upload dataset file to repo

lisa_data/pascal_part.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_pascal_part(base_image_dir):
+    coco_api_pascal_part = COCO(
+        os.path.join(base_image_dir, "vlpart", "pascal_part", "train.json")
+    )
+    all_classes = coco_api_pascal_part.loadCats(coco_api_pascal_part.getCatIds())
+    class_map_pascal_part = {}
+    for cat in all_classes:
+        cat_main, cat_part = cat["name"].strip().split(":")
+        name = (cat_main, cat_part)
+        class_map_pascal_part[cat["id"]] = name
+    img_ids = coco_api_pascal_part.getImgIds()
+    print("pascal_part: ", len(img_ids))
+    return class_map_pascal_part, img_ids, coco_api_pascal_part
+
+
+base_image_dir = '/mnt/workspace/workgroup/yuanyq/code/LISA/dataset'
+class_map, img_ids, coco_api = init_pascal_part(base_image_dir)
+final_data = []
+for idx in tqdm(range(len(img_ids))):
+    dic = {}
+    
+    img_id = img_ids[idx]
+    image_info = coco_api.loadImgs([img_id])[0]
+    file_name = image_info["file_name"]
+    file_name = os.path.join(
+        "VOCdevkit", "VOC2010", "JPEGImages", 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'] = 'vlpart/pascal_part/'+file_name
+    dic['cat'] = cats
+    dic['masks'] = masks
+    final_data.append(dic)
+    
+    
+print(len(final_data))
+with open('pascal_part.json', 'w') as f:
+    f.write(json.dumps(final_data))