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OpenCV（02）图像颜色处理，灰度化，二值化，仿射变换

网友: 技术文档 2025-07-26 19:56:03

【OpenCV（01）】基本图像操作、绘制，读取视频

目录

  • 图像颜色加法
  • 灰度化
  • 二值化
  • 仿射变换

图像颜色加法

  • 颜色加法
import cv2 as cvimport numpy as np#读图cao = cv.imread(\'E:\\hqyj\\code\\opencv\\images\\cao.png\')pig = cv.imread(\'E:\\hqyj\\code\\opencv\\images\\pig.png\')#饱和操作 cv.add(img1,img2) np.uint8dst1 = cv.add(cao,pig)#numpy直接相加 取模运算 对256取模 250+10=4dst2 = cao+pig# print(cao)# print(pig)# print(dst1)cv.imshow(\'dst1\',dst1)cv.imshow(\'dst2\',dst2)x = np.uint8([[250]])y = np.uint8([[10]])xy1 = cv.add(x,y)xy2 = x+yprint(xy1)print(xy2)dst3 = cv.addWeighted(cao,0.2,pig,0.8,0)cv.imshow(\'dst3\',dst3)cv.waitKey(0)cv.destroyAllWindows()
  • 图像转换
import cv2 as cvimport numpy as np# 读取图片img = cv.imread(\'E:\\hqyj\\code\\opencv\\images\\pig.png\')# 颜色转换 cv.cvtColor(img, cv.COLOR_BGR2HSV)->图像，转换方式# 灰度图gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)cv.imshow(\'gray\',gray)# 转hsvhsv = cv.cvtColor(img,cv.COLOR_BGR2HSV)cv.imshow(\'hsv\',hsv)# bgr转rgbrgb = cv.cvtColor(img,cv.COLOR_BGR2RGB)cv.imshow(\'rgb\',rgb)cv.waitKey(0)cv.destroyAllWindows()

灰度化

  • 最大值法
import cv2 as cvimport numpy as np#读取图像pig = cv.imread(\'E:\\hqyj\\code\\opencv\\images\\pig.png\')shape = pig.shape #(h,w,c)img = np.zeros((shape[0],shape[1]),dtype = np.uint8)#for循环遍历像素点for i in range(shape[0]): for j in range(shape[1]): img[i,j] = max(pig[i,j,0],pig[i,j,1],pig[i,j,2])cv.imshow(\'grey\',img)cv.waitKey(0)cv.destroyAllWindows()
  • 平均值法
import cv2 as cvimport numpy as np#读取图像pig = cv.imread(\'E:\\hqyj\\code\\opencv\\images\\pig.png\')shape = pig.shape #(h,w,c)img = np.zeros((shape[0],shape[1]),dtype = np.uint8)#for循环遍历像素点for i in range(shape[0]): for j in range(shape[1]): #将int()：转换为更大的类型，防止溢出 img[i,j] =np.uint8((int(pig[i,j,0])+int(pig[i,j,1])+int(pig[i,j,2]))/3)cv.imshow(\'grey\',img)cv.waitKey(0)cv.destroyAllWindows()
  • 加权平均值法
import cv2 as cvimport numpy as np#读取图像pig = cv.imread(\'E:\\hqyj\\code\\opencv\\images\\pig.png\')shape = pig.shape #(h,w,c)img = np.zeros((shape[0],shape[1]),dtype = np.uint8)#定义权重wb,wg,wr = 0.114,0.587,0.299#for循环遍历像素点for i in range(shape[0]): for j in range(shape[1]): #将int()：转换为更大的类型，防止溢出 img[i,j] = round((wb * pig[i,j,0] + wg * pig[i,j,1] + wr * pig[i,j,2])//3)cv.imshow(\'grey\',img)cv.waitKey(0)cv.destroyAllWindows()

