def __init__(self,folder_A,folder_B):
# folder A and folder B should have exactly the same content's file name
self.folder_A = folder_A
self.folder_B = folder_B
self.list_A = utils.get_dir_filelist_by_extension(folder_A,'jpg')
self.list_B = utils.get_dir_filelist_by_extension(folder_B,'jpg')
if self.list_A!=self.list_B:
print('两个文件夹内容不符！')
exit()
else:
self.start()
def __init__(self, config, shuffle=False, phase="train"):
image_extention = 'bmp'
if phase == "train":
img_list = get_dir_filelist_by_extension(
dir=config['train']['data_folder'] + '/images',
ext=image_extention)
elif phase == "test" or "val":
img_list = get_dir_filelist_by_extension(
dir=config['val']['data_folder'] + '/images',
ext=image_extention)
else:
raise Exception("wrong phase! should be train test or val")
img_list.sort()
all_image_and_anno_paths = []
for img_path in img_list:
xmlname = img_path.split('/')[-1].replace(image_extention, 'xml')
if phase == "train":
anno_path = config['train'][
'data_folder'] + '/annotations/' + xmlname
elif phase == "test" or "val":
anno_path = config['val'][
'data_folder'] + '/annotations/' + xmlname
all_image_and_anno_paths.append({
'image_path': img_path,
'anno_path': anno_path
})
self.image_anno_list = all_image_and_anno_paths
self.batch_size = config['model']['batch_size']
self.image_size = config['model']['image_size']
self.grid = config['model']['grid']
self.classes = config['model']['classes']
self.anchors = config['model']['anchors']
self.shuffle = shuffle
if self.shuffle:
self.shuffle_data()
# image augmentation
sometimes = lambda aug: iaa.Sometimes(0.5, aug)
self.aug_pipe = iaa.Sequential([
SquarePad(),
sometimes(iaa.Fliplr(p=1)),
sometimes(iaa.Flipud(p=1)),
iaa.Scale({
"height": self.image_size[0],
"width": self.image_size[1]
})
])
def __init__(self, dir, train_count):
self.dir = dir
self.file_list = utils.get_dir_filelist_by_extension(self.dir,
ext='jpg')
self.parent_dir, tail = os.path.split(os.path.abspath(
self.dir)) # 使用abspath函数先规范化路径
random.shuffle(self.file_list)
self.train_count = train_count
self.start_split()
def step2(tmpfile):
f = open(tmpfile)
working_dir = f.read()
f.close()
d = utils.get_dir_subdir(working_dir + '/predicts', True)
pics = []
for subd in d:
imgs_path = utils.get_dir_filelist_by_extension(subd, 'jpg', True)
for k in imgs_path:
img = cv2.imread(k, flags=cv2.IMREAD_GRAYSCALE)
pics.append(img)
print('%s 处理完成' % subd)
b = utils.get_dir_filelist_by_extension(d[0], 'jpg')
a = len(b)
utils.view_pics(pics,
a,
b,
output_full_path='./compareresult/' +
working_dir.replace('./', '').replace(' ', '-') + '.png')
def start_sift(self):
self.images_list = utils.get_dir_filelist_by_extension(self.images_dir,self.img_ext)
for i,f in enumerate(self.images_list):
img = cv2.imread(f)
height, width, channels = img.shape
ratio = width/height
if height >= self.min_height \
and width >= self.min_width \
and ratio >= self.ratio_limit[0] \
and ratio <= self.ratio_limit[1]:
dst_name = self.out_dir+f.split('/')[-1]
# dst_name = dst_name.replace('.'+self.img_ext,'.jpg')
cv2.imwrite(dst_name,img)
print('已保存',dst_name)
def export_image_and_label_selfdefine(labels_folder_path,images_folder):
