排名优化,关键词优化排名推广搜ノ牛霸天排名软件,网上商城下载,cn体育门户网站源码pip install matplotlib1简单的阈值化cv2.threshold第一个参数是源图像#xff0c;它应该是灰度图像. 第二个参数是用于对像素值进行分类的阈值, 第三个参数是maxVal#xff0c;它表示如果像素值大于(有时小于)阈值则要给出的值. OpenCV提供不同类型的阈值#xff0c;它由函…pip install matplotlib1简单的阈值化cv2.threshold第一个参数是源图像它应该是灰度图像. 第二个参数是用于对像素值进行分类的阈值, 第三个参数是maxVal它表示如果像素值大于(有时小于)阈值则要给出的值. OpenCV提供不同类型的阈值它由函数的第四个参数决定. 不同的类型是cv2.THRESH_BINARY如果 src(x,y)threshold ,dst(x,y) max_value; 否则,dst(x,y)0cv.THRESH_BINARY_INV如果 src(x,y)threshold,dst(x,y) 0; 否则,dst(x,y) max_valuecv.THRESH_TRUNC如果 src(x,y)thresholddst(x,y) max_value; 否则dst(x,y) src(x,y)cv.THRESH_TOZERO如果src(x,y)thresholddst(x,y) src(x,y) ; 否则 dst(x,y) 0cv.THRESH_TOZERO_INV如果 src(x,y)thresholddst(x,y) 0 ; 否则dst(x,y) src(x,y)代码importcv2importnumpy as npimportmatplotlib.pylab as pltimg cv2.imread(lena.jpg,0)ret,thresh1 cv2.threshold(img,127,255,cv2.THRESH_BINARY)ret,thresh2 cv2.threshold(img,127,255,cv2.THRESH_BINARY_INV)ret,thresh3 cv2.threshold(img,127,255,cv2.THRESH_TRUNC)ret,thresh4 cv2.threshold(img,127,255,cv2.THRESH_TOZERO)ret,thresh5 cv2.threshold(img,127,255,cv2.THRESH_TOZERO_INV)titles [Original Image,BINARY,BINARY_INV,TRUNC,TOZERO,TOZERO_INV]images[img, thresh1, thresh2, thresh3, thresh4, thresh5]for i in range(6):plt.subplot(2,3,i1),plt.imshow(images[i],gray)plt.title(titles[i])plt.xticks([]),plt.yticks([])plt.show()测试效果2自适应阈值化图像在不同区域具有不同照明条件时应进行自适应阈值处理.因此我们为同一图像的不同区域获得不同的阈值并且它为具有不同照明的图像提供了更好的结果.cv2.adaptiveThreshold(src, maxValue, adaptiveMethod, thresholdType, blockSize, C[, dst])adaptiveMethod:决定如何计算阈值cv2.ADAPTIVE_THRESH_MEAN_C:阈值是邻域的平均值cv2.ADAPTIVE_THRESH_GAUSSIAN_C:阈值是邻域值的加权和其中权重是高斯窗口blockSize:决定了邻域的大小C:从计算的平均值或加权平均值中减去的常数importcv2importnumpy as npimportmatplotlib.pylab as pltimg cv2.imread(lena.jpg,0)img cv2.medianBlur(img,5)ret,th1 cv2.threshold(img,127,255,cv2.THRESH_BINARY)th2 cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_MEAN_C,\cv2.THRESH_BINARY,11,2)th3 cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\cv2.THRESH_BINARY,11,2)titles [Original Image, Global Thresholding (v 127),Adaptive Mean Thresholding, Adaptive Gaussian Thresholding]images[img, th1, th2, th3]for i in range(4):plt.subplot(2,2,i1),plt.imshow(images[i],gray)plt.title(titles[i])plt.xticks([]),plt.yticks([])plt.show()3 大津阈值法根据双峰图像的图像直方图自动计算阈值。 (对于非双峰图像二值化不准确。)使用cv.threshold()但是传递了一个额外的标志v.THRESH_OTSU.对于阈值只需传递零.然后算法找到最佳阈值并返回为第二个输出retVal。如果未使用Otsu阈值法则retVal与之前使用的阈值相同.在第一种情况下将全局阈值应用为值127.在第二种情况下直接应用了Otsu的阈值.在第三种情况下使用5x5高斯内核过滤图像以消除噪声然后应用Otsu阈值处理.代码importcv2importnumpy as npimportmatplotlib.pylab as pltimg cv2.imread(lena.jpg,0)#global thresholdingret1,th1 cv2.threshold(img,127,255,cv2.THRESH_BINARY)#Otsus thresholdingret2,th2 cv2.threshold(img,0,255,cv2.THRESH_BINARYcv2.THRESH_OTSU)#Otsus thresholding after Gaussian filteringblur cv2.GaussianBlur(img,(5,5),0)ret3,th3 cv2.threshold(blur,0,255,cv2.THRESH_BINARYcv2.THRESH_OTSU)#plot all the images and their histogramsimages [img, 0, th1,img, 0, th2,blur, 0, th3]titles [Original Noisy Image,Histogram,Global Thresholding (v127),Original Noisy Image,Histogram,Otsus Thresholding,Gaussian filtered Image,Histogram,Otsus Thresholding]for i in range(3):plt.subplot(3,3,i*31),plt.imshow(images[i*3],gray)plt.title(titles[i*3]), plt.xticks([]), plt.yticks([])plt.subplot(3,3,i*32),plt.hist(images[i*3].ravel(),256)plt.title(titles[i*31]), plt.xticks([]), plt.yticks([])plt.subplot(3,3,i*33),plt.imshow(images[i*32],gray)plt.title(titles[i*32]), plt.xticks([]), plt.yticks([])plt.show()
