def resize_one_img_xml(save_dir, resize_ratio, img_xml):
"""将一张训练图片进行 resize"""
# 解析读到的数据
img_path, xml_path = img_xml
#
a = DeteRes(xml_path)
#
if (not os.path.exists(img_path)) or (not os.path.exists(xml_path)):
return
#
if len(a) < 1:
return
#
im = cv2.imdecode(np.fromfile(img_path, dtype=np.uint8), 1)
im_height, im_width = im.shape[:2]
im_height_new, im_width_new = int(im_height * resize_ratio), int(im_width * resize_ratio)
im_new = cv2.resize(im, (im_width_new, im_height_new))
#
# a.height = im_height_new
# a.width = im_width_new
# a.img_path =
# 将每一个 obj 进行 resize
for each_obj in a:
each_obj.x1 = max(1, int(each_obj.x1 * resize_ratio))
each_obj.x2 = min(im_width_new - 1, int(each_obj.x2 * resize_ratio))
each_obj.y1 = max(1, int(each_obj.y1 * resize_ratio))
each_obj.y2 = min(im_height_new - 1, int(each_obj.y2 * resize_ratio))
# 保存 img
save_img_path = os.path.join(save_dir, 'JPEGImages', FileOperationUtil.bang_path(xml_path)[1] + '.jpg')
cv2.imwrite(save_img_path, im_new)
# 保存 xml
a.img_path = save_img_path
save_xml_path = os.path.join(save_dir, 'Annotations', FileOperationUtil.bang_path(xml_path)[1] + '.xml')
a.save_to_xml(save_xml_path)
def resize_train_data(img_dir, xml_dir, save_dir, resize_ratio=0.5):
"""对训练数据进行resize，resize img 和 xml """
save_img_dir = os.path.join(save_dir, 'JPEGImages')
save_xml_dir = os.path.join(save_dir, 'Annotations')
os.makedirs(save_xml_dir, exist_ok=True)
os.makedirs(save_img_dir, exist_ok=True)
index = 0
for each_xml_path in FileOperationUtil.re_all_file(xml_dir, endswitch=['.xml']):
print(index, each_xml_path)
index += 1
each_img_path = os.path.join(img_dir, FileOperationUtil.bang_path(each_xml_path)[1] + '.jpg')
resize_one_img_xml(save_dir, resize_ratio, (each_img_path, each_xml_path))
# # 关键点检测
# # 模型的下载路径：http://dlib.net/files/
# predictor = dlib.shape_predictor(r'C:\Users\14271\Desktop\del\shape_predictor_68_face_landmarks.dat')
#
# for det in dets:
# shape = predictor(img, det)
# print(shape.parts())
#
# # 人脸对齐
# my_img = dlib.get_face_chip(img, shape, size=150)
#
# plt.imshow(my_img)
# plt.show()
#
img_dir = r"/home/ldq/20220112_img_from_iphone/img"
save_dir = r"/home/ldq/20220112_img_from_iphone/xml"
detector = dlib.get_frontal_face_detector()
for each_img_path in FileOperationUtil.re_all_file(img_dir, endswitch=['.jpg', '.JPG', '.png', '.PNG']):
print(each_img_path)
each_dete_res = DeteRes(assign_img_path=each_img_path)
img = cv2.imread(each_img_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
dets = detector(img, 1)
for each_shape in dets:
each_dete_res.add_obj(x1=int(each_shape.left()), y1=int(each_shape.top()), x2=int(each_shape.right()), y2=int(each_shape.bottom()), tag='face')
each_dete_res.save_to_xml(os.path.join(save_dir, FileOperationUtil.bang_path(each_img_path)[1] + '.xml'))
# -*- coding: utf-8 -*-
# -*- author: jokker -*-
import os
from JoTools.utils.FileOperationUtil import FileOperationUtil
img_dir = r"C:\Users\14271\Desktop\for_unet_data"
index = 0
for each_img_path in FileOperationUtil.re_all_file(img_dir,
endswitch=['.jpg', '.JPG']):
dir, name, suffix = FileOperationUtil.bang_path(each_img_path)
each_json_path = os.path.join(img_dir, name + ".json")
if not os.path.exists(each_json_path):
index += 1
print(index, each_img_path)
print(index, each_json_path)
os.remove(each_img_path)
from PIL import Image
import os
"""
* mask 的理想状态是每一个对象用一个不同的 int 值表示出来
* mask 的次理想状态是每一个对象用相同的 int 值表示出来
"""
img_dir = r"C:\Users\14271\Desktop\mask_test_res_019\img"
mask_dir = r"C:\Users\14271\Desktop\mask_test_res_019\mask"
save_dir = r"C:\Users\14271\Desktop\mask_test_res_019\json"
for each_img_path in FileOperationUtil.re_all_file(img_dir,
endswitch=['.jpg']):
each_mask_path = os.path.join(
mask_dir,
FileOperationUtil.bang_path(each_img_path)[1] + '_mask.png')
each_save_path = os.path.join(
save_dir,
FileOperationUtil.bang_path(each_img_path)[1] + '.json')