parent_dir, _ = utils.get_parent_dir(labels_folder_path)
target_dir = utils.create_new_empty_dir(parent_dir + '/images/')
file_list = utils.get_dir_filelist_by_extension(dir=labels_folder_path, ext='xml')
for file in file_list:
tree = ET.ElementTree(file=file)
for elem in tree.iter(tag='filename'):
file_name = elem.text
full_path = images_folder+'/'+file_name
try:
shutil.copy2(src=full_path, dst=target_dir + '/' + file_name)
except:
print(full_path + '复制失败')
def paste_one_bg(self):
#读取背景图及前景图
bg_names = utils.get_dir_filelist_by_extension(self.bg_dir, 'jpg')
rect_names = utils.get_dir_filelist_by_extension(self.rect_dir, 'jpg')
rect_num = random.randint(self.pic_min, self.pic_max)
bg = cv2.imread(random.choice(bg_names))
random_rects_name = random.choices(rect_names, k=rect_num)
self.rect_classes = []
for rect_name in random_rects_name:
rect_class = self.get_class_by_name(rect_name)
self.rect_classes.append(rect_class)
all_rects = []
if rect_num > 0:
for rect in random_rects_name:
all_rects.append(cv2.imread(rect))
#生成待粘贴区域
while True:
rois = utils.generate_rects(
num=rect_num,
width=all_rects[0].shape[0],
height=all_rects[0].shape[1],
prison=[0, 0, bg.shape[0], bg.shape[1]])
print('roi num:', len(rois))
if self.interaction_rects(rois) == True:
print('有相交')
else:
print('无相交')
print(rois)
break
#开始粘贴
for i, roi in enumerate(rois):
x1, y1, x2, y2 = roi[0], roi[1], roi[2], roi[3]
bg[y1:y2, x1:x2] = all_rects[i] # y1:y2,x1:x2
self.rois = rois
return bg
def start_scale(self):
self.images_list = utils.get_dir_filelist_by_extension(self.images_dir,self.img_ext)
for i,f in enumerate(self.images_list):
img = cv2.imread(f)
height, width, channels = img.shape
if self.scale != 0:
new_img = cv2.resize(img,dsize=(0,0),fx=self.scale,fy=self.scale)
if self.width !=0 and self.height==0:#按照宽度成比例缩放
new_height = int((height/width)*self.width)
new_img = cv2.resize(img,dsize=(self.width,new_height))
if self.height !=0 and self.width == 0:
new_width = int((width/height)*self.height)
new_img = cv2.resize(img,dsize=(new_width,self.width))
if self.height!=0 and self.width!=0:
new_img = cv2.resize(img,dsize=(self.width,self.height))
dst_name = self.out_dir+f.split('/')[-1]
dst_name = dst_name.replace('.'+self.img_ext,'.jpg')
cv2.imwrite(dst_name,new_img)
print('已保存',dst_name)
def delete_empty_label(labels_folder_path):
file_list = utils.get_dir_filelist_by_extension(dir=labels_folder_path, ext='xml')
# print(file_list)
delete_list = []
good_labels_count = 0
for file in file_list:
tree = ET.ElementTree(file=file)
root = tree.getroot()
contains_label = False
for elem in tree.iter(tag='object'):
contains_label = True
if not contains_label:
delete_list.append(file)
else:
good_labels_count += 1
for file in delete_list:
os.remove(file)
print(file + ' 已删除')
print('保留下来的标记文件共有%d个' % good_labels_count)
def step1(path):
kinds = ['Cr', 'In', 'Pa', 'PS', 'RS', 'Sc']
sets = ['new_plates', 'aluminum']
current_kind = ''
current_set = ''
for kind in kinds:
if kind in path:
current_kind = kind
break
for se in sets:
if se in path:
current_set = se
break
print('path:', path)
