3. OpenCV-Python——图像梯度算法、边缘检测、图像金字塔与轮廓检测、直方图与傅里叶变换

 1 # *******************图像梯度算法**********************开始
 2 import cv2
 3 # import numpy as np
 4 
 5 img = cv2.imread('pie.png',cv2.IMREAD_GRAYSCALE)
 6 cv2.imshow("img",img)
 7 cv2.waitKey()
 8 cv2.destroyAllWindows()
 9 
10 # 显示图像函数
11 def cv_show(img,name):
12     cv2.imshow(name,img)
13     cv2.waitKey()
14     cv2.destroyAllWindows()
15 
16 # Sobel算子——x轴
17 sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=3)   # 计算水平的
18 cv_show(sobelx,'sobelx')
19 
20 # 白到黑是正数，黑到白就是负数了，所有的负数会被截断成0，所以要取绝对值
21 sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=3)
22 sobelx = cv2.convertScaleAbs(sobelx)             # 取绝对值
23 cv_show(sobelx,'sobelx')
24 
25 # Sobel算子——y轴
26 sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=3)
27 sobely = cv2.convertScaleAbs(sobely)             # 取绝对值
28 cv_show(sobely,'sobely')
29 
30 # 求和
31 sobelxy = cv2.addWeighted(sobelx,0.5,sobely,0.5,0)  # 按权重计算
32 cv_show(sobelxy,'sobelxy')
33 
34 # 也有直接计算xy轴的————不推荐使用
35 # sobelxy=cv2.Sobel(img,cv2.CV_64F,1,1,ksize=3)
36 # sobelxy = cv2.convertScaleAbs(sobelxy)
37 # cv_show(sobelxy,'sobelxy')
38 # *******************图像梯度算法**********************结束

 1 # *******************图像梯度算法-实际操作**********************开始
 2 import cv2
 3 
 4 # 显示图像函数
 5 def cv_show(img,name):
 6     cv2.imshow(name,img)
 7     cv2.waitKey()
 8     cv2.destroyAllWindows()
 9 
10 img = cv2.imread('lena.jpg',cv2.IMREAD_GRAYSCALE)
11 cv_show(img,'img')
12 
13 # 分别计算x和y
14 img = cv2.imread('lena.jpg',cv2.IMREAD_GRAYSCALE)
15 sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=3)
16 sobelx = cv2.convertScaleAbs(sobelx)
17 sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=3)
18 sobely = cv2.convertScaleAbs(sobely)
19 sobelxy = cv2.addWeighted(sobelx,0.5,sobely,0.5,0)
20 cv_show(sobelxy,'sobelxy')
21 # *******************图像梯度算法-实际操作**********************结束

 1 # *******************图像梯度算子-Scharr+laplacian**********************开始
 2 import cv2
 3 import numpy as np
 4 
 5 #不同算子的差异
 6 img = cv2.imread('lena.jpg',cv2.IMREAD_GRAYSCALE)
 7 sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=3)
 8 sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=3)
 9 sobelx = cv2.convertScaleAbs(sobelx)
10 sobely = cv2.convertScaleAbs(sobely)
11 sobelxy =  cv2.addWeighted(sobelx,0.5,sobely,0.5,0)
12 
13 scharrx = cv2.Scharr(img,cv2.CV_64F,1,0)
14 scharry = cv2.Scharr(img,cv2.CV_64F,0,1)
15 scharrx = cv2.convertScaleAbs(scharrx)
16 scharry = cv2.convertScaleAbs(scharry)
17 scharrxy =  cv2.addWeighted(scharrx,0.5,scharry,0.5,0)
18 
19 laplacian = cv2.Laplacian(img,cv2.CV_64F)
20 laplacian = cv2.convertScaleAbs(laplacian)
21 
22 res = np.hstack((sobelxy,scharrxy,laplacian))
23 
24 # 显示图像函数
25 def cv_show(img,name):
26     cv2.imshow(name,img)
27     cv2.waitKey()
28     cv2.destroyAllWindows()
29 cv_show(res,'res')
30 # *******************图像梯度算子-Scharr+laplacian**********************结束

 1 # *******************边缘检测**********************开始
 2 import cv2
 3 import numpy as np
 4 
 5 img=cv2.imread("lena.jpg",cv2.IMREAD_GRAYSCALE)
 6 
 7 v1=cv2.Canny(img,80,150)  # 设置双阈值 最小和最大
 8 v2=cv2.Canny(img,50,100)
 9 
10 res = np.hstack((v1,v2))
11 
12 # 显示图像函数
13 def cv_show(img,name):
14     cv2.imshow(name,img)
15     cv2.waitKey()
16     cv2.destroyAllWindows()
17 cv_show(res,'res')
18 # *******************边缘检测**********************结束

