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import numpy as np |
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import json |
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import os |
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from pycocotools import mask as maskUtils |
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from PIL import Image |
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from tqdm import tqdm |
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def singleMask2rle(mask): |
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rle = maskUtils.encode(np.array(mask[:, :, None], order='F', dtype="uint8"))[0] |
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rle["counts"] = rle["counts"].decode("utf-8") |
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return rle |
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def init_ade20k(base_image_dir): |
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with open("../ade20k_classes.json", "r") as f: |
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ade20k_classes = json.load(f) |
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ade20k_classes = np.array(ade20k_classes) |
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image_ids = sorted( |
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os.listdir(os.path.join(base_image_dir, "ade20k/images", "training")) |
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) |
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ade20k_image_ids = [] |
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for x in image_ids: |
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if x.endswith(".jpg"): |
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ade20k_image_ids.append(x[:-4]) |
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ade20k_images = [] |
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for image_id in ade20k_image_ids: |
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ade20k_images.append( |
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os.path.join( |
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base_image_dir, |
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"ade20k", |
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"images", |
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"training", |
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"{}.jpg".format(image_id), |
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) |
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) |
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ade20k_labels = [ |
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x.replace(".jpg", ".png").replace("images", "annotations") |
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for x in ade20k_images |
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] |
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print("ade20k: ", len(ade20k_images)) |
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return ade20k_classes, ade20k_images, ade20k_labels |
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base_image_dir = '/mnt/workspace/workgroup/yuanyq/code/LISA/dataset' |
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classes, images, labels = init_ade20k(base_image_dir) |
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final_data = [] |
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for idx in tqdm(range(len(images))): |
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dic = {} |
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image_path = images[idx] |
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label_path = labels[idx] |
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label = Image.open(label_path) |
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label = np.array(label) |
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label[label == 0] = 255 |
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label -= 1 |
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label[label == 254] = 255 |
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unique_label = np.unique(label).tolist() |
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if 255 in unique_label: |
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unique_label.remove(255) |
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if len(unique_label) == 0: |
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continue |
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cats = [] |
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for class_id in unique_label: |
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cats.append(classes[class_id]) |
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masks = [] |
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for class_id in unique_label: |
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msk = label==class_id |
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rle = singleMask2rle(msk) |
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masks.append(rle) |
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dic['image'] = image_path.replace('/mnt/workspace/workgroup/yuanyq/code/LISA/dataset/', '') |
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dic['cat'] = cats |
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dic['masks'] = masks |
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final_data.append(dic) |
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print(len(final_data)) |
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with open('ade20k.json', 'w') as f: |
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f.write(json.dumps(final_data)) |
