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| import numpy as np | |
| import cv2 | |
| # This function is modified from the following code snippet: | |
| # https://github.com/StanislasBertrand/RetinaFace-tf2/blob/5f68ce8130889384cb8aca937a270cea4ef2d020/retinaface.py#L49-L74 | |
| def resize_image(img, scales, allow_upscaling): | |
| img_h, img_w = img.shape[0:2] | |
| target_size = scales[0] | |
| max_size = scales[1] | |
| if img_w > img_h: | |
| im_size_min, im_size_max = img_h, img_w | |
| else: | |
| im_size_min, im_size_max = img_w, img_h | |
| im_scale = target_size / float(im_size_min) | |
| if not allow_upscaling: | |
| im_scale = min(1.0, im_scale) | |
| if np.round(im_scale * im_size_max) > max_size: | |
| im_scale = max_size / float(im_size_max) | |
| if im_scale != 1.0: | |
| img = cv2.resize( | |
| img, | |
| None, | |
| None, | |
| fx=im_scale, | |
| fy=im_scale, | |
| interpolation=cv2.INTER_LINEAR | |
| ) | |
| return img, im_scale | |
| # This function is modified from the following code snippet: | |
| # https://github.com/StanislasBertrand/RetinaFace-tf2/blob/5f68ce8130889384cb8aca937a270cea4ef2d020/retinaface.py#L76-L96 | |
| def preprocess_image(img, allow_upscaling): | |
| pixel_means = np.array([0.0, 0.0, 0.0], dtype=np.float32) | |
| pixel_stds = np.array([1.0, 1.0, 1.0], dtype=np.float32) | |
| pixel_scale = float(1.0) | |
| scales = [1024, 1980] | |
| img, im_scale = resize_image(img, scales, allow_upscaling) | |
| img = img.astype(np.float32) | |
| im_tensor = np.zeros((1, img.shape[0], img.shape[1], img.shape[2]), dtype=np.float32) | |
| # Make image scaling + BGR2RGB conversion + transpose (N,H,W,C) to (N,C,H,W) | |
| for i in range(3): | |
| im_tensor[0, :, :, i] = (img[:, :, 2 - i] / pixel_scale - pixel_means[2 - i]) / pixel_stds[2 - i] | |
| return im_tensor, img.shape[0:2], im_scale | |