案例#

1.图片修复

opencv中给我们提供了一个用于修复图片的函数

cv.inpaint(src,inpaintMask,inpaintRadius,flags)
src : 表示输入的图像
inpaintMask: 掩膜，其实就是要修复哪些区域
inpaintRadius: 表示修复半径
flags: 表示修复时所使用的算法,有CV_INPAINT_TELEA和CV_INPAINT_NS可选，处理效果差不多

下面这里有一张我的示例图像，左边为待修复的图像，右边为修复之后的图像

鼠标控制示例：

src = cv.imread("img/lena.jpg")
cv.imshow("src",src)
def onMouse(e,x,y,flags,a):
    print(e,x,y,flags,a)
cv.setMouseCallback("src",onMouse,param=[23])
cv.waitKey()

参考示例代码:

import cv2 as cv
import numpy as np
src = cv.imread("img/itheima_inpaint.jpg")
cv.imshow("src",src)
height,width = src.shape[0:2]
inpaintMask = np.zeros((height,width), np.uint8)
def onMouse(e,x,y,flags,a):
    global src
    # print(e,x,y,flags,a)
    if e == cv.EVENT_MOUSEMOVE and flags == cv.EVENT_FLAG_LBUTTON:
        cv.circle(inpaintMask,(x,y),10,255,-1)
    elif e == cv.EVENT_LBUTTONUP:
        # 修复图像
        src = cv.inpaint(src,inpaintMask,5,cv.INPAINT_TELEA)
        # 更新图像
        cv.imshow("src",src)
cv.setMouseCallback("src",onMouse,param=[23])
cv.waitKey()

2.图像切边

import cv2 as cv
img = cv.imread("img/01_qiebian.jpg")
cv.imshow("img",img)
def fetchROI(img):
    # 将彩色图转成灰度图
    gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
    # 对图像进行二值化
    _,binary = cv.threshold(gray,0,255,cv.THRESH_BINARY_INV|cv.THRESH_OTSU)
    cv.imshow("binary",binary)
    # 查找最大的轮廓
    contours,_ = cv.findContours(binary,cv.RETR_EXTERNAL,cv.CHAIN_APPROX_SIMPLE)
    height,width = img.shape[0:2]
    rect = None
    for i,c in enumerate(contours):
        # 找到最大的轮廓矩形
        center,shape,angle = cv.minAreaRect(c)
        cv.circle(img,(int(center[0]),int(center[1])),3,(0,255,0))
        if shape[1] > width*0.75:
            rect = cv.boundingRect(c)
    x,y,w,h = rect
    return img[y:y+h,x:x+w]
dst = fetchROI(img)
cv.imshow("img",img)
cv.imshow("dst",dst)
cv.waitKey()

3.切边加旋转

import cv2 as cv
img = cv.imread("img/02_qiebian.jpg")
cv.imshow("img",img)
def fetchROI(img):
    # 将彩色图转成灰度图
    gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
    # 对图像进行二值化
    _,binary = cv.threshold(gray,0,255,cv.THRESH_BINARY_INV|cv.THRESH_OTSU)
    cv.imshow("binary",binary)
    # 查找最大的轮廓
    contours,_ = cv.findContours(binary,cv.RETR_EXTERNAL,cv.CHAIN_APPROX_SIMPLE)
    height,width = img.shape[0:2]
    rect = None
    for i,c in enumerate(contours):
        # 找到最大的轮廓矩形
        center,shape,angle = cv.minAreaRect(c)
        cv.circle(img,(int(center[0]),int(center[1])),3,(0,255,0))
        if shape[1] > width*0.75:
            rect = cv.boundingRect(c)
    x,y,w,h = rect
    return img[y:y+h,x:x+w]
def rotaIMG(img):
    # 将彩色图转成灰度图
    gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
    # 对图像进行二值化
    _, binary = cv.threshold(gray, 0, 255, cv.THRESH_BINARY_INV | cv.THRESH_OTSU)
    cv.imshow("binary", binary)
    # 查找最大的轮廓
    contours, _ = cv.findContours(binary, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
    height, width = img.shape[0:2]
    center, shape, angle = None,None,None
    for i, c in enumerate(contours):
        # 找到最大的轮廓矩形
        center1, shape1, angle1 = cv.minAreaRect(c)
        cv.circle(img, (int(center1[0]), int(center1[1])), 3, (0, 255, 0))
        if shape1[1] > width * 0.75:
            center, shape, angle = center1, shape1, angle1
    # 将图像旋转正确
    M = cv.getRotationMatrix2D((int(width/2),int(height/2)),angle,1)
    print(shape)
    # 旋转
    img = cv.warpAffine(img,M,(width,height))
    cv.imshow("img",img)
    return img
img = rotaIMG(img)
dst = fetchROI(img)
cv.imshow("img",img)
cv.imshow("dst",dst)
cv.waitKey()

4.直线检测

import cv2 as cv
import numpy as np
img = cv.imread("img/engline.jpg")
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
thresh,binary = cv.threshold(gray,0,255,cv.THRESH_BINARY_INV|cv.THRESH_OTSU)
kernel = cv.getStructuringElement(cv.MORPH_RECT,(20,1))
morph = cv.morphologyEx(binary,cv.MORPH_OPEN,kernel)
kernel = cv.getStructuringElement(cv.MORPH_RECT,(3,3))
morph = cv.dilate(morph, kernel)
lines = cv.HoughLinesP(morph,1,np.pi/180,30,20,0)
for line in lines:
    x1,y1,x2,y2 = line[0]
    cv.line(img,(x1,y1),(x2,y2),(0,0,255),1)
cv.imshow("img",img)
cv.waitKey()

5.零件计算

import cv2 as cv
img = cv.imread("img/lingjian.png")
cv.imshow("img",img)
# 将彩色图转成灰度图
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
# 对图像进行二值化
_, binary = cv.threshold(gray, 0, 255, cv.THRESH_BINARY | cv.THRESH_TRIANGLE)
cv.imshow("binary", binary)
dist = cv.distanceTransform(binary,cv.DIST_L2,3)
dist = cv.normalize(dist,0,1,norm_type=cv.NORM_MINMAX)
cv.imshow("dst",dist)
_,dist2 = cv.threshold(dist,0.5,1.0,cv.THRESH_BINARY)
cv.imshow("dist2",dist2)
dist2 = cv.convertScaleAbs(dist2,cv.CV_8UC1)
contours,_ = cv.findContours(dist2,cv.RETR_EXTERNAL,cv.CHAIN_APPROX_SIMPLE)
print("零件总数：",len(contours))
import numpy as np
np.random.seed(123456)
dst = np.zeros_like(img)
# 绘制轮廓
for i,c in enumerate(contours):
    cv.drawContours(dst,contours,i,(np.random.randint(0,255),np.random.randint(0,255),np.random.randint(0,255)),-1)
cv.imshow("idstg",dst)
cv.waitKey()