Upload LLaVA-Next-3D/data_precessing/MGrounding_process.py with huggingface_hub

LLaVA-Next-3D/data_precessing/MGrounding_process.py

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+import json
+import pdb
+import re
+from tqdm import tqdm
+
+data = json.load(open('/mnt/petrelfs/wangzehan/data/MGrounding-630k/Group_Grounding/gg_train_120k.json', 'r'))
+
+llava_data = []
+
+idx = 0
+
+for item in tqdm(data):
+    conv = item['conversations']
+
+    for i in range(len(conv)//2):
+        question = conv[2*i]['value']
+        answer = conv[2*i+1]['value']
+
+        caption = question.split('<|object_ref_start|>')[-1].split('<|object_ref_end|>')[0]
+        if "It's in the first image" in answer:
+            gt = 'The object is located at: Frame-1: '
+        elif "It's in the second image" in answer:
+            gt = 'The object is located at: Frame-1: '
+        elif "It's in the third image" in answer:
+            gt = 'The object is located at: Frame-3: '
+        elif "It's in the fourth image" in answer:
+            gt = 'The object is located at: Frame-4: '
+        elif "It's in the fifth image" in answer:
+            gt = 'The object is located at: Frame-5: ' 
+
+        coords_str = re.findall(r'(\d+)', answer)
+        coords_list = [round(int(coord)/1000, 2) for coord in coords_str]
+        gt += str(coords_list)
+
+        llava_item = {"id": idx, 
+                "video": item['images'],
+                "conversations": [{"value": f"<image> Identify the object according to the following description.\n {caption}", "from": "human"}, \
+                                    {"value": gt, "from": "gpt"}], 
+                "metadata": {"dataset": "MGrounding_refer"}}
+        llava_data.append(llava_item)
+        idx += 1
+
+pdb.set_trace()
+json.dump(llava_data, open('extra_data/annotation/MGrounding_group_grounding_llava_format.json', 'w'))