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from data import ABDataset
from utils.common.data_record import read_json
from PIL import Image
import os
from utils.common.file import ensure_dir
import numpy as np
from itertools import groupby
from skimage import morphology, measure
from PIL import Image
from scipy import misc
import tqdm
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
import shutil
def convert_seg_dataset_to_cls(seg_imgs_path, seg_labels_path, target_cls_data_dir, ignore_classes_idx, thread_i, min_img_size=224, label_after_hook=lambda x: x):
"""
Reference: https://blog.csdn.net/lizaijinsheng/article/details/119889946
NOTE:
Background class should not be considered.
However, if a seg dataset has only one valid class, so that the generated cls dataset also has only one class and
the cls accuracy will be 100% forever. But we do not use the generated cls dataset alone, so it is ok.
"""
assert len(seg_imgs_path) == len(seg_labels_path)
classes_imgs_id_map = {}
for seg_img_path, seg_label_path in tqdm.tqdm(zip(seg_imgs_path, seg_labels_path), total=len(seg_imgs_path),
dynamic_ncols=True, leave=False, desc=f'thread {thread_i}'):
try:
seg_img = Image.open(seg_img_path)
seg_label = Image.open(seg_label_path).convert('L')
seg_label = np.array(seg_label)
seg_label = label_after_hook(seg_label)
except Exception as e:
print(e)
print(f'file {seg_img_path} error, skip')
exit()
# seg_img = Image.open(seg_img_path)
# seg_label = Image.open(seg_label_path).convert('L')
# seg_label = np.array(seg_label)
this_img_classes = set(seg_label.reshape(-1).tolist())
# print(this_img_classes)
for class_idx in this_img_classes:
if class_idx in ignore_classes_idx:
continue
if class_idx not in classes_imgs_id_map.keys():
classes_imgs_id_map[class_idx] = 0
mask = np.zeros((seg_label.shape[0], seg_label.shape[1]), dtype=np.uint8)
mask[seg_label == class_idx] = 1
mask_without_small = morphology.remove_small_objects(mask, min_size=10, connectivity=2)
label_image = measure.label(mask_without_small)
for region in measure.regionprops(label_image):
bbox = region.bbox # (top, left, bottom, right)
bbox = [bbox[1], bbox[0], bbox[3], bbox[2]] # (left, top, right, bottom)
width, height = bbox[2] - bbox[0], bbox[3] - bbox[1]
if width < min_img_size or height < min_img_size:
continue
target_cropped_img_path = os.path.join(target_cls_data_dir, str(class_idx),
f'{classes_imgs_id_map[class_idx]}.{seg_img_path.split(".")[-1]}')
ensure_dir(target_cropped_img_path)
seg_img.crop(bbox).save(target_cropped_img_path)
# print(target_cropped_img_path)
# exit()
classes_imgs_id_map[class_idx] += 1
num_cls_imgs = 0
for k, v in classes_imgs_id_map.items():
# print(f'# class {k}: {v + 1}')
num_cls_imgs += v
# print(f'total: {num_cls_imgs}')
return classes_imgs_id_map
from concurrent.futures import ThreadPoolExecutor
# def convert_seg_dataset_to_cls_multi_thread(seg_imgs_path, seg_labels_path, target_cls_data_dir, ignore_classes_idx, num_threads):
# if os.path.exists(target_cls_data_dir):
# shutil.rmtree(target_cls_data_dir)
# assert len(seg_imgs_path) == len(seg_labels_path)
# n = len(seg_imgs_path) // num_threads
# pool = ThreadPoolExecutor(max_workers=num_threads)
# # threads = []
# futures = []
# for thread_i in range(num_threads):
# # thread = threading.Thread(target=convert_seg_dataset_to_cls,
# # args=(seg_imgs_path[thread_i * n: (thread_i + 1) * n],
# # seg_labels_path[thread_i * n: (thread_i + 1) * n],
# # target_cls_data_dir, ignore_classes_idx))
# # threads += [thread]
# future = pool.submit(convert_seg_dataset_to_cls, *(seg_imgs_path[thread_i * n: (thread_i + 1) * n],
# seg_labels_path[thread_i * n: (thread_i + 1) * n],
# target_cls_data_dir, ignore_classes_idx, thread_i))
# futures += [future]
# futures += [
# pool.submit(convert_seg_dataset_to_cls, *(seg_imgs_path[(thread_i + 1) * n: ],
# seg_labels_path[(thread_i + 1) * n: ],
# target_cls_data_dir, ignore_classes_idx, thread_i))
# ]
# for f in futures:
# f.done()
# res = []
# for f in futures:
# res += [f.result()]
# print(res[-1])
# res_dist = {}
# for r in res:
# for k, v in r.items():
# if k in res_dist.keys():
# res_dist[k] += v
# else:
# res_dist[k] = v
# print('results:')
# print(res_dist)
# pool.shutdown()
import random
def random_crop_aug(target_dir):
for class_dir in os.listdir(target_dir):
class_dir = os.path.join(target_dir, class_dir)
for img_path in os.listdir(class_dir):
img_path = os.path.join(class_dir, img_path)
img = Image.open(img_path)
w, h = img.width, img.height
for ri in range(5):
img.crop(
[
random.randint(0, w // 5),
random.randint(0, h // 5),
random.randint(w // 5 * 4, w),
random.randint(h // 5 * 4, h)
]
).save(
os.path.join(os.path.dirname(img_path), f'randaug_{ri}_' + os.path.basename(img_path))
)
