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| import os | |
| import cv2 | |
| import argparse | |
| import numpy as np | |
| from PIL import Image | |
| import onnx | |
| import onnxruntime | |
| class ModNet: | |
| def __init__(self, model_path): | |
| # Initialize session and get prediction | |
| self.session = onnxruntime.InferenceSession(model_path, None) | |
| # Get x_scale_factor & y_scale_factor to resize image | |
| def get_scale_factor(self, 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 | |
| def segment(self, image_path): | |
| ref_size = 512 | |
| ############################################## | |
| # Main Inference part | |
| ############################################## | |
| # read image | |
| im = cv2.imread(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 = self.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') | |
| input_name = self.session.get_inputs()[0].name | |
| output_name = self.session.get_outputs()[0].name | |
| result = self.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) | |
| # obtain predicted foreground | |
| image = cv2.imread(image_path) | |
| image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) | |
| if len(image.shape) == 2: | |
| image = image[:, :, None] | |
| if image.shape[2] == 1: | |
| image = np.repeat(image, 3, axis=2) | |
| elif image.shape[2] == 4: | |
| image = image[:, :, 0:3] | |
| matte = np.repeat(np.asarray(matte)[:, :, None], 3, axis=2) / 255 | |
| foreground = image * matte + np.full(image.shape, 255) * (1 - matte) | |
| return foreground | |