new script to restore mask of a single folder

iou_restore_mask_oneolderonly.py

+import os
+import numpy as np
+import matplotlib.pyplot as plt
+import tifffile
+from utils import *
+from segmentation import *
+import time
+import datetime
+from tqdm import tqdm
+
+# ---
+# Load threshold data
+# ---
+table_path = 'w:\\NPC_adult_new\\quantification_data\\iou_quantification\\iou_quantification_dlwithbiasx3_2023-07-20\\'
+table_list = os.listdir(table_path)
+table_list.sort()
+table_list = [x for x in table_list if x.find('.csv') > 0]
+
+# ---
+# Data Import
+# ---
+# results paths
+results_path = 'w:\\NPC_adult_new\\dl_training\\results\\contrastenhance_128x128px_maeloss_20pxblurrad_poissonnoise_adaptive0p6to0p5_unet5\\'
+
+pred1_mask_path = results_path+'results_20230129_x3\\'
+pred1_suffix_substring = '.tif'
+
+pred1_save_path = os.path.join(pred1_mask_path, 'masks')
+if not os.path.exists(pred1_save_path):
+    os.mkdir(pred1_save_path)
+
+# pred2_mask_path = results_path+'results_x2\\'
+# pred2_suffix_substring = '_x2.tif'
+
+# pred2_save_path = os.path.join(pred2_mask_path, 'masks')
+# if not os.path.exists(pred2_save_path):
+#     os.mkdir(pred2_save_path)
+
+# pred3_mask_path = results_path+'results_x3\\'
+# pred3_suffix_substring = '_x3.tif'
+
+# pred3_save_path = os.path.join(pred3_mask_path, 'masks')
+# if not os.path.exists(pred3_save_path):
+#     os.mkdir(pred3_save_path)
+
+time_start_script = time.time()
+
+for table_name in tqdm(table_list, total=len(table_list)):
+
+    data_table = []
+    with open(os.path.join(table_path, table_name), "r") as file:
+        for line in file:
+            line_proc = line.split(',')
+            try:
+                test = float(line_proc[0])
+            except:
+                continue
+            parsed_line = []
+            for i in range(len(line_proc)):
+                try:
+                    item = float(line_proc[i])
+                except:
+                    continue
+                parsed_line.append(item)
+            data_table.append(parsed_line)
+    
+    data_table = np.asarray(data_table, dtype='float32')
+    # print('data len: ', data_table.shape)
+    # print('line len: ', data_table[0].shape)
+
+
+    table_name_noext = table_name[12:table_name.index('_mask')]
+    print(table_name_noext)
+
+    time_image = time.time()
+    # to check if loaded images are the same
+    
+    print('Loading image: ', table_name_noext)
+
+    print('Loading pred1 image name: ')
+    table_1 = data_table[:, 2]
+    image_pred = tifffile.imread(os.path.join(pred1_mask_path, table_name_noext+pred1_suffix_substring)).astype('float32')
+    pred1_mask = make_mask_from_values(image_pred, table_1)
+    tifffile.imwrite(os.path.join(pred1_save_path, table_name_noext+pred1_suffix_substring), pred1_mask)
+    
+    # print('Loading pred2 image name: ')
+    # table_2 = data_table[:, 4]
+    # # print(table_2.shape)
+    # # print(table_2[:10])
+    # # stop
+    # image_pred2 = tifffile.imread(os.path.join(pred2_mask_path, table_name_noext+pred2_suffix_substring)).astype('float32')
+    # pred2_mask = make_mask_from_values(image_pred2, table_2)
+    # tifffile.imwrite(os.path.join(pred2_save_path, table_name_noext+pred2_suffix_substring), pred2_mask)
+    
+    # print('Loading pred3 image name: ')
+    # table_3 = data_table[:, 6]
+    # image_pred3 = tifffile.imread(os.path.join(pred3_mask_path, table_name_noext+pred3_suffix_substring)).astype('float32')
+    # pred3_mask = make_mask_from_values(image_pred3, table_3)
+    # tifffile.imwrite(os.path.join(pred3_save_path, table_name_noext+pred3_suffix_substring), pred3_mask)
+
+    print('Time per image: ', time.time() - time_image)
+
+print('Time: ', time.time() - time_start_script)
+print('Done')