upload dataset file to repo

lisa_data/mapillary.py

@@ -0,0 +1,66 @@
+import numpy as np
+import json
+import os
+from pycocotools import mask as maskUtils
+from PIL import Image
+from tqdm import tqdm
+import glob
+
+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_mapillary(base_image_dir):
+    mapillary_data_root = os.path.join(base_image_dir, "mapillary")
+    with open(os.path.join(mapillary_data_root, "config_v2.0.json")) as f:
+        mapillary_classes = json.load(f)["labels"]
+    mapillary_classes = [x["readable"].lower() for x in mapillary_classes]
+    mapillary_classes = np.array(mapillary_classes)
+    mapillary_labels = sorted(
+        glob.glob(
+            os.path.join(mapillary_data_root, "training", "v2.0", "labels", "*.png")
+        )
+    )
+    mapillary_images = [
+        x.replace(".png", ".jpg").replace("v2.0/labels", "images")
+        for x in mapillary_labels
+    ]
+    print("mapillary: ", len(mapillary_images))
+    return mapillary_classes, mapillary_images, mapillary_labels
+
+
+
+base_image_dir = '/mnt/workspace/workgroup/yuanyq/code/LISA/dataset'
+classes, images, labels = init_mapillary(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)
+   
+    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('mapillary.json', 'w') as f:
+    f.write(json.dumps(final_data))