print(current_set, current_kind)
# exit()
gan = CycleGAN('/plates/单独训练集_B_%s/%s' % (current_set, current_kind))
model_files_list = utils.get_dir_filelist_by_extension(
path + '/saved_model', ext='h5', with_parent_path=True)
mflist = []
for mf in model_files_list:
if 'gAB' in mf:
mflist.append(mf)
mflist.sort()
for model_file in mflist:
# np.random.seed(0)
random.seed(0)
a = model_file.split('.')[0].split('/')[-1].split('_')[-1]
gan.translate(model_file,
total_num=10,
output_dir=gan.output_dir + '/predicts/' + a)
f = open('./tmp.txt', mode='w')
f.write(gan.output_dir)
f.close()
from lbp_score import LBP, LBP_Score
from cyclegan_plates import CycleGAN
import utils
import json
from matplotlib import pyplot as plt
'''
1. 读取文件夹内的模型
2. 通过模型和原始样本，生成新样本，存储在临时文件夹下
3. 计算原始集合生成集的lbpscore，并存储lbpscore
4. 清空临时文件夹，开始下一个循环。
'''
a = utils.get_dir_filelist_by_extension('2018-03-27 09_11_11 Sc/saved_model',
'h5',
with_parent_path=True)
a.sort()
print(a)
source_folder = 'datasets/plates/单独训练集/Sc/trainA'
target_folder = './tmp/translation'
# for model in a:
b = utils.get_dir_filelist_by_extension(source_folder, ext='jpg')
all_lbp_scores = []
k = 1
for model in a:
gan = CycleGAN(dataset_name='/plates/单独训练集/Sc')
gan.translate(model_path=model, total_num=len(b), output_dir=target_folder)
ls = LBP_Score(folder_A=source_folder, folder_B=target_folder)
s = ls.get_lbp_score()
lbp_score = round(s, 4)
import random
if __name__ == "__main__":
root_dir = sys.argv[1]
parent_dir, _ = utils.get_parent_dir(root_dir)
target_dir = os.path.join(parent_dir, 'results')
utils.create_new_empty_dir(target_dir)
os.mkdir(os.path.join(target_dir, 'images'))
os.mkdir(os.path.join(target_dir, 'annotations'))
sub_dirs = utils.get_immediate_subdirectories(root_dir)
total_count = 0
for sub_dir in sub_dirs:
sub_dir = os.path.join(root_dir, sub_dir)
image_dir = os.path.join(sub_dir, 'images')
image_file_list = utils.get_dir_filelist_by_extension(image_dir, 'bmp')
total_count += len(image_file_list)
print('图像总数：%d' % total_count)
file_paths = []
for i in range(1, total_count + 1):
new_name = utils.fixed_length(i, 4)
a = {
'image':
os.path.join(target_dir, 'images', new_name + '.bmp'),
'annotation':
os.path.join(target_dir, 'annotations', new_name + '.xml')
}
file_paths.append(a)
random.seed(1)
random.shuffle(file_paths)
image_index = 0
def change_tag_text(file_list, target_dir):
for file in file_list:
tree = ET.ElementTree(file=file)
root = tree.getroot()
# print(root.tag)
# print(root.attrib)
print(file)
for elem in tree.iter(tag='path'):
# file_name = elem.text
# index = file_name.find('.')
# ext = file_name[index:]
_, fn = utils.get_parent_dir(file)
fn = fn.replace('xml', 'bmp')
elem.text = ''
# print(elem.text)
tree.write(target_dir + os.path.basename(file))
if __name__ == "__main__":
xmls_dir = '/Users/shidanlifuhetian/All/data/KHB_ANNO/USTB中厚板检测数据集/test/xmls1'
# 工具作用：规范xml内容，filename规范为真实文件名
file_list = utils.get_dir_filelist_by_extension(dir=xmls_dir, ext='xml')
parent_dir, _ = utils.get_parent_dir(xmls_dir)
utils.create_new_empty_dir(parent_dir + '/xmls2/')
target_dir = parent_dir + '/xmls2/'
change_tag_text(file_list, target_dir)