 1 # *******************图像金字塔--高斯金字塔**********************开始
 2 import cv2
 3 import numpy as np
 4 
 5 # 显示图像函数
 6 def cv_show(img,name):
 7     cv2.imshow(name,img)
 8     cv2.waitKey()
 9     cv2.destroyAllWindows()
10 
11 img=cv2.imread("AM.png")
12 # cv_show(img,'img')
13 print (img.shape)
14 
15 # 高斯金字塔-上采样 （可执行多次）
16 up=cv2.pyrUp(img)
17 # cv_show(up,'up')
18 print (up.shape)
19 
20 # 高斯金字塔-下采样 （可执行多次）
21 down=cv2.pyrDown(img)
22 # cv_show(down,'down')
23 print (down.shape)
24 
25 # 高斯金字塔-先上采样再下采样 （会损失信息-变模糊）
26 up=cv2.pyrUp(img)
27 up_down=cv2.pyrDown(up)
28 # cv_show(up_down,'up_down')
29 cv_show(np.hstack((img,up_down)),'up_down')
30 # *******************图像金字塔--高斯金字塔**********************结束

 1 # *******************图像金字塔-拉普拉斯金字塔**********************开始
 2 import cv2
 3 import numpy as np
 4 
 5 # 显示图像函数
 6 def cv_show(img,name):
 7     cv2.imshow(name,img)
 8     cv2.waitKey()
 9     cv2.destroyAllWindows()
10 
11 img=cv2.imread("AM.png")
12 down=cv2.pyrDown(img)
13 down_up=cv2.pyrUp(down)
14 l_1=img-down_up
15 cv_show(l_1,'l_1')
16 # *******************图像金字塔-拉普拉斯金字塔**********************结束

 1 # *******************图像轮廓**********************开始
 2 import cv2
 3 import numpy as np
 4 
 5 # 读入图像转换为二值图像
 6 img = cv2.imread('contours.png')
 7 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)     # 转换为灰度图
 8 ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)  # 转换成二值图
 9 
10 # 显示图像函数
11 def cv_show(img,name):
12     cv2.imshow(name,img)
13     cv2.waitKey()
14     cv2.destroyAllWindows()
15 # cv_show(thresh,'thresh')
16 
17 # 轮廓检测  第一个就是二值的结果  第二个是一堆轮廓点  第三个是层级
18 binary, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
19 
20 # 绘制轮廓
21 draw_img = img.copy()  # 注意需要copy,要不原图会变。。。
22 res = cv2.drawContours(draw_img, contours, -1, (0, 0, 255), 2) # 传入绘制图像，轮廓，轮廓索引(-1全部)，颜色模式，线条厚度
23 # cv_show(res,'res')
24 
25 draw_img = img.copy()
26 res = cv2.drawContours(draw_img, contours, 2, (0, 0, 255), 2)
27 cv_show(res,'res')
28 # *******************图像轮廓**********************结束

 1 import cv2
 2 
 3 # 读入图像转换为二值图像
 4 img = cv2.imread('contours.png')
 5 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)     # 转换为灰度图
 6 ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)  # 转换成二值图
 7 
 8 # 轮廓检测  第一个就是二值的结果  第二个是一堆轮廓点  第三个是层级
 9 binary, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
10 
11 # 绘制轮廓
12 draw_img = img.copy()
13 res = cv2.drawContours(draw_img, contours, 2, (0, 0, 255), 2)
14 
15 # 轮廓特征
16 cnt = contours[0]               # 获取轮廓
17 print(cv2.contourArea(cnt))     # 计算面积
18 print(cv2.arcLength(cnt, True)) # 计算周长，True表示闭合的

 1 import cv2
 2 
 3 img = cv2.imread('contours2.png')
 4 # 显示图像函数
 5 def cv_show(img,name):
 6     cv2.imshow(name,img)
 7     cv2.waitKey()
 8     cv2.destroyAllWindows()
 9 
10 # 二值+轮廓检测
11 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
12 ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
13 binary, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
14 cnt = contours[0]
15 # 轮廓绘制
16 draw_img = img.copy()
17 res = cv2.drawContours(draw_img, [cnt], -1, (0, 0, 255), 2)
18 # cv_show(res,'res')
19 
20 # 轮廓近似
21 epsilon = 0.05*cv2.arcLength(cnt,True)
22 approx = cv2.approxPolyDP(cnt,epsilon,True)
23 
24 draw_img = img.copy()
25 res = cv2.drawContours(draw_img, [approx], -1, (0, 0, 255), 2)
26 cv_show(res,'res')

 1 # *******************图像轮廓-边界矩形**********************开始
 2 import cv2
 3 
 4 # 显示图像函数
 5 def cv_show(img,name):
 6     cv2.imshow(name,img)
 7     cv2.waitKey()
 8     cv2.destroyAllWindows()
 9 
10 img = cv2.imread('contours.png')
11 
12 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
13 ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
14 binary, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
15 cnt = contours[0]
16 
17 # 边界矩形
18 x,y,w,h = cv2.boundingRect(cnt)
19 img = cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
20 cv_show(img,'img')
21 # 轮廓面积与边界矩形比
22 area = cv2.contourArea(cnt)
23 x, y, w, h = cv2.boundingRect(cnt)
24 rect_area = w * h
25 extent = float(area) / rect_area
26 print ('轮廓面积与边界矩形比',extent)
27 # *******************图像轮廓-边界矩形**********************结束

1 # 外接圆
2 (x,y),radius = cv2.minEnclosingCircle(cnt)
3 center = (int(x),int(y))
4 radius = int(radius)
5 img = cv2.circle(img,center,radius,(0,255,0),2)
6 cv_show(img,'img')