Add DifferentialDerivative class

Author: Spencer.Luo

Test.py

@@ -30,5 +30,5 @@
 # import advanced_pattern.ObjectPool
 # import advanced_pattern.Callback
 
-# import application.ImageProcessing
-import application.Principle
+import application.ImageProcessing
+# import application.Principle

application/ImageProcessing.py

@@ -66,6 +66,56 @@ def preProcessing(self, img):
         return cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
 
 
+
+# 一阶微分算子
+#=======================================================================================================================
+from abc import ABCMeta, abstractmethod
+# 引入ABCMeta和abstractmethod来定义抽象类和抽象方法
+import numpy as np
+# 引入numpy模块
+
+class DifferentialDerivative(metaclass=ABCMeta):
+    """微分求导算法"""
+
+    def imgProcessing(self, img, width, height):
+        """模板方法，进行图像处理"""
+        # 这里特别需要注意：OpenCv for Python中，(x, y)坐标点的像素用img[y, x]表示
+        newImg = np.zeros([height, width], dtype=np.uint8)
+        for y in range(0, height):
+            for x in range(0, width):
+                # 因为是采用(3*3)的核进行处理，所以最边上一圈的像素无法处理，需保留原值
+                if (y != 0 and y != height-1 and x != 0 and x != width-1):
+                    value = self.derivation(img, x, y)
+                    # 小于0的值置为0，大于255的值置为255
+                    value = 0 if value < 0 else (255 if value > 255 else value)
+                    newImg[y, x] = value
+                else:
+                    newImg[y, x] = img[y, x]
+        return newImg
+
+    @abstractmethod
+    def derivation(self, img, x, y):
+        """具体的步骤由子类实现"""
+        pass
+
+class DifferentialDerivativeX(DifferentialDerivative):
+    """水平微分求导算法"""
+
+    def derivation(self, img, x, y):
+        """Gx=f(x-1,y-1) + 2f(x-1,y) + f(x-1,y+1) - f(x+1,y-1) - 2f(x+1,y) - f(x+1, y+1)"""
+        pix = img[y-1, x-1] + 2 * img[y, x-1] + img[y+1, x-1] - img[y-1, x+1] - 2 *img[y, x+1] - img[y+1, x+1]
+        return pix
+
+
+class DifferentialDerivativeY(DifferentialDerivative):
+    """垂直微分求导算法"""
+
+    def derivation(self, img, x, y):
+        """Gy=f(x-1,y-1) + 2f(x,y-1) + f(x+1,y-1) - f(x-1,y+1) - 2f(x,y+1) - f(x+1,y+1)"""
+        pix = img[y-1, x-1] + 2*img[y-1, x] + img[y-1, x+1] - img[y+1, x-1] - 2*img[y+1, x] - img[y+1, x+1]
+        return pix
+
+
 def testImageProcessing():
     img = cv2.imread("E:\\TestImages\\bird.jpg")
     print("灰度化 --> 梯度化 --> 核心算法:边缘提取算法：")
@@ -81,4 +131,51 @@ def testImageProcessing():
     cv2.waitKey(0)
     cv2.destroyAllWindows()
 
-testImageProcessing()
+def differentialDerivativeOpenCv():
+    img = cv2.imread("E:\\TestImages\\person.jpg")
+
+    # 转换成单通道灰度图
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+    x = cv2.Sobel(img, cv2.CV_16S, 1, 0)
+    y = cv2.Sobel(img, cv2.CV_16S, 0, 1)
+    # 进行微分计算后，可能会出现负值，将每个像素加上最小负数的绝对值
+    absX = cv2.convertScaleAbs(x)  # 转回uint8
+    absY = cv2.convertScaleAbs(y)
+    # img = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
+
+    cv2.imshow("First order differential X", absX)
+    cv2.imshow("First order differential Y", absY)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+
+def differentialDerivative():
+    img = cv2.imread("E:\\TestImages\\person.jpg")
+
+    # 转换成单通道的灰度图
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    # 均值滤波
+    # img = cv2.blur(img, (3, 3))
+
+    # 获取图片的宽和高
+    width = img.shape[1]
+    height = img.shape[0]
+    # 进行微分求导
+    derivativeX = DifferentialDerivativeX()
+    imgX = derivativeX.imgProcessing(img, width, height)
+    derivativeY = DifferentialDerivativeY()
+    imgY = derivativeY.imgProcessing(img, width, height)
+    imgScobel = cv2.addWeighted(imgX, 0.5, imgY, 0.5, 0)
+
+    cv2.imshow("First order differential X", imgX)
+    cv2.imshow("First order differential Y", imgY)
+    cv2.imshow("First order differential Scobel", imgScobel)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+
+# testImageProcessing()
+# differentialDerivativeOpenCv()
+differentialDerivative()
+