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))
# -*- coding: utf-8 -*-
# -*- author: jokker -*-
import os
from JoTools.utils.FileOperationUtil import FileOperationUtil, FilterFun
img_dir = r"C:\Users\14271\Desktop\del"
for each_img_path in FileOperationUtil.re_all_file(img_dir, func=FilterFun.get_filter_about_file_size(1, mode='bt')):
img_size = os.path.getsize(each_img_path)
print(img_size)
# if img_size == 0:
# os.remove(each_img_path)
# print(each_img_path)
# -*- coding: utf-8 -*-
# -*- author: jokker -*-
import random
import os
import shutil
import cv2
import PIL.Image as Image
from JoTools.operateDeteRes import OperateDeteRes
from JoTools.txkjRes.deteRes import DeteRes, DeteObj
from JoTools.utils.FileOperationUtil import FileOperationUtil
from JoTools.utils.RandomUtil import RandomUtil
xml_dir = r"/home/ldq/tj_dete/merge"
save_dir = r"/home/ldq/tj_dete/merge_new"
OperateDeteRes.get_class_count(xml_dir, print_count=True)
for each_xml_path in FileOperationUtil.re_all_file(xml_dir,
endswitch=['.xml']):
#
a = DeteRes(each_xml_path)
a.filter_by_tags(need_tag=["2"])
save_path = os.path.join(save_dir, os.path.split(each_xml_path)[1])
if len(a) > 0:
a.save_to_xml(save_path)
OperateDeteRes.get_class_count(save_dir, print_count=True)
# -*- coding: utf-8 -*-
# -*- author: jokker -*-
import os
from JoTools.utils.LivpUtil import LivpUtil
from JoTools.utils.FileOperationUtil import FileOperationUtil
livp_dir = r"/home/ldq/livp2jpg/img/heic"
temp_folder = r"C:\Users\14271\Desktop\del\livp\tmp"
save_folder = r"/home/ldq/livp2jpg/res"
for each_heic_path in FileOperationUtil.re_all_file(livp_dir,
endswitch=['.heic']):
save_path = os.path.join(save_folder, each_heic_path[:-4] + 'jpg')
LivpUtil.heic_to_jpg(each_heic_path, save_path)
# assign_code_list = ['040500021','040500022','040500023','040501031','040501032','040501033']
assign_code_list = ['040303021', '040303022']
save_dir = r"C:\Users\14271\Desktop\del\新防振锤数据武汉电科院"
img_dir_list = [
r"\\192.168.3.80\数据\9eagle数据库\peiyu_06.library\images",
r"\\192.168.3.80\数据\9eagle数据库\peiyu_07.library\images",
r"\\192.168.3.80\数据\9eagle数据库\peiyu_11.library\images"
]
for dir_index, img_dir in enumerate(img_dir_list):
for index, each_json_path in enumerate(
FileOperationUtil.re_all_file(img_dir,
lambda x: str(x).endswith('.json'))):
try:
print(dir_index, index, each_json_path)
b = DeteRes()
a = EagleMetaData()
a.load_atts_from_json(each_json_path)
b.img_path = os.path.join(os.path.dirname(each_json_path),
a.name + '.jpg')
if not os.path.exists(b.img_path):
continue
if a.comments is None:
continue
from JoTools.utils.FileOperationUtil import FileOperationUtil
from JoTools.operateDeteRes import OperateDeteRes
from JoTools.utils.JsonUtil import JsonUtil
import prettytable
standard_dir = r"C:\data\fzc_优化相关资料\防振锤优化\000_标准分类测试集\crop_add_broken"
customer_dir = r"C:\Users\14271\Desktop\fzc分类验证结果\fzc_test_res_006"
# OperateDeteRes.cal_acc_classify(standard_dir, customer_dir)
label_list = ["yt", "sm", "gt", "zd_yt", "fzc_broken"]
# todo 解析 json 文件，对比每一个类型在 各个模型上的正确率和召回率
model_dir = r"C:\Users\14271\Desktop\003_test_res"
model_list = FileOperationUtil.re_all_file(model_dir,
lambda x: str(x).endswith('.json'))
all_res = {}
for each_json_path in model_list:
epoch_num = int(each_json_path.split('_')[-2])
js_file = JsonUtil.load_data_from_json_file(each_json_path)
each_res = {'rec': {}, 'acc': {}}
for each in js_file:
type_str, label, val = each[0], each[1], each[3]
