update opencv

Author: xiaokaijun

opencv/day03/30_图像卷积.py

@@ -0,0 +1,15 @@
+#encoding:utf-8
+import cv2 as cv
+import numpy as np
+
+# 定义一个卷积核
+# kernel = np.ones((3,3),np.float32)/9
+
+src = cv.imread("../img/itheima_salt.jpg")
+cv.imshow("src",src)
+
+# dst = cv.filter2D(src, -1, kernel)
+dst = cv.blur(src, (3, 3))
+cv.imshow("dst",dst)
+
+cv.waitKey()

opencv/day03/31_中值滤波.py

@@ -0,0 +1,12 @@
+#encoding:utf-8
+import cv2 as cv
+
+src = cv.imread("../img/itheima_salt.jpg")
+
+# 中值滤波
+dst = cv.medianBlur(src, 5)
+
+
+cv.imshow("src",src)
+cv.imshow("dst",dst)
+cv.waitKey()

opencv/day03/32_高斯模糊.py

@@ -0,0 +1,10 @@
+#encoding: utf-8
+import cv2 as cv
+
+src = cv.imread("../img/itheima_salt.jpg")
+
+dst = cv.GaussianBlur(src, (3, 3), 5)
+
+cv.imshow("dst",dst)
+cv.imshow("src",src)
+cv.waitKey()

opencv/day03/33_Sobel算子.py

@@ -0,0 +1,31 @@
+#encoding:utf-8
+import cv2 as cv
+
+src = cv.imread("../img/brain.jpg")
+
+x_sobel = cv.Sobel(src,cv.CV_32F,1,0)
+
+x_sobel = cv.convertScaleAbs(x_sobel);
+
+cv.imshow("xsobel",x_sobel)
+
+y_sobel = cv.Sobel(src,cv.CV_32F,0,1)
+
+y_sobel = cv.convertScaleAbs(y_sobel);
+
+cv.imshow("ysobel",y_sobel)
+
+xy_sobel = cv.addWeighted(x_sobel,0.5,y_sobel,0.5,0);
+cv.imshow("xysobel",xy_sobel)
+
+cv.imshow("src",src)
+# xy_sobel = cv.Sobel(src,cv.CV_32F,1,1)
+#
+# xy_sobel = cv.convertScaleAbs(xy_sobel);
+#
+# cv.imshow("xysobel",xy_sobel)
+
+
+
+
+cv.waitKey()

opencv/day03/34_拉普拉斯算子.py

@@ -0,0 +1,12 @@
+#encoding:utf-8
+import cv2 as cv
+
+src = cv.imread("../img/grbq.jpg")
+cv.imshow("src",src)
+# 使用拉普拉斯算子
+dst = cv.Laplacian(src,cv.CV_32F)
+# 将数据缩放到0-255
+dst = cv.convertScaleAbs(dst)
+
+cv.imshow("dst",dst)
+cv.waitKey()

opencv/day03/35_Canny算法.py

@@ -0,0 +1,21 @@
+#encoding:utf-8
+import cv2 as cv
+
+src = cv.imread("../img/itheima.jpg",cv.IMREAD_COLOR)
+cv.imshow("src",src);
+
+# 将彩色图像转成灰度图像
+grayImg = cv.cvtColor(src,cv.COLOR_BGR2GRAY)
+
+
+
+def onChange(value):
+    # 调用Canny算法
+    dst = cv.Canny(grayImg, value, value*3);
+    cv.imshow("canny", dst)
+# 先命名窗口
+cv.namedWindow("canny")
+cv.createTrackbar("lowthresh","canny",0,255,onChange)
+
+
+cv.waitKey()

opencv/day03/36_双边滤波器.py

@@ -0,0 +1,29 @@
+#encoding:utf-8
+import cv2 as cv
+
+src = cv.imread("../img/timg.jpg",cv.IMREAD_COLOR)
+cv.imshow("src",src)
+
+# dst = cv.bilateralFilter(src,10,50,50);
+d = 0;
+sigmaColor = 0;
+sigmaSpace = 0;
+def onChange(value,index):
+    global d,sigmaSpace,sigmaColor
+    if index == 0 : # 修改d值
+        d = value
+    elif index == 1: # 修改颜色域值
+        sigmaColor = value
+    elif index == 2: # 修改空间域范围
+        sigmaSpace = value
+
+    dst = cv.bilateralFilter(src, d, sigmaColor, sigmaSpace);
+    cv.imshow("filter", dst)
+
+cv.namedWindow("filter")
+cv.createTrackbar("d:","filter",0,255,lambda value: onChange(value,0))
+cv.createTrackbar("sigmaColor:","filter",0,255,lambda value: onChange(value,1))
+cv.createTrackbar("sigmaSpace:","filter",0,255,lambda value: onChange(value,2))
+
+
+cv.waitKey()

opencv/day03/37_图像的锐化.py

@@ -0,0 +1,15 @@
+#encoding: utf-8
+import cv2 as cv
+import numpy as np
+
+src = cv.imread("../img/hehua.jpg")
+cv.imshow("src",src)
+
+k = 1
+
+kernel = np.array([[-k,-k,-k],[-k,8*k+1,-k],[-k,-k,-k]])
+
+dst = cv.filter2D(src, -1, kernel)
+
+cv.imshow("dst",dst)
+cv.waitKey()

opencv/day03/38_获取图像中的直线.py

@@ -0,0 +1,31 @@
+#encoding:utf-8
+import cv2 as cv
+import numpy as np
+
+src = cv.imread("../img/weiqi.jpg")
+
+# 将彩图转成灰度图
+gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
+
+# 将图像转成二值图
+value,binary = cv.threshold(gray,100,255,cv.THRESH_BINARY_INV);
+print value
+# 霍夫变换寻找直线
+# 距离精度1
+rho = 1
+# 弧度变换的步长
+theta = np.pi/180
+# 累加的阈值， 最终的结果，一条直线上至少要包含十个像素点
+thresh = 10
+
+lines = cv.HoughLinesP(binary,rho,theta,thresh,minLineLength=25,maxLineGap=3);
+
+for line in lines: # [[ 14 263 464 263]]
+    x1,y1,x2,y2 = line[0]
+    cv.line(src,(x1,y1),(x2,y2),(0,0,255),2)
+
+
+cv.imshow("src",src)
+cv.imshow("gray",gray)
+cv.imshow("binary",binary)
+cv.waitKey()

opencv/day03/39_霍夫圆.py

@@ -0,0 +1,30 @@
+#encoding:utf-8
+import cv2 as cv
+
+src = cv.imread("../img/weiqi.jpg")
+
+# 将彩图转成灰度图
+gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
+
+# 注意 霍夫圆不需要转换陈成二值图像
+method = cv.HOUGH_GRADIENT
+# 累加器的分辨率，相当于循环的步长
+dp = 1
+# 检测到的圆心之间最小的距离
+minDist = 20
+# canny边缘检测算法的阈值上限
+param1 = 50;
+# 累计器阈值
+param2 = 30
+
+circles = cv.HoughCircles(gray,method,dp,minDist,param1=param1,param2=param2,minRadius=3,maxRadius=20)
+
+for circle in circles[0,:]:
+    x,y,r = circle
+    # 绘制圆心
+    cv.circle(src,(x,y),2,(255,255,0),-1)
+    # 绘制圆
+    cv.circle(src,(x,y),r,(0,0,255),2)
+
+cv.imshow("src",src)
+cv.waitKey()