if not os.path.exists(each_mask_path):
print("* mask 文件不存在")
continue
else:
print(each_mask_path)
a = SegmentRes()
a.img_path = each_img_path
a.get_segment_obj_from_mask(each_mask_path, each_mask_point_numb=60)
a.save_to_josn(each_save_path)
if isinstance(img_mat, str):
img_mat = cv2.imdecode(np.fromfile(img_mat, dtype=np.uint8), 1)
#
rect = four_point_transform(img_mat, np.array(four_points))
#
if save_path:
# cv2.imwrite(save_path, rect)
cv2.imencode('.jpg', rect)[1].tofile(save_path)
return rect
if __name__ == "__main__":
xml_point_dir = r"C:\Users\14271\Desktop\jizhuangxiang\img"
img_dir = r"C:\Users\14271\Desktop\jizhuangxiang\img"
save_dir = r"C:\Users\14271\Desktop\jizhuangxiang\crop"
for each_json_path in FileOperationUtil.re_all_file(xml_point_dir,
endswitch=['.json']):
img_path = os.path.join(
img_dir,
FileOperationUtil.bang_path(each_json_path)[1] + '.png')
four_points = JsonUtil.load_data_from_json_file(
each_json_path)["shapes"][0]['points']
each_save_path = os.path.join(
save_dir,
FileOperationUtil.bang_path(each_json_path)[1] + '.jpg')
# transform
transform_img_with_4_point(img_path, four_points, each_save_path)
import numpy as np
from labelme import utils
import labelme
import cv2
from PIL import Image
import os
json_dir = r"C:\data\004_绝缘子污秽\002_测试标图流程\json"
crop_dir = r"C:\data\004_绝缘子污秽\002_测试标图流程\crop_box_fix\crop_box"
save_dir = r"C:\data\004_绝缘子污秽\002_测试标图流程\json_fix"
index = 1
for each_json_path in FileOperationUtil.re_all_file(json_dir,
endswitch=['.json']):
img_name = FileOperationUtil.bang_path(each_json_path)[1]
save_json_path = os.path.join(save_dir, img_name + '.json')
include_labels = []
a = SegmentRes()
a.parse_json_info(json_path=each_json_path,
parse_mask=False,
parse_img=False)
# 找到未被删除的 crop
for i in range(len(a)):
each_label = "test{0}".format(i + 1)
each_crop_path = os.path.join(
crop_dir, "{0}_{1}.jpg".format(img_name, each_label))
if os.path.exists(each_crop_path):
include_labels.append(each_label)
region_img_dir = r"C:\data\fzc_优化相关资料\dataset_fzc\000_train_data_step_1\JPEGImages"
region_xml_dir = r"C:\data\fzc_优化相关资料\dataset_fzc\000_train_data_step_1\Annotations"
new_img_dir = r"C:\Users\14271\Desktop\wuhan_006_fzc\JPEGImages"
new_xml_dir = r"C:\Users\14271\Desktop\wuhan_006_fzc\Annotations"
new_xml_path_list = []
new_img_path_list = []
index = 0
for each_img_path in FileOperationUtil.re_all_file(
new_img_dir, endswitch=['.jpg', '.JPG', '.png', '.PNG']):
# print(index, each_img_path)
index += 1
each_xml_path = os.path.join(
new_xml_dir,
FileOperationUtil.bang_path(each_img_path)[1] + '.xml')
#
each_new_img_path = os.path.join(region_img_dir,
os.path.split(each_img_path)[1])
each_new_xml_path = os.path.join(region_xml_dir,
os.path.split(each_xml_path)[1])
if os.path.exists(each_new_img_path):
print("* img path exists : {0}".format(each_new_img_path))
continue
if os.path.exists(each_new_xml_path):
print("* xml path exists : {0}".format(each_new_xml_path))
continue
new_img_path_list.append(each_img_path)
save_img_dir = os.path.join(save_dir, 'JPEGImages')
os.makedirs(save_img_dir, exist_ok=True)
save_xml_dir = os.path.join(save_dir, 'Annotations')
os.makedirs(save_xml_dir, exist_ok=True)
for each_img_path in FileOperationUtil.re_all_file(img_dir, endswitch=['.jpg', '.JPG', '.png', '.PNG']):
print(each_img_path)
each_md5 = HashLibUtil.get_file_md5(each_img_path)
img_new_path = os.path.join(save_img_dir, "{0}.jpg".format(each_md5))
xml_new_path = os.path.join(save_xml_dir, "{0}.xml".format(each_md5))
#
each_xml_path = os.path.join(xml_dir, FileOperationUtil.bang_path(each_img_path)[1] + '.xml')
#
if os.path.exists(each_xml_path):
# shutil.copy(each_img_path, img_new_path)
# shutil.copy(each_xml_path, xml_new_path)
a = DeteRes(each_xml_path)
if len(a) < 1:
continue
shutil.move(each_img_path, img_new_path)
shutil.move(each_xml_path, xml_new_path)