oiv6 process

data/process.py

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+'''
+Rename the Open Images V6 images to adapt pycocotools and produce the corresponding annotations.
+The Visual Genome annotations are produced in the same way.
+'''
+import json
+import os
+
+root_path = 'your_path/open-imagev6/' # Download images from Rongjie's repo and unzip it.
+
+
+with open(root_path +'annotations/'+ 'categories_dict.json') as f:
+    categories = json.load(f)
+obj_categories = categories['obj']
+rel_categories = categories['rel']
+
+categories = []
+for idx, i in enumerate(obj_categories):
+    category = {'supercategory': i, 'id': idx, 'name': i}
+    categories.append(category)
+
+with open(root_path+'annotations/'+'vrd-train-anno.json') as f:
+    train_image_list = json.load(f)
+
+counter = 0
+new_image_name = 1
+
+images = []
+annotations = []
+train_rel = {}
+#
+
+for row in train_image_list:
+    image_path = root_path+'images/'+row['img_fn']+'.jpg' #TODO image_id
+    w, h = row['img_size']
+    os.rename(image_path, root_path+'images/'+str(new_image_name)+'.jpg')
+
+    image = {'file_name': str(new_image_name)+'.jpg',
+              'height': h,
+              'width': w,
+              'id': new_image_name}
+    images.append(image)
+
+    for index, j in enumerate(row['bbox']):
+
+        bbox = [j[0], j[1], j[2]-j[0], j[3]-j[1]] #cxcywh
+        area = int(bbox[2] * bbox[3])
+        anno_id = counter
+        counter = counter + 1
+
+        annotation = {'segmentation': None,
+                      'area': area,
+                      'bbox': bbox,
+                      'iscrowd': 0,
+                      'image_id': new_image_name,
+                      'id': anno_id,
+                      'category_id': row['det_labels'][index]}
+        annotations.append(annotation)
+
+    train_rel[new_image_name] = row['rel']
+
+    new_image_name = new_image_name + 1
+
+train_database = {'images': images,
+                 'annotations': annotations,
+                 'categories': categories}
+
+print('train finish')
+
+with open(root_path+'annotations/'+'vrd-test-anno.json') as f:
+    test_image_list = json.load(f)
+
+images = []
+annotations = []
+test_rel = {}
+
+for row in test_image_list:
+    image_path = root_path+'images/'+row['img_fn']+'.jpg' #TODO image_id
+    w, h = row['img_size']
+    os.rename(image_path, root_path+'images/'+str(new_image_name)+'.jpg')
+
+    image = {'file_name': str(new_image_name)+'.jpg',
+              'height': h,
+              'width': w,
+              'id': new_image_name}
+    images.append(image)
+
+    for index, j in enumerate(row['bbox']):
+
+        bbox = [j[0], j[1], j[2]-j[0], j[3]-j[1]] #cxcywh
+        area = int(bbox[2] * bbox[3])
+        anno_id = counter
+        counter = counter + 1
+
+        annotation = {'segmentation': None,
+                      'area': area,
+                      'bbox': bbox,
+                      'iscrowd': 0,
+                      'image_id': new_image_name,
+                      'id': anno_id,
+                      'category_id': row['det_labels'][index]}
+        annotations.append(annotation)
+
+    test_rel[new_image_name] = row['rel']
+
+    new_image_name = new_image_name + 1
+
+test_database = {'images': images,
+                 'annotations': annotations,
+                 'categories': categories}
+
+print('test finish')
+
+
+with open(root_path+'annotations/'+'vrd-val-anno.json') as f:
+    val_image_list = json.load(f)
+
+images = []
+annotations = []
+val_rel = {}
+
+for row in val_image_list:
+    image_path = root_path+'images/'+row['img_fn']+'.jpg' #TODO image_id
+    w, h = row['img_size']
+    os.rename(image_path, root_path+'images/'+str(new_image_name)+'.jpg')
+
+    image = {'file_name': str(new_image_name)+'.jpg',
+              'height': h,
+              'width': w,
+              'id': new_image_name}
+    images.append(image)
+
+    for index, j in enumerate(row['bbox']):
+
+        bbox = [j[0], j[1], j[2]-j[0], j[3]-j[1]] #cxcywh
+        area = int(bbox[2] * bbox[3])
+        anno_id = counter
+        counter = counter + 1
+
+        annotation = {'segmentation': None,
+                      'area': area,
+                      'bbox': bbox,
+                      'iscrowd': 0,
+                      'image_id': new_image_name,
+                      'id': anno_id,
+                      'category_id': row['det_labels'][index]}
+        annotations.append(annotation)
+
+    val_rel[new_image_name] = row['rel']
+
+    new_image_name = new_image_name + 1
+
+val_database = {'images': images,
+                 'annotations': annotations,
+                 'categories': categories}
+
+print('test finish')
+
+rel_database = {'train': train_rel,
+                'val': val_rel,
+                'test': test_rel,
+                'rel_categories': rel_categories}
+
+
+json.dump(train_database, open('train.json', 'w'))
+json.dump(val_database, open('val.json', 'w'))
+json.dump(test_database, open('test.json', 'w'))
+json.dump(rel_database, open('rel.json', 'w'))