1：标注数据集
2：截取数据集新json
3：生成label（利用labelme自带的labelme_json_to_dataset）生成jsondir
4：
分离数据集image，label
import os
import cv2
import numpy as np
#将json文件label转换为到data文件夹
# n=2#n为总共标注的图片数
# for i in range(n):
# os.system('activate labelme & labelme_json_to_dataset F:/项目/线路标杆坠陀/data/txt/json/%d.json -o F:/项目/线路标杆坠陀/data/txt/data/%d_json'%(i,i))
#dst_w=512
#dst_h=512
#dst_shape=(dst_w,dst_h,3)
train_image = './train_image/'
if not os.path.exists(train_image):
os.makedirs(train_image)
train_label = './train_label/'
if not os.path.exists(train_label):
os.makedirs(train_label)
dirlist = os.listdir('newjson/json_dir')
for dir in dirlist:
print(dir)
img=cv2.imread('./newjson/json_dir/%s/img.png'%dir)
label=cv2.imread('./newjson/json_dir/%s/label.png'%dir)
print(img.shape)
label=label/np.max(label[:,:,2])*255
label[:,:,0]=label[:,:,1]=label[:,:,2]
print(np.max(label[:,:,2]))
# cv2.imshow('l',label)
# cv2.waitKey(0)
print(set(label.ravel()))
# image_name = dir.split('_')[0]+'_'+ dir.split('_')[1]
image_name =dir[0:-5]
print(image_name)
cv2.imwrite(train_image+'%s.png'%image_name,img)
cv2.imwrite(train_label+'%s.png'%image_name,label)生成两个文件夹，一个放原图，一个放label的mask二值化后的
5：摆正数据集防止倾斜，mask倾斜情况，保存摆正后的图片
# -*- coding:utf-8 -*-
# author: DuanshengLiu
import cv2
import numpy as np
from numpy.lib.npyio import savez_compressed
def locate_and_correct(img_src, img_mask):
"""
该函数通过cv2对img_mask进行边缘检测，获取车牌区域的边缘坐标(存储在contours中)和最小外接矩形4个端点坐标,
再从车牌的边缘坐标中计算出和最小外接矩形4个端点最近的点即为平行四边形车牌的四个端点,从而实现车牌的定位和矫正
:param img_src: 原始图片
:param img_mask: 通过u_net进行图像分隔得到的二值化图片，车牌区域呈现白色，背景区域为黑色
:return: 定位且矫正后的车牌
"""
# cv2.imshow('img_mask',img_mask)
# cv2.waitKey(0)
# ret,thresh = cv2.threshold(img_mask[:,:,0],0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU) #二值化
# cv2.imshow('thresh',thresh)
# cv2.waitKey(0)
mask=img_mask[:, :, 0].astype(np.uint8)
try:
contours, hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
except: # 防止opencv版本不一致报错
ret, contours, hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if not len(contours): # contours1长度为0说明未检测到车牌
# print("未检测到车牌")
return [], []
else:
Lic_img = []
img_src_copy = img_src.copy() # img_src_copy用于绘制出定位的车牌轮廓
for ii, cont in enumerate(contours):
x, y, w, h = cv2.boundingRect(cont) # 获取最小外接矩形
img_cut_mask = img_mask[y:y + h, x:x + w] # 将标签车牌区域截取出来
# cv2.imshow('img_cut_mask',img_cut_mask)
# cv2.waitKey(0)
# print('w,h,均值,宽高比',w,h,np.mean(img_cut_mask),w/h)
# contours中除了车牌区域可能会有宽或高都是1或者2这样的小噪点，
# 而待选车牌区域的均值应较高，且宽和高不会非常小，因此通过以下条件进行筛选
if np.mean(img_cut_mask) >= 75 and w > 15 and h > 15:
rect = cv2.minAreaRect(cont) # 针对坐标点获取带方向角的最小外接矩形，中心点坐标，宽高，旋转角度
box = cv2.boxPoints(rect).astype(np.int32) # 获取最小外接矩形四个顶点坐标
# cv2.drawContours(img_mask, contours, -1, (0, 0, 255), 2)
# cv2.drawContours(img_mask, [box], 0, (0, 255, 0), 2)
# cv2.imshow('img_mask',img_mask)
# cv2.waitKey(0)
cont = cont.reshape(-1, 2).tolist()
