upload dataset file to repo

lisa_data/ade.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
+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_ade20k(base_image_dir):
+    with open("../ade20k_classes.json", "r") as f:
+        ade20k_classes = json.load(f)
+    ade20k_classes = np.array(ade20k_classes)
+    image_ids = sorted(
+        os.listdir(os.path.join(base_image_dir, "ade20k/images", "training"))
+    )
+    ade20k_image_ids = []
+    for x in image_ids:
+        if x.endswith(".jpg"):
+            ade20k_image_ids.append(x[:-4])
+    ade20k_images = []
+    for image_id in ade20k_image_ids:  # self.descriptions:
+        ade20k_images.append(
+            os.path.join(
+                base_image_dir,
+                "ade20k",
+                "images",
+                "training",
+                "{}.jpg".format(image_id),
+            )
+        )
+    ade20k_labels = [
+        x.replace(".jpg", ".png").replace("images", "annotations")
+        for x in ade20k_images
+    ]
+    print("ade20k: ", len(ade20k_images))
+    return ade20k_classes, ade20k_images, ade20k_labels
+
+base_image_dir = '/mnt/workspace/workgroup/yuanyq/code/LISA/dataset'
+classes, images, labels = init_ade20k(base_image_dir)
+final_data = []
+for idx in tqdm(range(len(images))):
+    dic = {}
+    image_path = images[idx]
+    label_path = labels[idx]
+    label = Image.open(label_path)
+    label = np.array(label)
+   
+    label[label == 0] = 255
+    label -= 1
+    label[label == 254] = 255
+    
+    unique_label = np.unique(label).tolist()
+    if 255 in unique_label:
+        unique_label.remove(255)
+    if len(unique_label) == 0:
+        continue
+    
+    cats = []
+    for class_id in unique_label:
+        cats.append(classes[class_id])
+    masks = []
+    for class_id in unique_label:
+        msk = label==class_id
+        rle = singleMask2rle(msk)
+        masks.append(rle)
+    
+    dic['image'] = image_path.replace('/mnt/workspace/workgroup/yuanyq/code/LISA/dataset/', '')
+    dic['cat'] = cats
+    dic['masks'] = masks
+    final_data.append(dic)
+    
+print(len(final_data))
+with open('ade20k.json', 'w') as f:
+    f.write(json.dumps(final_data))