OpenCV的鼠标操作

实现获取像素点的功能主要基于OpenCV的内置函数cv2.setMouseCallback()，即鼠标事件回调

setMouseCallback(winname, onMouse,userdata=0)

winname: 接收鼠标事件的窗口名称
onMouse: 处理鼠标事件的回调函数指针
userdata: 传给回调函数的用户数据

 代码实现：鼠标点击图片时，读取当前鼠标对应位置的像素值（顺序为BGR）,在鼠标所在位置的左上角显示当前坐标值和像素值；鼠标移动时，旧的文本框消失

import cv2
import numpy as npimg = cv2.imread('./158.jpg')#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:#输出坐标print(x,y)#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[x,y])#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()

IPM代码：

import cv2  
import numpy as np  def multi_transform(img, pts1):  ROI_HEIGHT = 30000  ROI_WIDTH = 3750  # 设定逆透视图的宽度  IPM_WIDTH = 500  N = 5  # 保证逆透视图的宽度大概为N个车头宽  sacale=(IPM_WIDTH/N)/ROI_WIDTH  IPM_HEIGHT=ROI_HEIGHT*sacale  pts2 = np.float32([[IPM_WIDTH/2-IPM_WIDTH/(2*N), 0],  [IPM_WIDTH/2+IPM_WIDTH/(2*N), 0],  [IPM_WIDTH/2-IPM_WIDTH/(2*N), IPM_HEIGHT],  [IPM_WIDTH/2+IPM_WIDTH/(2*N), IPM_HEIGHT]])  print(IPM_HEIGHT,IPM_WIDTH)  matrix = cv2.getPerspectiveTransform(pts1, pts2)  output = cv2.warpPerspective(img, matrix, (int(IPM_WIDTH),int(IPM_HEIGHT+50)))  for i in range(0, 4):  cv2.circle(img, (pts1[i][0], pts1[i][1]), 6, (0, 0, 255), cv2.FILLED)  for i in range(0,4):  cv2.circle(output, (pts2[i][0], pts2[i][1]),6, (0, 0, 255), cv2.FILLED)  # p1 = (0, 250)  # p2 = (img.shape[1], img.shape[0]-100)  # point_color = (255, 0, 0)  # cv2.rectangle(img, p1, p2, point_color, 2)  cv2.imshow("src image", img)  cv2.imshow("output image", output)  cv2.imwrite("output.jpg", output)  # 新增的代码，将处理后的图像保存为 "output.jpg"  cv2.waitKey(0)  if __name__ == '__main__':  # 图像1  img = cv2.imread("./158.jpg")  pts1 = np.float32([[543, 462],       # p1  [749, 466],       # p2  [277, 536],       # p3  [937, 546]])      # p4  # 图像2  # img = cv2.imread("./789.jpeg")  # pts1 = np.float32([[243, 189],       # p1  #                    [383, 186],       # p2  #                    [77, 253],       # p3  #                    [533, 253]])      # p4  multi_transform(img, pts1)

 原图：

效果图：

二值化处理：

import cv2  # 读取原始图像  
img = cv2.imread('./498_1.jpg')  # 将图像转换为灰度图像  
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # 对灰度图像进行二值化处理  
thresh, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)  # 保存二值化后的图像  
cv2.imwrite('./498_3.jpg', binary)