# 由于转换矩阵的两组坐标位置需要一一对应，因此需要将最小外接矩形的坐标进行排序，最终排序为[左上，左下，右上，右下]
box = sorted(box, key=lambda xy: xy[0]) # 先按照左右进行排序，分为左侧的坐标和右侧的坐标
box_left, box_right = box[:2], box[2:] # 此时box的前2个是左侧的坐标，后2个是右侧的坐标
box_left = sorted(box_left, key=lambda x: x[1]) # 再按照上下即y进行排序，此时box_left中为左上和左下两个端点坐标
box_right = sorted(box_right, key=lambda x: x[1]) # 此时box_right中为右上和右下两个端点坐标
box = np.array(box_left + box_right) # [左上，左下，右上，右下]
# print(box)
x0, y0 = box[0][0], box[0][1] # 这里的4个坐标即为最小外接矩形的四个坐标，接下来需获取平行(或不规则)四边形的坐标
x1, y1 = box[1][0], box[1][1]
x2, y2 = box[2][0], box[2][1]
x3, y3 = box[3][0], box[3][1]
def point_to_line_distance(X, Y):
if x2 - x0:
k_up = (y2 - y0) / (x2 - x0) # 斜率不为无穷大
d_up = abs(k_up * X - Y + y2 - k_up * x2) / (k_up ** 2 + 1) ** 0.5
else: # 斜率无穷大
d_up = abs(X - x2)
if x1 - x3:
k_down = (y1 - y3) / (x1 - x3) # 斜率不为无穷大
d_down = abs(k_down * X - Y + y1 - k_down * x1) / (k_down ** 2 + 1) ** 0.5
else: # 斜率无穷大
d_down = abs(X - x1)
return d_up, d_down
d0, d1, d2, d3 = np.inf, np.inf, np.inf, np.inf
l0, l1, l2, l3 = (x0, y0), (x1, y1), (x2, y2), (x3, y3)
for each in cont: # 计算cont中的坐标与矩形四个坐标的距离以及到上下两条直线的距离，对距离和进行权重的添加，成功计算选出四边形的4个顶点坐标
x, y = each[0], each[1]
dis0 = (x - x0) ** 2 + (y - y0) ** 2
dis1 = (x - x1) ** 2 + (y - y1) ** 2
dis2 = (x - x2) ** 2 + (y - y2) ** 2
dis3 = (x - x3) ** 2 + (y - y3) ** 2
d_up, d_down = point_to_line_distance(x, y)
weight = 0.975
if weight * d_up + (1 - weight) * dis0 < d0: # 小于则更新
d0 = weight * d_up + (1 - weight) * dis0
l0 = (x, y)
if weight * d_down + (1 - weight) * dis1 < d1:
d1 = weight * d_down + (1 - weight) * dis1
l1 = (x, y)
if weight * d_up + (1 - weight) * dis2 < d2:
d2 = weight * d_up + (1 - weight) * dis2
l2 = (x, y)
if weight * d_down + (1 - weight) * dis3 < d3:
d3 = weight * d_down + (1 - weight) * dis3
l3 = (x, y)
print([l0,l1,l2,l3])
for l in [l0, l1, l2, l3]:
cv2.circle(img=img_mask, color=(0, 255, 255), center=tuple(l), thickness=2, radius=2)
# cv2.imshow('img_mask',img_mask)
# cv2.waitKey(0)
p0 = np.float32([l0, l1, l2, l3]) # 左上角，左下角，右上角，右下角，p0和p1中的坐标顺序对应，以进行转换矩阵的形成
p1 = np.float32([(0, 0), (0, 160), (32, 0), (32, 160)]) # 我们所需的长方形
transform_mat = cv2.getPerspectiveTransform(p0, p1) # 构成转换矩阵
lic = cv2.warpPerspective(img_src, transform_mat, (32, 160)) # 进行车牌矫正
# cv2.imshow('lic',lic)
# cv2.waitKey(0)
Lic_img.append(lic)
cv2.drawContours(img_src_copy, [np.array([l0, l1, l3, l2])], -1, (0, 255, 0), 2) # 在img_src_copy上绘制出定位的车牌轮廓，(0, 255, 0)表示绘制线条为绿色
return img_src_copy, Lic_img
# for i in range(1):
# print(i
# )
import os
save_path = 'jiuzhenghoutup'
dirlist = os.listdir('./train_image')
for dir in dirlist:
img=cv2.imread('./train_image/%s'%dir)
label=cv2.imread('./train_label/%s'%dir,cv2.COLOR_BGR2GRAY)
# print(label.shape)
srcimg,licimg=locate_and_correct(img,label)
