模式识别与图像处理课程实验一：图像处理实验(颜色算子实验、Susan、Harris角点检测实验、 sobel边缘算子检测实验)(模式识别与图像处理能做什么)

模式识别与图像处理课程实验一：图像处理实验(颜色算子实验、Susan、Harris角点检测实验、 sobel边缘算子检测实验) 模式识别与图像处理课程实验一：图像处理实验-->> 颜色算子实验、Susan、Harris角点检测实验、 sobel边缘算子检测实验一、 实验内容二、 颜色算子实验2.1、 提取红色2.2、 提取绿色2.3、 提取蓝色三、 Susan、Harris角点检测实验3. 1、 实验程序3.1.1、Susan角点检测3.1.2、Harris角点检测四、 sobel边缘算子检测实验4.1、sobel边缘算子检五、 实验总结

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一、 实验内容

要求编写一个包含颜色算子，Susan，Harris，角点，sobel边缘算子的程。

二、 颜色算子实验2.1、 提取红色实验的程序如下import numpy as npimport cv2 as cvimage = cv.imread("1.jpg")image = image / np.ones([1, 1, 3]).astype(np.float32)image = cv.cvtColor(image, cv.COLOR_BGR2RGB)print(image.shape)# 颜色算子# redredAdd = np.ones([1, 1, 3]).astype(np.float32)redAdd[0, 0, 0] = 1.0redAdd[0, 0, 1] = 0.5redAdd[0, 0, 2] = 0.25redSub = np.ones([1, 1, 3]).astype(np.float32)redSub[0, 0, 0] = 0.25redSub[0, 0, 1] = 0.5redSub[0, 0, 2] = 1.0image1 = np.mean(image * redAdd, 2)image2 = np.mean(image * redSub, 2) + 100imageRed = image1 / image2redMax = np.max(imageRed)redMin = np.min(imageRed)imageRed = 255 * (imageRed - redMin) / (redMax - redMin)cv.imwrite("1red.png", imageRed)

运行结果如下

实验原图 实验结果图 2.2、 提取绿色

实验的程序如下

import numpy as npimport cv2 as cvimage = cv.imread("1.jpg")image = image / np.ones([1, 1, 3]).astype(np.float32)image = cv.cvtColor(image, cv.COLOR_BGR2RGB)print(image.shape)# greengreenAdd = np.ones([1, 1, 3]).astype(np.float32)greenAdd[0, 0, 0] = 0.5greenAdd[0, 0, 1] = 1.0greenAdd[0, 0, 2] = 0.25greenSub = np.ones([1, 1, 3]).astype(np.float32)greenSub[0, 0, 0] = 0.5greenSub[0, 0, 1] = 0.25greenSub[0, 0, 2] = 1.0image1 = np.mean(image * greenAdd, 2)image2 = np.mean(image * greenSub, 2) + 100imageGreen = image1 / image2greenMax = np.max(imageGreen)greenMin = np.min(imageGreen)imageRed = 255 * (imageGreen - greenMin) / (greenMax - greenMin)cv.imwrite("1green.png", imageRed)

运行结果如下

实验原图

实验结果图

2.3、 提取蓝色实验的程序如下import numpy as npimport cv2 as cvimage = cv.imread("1.jpg")image = image / np.ones([1, 1, 3]).astype(np.float32)image = cv.cvtColor(image, cv.COLOR_BGR2RGB)print(image.shape)# bulebuleAdd = np.ones([1, 1, 3]).astype(np.float32)buleAdd[0, 0, 0] = 0.25buleAdd[0, 0, 1] = 0.5buleAdd[0, 0, 2] = 1.0buleSub = np.ones([1, 1, 3]).astype(np.float32)buleSub[0, 0, 0] = 1.0buleSub[0, 0, 1] = 0.5buleSub[0, 0, 2] = 0.25image1 = np.mean(image * buleAdd, 2)image2 = np.mean(image * buleSub, 2) + 100imageBlue = image1 / image2blueMax = np.max(imageBlue)blueMin = np.min(imageBlue)imageBlue = 255 * (imageBlue - blueMin) / (blueMax - blueMin)cv.imwrite("1blue.png", imageBlue)

