一、和图像交互获得图像的坐标和像素值

import cv2
import numpy as np
import signal
import threading
import timeif __name__ == '__main__':img = cv2.imread('XXX',0)#读取图片font_face,font_scale,thickness=cv2.FONT_HERSHEY_SIMPLEX,0.5,1#鼠标交互def mouseHandler(event,x,y,flags,param):points = (x,y)global imgCopy#鼠标左键双击事件if event == cv2.EVENT_LBUTTONDBLCLK:#拷贝一张与原图像格式相同的新图像imgCopy = img.copy()#拼接文字text = '['+str(x)+','+str(y)+']'+str(img[y,x])print(text)#读取文字（宽，高），下基线(t_w,t_h),baseLine = cv2.getTextSize(text,font_face,font_scale,thickness)#在鼠标当前位置的左上角显示文字cv2.putText(imgCopy,text,(x-t_w,y),font_face,font_scale,(125,125,125))cv2.imshow('win',imgCopy)#鼠标移动事件elif event == cv2.EVENT_MOUSEMOVE:#显示原图片能使文本框消失cv2.imshow('win',img)cv2.namedWindow('win')#窗口与回调函数绑定cv2.setMouseCallback('win',mouseHandler)cv2.imshow('win',img)cv2.waitKey()

二、二值化图像

import cv2
import numpy as np
import signal
import threading
import timeif __name__ == '__main__':img = cv2.imread('path',0)#读取图片ret, binary = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)print("threshold value %s" % ret)  #打印阈值，超过阈值显示为白色，低于该阈值显示为黑色cv2.imshow("threshold", binary) #显示二值化图像cv2.waitKey(0)cv2.destroyAllWindows()

批量图像二值化

import cv2
import numpy as np
import signal
import threading
import time
import os
import sys
import random
import datetime
import argparsedef get_files(path):files = []for filename in os.listdir(path):if os.path.isfile(os.path.join(path, filename)):files.append(filename)return filesif __name__ == '__main__':files_path="XXX"#print(files_path)image_files = get_files(files_path)i=1#print(image_files)for image_file in image_files:image_path=os.path.join(files_path , image_file)print(image_path)img = cv2.imread(image_path,0)#读取图片start_time_init = time.perf_counter()ret, binary = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)end_time_init = time.perf_counter()elapsed_time_init = (end_time_init - start_time_init)*1000print("二值化时间： {} ms".format(elapsed_time_init))print("threshold value %s" % ret)  #打印阈值，超过阈值显示为白色，低于该阈值显示为黑色scv2.imwrite(files_path+"/binary/"+str(i)+".png",binary)i=i+1

三、区域合并提取最大连通域

import cv2
import numpy as np
import signal
import threading
import time
import os
import sys
import random
import datetime
import argparsedef get_files(path):files = []for filename in os.listdir(path):if os.path.isfile(os.path.join(path, filename)):files.append(filename)return filesif __name__ == '__main__':#files_path="/home/robot/PaddleOCR-2.6.0/data/OK0828/raw_data/"files_path="/home/robot/PaddleOCR-2.6.0/data/829/"files_path="/home/robot/PaddleOCR-2.6.0/data/NG0823/"#print(files_path)image_files = get_files(files_path)i=1#print(image_files)for image_file in image_files:image_path=os.path.join(files_path , image_file)print(image_path)img = cv2.imread(image_path,0)#读取图片start_time_init = time.perf_counter()ret, binary = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)end_time_init = time.perf_counter()elapsed_time_init = (end_time_init - start_time_init)*1000print("二值化时间： {} ms".format(elapsed_time_init))print("threshold value %s" % ret)  #打印阈值，超过阈值显示为白色，低于该阈值显示为黑色scv2.imwrite(files_path+"/binary/"+str(i)+".png",binary)i=i+1# cv2.imshow("threshold", binary) #显示二值化图像# cv2.waitKey(0)# cv2.destroyAllWindows()start_time = time.perf_counter()num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(binary)end_time = time.perf_counter()elapsed_time = (end_time - start_time)*1000print("连通域的时间： {} ms".format(elapsed_time))max_area=0j=0for st in stats[1:]:j=j+1area=st[4]if(max_area<area):max_area=areaindex=jprint("index",index)print("max_area",max_area)#index=index+1print('num_labels: ', num_labels)labels[labels>0] = 255labels = labels.astype(np.uint8)# #将一维灰度图像扩展到三维labels= np.expand_dims(labels,axis=2).repeat(3,axis=2).astype(np.uint8)# for st in stats[1:]:cv2.rectangle(labels, (stats[index][0], stats[index][1]), (stats[index][0]+stats[index][2], stats[index][1]+stats[index][3]), (0, 255, 0), 3)#cv2.imshow('labels', labels)#cv2.waitKey(0)cv2.imwrite(files_path+"/labels/"+str(i)+".png",labels)