| import cv2
|
| import os
|
|
|
| from sklearn.preprocessing import normalize
|
| import numpy as np
|
|
|
| def visualizer(pathes, anomaly_map, img_size, save_path, cls_name):
|
| for idx, path in enumerate(pathes):
|
| cls = path.split('/')[-2]
|
| filename = path.split('/')[-1]
|
| vis = cv2.cvtColor(cv2.resize(cv2.imread(path), (img_size, img_size)), cv2.COLOR_BGR2RGB)
|
| mask = normalize(anomaly_map[idx])
|
| vis = apply_ad_scoremap(vis, mask)
|
| vis = cv2.cvtColor(vis, cv2.COLOR_RGB2BGR)
|
| save_vis = os.path.join(save_path, 'imgs', cls_name[idx], cls)
|
| if not os.path.exists(save_vis):
|
| os.makedirs(save_vis)
|
| cv2.imwrite(os.path.join(save_vis, filename), vis)
|
|
|
| def apply_ad_scoremap(image, scoremap, alpha=0.5):
|
| np_image = np.asarray(image, dtype=float)
|
| scoremap = (scoremap * 255).astype(np.uint8)
|
| scoremap = cv2.applyColorMap(scoremap, cv2.COLORMAP_JET)
|
| scoremap = cv2.cvtColor(scoremap, cv2.COLOR_BGR2RGB)
|
| return (alpha * np_image + (1 - alpha) * scoremap).astype(np.uint8)
|
|
|