# print(img_path)
# exit()
if __name__ == '__main__':
# SuperviselyPerson
# root_dir = '/data/zql/datasets/supervisely_person/Supervisely Person Dataset'
# images_path, labels_path = [], []
# for p in os.listdir(root_dir):
# if p.startswith('ds'):
# p1 = os.path.join(root_dir, p, 'img')
# images_path += [(p, os.path.join(p1, n)) for n in os.listdir(p1)]
# for dsi, img_p in images_path:
# target_p = os.path.join(root_dir, p, dsi, img_p.split('/')[-1])
# labels_path += [target_p]
# images_path = [i[1] for i in images_path]
# target_dir = '/data/zql/datasets/supervisely_person_for_cls_task'
# if os.path.exists(target_dir):
# shutil.rmtree(target_dir)
# convert_seg_dataset_to_cls(
# seg_imgs_path=images_path,
# seg_labels_path=labels_path,
# target_cls_data_dir=target_dir,
# ignore_classes_idx=[0, 2],
# # num_threads=8
# thread_i=0
# )
# random_crop_aug('/data/zql/datasets/supervisely_person_for_cls_task')
# GTA5
# root_dir = '/data/zql/datasets/GTA-ls-copy/GTA5'
# images_path, labels_path = [], []
# for p in os.listdir(os.path.join(root_dir, 'images')):
# p = os.path.join(root_dir, 'images', p)
# if not p.endswith('png'):
# continue
# images_path += [p]
# labels_path += [p.replace('images', 'labels_gt')]
# target_dir = '/data/zql/datasets/gta5_for_cls_task'
# if os.path.exists(target_dir):
# shutil.rmtree(target_dir)
# convert_seg_dataset_to_cls(
# seg_imgs_path=images_path,
# seg_labels_path=labels_path,
# target_cls_data_dir=target_dir,
# ignore_classes_idx=[],
# thread_i=0
# )
# cityscapes
# root_dir = '/data/zql/datasets/cityscape/'
# def _get_target_suffix(mode: str, target_type: str) -> str:
# if target_type == 'instance':
# return '{}_instanceIds.png'.format(mode)
# elif target_type == 'semantic':
# return '{}_labelIds.png'.format(mode)
# elif target_type == 'color':
# return '{}_color.png'.format(mode)
# else:
# return '{}_polygons.json'.format(mode)
# images_path, labels_path = [], []
# split = 'train'
# images_dir = os.path.join(root_dir, 'leftImg8bit', split)
# targets_dir = os.path.join(root_dir, 'gtFine', split)
# for city in os.listdir(images_dir):
# img_dir = os.path.join(images_dir, city)
# target_dir = os.path.join(targets_dir, city)
# for file_name in os.listdir(img_dir):
# target_types = []
# for t in ['semantic']:
# target_name = '{}_{}'.format(file_name.split('_leftImg8bit')[0],
# _get_target_suffix('gtFine', t))
# target_types.append(os.path.join(target_dir, target_name))
# images_path.append(os.path.join(img_dir, file_name))
# labels_path.append(target_types[0])
# print(images_path[0: 5], '\n', labels_path[0: 5])
# target_dir = '/data/zql/datasets/cityscapes_for_cls_task'
# if os.path.exists(target_dir):
# shutil.rmtree(target_dir)
# convert_seg_dataset_to_cls(
# seg_imgs_path=images_path,
# seg_labels_path=labels_path,
# target_cls_data_dir=target_dir,
# ignore_classes_idx=[],
# # num_threads=8
# thread_i=0
# )
# import shutil
# ignore_target_dir = '/data/zql/datasets/cityscapes_for_cls_task_ignored'
# ignore_label = 255
# raw_idx_map_in_y_transform = {-1: ignore_label, 0: ignore_label, 1: ignore_label, 2: ignore_label,
# 3: ignore_label, 4: ignore_label, 5: ignore_label, 6: ignore_label,
# 7: 0, 8: 1, 9: ignore_label, 10: ignore_label, 11: 2, 12: 3, 13: 4,
# 14: ignore_label, 15: ignore_label, 16: ignore_label, 17: 5,
# 18: ignore_label, 19: 6, 20: 7, 21: 8, 22: 9, 23: 10, 24: 11, 25: 12, 26: 13, 27: 14,
# 28: 15, 29: ignore_label, 30: ignore_label, 31: 16, 32: 17, 33: 18}
# ignore_classes_idx = [k for k, v in raw_idx_map_in_y_transform.items() if v == ignore_label]
# ignore_classes_idx = sorted(ignore_classes_idx)
# for class_dir in os.listdir(target_dir):
# if int(class_dir) in ignore_classes_idx:
# continue
# shutil.move(
# os.path.join(target_dir, class_dir),
# os.path.join(ignore_target_dir, class_dir)
# )
# else:
# shutil.move(
# os.path.join(target_dir, class_dir),
# os.path.join(target_dir, str(raw_idx_map_in_y_transform[int(class_dir)]))
# )
# continue
# print(class_dir)
# exit()
# baidu person
# root_dir = '/data/zql/datasets/baidu_person/clean_images/'
# images_path, labels_path = [], []
# for p in os.listdir(os.path.join(root_dir, 'images')):
# images_path += [os.path.join(root_dir, 'images', p)]
# labels_path += [os.path.join(root_dir, 'profiles', p.split('.')[0] + '-profile.jpg')]
# target_dir = '/data/zql/datasets/baiduperson_for_cls_task'
# # if os.path.exists(target_dir):
# # shutil.rmtree(target_dir)
# def label_after_hook(x):
# x[x > 1] = 1
# return x
# convert_seg_dataset_to_cls(
# seg_imgs_path=images_path,
# seg_labels_path=labels_path,
# target_cls_data_dir=target_dir,
# ignore_classes_idx=[1],
# # num_threads=8
# thread_i=1,
# min_img_size=224,
# label_after_hook=label_after_hook
# )
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