rm labelme2yolo.py
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Wang Xin
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labelme2yolo.py
358
labelme2yolo.py
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'''
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Created on Aug 18, 2021
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@author: xiaosonh
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@author: GreatV(Wang Xin)
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'''
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import os
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import sys
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import argparse
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import shutil
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import math
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import base64
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import io
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from collections import OrderedDict
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from multiprocessing import Pool
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import json
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import cv2
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from sklearn.model_selection import train_test_split
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import numpy as np
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import PIL.ExifTags
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import PIL.Image
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import PIL.ImageOps
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# copy form https://github.com/wkentaro/labelme/blob/main/labelme/utils/image.py
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def img_data_to_pil(img_data):
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f = io.BytesIO()
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f.write(img_data)
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img_pil = PIL.Image.open(f)
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return img_pil
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# copy form https://github.com/wkentaro/labelme/blob/main/labelme/utils/image.py
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def img_data_to_arr(img_data):
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img_pil = img_data_to_pil(img_data)
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img_arr = np.array(img_pil)
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return img_arr
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# copy form https://github.com/wkentaro/labelme/blob/main/labelme/utils/image.py
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def img_b64_to_arr(img_b64):
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img_data = base64.b64decode(img_b64)
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img_arr = img_data_to_arr(img_data)
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return img_arr
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# copy form https://github.com/wkentaro/labelme/blob/main/labelme/utils/image.py
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def img_pil_to_data(img_pil):
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f = io.BytesIO()
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img_pil.save(f, format="PNG")
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img_data = f.getvalue()
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return img_data
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# copy form https://github.com/wkentaro/labelme/blob/main/labelme/utils/image.py
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def img_arr_to_b64(img_arr):
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img_pil = PIL.Image.fromarray(img_arr)
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f = io.BytesIO()
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img_pil.save(f, format="PNG")
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img_bin = f.getvalue()
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if hasattr(base64, "encodebytes"):
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img_b64 = base64.encodebytes(img_bin)
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else:
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img_b64 = base64.encodestring(img_bin)
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return img_b64
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# copy form https://github.com/wkentaro/labelme/blob/main/labelme/utils/image.py
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def img_data_to_png_data(img_data):
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with io.BytesIO() as f:
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f.write(img_data)
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img = PIL.Image.open(f)
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with io.BytesIO() as f:
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img.save(f, "PNG")
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f.seek(0)
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return f.read()
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# copy form https://github.com/wkentaro/labelme/blob/main/labelme/utils/image.py
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def apply_exif_orientation(image):
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try:
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exif = image._getexif()
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except AttributeError:
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exif = None
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if exif is None:
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return image
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exif = {
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PIL.ExifTags.TAGS[k]: v
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for k, v in exif.items()
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if k in PIL.ExifTags.TAGS
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}
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orientation = exif.get("Orientation", None)
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if orientation == 1:
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# do nothing
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return image
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elif orientation == 2:
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# left-to-right mirror
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return PIL.ImageOps.mirror(image)
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elif orientation == 3:
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# rotate 180
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return image.transpose(PIL.Image.ROTATE_180)
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elif orientation == 4:
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# top-to-bottom mirror
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return PIL.ImageOps.flip(image)
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elif orientation == 5:
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# top-to-left mirror
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return PIL.ImageOps.mirror(image.transpose(PIL.Image.ROTATE_270))
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elif orientation == 6:
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# rotate 270
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return image.transpose(PIL.Image.ROTATE_270)
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elif orientation == 7:
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# top-to-right mirror
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return PIL.ImageOps.mirror(image.transpose(PIL.Image.ROTATE_90))
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elif orientation == 8:
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# rotate 90
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return image.transpose(PIL.Image.ROTATE_90)
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else:
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return image
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class Labelme2YOLO(object):
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def __init__(self, json_dir):
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self._json_dir = json_dir
