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 yolox import YoloX # 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=(640, 640)): padded_img = np.ones((target_size[0], target_size[1], 3)).astype(np.float32) * 114.0 ratio = min(target_size[0] / srcimg.shape[0], target_size[1] / srcimg.shape[1]) resized_img = cv.resize( srcimg, (int(srcimg.shape[1] * ratio), int(srcimg.shape[0] * ratio)), interpolation=cv.INTER_LINEAR ).astype(np.float32) padded_img[: int(srcimg.shape[0] * ratio), : int(srcimg.shape[1] * ratio)] = resized_img return padded_img, ratio def unletterbox(bbox, letterbox_scale): return bbox / letterbox_scale def vis(dets, srcimg, letterbox_scale, fps=None): res_img = srcimg.copy() if fps is not None: fps_label = "FPS: %.2f" % fps cv.putText(res_img, fps_label, (10, 25), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2) for det in dets: box = unletterbox(det[:4], letterbox_scale).astype(np.int32) score = det[-2] cls_id = int(det[-1]) x0, y0, x1, y1 = box text = '{}:{:.1f}%'.format(classes[cls_id], score * 100) font = cv.FONT_HERSHEY_SIMPLEX txt_size = cv.getTextSize(text, font, 0.4, 1)[0] cv.rectangle(res_img, (x0, y0), (x1, y1), (0, 255, 0), 2) cv.rectangle(res_img, (x0, y0 + 1), (x0 + txt_size[0] + 1, y0 + int(1.5 * txt_size[1])), (255, 255, 255), -1) cv.putText(res_img, text, (x0, y0 + txt_size[1]), font, 0.4, (0, 0, 0), thickness=1) return res_img 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_yolox_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=0.5, type=float, help='Class confidence') parser.add_argument('--nms', default=0.5, type=float, help='Enter nms IOU threshold') parser.add_argument('--obj', default=0.5, type=float, help='Enter object 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_net = YoloX(modelPath= args.model, confThreshold=args.confidence, nmsThreshold=args.nms, objThreshold=args.obj, backendId=backend_id, targetId=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) input_blob, letterbox_scale = letterbox(input_blob) # Inference tm.start() preds = model_net.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_net.infer(input_blob) tm.stop() img = vis(preds, frame, letterbox_scale, fps=tm.getFPS()) cv.imshow("YoloX Demo", img) tm.reset()