print(len(licimg))
print(licimg[0].shape)
# cv2.imshow('xiugai',srcimg)
# cv2.imshow('xiugai1',licimg[0])
cv2.imencode('.jpg',licimg[0])[1].tofile(save_path + '/'+dir)
# cv2.waitKey(0)
# cv2.WIN6：这里是ocr，需要对摆正后的图片进行文字标注利用，这个代码网上有，如有需要可联系我。
import sys, os
from PyQt5.QtWidgets import QMainWindow, QFileDialog, QGraphicsPixmapItem, QGraphicsScene, QApplication,QMessageBox
from PyQt5.QtGui import QPixmap
import LabelOCR, LogBrowser
from glob import glob
import time
img_formats = ['.bmp', '.jpg', '.jpeg', '.png'] # 支持的图片格式
label_formats = [".txt"] # 支持的标注文件格式
# 标识符定义
LAST_PAGE = 0
NEXT_PAGE = 2
TO_PAGE = 1
# logger标识符
SAVE_LABEL = 0
JUMP_PAGE = 1
SHOW_PAGE = 2
LOAD_IMG = 3
LOAD_LABEL = 4
class MainCode(QMainWindow, LabelOCR.Ui_MainWindow):
def __init__(self):
QMainWindow.__init__(self)
LabelOCR.Ui_MainWindow.__init__(self)
self.setupUi(self)
self.btn_load_img.clicked.connect(self.load_img)
self.btn_load_text.clicked.connect(self.load_text)
self.btn_last_page.clicked.connect(lambda: self.go_to_page(LAST_PAGE)) # 传参0,1,2 表示 上一页，指定页，下一页
self.btn_next_page.clicked.connect(lambda: self.go_to_page(NEXT_PAGE))
self.btn_go.clicked.connect(lambda: self.go_to_page(TO_PAGE))
self.horizontalSlider.valueChanged.connect(self.show_img_rescled)
self.btn_save.clicked.connect(self.save_file)
self.btn_show_log.clicked.connect(self.show_history_log)
self.log_window = LogWindow()
self.init()
def init(self):
self.img_dir = None
self.img_list = None
self.image_files = [] # 图片文件名list
self.text_dir = None
self.text_list = None
self.img_now = None
self.text_now = None
self.text_now_path = None
self.page_now = 0
self.img_num_total = 0
self.pixmap = None
self.page_init()
self.screen_size_init()
def page_init(self):
self.resize_scale = 10 # 10 就是1倍， 50就是5倍
self.horizontalSlider.setSliderPosition(self.resize_scale)
def screen_size_init(self):
self.resize(800, 600)
pass
def load_img(self):
img_dir = QFileDialog.getExistingDirectory(self, "选取文件夹") # 起始路径
if len(img_dir) == 0 or img_dir == self.img_dir:
return
else:
self.img_dir = img_dir
f = sorted(glob(img_dir + os.sep + "*.*"))
image_files = [x.replace('/', os.sep) for x in f if os.path.splitext(x)[-1].lower() in img_formats]
if len(image_files) == 0:
reply = QMessageBox.question(self, 'Ooooops', '目录里没有支持的图片。', QMessageBox.Yes)
return
else:
self.image_files= image_files
self.img_num_total = len(self.image_files)
self.logger(self.img_dir, LOAD_IMG)
self.spinBox_page.setValue(0) # 打开新目录时，页码回到1
self.go_to_page(TO_PAGE)
self.label_img_dir.setText(self.img_dir)
self.label_num_total.setText("当前: {}/{}".format(self.page_now + 1, self.img_num_total))
self.spinBox_page.setMaximum(self.img_num_total)
def show_img(self, path):
try:
self.pixmap = QPixmap(path)
self.item = QGraphicsPixmapItem(self.pixmap)
self.scene = QGraphicsScene() # 创建场景
self.scene.addItem(self.item)