each_res[type_str][label] = val
all_res[epoch_num] = each_res
epoch_num_list = list(all_res.keys())
from JoTools.txkjRes.deteRes import DeteRes
from JoTools.utils.FileOperationUtil import FileOperationUtil
import base64
import numpy as np
from labelme import utils
import labelme
import cv2
from PIL import Image
json_dir = r"C:\data\004_绝缘子污秽\val\json"
a = SegmentJson()
dete_res = DeteRes()
for each_json_path in list(
FileOperationUtil.re_all_file(json_dir, endswitch=['.json']))[20:]:
print(each_json_path)
a.parse_json_info(each_json_path, parse_img=True, parse_mask=True)
dete_res.img = Image.fromarray(a.image_data)
for each_obj in a.shapes:
print(each_obj.box)
box = each_obj.box
dete_res.add_obj(box[0], box[1], box[2], box[3], tag=each_obj.label)
b = Image.fromarray(a.mask.astype(np.uint8) * 100)
b.save(r"C:\Users\14271\Desktop\del\112233.png")
# -*- coding: utf-8 -*-
# -*- author: jokker -*-
import os
import random
from JoTools.utils.FileOperationUtil import FileOperationUtil
img_dir = r"D:\data\001_fzc_优化相关资料\dataset_fzc\001_train_data_step_1.5\jieya\zd"
save_dir = r"C:\Users\14271\Desktop\train_vit\3"
# for each_img_path in FileOperationUtil.re_all_file(img_dir, endswitch=['.jpg', '.JPG']):
#
# random_num = random.randrange(1, 1000)
#
# print(random_num)
#
# if random_num > 250:
# os.remove(each_img_path)
img_path_list = list(
FileOperationUtil.re_all_file(img_dir, endswitch=['.jpg', '.JPG']))
FileOperationUtil.move_file_to_folder(img_path_list, save_dir, is_clicp=True)
import numpy as np
from labelme import utils
import labelme
import cv2
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
# -*- author: jokker -*-
import os
from JoTools.utils.FileOperationUtil import FileOperationUtil
from JoTools.utils.PrintUtil import PrintUtil
from JoTools.utils.HashlibUtil import HashLibUtil
# todo 添加移动的记录，这样方便数据的还原
region_img_dir_list = [
r"D:\data\001_fzc_优化相关资料\dataset_fzc\000_train_data_step_1\JPEGImages",
]
new_img_dir = r"F:\20211019_防震锤锈蚀数据清洗\fix_data"
index = 0
for img_index, each_img_path in enumerate(FileOperationUtil.re_all_file(new_img_dir, endswitch=['.jpg', '.JPG', '.png', '.PNG'])):
# 计算 md5 值
md5_str = HashLibUtil.get_file_md5(each_img_path)
for each_img_dir in region_img_dir_list:
# 数据集中的名字
region_img_path = os.path.join(each_img_dir, md5_str + '.jpg')
#
if os.path.exists(region_img_path):
# os.remove(each_img_path)
index += 1
print("{0} | {2} remove : {1}".format(index, each_img_path, img_index))
# -*- coding: utf-8 -*-
# -*- author: jokker -*-
import os
import shutil
from JoTools.utils.FileOperationUtil import FileOperationUtil
img_dir = r"D:\data\001_fzc_优化相关资料\dataset_fzc\001_train_data_step_1.5\jieya\zd\extend"
for each_img_path in FileOperationUtil.re_all_file(img_dir):
print(each_img_path)
img_dir, img_name, suffix = FileOperationUtil.bang_path(each_img_path)
new_img_path = os.path.join(img_dir, img_name + '_extend.' + suffix)
shutil.move(each_img_path, new_img_path)
face_num = res['result']['face_num']
for i in range(face_num):
loc = res['result']['face_list'][i]['location']
x1, y1 = loc['left'], loc['top']
width, height = loc['width'], loc['height']
x2, y2 = x1 + width, y1 + height
face_info.append([int(x1), int(y1), int(x2), int(y2)])
return face_info
# OperateDeteRes.crop_imgs(img_dir, xml_dir=img_dir, save_dir=save_dir)
# todo 测试正脸的图片
for img_path in FileOperationUtil.re_all_file(
img_dir, lambda x: str(x).endswith(('.JPG', '.jpg'))):
dete_res = DeteRes(assign_img_path=img_path)
