add data processing demo script

utils/data_proc_demo.py

@@ -0,0 +1,83 @@
+import os
+import numpy as np
+import json
+import cv2
+import glob
+
+def get_mask_from_json(json_path, img):
+    try:
+        with open(json_path, 'r') as r:
+            anno = json.loads(r.read())     
+    except:
+        with open(json_path, 'r', encoding="cp1252") as r:
+            anno = json.loads(r.read())     
+
+    inform = anno['shapes']
+    comments = anno['text']
+    is_sentence = anno['is_sentence']
+
+    height, width = img.shape[:2]    
+
+    ### sort polies by area
+    area_list = []
+    valid_poly_list = []
+    for i in inform:
+        label_id = i['label']
+        points = i['points']
+        if 'flag' == label_id.lower():  ## meaningless deprecated annotations
+            continue     
+                        
+        tmp_mask = np.zeros((height, width), dtype=np.uint8)
+        cv2.polylines(tmp_mask, np.array([points], dtype=np.int32), True, 1, 1)
+        cv2.fillPoly(tmp_mask, np.array([points], dtype=np.int32), 1)
+        tmp_area = tmp_mask.sum()
+
+        area_list.append(tmp_area)
+        valid_poly_list.append(i)
+
+    ### ground-truth mask
+    sort_index = np.argsort(area_list)[::-1].astype(np.int32)
+    sort_index = list(sort_index)
+    sort_inform = []
+    for s_idx in sort_index:
+        sort_inform.append(valid_poly_list[s_idx])
+
+    mask = np.zeros((height, width), dtype=np.uint8)
+    for i in sort_inform:
+        label_id = i['label']
+        points = i['points']
+        
+        if 'ignore' in label_id.lower():  
+            label_value = 255       # ignored during evaluation
+        else:   
+            label_value = 1         # target
+                        
+        cv2.polylines(mask, np.array([points], dtype=np.int32), True, label_value, 1)
+        cv2.fillPoly(mask, np.array([points], dtype=np.int32), label_value)
+
+    return mask, comments, is_sentence
+
+
+if __name__ == '__main__':
+    data_dir = './train'
+    vis_dir = './vis'
+
+    if not os.path.exists(vis_dir):
+        os.makedirs(vis_dir)
+
+    json_path_list = sorted(glob.glob(data_dir + '/*.json'))
+    for json_path in json_path_list:
+        img_path = json_path.replace('.json', '.jpg')
+        img = cv2.imread(img_path)[:,:,::-1]
+
+        # In generated mask, value 1 denotes valid target region, and value 255 stands for region ignored during evaluaiton.   
+        mask, comments, is_sentence = get_mask_from_json(json_path, img)   
+
+        ## visualization. Green for target, and red for ignore.
+        valid_mask = (mask == 1).astype(np.float32)[:,:,None]
+        ignore_mask = (mask == 255).astype(np.float32)[:,:,None]
+        vis_img = img * (1 - valid_mask) * (1 - ignore_mask) + ((np.array([0,255,0]) * 0.6 + img * 0.4) * valid_mask + (np.array([255,0,0]) * 0.6 + img * 0.4) * ignore_mask) 
+        vis_img = np.concatenate([img, vis_img], 1)
+        vis_path = os.path.join(vis_dir, json_path.split('/')[-1].replace('.json', '.jpg'))
+        cv2.imwrite(vis_path, vis_img[:,:,::-1])
+        print('Visualization has been saved to: ', vis_path)