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()