【OpenCV应用】python处理行李图像匹配项目——图像(简单)清晰化

图像清晰化尝试

是否可以加强图像边缘、轮廓使得图像清晰化

1.图像锐化–自定义卷积核滤波

https://blog.csdn.net/Miracle0_0/article/details/82051497

采用cv2.filter2D进行卷积核滤波

#图像锐化卷积核
kernel_sharpen_1 = np.array([
        [-1,-1,-1],
        [-1,9,-1],
        [-1,-1,-1]])
kernel_sharpen_2 = np.array([
        [1,1,1],
        [1,-7,1],
        [1,1,1]])
kernel_sharpen_3 = np.array([
        [-1,-1,-1,-1,-1],
        [-1,2,2,2,-1],
        [-1,2,8,2,-1],
        [-1,2,2,2,-1], 
        [-1,-1,-1,-1,-1]])/8.0
kernel_sharpen_4 = np.array([
        [0,-1,0],
        [-1,5,-1],
        [0,-1,0]])

2.图像边缘检测–自定义卷积核

https://blog.csdn.net/zouxy09/article/details/49080029

kernel_edge_1 = np.array([
        [0,0,0,0,0],
        [0,0,0,0,0],
        [-1,-1,2,0,0],
        [0,0,0,0,0],
        [0,0,0,0,0],])
kernel_edge_2 = np.array([
        [0,0,-1,0,0],
        [0,0,-1,0,0],
        [0,0,4,0,0],
        [0,0,-1,0,0],
        [0,0,-1,0,0],])
kernel_edge_3 = np.array([
        [-1,0,0,0,0],
        [0,-2,0,0,0],
        [0,0,6,0,0],
        [0,0,0,-2,0],
        [0,0,0,0,-1],])
kernel_edge_4 = np.array([
        [-1,-1,-1],
        [-1,8,-1],
        [-1,-1,-1]])

3.图像边缘检测–skimage包

https://www.cnblogs.com/denny402/p/5125253.html

sobel、roberts、scharr、prewitt算子

canny算子

TODO：这个skimage的跑不出效果噻，不知道为啥，用openCV的

edges = cv.Canny(img,100,200)
edges2 = cv.Canny(img,50,250)
edges3 = cv.Canny(img,0,255)

gabor滤波

gabor滤波可用来进行边缘检测和纹理特征提取。通过修改frequency值来调整滤波效果，返回一对边缘结果，一个是用真实滤波核的滤波结果，一个是想象的滤波核的滤波结果。

4.图像轮廓检测

https://www.cnblogs.com/denny402/p/5160955.html

这种方法应该是检测亚像素或基础图形的拟合，效果太不好

5.其它：高级滤波

https://www.cnblogs.com/denny402/p/5133086.html

6.Image Pyramids: 图像放缩后的效果观察

https://docs.opencv.org/master/dc/dff/tutorial_py_pyramids.html

7.Image Gradients：图像渐变

https://docs.opencv.org/master/d5/d0f/tutorial_py_gradients.html

1. Sobel and Scharr Derivatives

2. Laplacian Derivatives

laplacian = cv.Laplacian(img,cv.CV_64F)
sobelx = cv.Sobel(img,cv.CV_64F,1,0,ksize=5)
sobely = cv.Sobel(img,cv.CV_64F,0,1,ksize=5)