import numpy as np import cv2 import argparse from nanodet import NanoDet def str2bool(v): if v.lower() in ['on', 'yes', 'true', 'y', 't']: return True elif v.lower() in ['off', 'no', 'false', 'n', 'f']: return False else: raise NotImplementedError backends = [cv2.dnn.DNN_BACKEND_OPENCV, cv2.dnn.DNN_BACKEND_CUDA] targets = [cv2.dnn.DNN_TARGET_CPU, cv2.dnn.DNN_TARGET_CUDA, cv2.dnn.DNN_TARGET_CUDA_FP16] help_msg_backends = "Choose one of the computation backends: {:d}: OpenCV implementation (default); {:d}: CUDA" help_msg_targets = "Chose one of the target computation devices: {:d}: CPU (default); {:d}: CUDA; {:d}: CUDA fp16" try: backends += [cv2.dnn.DNN_BACKEND_TIMVX] targets += [cv2.dnn.DNN_TARGET_NPU] help_msg_backends += "; {:d}: TIMVX" help_msg_targets += "; {:d}: NPU" except: print('This version of OpenCV does not support TIM-VX and NPU. Visit https://github.com/opencv/opencv/wiki/TIM-VX-Backend-For-Running-OpenCV-On-NPU for more information.') 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 = cv2.resize(img, (neww, newh), interpolation=cv2.INTER_AREA) left = int((target_size[1] - neww) * 0.5) img = cv2.copyMakeBorder(img, 0, 0, left, target_size[1] - neww - left, cv2.BORDER_CONSTANT, value=0) # add border else: newh, neww = int(target_size[0] * hw_scale), target_size[1] img = cv2.resize(img, (neww, newh), interpolation=cv2.INTER_AREA) top = int((target_size[0] - newh) * 0.5) img = cv2.copyMakeBorder(img, top, target_size[0] - newh - top, 0, 0, cv2.BORDER_CONSTANT, value=0) else: img = cv2.resize(img, target_size, interpolation=cv2.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 cv2.putText(ret, fps_label, (10, 25), cv2.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) cv2.rectangle(ret, (xmin, ymin), (xmax, ymax), (0, 255, 0), thickness=2) # label label = "{:s}: {:.2f}".format(classes[classid], conf) cv2.putText(ret, label, (xmin, ymin - 10), cv2.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', '-b', type=int, default=backends[0], help=help_msg_backends.format(*backends)) parser.add_argument('--target', '-t', type=int, default=targets[0], help=help_msg_targets.format(*targets)) 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', type=str2bool, default=False, help='Set true to save results. This flag is invalid when using camera.') parser.add_argument('--vis', '-v', type=str2bool, default=True, help='Set true to open a window for result visualization. This flag is invalid when using camera.') args = parser.parse_args() model = NanoDet(modelPath= args.model, prob_threshold=args.confidence, iou_threshold=args.nms, backend_id=args.backend, target_id=args.target) tm = cv2.TickMeter() tm.reset() if args.input is not None: image = cv2.imread(args.input) input_blob = cv2.cvtColor(image, cv2.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('Resutls saved to result.jpg\n') cv2.imwrite('result.jpg', img) if args.vis: cv2.namedWindow(args.input, cv2.WINDOW_AUTOSIZE) cv2.imshow(args.input, img) cv2.waitKey(0) else: print("Press any key to stop video capture") deviceId = 0 cap = cv2.VideoCapture(deviceId) while cv2.waitKey(1) < 0: hasFrame, frame = cap.read() if not hasFrame: print('No frames grabbed!') break input_blob = cv2.cvtColor(frame, cv2.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()) cv2.imshow("NanoDet Demo", img) tm.reset()