoDet(modelPath= args.model,
                    prob_threshold=args.confidence,
                    iou_threshold=args.nms,
                    backend_id=backend_id,
                    target_id=target_id)

    tm = cv.TickMeter()
    tm.reset()
    if args.input is not None:
        image = cv.imread(args.input)
        input_blob = cv.cvtColor(image, cv.COLOR_BGR2RGB)

        # Letterbox transformation
        input_blob, letterbox_scale = letterbox(input_blob)

        # Inference
        tm.start()
        preds = model.infer(input_blob)
        tm.stop()
        print("Inference time: {:.2f} ms".format(tm.getTimeMilli()))

        img = vis(preds, image, letterbox_scale)

        if args.save:
            print('Results saved to result.jpg\n')
            cv.imwrite('result.jpg', img)

        if args.vis:
            cv.namedWindow(args.input, cv.WINDOW_AUTOSIZE)
            cv.imshow(args.input, img)
            cv.waitKey(0)

    else:
        print("Press any key to stop video capture")
        deviceId = 0
        cap = cv.VideoCapture(deviceId)

        while cv.waitKey(1) < 0:
            hasFrame, frame = cap.read()
            if not hasFrame:
                print('No frames grabbed!')
                break

            input_blob = cv.cvtColor(frame, cv.COLOR_BGR2RGB)
            input_blob, letterbox_scale = letterbox(input_blob)
            # Inference
            tm.start()
            preds = model.infer(input_blob)
            tm.stop()

            img = vis(preds, frame, letterbox_scale, fps=tm.getFPS())

            cv.imshow("NanoDet Demo", img)

            tm.reset()