import argparse import numpy as np import cv2 as cv from mobilenet import MobileNet 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 = [cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_BACKEND_CUDA] targets = [cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_CUDA, cv.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 += [cv.dnn.DNN_BACKEND_TIMVX] targets += [cv.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.') all_mobilenets = [ 'image_classification_mobilenetv1_2022apr.onnx', 'image_classification_mobilenetv2_2022apr.onnx', 'image_classification_mobilenetv1_2022apr-int8-quantized.onnx', 'image_classification_mobilenetv2_2022apr-int8-quantized.onnx' ] parser = argparse.ArgumentParser(description='Demo for MobileNet V1 & V2.') parser.add_argument('--input', '-i', type=str, help='Usage: Set input path to a certain image, omit if using camera.') parser.add_argument('--model', '-m', type=str, choices=all_mobilenets, default=all_mobilenets[0], help='Usage: Set model type, defaults to image_classification_mobilenetv1_2022apr.onnx (v1).') 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)) args = parser.parse_args() if __name__ == '__main__': # Instantiate MobileNet model = MobileNet(modelPath=args.model, backendId=args.backend, targetId=args.target) # Read image and get a 224x224 crop from a 256x256 resized image = cv.imread(args.input) image = cv.cvtColor(image, cv.COLOR_BGR2RGB) image = cv.resize(image, dsize=(256, 256)) image = image[16:240, 16:240, :] # Inference result = model.infer(image) # Print result print('label: {}'.format(result))