self.graphicsView.setScene(self.scene)
self.item.setScale(self.horizontalSlider.value() / 10)
self.img_now = path
return True
except:
QMessageBox.question(self, 'Ooooops', '目录里没有支持的图片。', QMessageBox.Yes)
return False
def show_img_rescled(self):
if self.img_now is None:
return
self.item.setScale(self.horizontalSlider.value() / 10)
self.scene = QGraphicsScene() # 创建场景
self.scene.addItem(self.item)
self.graphicsView.setScene(self.scene)
def load_text(self):
text_dir = QFileDialog.getExistingDirectory(self, "选取文件夹") # 起始路径
if len(text_dir) == 0 or text_dir == self.text_dir:
return
else:
self.text_dir = text_dir
f = sorted(glob(text_dir + os.sep + "*.*"))
self.text_files = [x.replace('/', os.sep) for x in f if os.path.splitext(x)[-1].lower() in label_formats]
self.label_text_dir.setText(self.text_dir)
self.logger(self.text_dir, LOAD_LABEL)
self.go_to_page(TO_PAGE)
def show_text(self, path):
if os.path.exists(path):
f = open(path, "r", encoding="utf-8")
self.text_now = f.readline()
self.label_editor.setText(self.text_now)
f.close()
else:
self.statusbar.showMessage("找不到相应label")
self.label_editor.setText("")
self.text_now = None
def go_to_page(self, state):
if self.img_dir is None:
return
if state == LAST_PAGE:
if self.page_now > 0:
self.page_now -= 1
else:
return
if state == NEXT_PAGE:
if self.page_now + 1 < self.img_num_total:
self.page_now += 1
else:
return
if state == TO_PAGE:
if 0 <= self.spinBox_page.value() - 1 < self.img_num_total:
self.page_now = self.spinBox_page.value() - 1
else:
QMessageBox.question(self, 'Ooooops', '跳转页码超范围啦。', QMessageBox.Yes)
return
if state == LAST_PAGE or state == NEXT_PAGE:
self.save_file() # 翻页自动保存
img_path = self.image_files[self.page_now]
if self.text_dir is not None:
basename = os.path.basename(img_path).split(".")[:-1]
basename = ".".join(basename) + label_formats[0]
self.text_now_path = os.path.join(self.text_dir, basename)
self.show_text(self.text_now_path)
show_img_flag = self.show_img(img_path)
if show_img_flag: # 显示成功的话
self.label_file_now.setText(img_path)
self.label_num_total.setText("当前: {}/{}".format(self.page_now + 1, self.img_num_total))
self.spinBox_page.setValue(self.page_now + 1)
self.logger(self.text_now_path, JUMP_PAGE, "当前: {}/{}".format(self.page_now + 1, self.img_num_total))
def save_file(self):
if self.text_now is None and self.label_editor.text().rstrip().lstrip() is "" :
return
elif self.text_now is None and self.label_editor.text().rstrip().lstrip() is not "" :
basename = os.path.basename(self.img_now).split(".")[:-1]
basename = ".".join(basename) + label_formats[0]
self.text_now_path = os.path.join(self.text_dir, basename)
f = open(self.text_now_path, "w", encoding="utf-8")
f.writelines(self.label_editor.text().rstrip().lstrip())
f.close()
self.logger(self.text_now_path, SAVE_LABEL)