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self._label_id_map = self._get_label_id_map(self._json_dir)
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def _make_train_val_dir(self):
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self._label_dir_path = os.path.join(self._json_dir,
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'YOLODataset/labels/')
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self._image_dir_path = os.path.join(self._json_dir,
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'YOLODataset/images/')
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for yolo_path in (os.path.join(self._label_dir_path + 'train/'),
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os.path.join(self._label_dir_path + 'val/'),
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os.path.join(self._label_dir_path + 'test/'),
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os.path.join(self._image_dir_path + 'train/'),
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os.path.join(self._image_dir_path + 'val/'),
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os.path.join(self._image_dir_path + 'test/')):
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if os.path.exists(yolo_path):
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shutil.rmtree(yolo_path)
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os.makedirs(yolo_path)
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def _get_label_id_map(self, json_dir):
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label_set = set()
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for file_name in os.listdir(json_dir):
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if file_name.endswith('json'):
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json_path = os.path.join(json_dir, file_name)
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data = json.load(open(json_path))
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for shape in data['shapes']:
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label_set.add(shape['label'])
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return OrderedDict([(label, label_id) \
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for label_id, label in enumerate(label_set)])
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def _train_test_split(self, folders, json_names, val_size, test_size):
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if len(folders) > 0 and 'train' in folders and 'val' in folders and 'test' in folders:
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train_folder = os.path.join(self._json_dir, 'train/')
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train_json_names = [train_sample_name + '.json' \
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for train_sample_name in os.listdir(train_folder) \
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if os.path.isdir(os.path.join(train_folder, train_sample_name))]
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val_folder = os.path.join(self._json_dir, 'val/')
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val_json_names = [val_sample_name + '.json' \
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for val_sample_name in os.listdir(val_folder) \
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if os.path.isdir(os.path.join(val_folder, val_sample_name))]
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test_folder = os.path.join(self._json_dir, 'test/')
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test_json_names = [test_sample_name + '.json' \
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for test_sample_name in os.listdir(test_folder) \
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if os.path.isdir(os.path.join(test_folder, test_sample_name))]
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return train_json_names, val_json_names, test_json_names
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train_idxs, val_idxs = train_test_split(range(len(json_names)),
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test_size=val_size)
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tmp_train_len = len(train_idxs)
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test_idxs = []
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if test_size > 1e-8:
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train_idxs, test_idxs = train_test_split(range(tmp_train_len), test_size=test_size / (1 - val_size))
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train_json_names = [json_names[train_idx] for train_idx in train_idxs]
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val_json_names = [json_names[val_idx] for val_idx in val_idxs]
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test_json_names = [json_names[test_idx] for test_idx in test_idxs]
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return train_json_names, val_json_names, test_json_names
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def convert(self, val_size, test_size):
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json_names = [file_name for file_name in os.listdir(self._json_dir) \
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if os.path.isfile(os.path.join(self._json_dir, file_name)) and \
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file_name.endswith('.json')]
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folders = [file_name for file_name in os.listdir(self._json_dir) \
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if os.path.isdir(os.path.join(self._json_dir, file_name))]
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train_json_names, val_json_names, test_json_names = self._train_test_split(folders, json_names, val_size, test_size)
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self._make_train_val_dir()
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# convert labelme object to yolo format object, and save them to files
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# also get image from labelme json file and save them under images folder
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for target_dir, json_names in zip(('train/', 'val/', 'test/'),
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(train_json_names, val_json_names, test_json_names)):
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pool = Pool(os.cpu_count() - 1)
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for json_name in json_names:
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pool.apply_async(self.covert_json_to_text, args=(target_dir, json_name))
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pool.close()
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pool.join()
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print('Generating dataset.yaml file ...')
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self._save_dataset_yaml()
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def covert_json_to_text(self, target_dir, json_name):
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json_path = os.path.join(self._json_dir, json_name)
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json_data = json.load(open(json_path))
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print('Converting %s for %s ...' % (json_name, target_dir.replace('/', '')))
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img_path = self._save_yolo_image(json_data,
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json_name,
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self._image_dir_path,
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target_dir)
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yolo_obj_list = self._get_yolo_object_list(json_data, img_path)
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self._save_yolo_label(json_name,
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self._label_dir_path,
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target_dir,
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yolo_obj_list)
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def convert_one(self, json_name):
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json_path = os.path.join(self._json_dir, json_name)
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json_data = json.load(open(json_path))
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print('Converting %s ...' % json_name)
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img_path = self._save_yolo_image(json_data, json_name,
