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| import cv2 | |
| import random | |
| import numpy as np | |
| from PIL import Image | |
| def get_strided_size(orig_size, stride): | |
| return ((orig_size[0]-1)//stride+1, (orig_size[1]-1)//stride+1) | |
| def get_strided_up_size(orig_size, stride): | |
| strided_size = get_strided_size(orig_size, stride) | |
| return strided_size[0]*stride, strided_size[1]*stride | |
| def imshow(image, delay=0, mode='RGB', title='show'): | |
| if mode == 'RGB': | |
| demo_image = image[..., ::-1] | |
| else: | |
| demo_image = image | |
| cv2.imshow(title, demo_image) | |
| if delay >= 0: | |
| cv2.waitKey(delay) | |
| def transpose(image): | |
| return image.transpose((1, 2, 0)) | |
| def denormalize(image, mean=None, std=None, dtype=np.uint8, tp=True): | |
| if tp: | |
| image = transpose(image) | |
| if mean is not None: | |
| image = (image * std) + mean | |
| if dtype == np.uint8: | |
| image *= 255. | |
| return image.astype(np.uint8) | |
| else: | |
| return image | |
| def colormap(cam, shape=None, mode=cv2.COLORMAP_JET): | |
| if shape is not None: | |
| h, w, c = shape | |
| cam = cv2.resize(cam, (w, h)) | |
| cam = cv2.applyColorMap(cam, mode) | |
| return cam | |
| def decode_from_colormap(data, colors): | |
| ignore = (data == 255).astype(np.int32) | |
| mask = 1 - ignore | |
| data *= mask | |
| h, w = data.shape | |
| image = colors[data.reshape((h * w))].reshape((h, w, 3)) | |
| ignore = np.concatenate([ignore[..., np.newaxis], ignore[..., np.newaxis], ignore[..., np.newaxis]], axis=-1) | |
| image[ignore.astype(np.bool)] = 255 | |
| return image | |
| def normalize(cam, epsilon=1e-5): | |
| cam = np.maximum(cam, 0) | |
| max_value = np.max(cam, axis=(0, 1), keepdims=True) | |
| return np.maximum(cam - epsilon, 0) / (max_value + epsilon) | |
| def crf_inference(img, probs, t=10, scale_factor=1, labels=21): | |
| import pydensecrf.densecrf as dcrf | |
| from pydensecrf.utils import unary_from_softmax | |
| h, w = img.shape[:2] | |
| n_labels = labels | |
| d = dcrf.DenseCRF2D(w, h, n_labels) | |
| unary = unary_from_softmax(probs) | |
| unary = np.ascontiguousarray(unary) | |
| d.setUnaryEnergy(unary) | |
| d.addPairwiseGaussian(sxy=3/scale_factor, compat=3) | |
| d.addPairwiseBilateral(sxy=80/scale_factor, srgb=13, rgbim=np.copy(img), compat=10) | |
| Q = d.inference(t) | |
| return np.array(Q).reshape((n_labels, h, w)) | |
| def crf_with_alpha(ori_image, cams, alpha): | |
| # h, w, c -> c, h, w | |
| # cams = cams.transpose((2, 0, 1)) | |
| bg_score = np.power(1 - np.max(cams, axis=0, keepdims=True), alpha) | |
| bgcam_score = np.concatenate((bg_score, cams), axis=0) | |
| cams_with_crf = crf_inference(ori_image, bgcam_score, labels=bgcam_score.shape[0]) | |
| # return cams_with_crf.transpose((1, 2, 0)) | |
| return cams_with_crf | |
| def crf_inference_label(img, labels, t=10, n_labels=21, gt_prob=0.7): | |
| import pydensecrf.densecrf as dcrf | |
| from pydensecrf.utils import unary_from_labels | |
| h, w = img.shape[:2] | |
| d = dcrf.DenseCRF2D(w, h, n_labels) | |
| unary = unary_from_labels(labels, n_labels, gt_prob=gt_prob, zero_unsure=False) | |
| d.setUnaryEnergy(unary) | |
| d.addPairwiseGaussian(sxy=3, compat=3) | |
| d.addPairwiseBilateral(sxy=50, srgb=5, rgbim=np.ascontiguousarray(np.copy(img)), compat=10) | |
| q = d.inference(t) | |
| return np.argmax(np.array(q).reshape((n_labels, h, w)), axis=0) |