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| """ | |
| Inference ONNX model of MODNet | |
| Arguments: | |
| --image-path: path of the input image (a file) | |
| --output-path: path for saving the predicted alpha matte (a file) | |
| --model-path: path of the ONNX model | |
| Example: | |
| python inference_onnx.py \ | |
| --image-path=demo.jpg --output-path=matte.png --model-path=modnet.onnx | |
| """ | |
| import os | |
| import cv2 | |
| import argparse | |
| import numpy as np | |
| from PIL import Image | |
| import onnx | |
| import onnxruntime | |
| if __name__ == '__main__': | |
| # define cmd arguments | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--image-path', type=str, help='path of the input image (a file)') | |
| parser.add_argument('--output-path', type=str, help='paht for saving the predicted alpha matte (a file)') | |
| parser.add_argument('--model-path', type=str, help='path of the ONNX model') | |
| args = parser.parse_args() | |
| # check input arguments | |
| if not os.path.exists(args.image_path): | |
| print('Cannot find the input image: {0}'.format(args.image_path)) | |
| exit() | |
| if not os.path.exists(args.model_path): | |
| print('Cannot find the ONXX model: {0}'.format(args.model_path)) | |
| exit() | |
| ref_size = 512 | |
| # Get x_scale_factor & y_scale_factor to resize image | |
| def get_scale_factor(im_h, im_w, ref_size): | |
| if max(im_h, im_w) < ref_size or min(im_h, im_w) > ref_size: | |
| if im_w >= im_h: | |
| im_rh = ref_size | |
| im_rw = int(im_w / im_h * ref_size) | |
| elif im_w < im_h: | |
| im_rw = ref_size | |
| im_rh = int(im_h / im_w * ref_size) | |
| else: | |
| im_rh = im_h | |
| im_rw = im_w | |
| im_rw = im_rw - im_rw % 32 | |
| im_rh = im_rh - im_rh % 32 | |
| x_scale_factor = im_rw / im_w | |
| y_scale_factor = im_rh / im_h | |
| return x_scale_factor, y_scale_factor | |
| ############################################## | |
| # Main Inference part | |
| ############################################## | |
| # read image | |
| im = cv2.imread(args.image_path) | |
| im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB) | |
| # unify image channels to 3 | |
| if len(im.shape) == 2: | |
| im = im[:, :, None] | |
| if im.shape[2] == 1: | |
| im = np.repeat(im, 3, axis=2) | |
| elif im.shape[2] == 4: | |
| im = im[:, :, 0:3] | |
| # normalize values to scale it between -1 to 1 | |
| im = (im - 127.5) / 127.5 | |
| im_h, im_w, im_c = im.shape | |
| x, y = get_scale_factor(im_h, im_w, ref_size) | |
| # resize image | |
| im = cv2.resize(im, None, fx = x, fy = y, interpolation = cv2.INTER_AREA) | |
| # prepare input shape | |
| im = np.transpose(im) | |
| im = np.swapaxes(im, 1, 2) | |
| im = np.expand_dims(im, axis = 0).astype('float32') | |
| # Initialize session and get prediction | |
| session = onnxruntime.InferenceSession(args.model_path, None) | |
| input_name = session.get_inputs()[0].name | |
| output_name = session.get_outputs()[0].name | |
| result = session.run([output_name], {input_name: im}) | |
| # refine matte | |
| matte = (np.squeeze(result[0]) * 255).astype('uint8') | |
| matte = cv2.resize(matte, dsize=(im_w, im_h), interpolation = cv2.INTER_AREA) | |
| cv2.imwrite(args.output_path, matte) | |