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self._json_dir, '')
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yolo_obj_list = self._get_yolo_object_list(json_data, img_path)
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self._save_yolo_label(json_name, self._json_dir,
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'', yolo_obj_list)
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def _get_yolo_object_list(self, json_data, img_path):
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yolo_obj_list = []
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img_h, img_w, _ = cv2.imread(img_path).shape
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for shape in json_data['shapes']:
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# labelme circle shape is different from others
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# it only has 2 points, 1st is circle center, 2nd is drag end point
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if shape['shape_type'] == 'circle':
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yolo_obj = self._get_circle_shape_yolo_object(shape, img_h, img_w)
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else:
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yolo_obj = self._get_other_shape_yolo_object(shape, img_h, img_w)
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yolo_obj_list.append(yolo_obj)
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return yolo_obj_list
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def _get_circle_shape_yolo_object(self, shape, img_h, img_w):
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obj_center_x, obj_center_y = shape['points'][0]
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radius = math.sqrt((obj_center_x - shape['points'][1][0]) ** 2 +
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(obj_center_y - shape['points'][1][1]) ** 2)
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obj_w = 2 * radius
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obj_h = 2 * radius
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yolo_center_x= round(float(obj_center_x / img_w), 6)
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yolo_center_y = round(float(obj_center_y / img_h), 6)
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yolo_w = round(float(obj_w / img_w), 6)
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yolo_h = round(float(obj_h / img_h), 6)
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label_id = self._label_id_map[shape['label']]
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return label_id, yolo_center_x, yolo_center_y, yolo_w, yolo_h
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def _get_other_shape_yolo_object(self, shape, img_h, img_w):
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def __get_object_desc(obj_port_list):
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__get_dist = lambda int_list: max(int_list) - min(int_list)
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x_lists = [port[0] for port in obj_port_list]
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y_lists = [port[1] for port in obj_port_list]
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return min(x_lists), __get_dist(x_lists), min(y_lists), __get_dist(y_lists)
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obj_x_min, obj_w, obj_y_min, obj_h = __get_object_desc(shape['points'])
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yolo_center_x= round(float((obj_x_min + obj_w / 2.0) / img_w), 6)
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yolo_center_y = round(float((obj_y_min + obj_h / 2.0) / img_h), 6)
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yolo_w = round(float(obj_w / img_w), 6)
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yolo_h = round(float(obj_h / img_h), 6)
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label_id = self._label_id_map[shape['label']]
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return label_id, yolo_center_x, yolo_center_y, yolo_w, yolo_h
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def _save_yolo_label(self, json_name, label_dir_path, target_dir, yolo_obj_list):
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txt_path = os.path.join(label_dir_path,
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target_dir,
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json_name.replace('.json', '.txt'))
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with open(txt_path, 'w+') as f:
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for yolo_obj_idx, yolo_obj in enumerate(yolo_obj_list):
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yolo_obj_line = '%s %s %s %s %s\n' % yolo_obj \
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if yolo_obj_idx + 1 != len(yolo_obj_list) else \
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'%s %s %s %s %s' % yolo_obj
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f.write(yolo_obj_line)
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def _save_yolo_image(self, json_data, json_name, image_dir_path, target_dir):
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img_name = json_name.replace('.json', '.png')
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img_path = os.path.join(image_dir_path, target_dir,img_name)
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if not os.path.exists(img_path):
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img = img_b64_to_arr(json_data['imageData'])
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PIL.Image.fromarray(img).save(img_path)
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return img_path
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def _save_dataset_yaml(self):
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yaml_path = os.path.join(self._json_dir, 'YOLODataset/', 'dataset.yaml')
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with open(yaml_path, 'w+') as yaml_file:
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yaml_file.write('train: %s\n' % \
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os.path.join(self._image_dir_path, 'train/'))
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yaml_file.write('val: %s\n\n' % \
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os.path.join(self._image_dir_path, 'val/'))
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yaml_file.write('test: %s\n\n' % \
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os.path.join(self._image_dir_path, 'test/'))
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yaml_file.write('nc: %i\n\n' % len(self._label_id_map))
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names_str = ''
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for label, _ in self._label_id_map.items():
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names_str += "'%s', " % label
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names_str = names_str.rstrip(', ')
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yaml_file.write('names: [%s]' % names_str)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--json_dir',type=str,
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help='Please input the path of the labelme json files.')
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parser.add_argument('--val_size',type=float, nargs='?', default=None,
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help='Please input the validation dataset size, for example 0.1 ')
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parser.add_argument('--test_size',type=float, nargs='?', default=0.0,
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help='Please input the validation dataset size, for example 0.1 ')
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parser.add_argument('--json_name',type=str, nargs='?', default=None,
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help='If you put json name, it would convert only one json file to YOLO.')
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args = parser.parse_args(sys.argv[1:])
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convertor = Labelme2YOLO(args.json_dir)
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if args.json_name is None:
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convertor.convert(val_size=args.val_size, test_size=args.test_size)
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else:
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convertor.convert_one(args.json_name)
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