models/image_classification_ppresnet/demo.py

# This file is part of OpenCV Zoo project.
# It is subject to the license terms in the LICENSE file found in the same directory.
#
# Copyright (C) 2021, Shenzhen Institute of Artificial Intelligence and Robotics for Society, all rights reserved.
# Third party copyrights are property of their respective owners.

import argparse

import numpy as np
import cv2 as cv

from ppresnet import PPResNet

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

parser = argparse.ArgumentParser(description='Deep Residual Learning for Image Recognition (https://arxiv.org/abs/1512.03385, https://github.com/PaddlePaddle/PaddleHub)')
parser.add_argument('--input', '-i', type=str, help='Path to the input image.')
parser.add_argument('--model', '-m', type=str, default='image_classification_ppresnet50_2022jan.onnx', help='Path to the model.')
parser.add_argument('--label', '-l', type=str, default='./imagenet_labels.txt', help='Path to the dataset labels.')
args = parser.parse_args()

if __name__ == '__main__':
    # Instantiate ResNet
    model = PPResNet(modelPath=args.model, labelPath=args.label)

    # 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))