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| import cv2 | |
| import matplotlib | |
| matplotlib.use("Agg") | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| import os | |
| import seaborn as sns | |
| ## | |
| from sklearn.manifold import TSNE | |
| from sklearn.decomposition import PCA | |
| ## | |
| import matplotlib.ticker as mtick | |
| def plot_sample_cv2(names, imgs, scores_: dict, gts, save_folder=None): | |
| os.makedirs(save_folder, exist_ok=True) | |
| # get subplot number | |
| total_number = len(imgs) | |
| scores = scores_.copy() | |
| # normarlisze anomalies | |
| for k, v in scores.items(): | |
| max_value = np.max(v) | |
| min_value = np.min(v) | |
| scores[k] = (scores[k] - min_value) / max_value * 255 | |
| scores[k] = scores[k].astype(np.uint8) | |
| # draw gts | |
| mask_imgs = [] | |
| for idx in range(total_number): | |
| gts_ = gts[idx] | |
| mask_imgs_ = imgs[idx].copy() | |
| mask_imgs_[gts_ > 0.5] = (0, 0, 255) | |
| mask_imgs.append(mask_imgs_) | |
| # save imgs | |
| for idx in range(total_number): | |
| cv2.imwrite(os.path.join(save_folder, f'{names[idx]}_ori.jpg'), imgs[idx]) | |
| cv2.imwrite(os.path.join(save_folder, f'{names[idx]}_gt.jpg'), mask_imgs[idx]) | |
| for key in scores: | |
| heat_map = cv2.applyColorMap(scores[key][idx], cv2.COLORMAP_JET) | |
| visz_map = cv2.addWeighted(heat_map, 0.5, imgs[idx], 0.5, 0) | |
| cv2.imwrite(os.path.join(save_folder, f'{names[idx]}_{key}.jpg'), | |
| visz_map) | |
| def plot_feat_cv2(names, feat, save_folder=None): | |
| # get subplot number | |
| total_number = len(feat) | |
| # save imgs | |
| for idx in range(total_number): | |
| feat[idx] = cv2.resize(feat[idx], (256, 256), interpolation=cv2.INTER_NEAREST) | |
| cv2.imwrite(os.path.join(save_folder, f'{names[idx]}_feat.jpg'), feat[idx]) | |
| valid_feature_visualization_methods = ['TSNE', 'PCA'] | |
| def visualize_feature(features, labels, legends, n_components=3, method='TSNE'): | |
| assert method in valid_feature_visualization_methods | |
| assert n_components in [2, 3] | |
| if method == 'TSNE': | |
| model = TSNE(n_components=n_components) | |
| elif method == 'PCA': | |
| model = PCA(n_components=n_components) | |
| else: | |
| raise NotImplementedError | |
| feat_proj = model.fit_transform(features) | |
| if n_components == 2: | |
| ax = scatter_2d(feat_proj, labels) | |
| elif n_components == 3: | |
| ax = scatter_3d(feat_proj, labels) | |
| else: | |
| raise NotImplementedError | |
| plt.legend(legends) | |
| plt.axis('off') | |
| def scatter_3d(feat_proj, label): | |
| plt.clf() | |
| ax1 = plt.axes(projection='3d') | |
| label_unique = np.unique(label) | |
| for l in label_unique: | |
| ax1.scatter3D(feat_proj[label == l, 0], | |
| feat_proj[label == l, 1], | |
| feat_proj[label == l, 2], s=5) | |
| return ax1 | |
| def scatter_2d(feat_proj, label): | |
| plt.clf() | |
| ax1 = plt.axes() | |
| label_unique = np.unique(label) | |
| for l in label_unique: | |
| ax1.scatter(feat_proj[label == l, 0], | |
| feat_proj[label == l, 1], s=5) | |
| return ax1 | |