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))