text = self.label_editor.text().rstrip().lstrip() # 左右空格都去掉
if text == self.text_now:
self.statusbar.showMessage("没有改变标注哦")
else:
self.text_now = text
f = open(self.text_now_path, "w", encoding="utf-8")
f.writelines(text)
f.close()
self.logger(self.text_now_path, SAVE_LABEL)
def logger(self, file_name, behavior, memo=""):
timestamp = time.strftime("%a %b %d %H:%M:%S %Y", time.localtime())
if behavior == SAVE_LABEL:
contect = "保存文件 " + file_name
elif behavior == JUMP_PAGE:
contect = "浏览 " + memo
elif behavior == LOAD_IMG:
contect = "载入图片目录 " + file_name
elif behavior == LOAD_LABEL:
contect = "载入标签目录 " + file_name
else:
contect = ""
log = " ".join([timestamp, contect])
self.log_window.textBrowser.append(log)
self.statusbar.showMessage(log)
def show_history_log(self):
self.log_window.show()
def keyPressEvent(self, event):
if str(event.key()) == "65":
self.go_to_page(LAST_PAGE)
elif str(event.key()) == "68":
self.go_to_page(NEXT_PAGE)
elif str(event.key()) == "83":
self.save_file()
elif str(event.key()) == "72":
self.show_history_log()
def closeEvent(self, event):
self.log_window.close()
class LogWindow(QMainWindow, LogBrowser.Ui_MainWindow):
# _signal = QtCore.pyqtSignal(str)
def __init__(self):
QMainWindow.__init__(self)
LabelOCR.Ui_MainWindow.__init__(self)
self.setupUi(self)
self.logger = None
if __name__ == '__main__':
app = QApplication(sys.argv)
main_window = MainCode()
log_window = LogWindow()
with open('qss/blue.txt') as file:
style = file.readlines()
style = ''.join(style).strip('\n')
app.setStyleSheet(style)
main_window.show()
sys.exit(app.exec_())
7：标注完后需要和字典建立联系，字典是：char_std_5990.txt，并且将竖型文字横着来
建立联系需要用到一下工具：
# -*- coding: utf-8 -*-
import os
import numpy as np
import cv2
def gen_labetxt(path,dist_txt,org_labelpath,out_file):
path_list = os.listdir(path)
for image in path_list:
file_name=image.split('.')[0]
print(file_name)
labe_path = org_labelpath+'/'+str(file_name)+'.txt'
f=open(labe_path,encoding = 'UTF-8')
content=f.read()
f = open(dist_txt, encoding='utf-8')
filecontent = f.readlines()#.rstrip('\n')
dic_list=[]
for i in content:
index =0
for data in filecontent:
if i in data:
dic_list.append(index)
break
index = index +1
save_path= (out_file)
f = open(save_path, 'a+', encoding='utf-8') ##
f.write(str(image)+' '+str(dic_list).replace(', ',' ').replace('[','').replace(']','')+'\n')
f.close()
def rot90_image(path,saveimagepath):
path_list = os.listdir(path)
for image in path_list:
imagepath = path + '/' + image
imagedata = cv2.imread(imagepath)
img=np.rot90(imagedata)
cv2.imwrite(saveimagepath+'/'+image,img)
print(image)
save_path= ('3.txt')
f = open(save_path, 'w', encoding='utf-8') ##
path = 'txtimage'
labelpath ='txtlabe'
dist_txt = 'char_std_5990.txt'
gen_labetxt(path,dist_txt,labelpath,save_path)
saveimagepath = 'newimage'
rot90_image(path,saveimagepath)