File size: 2,051 Bytes
32d7e3b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 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)) |