update to script with fix to progress

main_batch_iou.py

@@ -12,8 +12,8 @@ from tqdm import tqdm
 # Data Import
 # ---
 #raw data path
-raw_mask_path = 'w:\\NPC_adult_new\\dl_training\\images\\'
-raw_prefix_substring = '.tif'
+raw_mask_path = 'w:\\NPC_adult_new\\dl_training\\images\\mask_gt\\'
+raw_prefix_substring = '_mask'
 
 # results paths
 results_path = 'w:\\NPC_adult_new\\dl_training\\fcenet\\results\\'
@@ -27,7 +27,7 @@ pred2_prefix_substring = '_x2'
 pred3_mask_path = results_path+'results_x3\\'
 pred3_prefix_substring = '_x3'
 
-quantification_name = 'dlbiasfree'
+quantification_name = 'fcenet'
 
 raw_list = os.listdir(raw_mask_path)
 raw_list.sort()
@@ -52,12 +52,12 @@ if not os.path.exists(save_path):
 
 time_start_script = time.time()
 
-for raw_name, pred_name, pred_name2, pred_name3 in tqdm(zip(raw_list, pred_list, pred_list_x2, pred_list_x3)):
+for raw_name, pred_name, pred_name2, pred_name3 in tqdm(zip(raw_list, pred_list, pred_list_x2, pred_list_x3), total=len(raw_list)):
     time_image = time.time()
     # to check if loaded images are the same
-    assert raw_name[:raw_name.index(raw_prefix_substring)] == pred_name[:pred_name.index(pred1_prefix_substring)], 'File names do not match :'+raw_name+', '+pred_name
-    assert pred_name[:pred_name.index(pred1_prefix_substring)] == pred_name2[:pred_name2.index(pred2_prefix_substring)], 'File names do not match.'
-    assert pred_name2[:pred_name2.index(pred2_prefix_substring)] == pred_name3[:pred_name3.index(pred3_prefix_substring)], 'File names do not match'
+    assert raw_name[:raw_name.index(raw_prefix_substring)] == pred_name[:pred_name.index(pred1_prefix_substring)], print('File names do not match :'+raw_name+', '+pred_name)
+    assert pred_name[:pred_name.index(pred1_prefix_substring)] == pred_name2[:pred_name2.index(pred2_prefix_substring)], print('File names do not match.')
+    assert pred_name2[:pred_name2.index(pred2_prefix_substring)] == pred_name3[:pred_name3.index(pred3_prefix_substring)], print('File names do not match')
 
     print('Loading raw image name: ', raw_name)
     image_raw = tifffile.imread(os.path.join(raw_mask_path, raw_name)).astype('float32')
@@ -113,9 +113,10 @@ for raw_name, pred_name, pred_name2, pred_name3 in tqdm(zip(raw_list, pred_list,
     else:
         print('Mean IoU of {} and {}: {}'.format(pred_name2, pred_name3, np.mean(iou_data3[0, :])))
 
-    data_metrics = np.hstack((iou_data, iou_data2, iou_data3)).tolist()
-    print(data_metrics.shape)
-    stop
+    data_metrics = np.vstack((iou_data, iou_data2, iou_data3)).T.tolist()
+    # print(data_metrics.shape)
+    # stop
+    # data_metrics = data_metrics.tolist()
     
     # ---
     # Saving data
@@ -125,7 +126,7 @@ for raw_name, pred_name, pred_name2, pred_name3 in tqdm(zip(raw_list, pred_list,
     table_metrics = 'mse_metrics_%s.csv' % image_name_noext
 
     with open(os.path.join(save_path, table_metrics), 'a') as outfile_metrics:
-        outfile_metrics.write('z_index,Raw vs x1,x1 vs x2,x2 vs x3,\n')
+        outfile_metrics.write('z_index,iou x1,threshold x1,iou x2,threshold x2,iou x3,threshold x3,\n')
         for i in range(len(data_metrics)):
             line = str(i+1)+','
             for j in range(len(data_metrics[i])):