二值化

  • 二值化（阈值法）格式：阈值法：
    _,binary = cv2.threshold(img,thresh,maxval,type)
    _和binary：cv2.threshold 函数返回两个值：一个是计算出的最佳阈值（retval），另一个是阈值处理后的图像（binary），_ 被用来忽略返回值中的第一个元素，即计算出的最佳阈值
    img：输入图像，要进行二值化处理的灰度图。
    thresh：设定的阈值。当像素值大于(或小于，取决于阈值类型)thresh时，该像素被赋予的值。
    type：阈值处理的类型。
  • 自适应二值化格式：
    cv2.adaptiveThreshold(image_np_gray, maxval, adaptiveMethod, thresholdType, blockSize, c)
    其中各个参数的含义如下：
    maxval：最大阈值，一般为255
    adaptiveMethod：小区域阈值的计算方式：
    ADAPTIVE_THRESH_MEAN_C：小区域内取均值
    ADAPTIVE_THRESH_GAUSSIAN_C：小区域内加权求和，权重是个高斯核
    thresholdType：二值化方法，只能使用THRESH_BINARY、THRESH_BINARY_INV，也就是阈值法和反阈值法
    blockSize：选取的小区域的面积，如7就是7*7的小块。(只能取奇数)
    c：最终阈值等于小区域计算出的阈值再减去此值
import cv2 as cv#读图flower = cv.imread(\'E:\\hqyj\\code\\opencv\\images\\\\flower.png\')#灰度化处理gray = cv.cvtColor(flower,cv.COLOR_BGR2GRAY)gray = cv.resize(gray,(360,360))cv.imshow(\'1.gray\',gray)#二值化 1.阈值法 cv.THRESH_BINARYthresh,binary = cv.threshold(gray,127,255,cv.THRESH_BINARY)print(thresh)cv.imshow(\'2.binary\',binary)#2.反阈值法 cv.THRESH_BINARY_INV_,binary_inv = cv.threshold(gray,127,255,cv.THRESH_BINARY_INV)cv.imshow(\'3.binary_inv\',binary_inv)#3.截断阈值法 cv.THRESH_TRUNC_,binary_trunc = cv.threshold(gray,170,255,cv.THRESH_TRUNC)cv.imshow(\'4.binary_trunc\',binary_trunc)#4.低阈值零处理法 cv.THRESH_TOZERO_,binary_tozero = cv.threshold(gray,127,255,cv.THRESH_TOZERO)cv.imshow(\'5.binary_tozero\',binary_tozero)#5.超阈值零处理法 cv.THRESH_TOZERO_INV_,binary_tozero_inv = cv.threshold(gray,127,255,cv.THRESH_TOZERO_INV)cv.imshow(\'6.binary_tozero_inv\',binary_tozero_inv)#6.OTSU阈值法shresh1,otsu=cv.threshold(gray,200,255,cv.THRESH_OTSU)#7.OTSU+反阈值法shresh2,otsu_inv = cv.threshold(gray,shresh1,255,cv.THRESH_BINARY_INV)print(shresh1)print(shresh2)cv.imshow(\'7.otsu\',otsu)#自适应二值化 小区域计算 必须只能结合阈值法或反阈值法#1.取均值binary_adaptive = cv.adaptiveThreshold(gray,255,cv.ADAPTIVE_THRESH_MEAN_C,cv.THRESH_BINARY,7,10)#2.加权求和法 高斯核adaptive_binary = cv.adaptiveThreshold(gray,255,cv.ADAPTIVE_THRESH_GAUSSIAN_C,cv.THRESH_BINARY,11,10)cv.imshow(\'8.binary_adaptive\',binary_adaptive)cv.imshow(\'9.adaptive_binary\',adaptive_binary)cv.waitKey(0)cv.destroyAllWindows()

仿射变换

  • 仿射变换流程
    1.读图cv.imread()，调尺寸cv.resize()
    2.获取仿射变换矩阵
1.旋转：M = cv.getRotationMatrix2D(center,angle,scale) center：旋转中心点的坐标，格式为`(x,y)`。angle：旋转角度，单位为度，正值表示逆时针旋转负值表示顺时针旋转。scale：缩放比例，若设为1，则不缩放。2.平移：M = np.float32([[1,0,tx],[0,1,ty]])3.缩放：M = np.float32([[sx,0,0],[0,sy,0]])4.剪切：M = np.float32([[shx,1,0],[1,shy,0]])
  • 旋转
import cv2 as cv#读图cat = cv.imread(\'E:\\hqyj\\code\\opencv\\images\\\\cat1.png\')cat = cv.resize(cat,(520,520))#获取旋转矩阵 cvv2.getRotationMatrix2D(center,angle,scale) 2x3M = cv.getRotationMatrix2D((260,260),-45,1)#仿射变换函数 cv.warpAffine(img,M,(w,h))img = cv.warpAffine(cat,M,(520,520))cv.imshow(\'img\',img)cv.waitKey(0)cv.destroyAllWindows()
  • 平移
import cv2 as cvimport numpy as np#读图cat = cv.imread(\'E:\\hqyj\\code\\opencv\\images\\\\cat1.png\')cat = cv.resize(cat,(520,520))#定义平移量tx,ty = 80,120#定义平移矩阵M = np.float32([[1,0,tx],[0,1,ty]])#仿射变换函数 M = cv.warpAffine(img,M,(w,h))img = cv.warpAffine(cat,M,(520,520))cv.imshow(\'img\',img)cv.waitKey(0)cv.destroyAllWindows()
  • 缩放
import cv2 as cvimport numpy as np#读图cat = cv.imread(\'E:\\hqyj\\code\\opencv\\images\\\\cat1.png\')cat = cv.resize(cat,(520,520))#定义缩放量sx,sy = 0.5,0.5#定义缩放矩阵M = np.float32([[sx,0,0],[0,sy,0]])#仿射变换函数 cv.warpAffine(img,M,(w,h))img = cv.warpAffine(cat,M,(520,520))cv.imshow(\'img\',img)cv.waitKey(0)cv.destroyAllWindows()
  • 剪切
import cv2 as cvimport numpy as np#读图cat = cv.imread(\'E:\\hqyj\\code\\opencv\\images\\\\cat1.png\')cat = cv.resize(cat,(520,520))#定义缩放量shx,shy = 0.5,0.5#定义缩放矩阵M = np.float32([[shx,1,0],[1,shy,0]])#仿射变换函数 cv.warpAffine(img,M,(w,h))img = cv.warpAffine(cat,M,(520,520))cv.imshow(\'img\',img)cv.waitKey(0)cv.destroyAllWindows()
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