upload dataset file to repo

lisa_data/paco.py

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+import numpy as np
+import json
+import os
+from pycocotools import mask as maskUtils
+from PIL import Image
+from tqdm import tqdm
+from pycocotools.coco import COCO
+import random
+def singleMask2rle(mask):
+    rle = maskUtils.encode(np.array(mask[:, :, None], order='F', dtype="uint8"))[0]
+    rle["counts"] = rle["counts"].decode("utf-8")
+    return rle
+
+def init_paco_lvis(base_image_dir):
+    coco_api_paco_lvis = COCO(
+        os.path.join(
+            base_image_dir, "vlpart", "paco", "annotations", "paco_lvis_v1_train.json"
+        )
+    )
+    all_classes = coco_api_paco_lvis.loadCats(coco_api_paco_lvis.getCatIds())
+    class_map_paco_lvis = {}
+    for cat in all_classes:
+        cat_split = cat["name"].strip().split(":")
+        if len(cat_split) == 1:
+            name = cat_split[0].split("_(")[0]
+        else:
+            assert len(cat_split) == 2
+            obj, part = cat_split
+            obj = obj.split("_(")[0]
+            part = part.split("_(")[0]
+            name = (obj, part)
+        class_map_paco_lvis[cat["id"]] = name
+    img_ids = coco_api_paco_lvis.getImgIds()
+    print("paco_lvis: ", len(img_ids))
+    return class_map_paco_lvis, img_ids, coco_api_paco_lvis
+
+
+base_image_dir = '/mnt/workspace/workgroup/yuanyq/code/LISA/dataset'
+class_map, img_ids, coco_api = init_paco_lvis(base_image_dir)
+final_data = []
+for idx in tqdm(range(len(img_ids))):
+    try:
+        dic = {}
+        
+        img_id = img_ids[idx]
+        image_info = coco_api.loadImgs([img_id])[0]
+        file_name = image_info["file_name"]
+
+        annIds = coco_api.getAnnIds(imgIds=image_info["id"])
+        anns = coco_api.loadAnns(annIds)
+        cats = []
+        
+        for ann in anns:
+            sampled_cls = class_map[ann["category_id"]]
+            if isinstance(sampled_cls, tuple):
+                obj, part = sampled_cls
+                if random.random() < 0.5:
+                    name = obj + " " + part
+                else:
+                    name = "the {} of the {}".format(part, obj)
+            else:
+                name = sampled_cls
+            cats.append(name)
+        masks = []  
+        for ann in anns:
+            masks.append(singleMask2rle(coco_api.annToMask(ann)))
+
+        dic['image'] = 'coco/'+file_name
+        dic['cat'] = cats
+        dic['masks'] = masks
+        final_data.append(dic)
+
+    except:
+        continue
+
+    
+print(len(final_data))
+with open('paco_lvis.json', 'w') as f:
+    f.write(json.dumps(final_data))