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110 lines
3.9 KiB
110 lines
3.9 KiB
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. |
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# |
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# Licensed under the Apache License, Version 2.0 (the "License"); |
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# you may not use this file except in compliance with the License. |
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# You may obtain a copy of the License at |
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# |
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# http://www.apache.org/licenses/LICENSE-2.0 |
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# |
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# Unless required by applicable law or agreed to in writing, software |
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# distributed under the License is distributed on an "AS IS" BASIS, |
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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# See the License for the specific language governing permissions and |
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# limitations under the License. |
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import os |
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import os.path as osp |
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import shutil |
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import cv2 |
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import numpy as np |
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import json |
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import argparse |
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import glob |
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from tqdm import tqdm |
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from PIL import Image |
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from collections import defaultdict |
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from utils import Timer |
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def _mkdir_p(path): |
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if not osp.exists(path): |
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os.makedirs(path) |
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def _save_palette(label, save_path): |
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bin_colormap = np.ones((256, 3)) * 255 |
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bin_colormap[0, :] = [0, 0, 0] |
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bin_colormap = bin_colormap.astype(np.uint8) |
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visualimg = Image.fromarray(label, "P") |
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palette = bin_colormap |
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visualimg.putpalette(palette) |
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visualimg.save(save_path, format='PNG') |
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def _save_mask(annotation, image_size, save_path): |
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mask = np.zeros(image_size, dtype=np.int32) |
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for contour_points in annotation: |
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contour_points = np.array(contour_points).reshape((-1, 2)) |
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contour_points = np.round(contour_points).astype(np.int32)[ |
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np.newaxis, :] |
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cv2.fillPoly(mask, contour_points, 1) |
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_save_palette(mask.astype("uint8"), save_path) |
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def _read_geojson(json_path): |
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with open(json_path, "r") as f: |
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jsoner = json.load(f) |
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imgs = jsoner["images"] |
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images = defaultdict(list) |
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sizes = defaultdict(list) |
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for img in imgs: |
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images[img["id"]] = img["file_name"] |
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sizes[img["file_name"]] = (img["height"], img["width"]) |
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anns = jsoner["annotations"] |
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annotations = defaultdict(list) |
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for ann in anns: |
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annotations[images[ann["image_id"]]].append(ann["segmentation"]) |
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return annotations, sizes |
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@Timer |
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def convert_data(raw_folder, end_folder): |
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print("-- Initializing --") |
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img_folder = osp.join(raw_folder, "images") |
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save_img_folder = osp.join(end_folder, "img") |
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save_lab_folder = osp.join(end_folder, "gt") |
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_mkdir_p(save_img_folder) |
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_mkdir_p(save_lab_folder) |
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names = os.listdir(img_folder) |
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print("-- Loading annotations --") |
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anns = {} |
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sizes = {} |
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jsons = glob.glob(osp.join(raw_folder, "*.json")) |
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for json in jsons: |
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j_ann, j_size = _read_geojson(json) |
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anns.update(j_ann) |
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sizes.update(j_size) |
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print("-- Converting datas --") |
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for k in tqdm(names): |
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# for k in tqdm(anns.keys()): |
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img_path = osp.join(img_folder, k) |
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img_save_path = osp.join(save_img_folder, k) |
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ext = "." + k.split(".")[-1] |
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lab_save_path = osp.join(save_lab_folder, k.replace(ext, ".png")) |
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shutil.copy(img_path, img_save_path) |
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if k in anns.keys(): |
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_save_mask(anns[k], sizes[k], lab_save_path) |
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else: # have not anns |
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_save_palette(np.zeros(sizes[k], dtype="uint8"), \ |
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lab_save_path) |
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parser = argparse.ArgumentParser(description="input parameters") |
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parser.add_argument("--raw_folder", type=str, required=True, \ |
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help="The folder path about original data, where `images` saves the original image, `annotation.json` saves the corresponding annotation information.") |
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parser.add_argument("--save_folder", type=str, required=True, \ |
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help="The folder path to save the results, where `img` saves the image and `gt` saves the label.") |
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if __name__ == "__main__": |
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args = parser.parse_args() |
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convert_data(args.raw_folder, args.save_folder)
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