upload dataset file to repo

lisa_data/ref_merge.py

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+import json
+import random
+from tqdm import tqdm
+DEFAULT_IMAGE_TOKEN = '<image>'
+
+SHORT_QUESTION = [
+    "Can you segment the {class_name} in this image?",
+    "Please segment the {class_name} in this image.",
+    "What is {class_name} in this image? Please respond with segmentation mask.",
+    "What is {class_name} in this image? Please output segmentation mask.",
+    "Could you identify and segment the {class_name} in this image?",
+    "Would you be able to segment the {class_name} in this image?",
+    "Can you provide a segmentation mask for the {class_name} in this image?",
+    "Please provide a segmentation mask for the {class_name} in this image.",
+    "Could you please segment the {class_name} in this image for me?",
+    "What {class_name} is present in this image? Kindly respond with a segmentation mask.",
+    "Which part of this image contains {class_name}? Please output with segmentation mask.",
+    "Is there a {class_name} in this image? If so, please provide the segmentation mask.",
+    "Can you segment out the {class_name} visible in this image?",
+    "Would you identify and provide a segmentation mask for the {class_name} in this image?",
+]
+    
+ANSWER_LIST = [
+    "It is [SEG].",
+    "Sure, [SEG].",
+    "Sure, it is [SEG].",
+    "Sure, the segmentation result is [SEG].",
+    "[SEG].",
+]
+
+NO_MASK_LIST = [
+    "Sorry, there is no {class_name} in the image.",
+    "There is no {class_name} in the image.",
+    "It seems that there is no {class_name} in the image.",
+    "It appears that the image doesn't contain any {class_name}.",
+    "Unfortunately, I'm unable to find any {class_name} in the image.",
+    "I regret to inform you that there is no {class_name} detected in the image.",
+    "The image seems to lack any presence of {class_name}.",
+    "It looks like there are no {class_name} visible in the image.",
+    "I'm sorry, but there doesn't appear to be any {class_name} in the image.",
+    "It seems like the image does not include any {class_name}.",
+    "I'm afraid there is no sign of {class_name} in the image.",
+    "Regrettably, the image does not feature any {class_name}.",
+    "There doesn't seem to be any {class_name} found in the image.",
+]
+    
+data_list = [
+    'refcoco.json',
+    'refcocog.json',
+    'refcoco+.json',
+    # 'grefcoco.json'
+]
+
+img_dic = {}
+final_data = []
+json_data = json.load(open('grefcoco.json'))
+for data in json_data:
+    img_dic[data['image']] = data
+
+for d_ in tqdm(data_list):
+    json_data = json.load(open(d_))
+    for data in tqdm(json_data):
+        if data['image'] in img_dic:
+            for i in range(len(data['cat'])):
+                if data['cat'][i] in img_dic[data['image']]['cat']:
+                    continue
+                img_dic[data['image']]['cat'].append(data['cat'][i])
+                img_dic[data['image']]['masks'].append([data['masks'][i]])
+            
+idx = 0
+for k in tqdm(img_dic):
+    data = img_dic[k]
+    if len(data['masks'])!=len(data['cat']):
+        print('err')
+    indices = list(range(len(data['masks'])))
+
+    # 打乱索引
+    random.shuffle(indices)
+
+    data['masks'] = [data['masks'][i] for i in indices]
+    data['cat'] = [data['cat'][i] for i in indices]
+    
+    s = 0
+    while s<len(data['masks']):
+        num = random.randint(2, 5)
+        dic = {}
+        dic['id'] = f'ref_{idx}'
+        idx+=1
+        dic['image'] = data['image']
+        dic['height'] = data['height']
+        dic['width'] = data['width']
+        dic['conversations'] = []
+        dic['masks'] = []
+        for i in range(num):
+            if i+s>=len(data['masks']):
+                break
+            dic['conversations'].append({'from': 'human', 'value': random.choice(SHORT_QUESTION).format(class_name=data['cat'][i+s].lower())})
+            if data['masks'][i+s] is None:
+                dic['conversations'].append({'from': 'gpt', 'value': random.choice(NO_MASK_LIST).format(class_name=data['cat'][i+s].lower())})
+            else:
+                dic['conversations'].append({'from': 'gpt', 'value': random.choice(ANSWER_LIST).replace('[SEG]', '[SEG]'*len(data['masks'][i+s]))})
+                for ann in data['masks'][i+s]:
+                    dic['masks'].append(ann)
+        dic['conversations'][0]['value'] = '<image>\n' + dic['conversations'][0]['value']
+        s+=num
+        final_data.append(dic)
+print(len(final_data))
+
+with open('ref_all.json', 'w') as f:
+    f.write(json.dumps(final_data, indent=4))