运行结果如下

实验原图

实验结果图

三、 Susan、Harris角点检测实验3. 1、 实验程序3.1.1、Susan角点检测

Susan角点检测程序如下

import numpy as npimport cv2 as cvimage = cv.imread("2.jpg")image = np.mean(image, 2)height = image.shape[0]width = image.shape[1]print(image.shape)#susan 算子radius = 5imageSusan = np.zeros([height, width]).astype(np.float32)for h in range(radius, height-radius): for w in range(radius, width-radius): numSmall = 0 numLarge = 0 numAll = 0 for y in range(-radius, radius + 1): for x in range(-radius, radius+1): distance = np.sqrt(y**2 + x**2) if distance <= radius: numAll += 1 if image[h + y, w + x] < image[h, w] - 27: numSmall += 1 if image[h + y, w + x] > image[h, w] + 27: numLarge += 1 ratio = 1.0 * numSmall / numAll ratio2 = 1.0 * numLarge / numAll if ratio < 0.3: imageSusan[h, w] = 0.3 - ratio if ratio2 > 0.7: imageSusan[h, w] = ratio2 - 0.7imageMax = np.max(imageSusan)imageMin = np.min(imageSusan)imageSusan = 255*(imageSusan - imageMin)/(imageMax - imageMin)print(imageSusan.shape)cv.imwrite("2.png", imageSusan)运行结果如下

实验原图

实验结果图

3.1.2、Harris角点检测Harris角点检测程序如下import cv2 as cvimport numpy as npimport matplotlib.pyplot as plt# 读取图像img = cv.imread('3.jpg')lenna_img = cv.cvtColor(img, cv.COLOR_BGR2RGB)# 图像转换成灰度图像grayImage = cv.cvtColor(img, cv.COLOR_BGR2GRAY)grayImage = np.float32(grayImage)# Harris算子harrisImage = cv.cornerHarris(grayImage, 2, 3, 0.04)harrisImage = cv.dilate(harrisImage, None)# 设置阈值thresImage = 0.006 * harrisImage.max()img[harrisImage > thresImage] = [255, 0, 0]# 显示正常中文的标签plt.rcParams['font.sans-serif'] = ['SimHei']titles = [u'(a)原始图像', u'(b)Harris图像']images = [lenna_img, img]for i in range(2): plt.subplot(1, 2, i + 1), plt.imshow(images[i], 'gray') plt.title(titles[i]) plt.xticks([]), plt.yticks([])plt.show()

运行结果如下

四、 sobel边缘算子检测实验4.1、sobel边缘算子检sobel边缘算子检程序如下import numpy as npimport cv2image = cv2.imread("3.jpg")height = image.shape[0]width = image.shape[1]sobelResult = np.zeros([height - 2, width - 2, 1]).astype(np.float32)sobelX = np.zeros([3, 3, 1]).astype(np.float32)sobelY = np.zeros([3, 3, 1]).astype(np.float32)sobelX[0, 0, 0] = -1sobelX[1, 0, 0] = -2sobelX[2, 0, 0] = -1sobelX[0, 2, 0] = 1sobelX[1, 2, 0] = 2sobelX[2, 2, 0] = 1sobelY[0, 0, 0] = -1sobelY[0, 1, 0] = -2sobelY[0, 2, 0] = -1sobelY[2, 0, 0] = 1sobelY[2, 1, 0] = 2sobelY[2, 2, 0] = 1for h in range(0, height - 3): for w in range(0, width - 3): #求方向梯度 imageIncre = image[h:h + 3, w:w + 3] gradientX = np.sum(imageIncre * sobelX) gradientY = np.sum(imageIncre * sobelY) gradient = np.sqrt(gradientX**2 + gradientY**2) sobelResult[h, w, 0] = gradientimageMax = np.max(sobelResult)imageMin = np.min(sobelResult)sobelResult = 255*(sobelResult - imageMin) / (imageMax - imageMin)cv2.imwrite("3.png", sobelResult)

2、 运行结果如下

实验原图

实验结果图

五、 实验总结1、 掌握了编写含颜色算子图像处理、Susan与Harris角点图像检测、sobel边缘算子图像检测的程序编写方法。2、 通过实验、对于边缘检测算子与角点检测算子有了进一步的掌握。