res = dete_face(img_path)
print(res)
for index, each_res in enumerate(res):
x1, y1, x2, y2 = each_res
dete_res.add_obj(x1=x1,
y1=y1,
x2=x2,
y2=y2,
tag='face',
assign_id=index)
save_path = os.path.join(save_dir, os.path.split(img_path)[1])
dete_res.draw_dete_res(save_path)
time.sleep(3)
# -*- coding: utf-8 -*-
# -*- author: jokker -*-
from JoTools.txkjRes.deteRes import DeteRes, DeteObj
from JoTools.utils.FileOperationUtil import FileOperationUtil
xml_dir = r"C:\data\fzc_优化相关资料\dataset_fzc\999_wait_for_train\武汉电科院_2021_04\xml_new_0.05"
for i in FileOperationUtil.re_all_file(xml_dir, endswitch=['.xml']):
a = DeteRes(i)
a.do_augment([0.05, 0.05, 0.05, 0.05], is_relative=True)
a.save_to_xml(i)
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)
# -*- coding: utf-8 -*- # -*- author: jokker -*- from JoTools.utils.FileOperationUtil import FileOperationUtil assign_dir = r"\\192.168.3.80\大金具-算法\qfm\连接件训练数据集" save_dir = r"C:\Users\14271\Desktop\连接件" FileOperationUtil.move_file_to_folder(FileOperationUtil.re_all_file(assign_dir, endswitch=['.xml']), save_dir, is_clicp=False) config_path = r"D:\Algo\saturn_database\config.ini"
# # 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'))
start_time = time.time()
args = parse_args()
portNum = args.port
# ------------------------------------------------------------------------------------------
model_name = "kkxTC"
save_dir = r"./result"
img_dir = input("输入要测试的文件夹地址: ")
# ------------------------------------------------------------------------------------------
print("-" * 100)
url = 'http://192.168.3.109:' + str(portNum) + '//' + model_name
for each_img_path in FileOperationUtil.re_all_file(
img_dir, lambda x: str(x).endswith((".jpg", ".JPG"))):
each_img_name = os.path.split(each_img_path)[1]
files = {'image': open(each_img_path, 'rb')}
data = {'filename': each_img_name}
res = requests.post(url=url, data=data, files=files)
#
if res.status_code == 200:
res = json.loads(res.text)
#
print('-' * 50)
print(each_img_path)
for alarm_index, each in enumerate(res["alarms"]):
print(" * {0}, {1}".format(alarm_index, each))
a = DeteRes(assign_img_path=each_img_path)
#
import time
def print_img_shape(img_path, times=5):
for _ in range(times):
img = cv2.imread(img_path)
print(img.shape)
if __name__ == "__main__":
start_time = time.time()
img_dir = r"C:\Users\14271\Desktop\del\pillow_cv2"
image_list = list(FileOperationUtil.re_all_file(img_dir, endswitch=['.jpg']))
pool = Pool(4)
for each_img_path in image_list:
pool.apply_async(print_img_shape, (each_img_path, 5, ))
pool.close()
pool.join()
end_time = time.time()
print("use time : {0} s".format(end_time - start_time))
# import PDFMiner
import pdfkit
from JoTools.utils.FileOperationUtil import FileOperationUtil
# 读取 pdf 中的内容:http://www.ityouknow.com/python/2020/01/02/python-pdf-107.html
# url页面转化为pdf
# url = r'https://blog.csdn.net/qq_41185868/article/details/79907936#pdfkit%E4%BD%BF%E7%94%A8%E6%96%B9%E6%B3%95'
file_path = r'C:\Users\Administrator\Desktop\SnowDepth.pdf'
dir_path = r"C:\data\深度学习资料\001_要打印的论文\detection"
# pdfkit.from_url(url, file_path)
# 文本内容转化为pdf
# pdfkit.from_string(u"jokker，呵呵，你说呢", file_path)
pdf_path_list = FileOperationUtil.re_all_file(
dir_path, lambda x: str(x).endswith('.pdf'))
for each_pdf_path in pdf_path_list:
# print(each_pdf_path)
pass
print(pdf_path_list[1])
# # 文件转化为pdf
# pdfkit.from_file(file, file_path)
#
# # 也可以是打开的文件
# with open('file.html') as f:
# pdfkit.from_file(f, 'out.pdf')
#
# print('OK')