# Scharr 算子
scharr_x = cv2.Scharr(img, cv2.CV_64F, 1, 0)
scharr_y = cv2.Scharr(img, cv2.CV_64F, 0, 1)

# 取絕對值
scharr_x = cv2.convertScaleAbs(scharr_x)
scharr_y = cv2.convertScaleAbs(scharr_y)

# 融合
scharr_xy = cv2.addWeighted(scharr_x, 0.5, scharr_y, 0.5, 0)

# 展示圖片
cv2.imshow("scharr_xy", scharr_xy)
cv2.waitKey(0)
cv2.destroyAllWindows()

# 讀取圖片, 并準換成灰度圖
img = cv2.imread("Mona_Lisa.jpg", cv2.IMREAD_GRAYSCALE)

# 高斯濾波器 (3 X 3)
img = cv2.GaussianBlur(img, (3, 3), sigmaX=0.1)

# Laplacian 算子
laplacian = cv2.Laplacian(img, cv2.CV_64F)

# 取絕對值
laplacian = cv2.convertScaleAbs(laplacian)

# 展示圖片
cv2.imshow("laplacian", laplacian)
cv2.waitKey(0)
cv2.destroyAllWindows()

# 讀取圖片, 并轉換成灰度圖
img = cv2.imread("Mona_Lisa.jpg", cv2.IMREAD_GRAYSCALE)

# Canny邊緣檢測
out1 = cv2.Canny(img, 50, 150)
out2 = cv2.Canny(img, 100, 150)

# 合并
canny = np.hstack((out1, out2))

# 展示圖片
cv2.imshow("canny", canny)
cv2.waitKey(0)
cv2.destroyAllWindows()