差分滤波器的介绍及用于图像特征检测

import cv2

import numpy as np

# Gray scale

def BGR2GRAY(img):

    b = img[:, :, 0].copy()

    g = img[:, :, 1].copy()

    r = img[:, :, 2].copy()

    # Gray scale

    out = 0.2126 * r + 0.7152 * g + 0.0722 * b

    out = out.astype(np.uint8)

    return out

# different filter

def different_filter(img, K_size=3):

    H, W = img.shape

    # Zero padding

    pad = K_size // 2

    out = np.zeros((H + pad * 2, W + pad * 2), dtype=np.float)

    out[pad: pad + H, pad: pad + W] = img.copy().astype(np.float)

    tmp = out.copy()

    out_v = out.copy()

    out_h = out.copy()

    out_d = out.copy()

    # vertical kernel

    Kv = [[0., -1., 0.],[0., 1., 0.],[0., 0., 0.]]

    # horizontal kernel

    Kh = [[0., 0., 0.],[-1., 1., 0.], [0., 0., 0.]]

    # diagonal kernel

    Kd = [[-1.,0.,0.],[0.,1.,0.],[0.,0.,0.]]

    # filtering

    for y in range(H):

        for x in range(W):

            out_v[pad + y, pad + x] = np.sum(Kv * (tmp[y: y + K_size, x: x + K_size]))

            out_h[pad + y, pad + x] = np.sum(Kh * (tmp[y: y + K_size, x: x + K_size]))

            out_d[pad + y, pad + x] = np.sum(Kd * (tmp[y: y + K_size, x: x + K_size]))

    out_v = np.clip(out_v, 0, 255)

    out_h = np.clip(out_h, 0, 255)

    out_d = np.clip(out_d, 0, 255)

    out_v = out_v[pad: pad + H, pad: pad + W].astype(np.uint8)

    out_h = out_h[pad: pad + H, pad: pad + W].astype(np.uint8)

    out_d = out_d[pad: pad + H, pad: pad + W].astype(np.uint8)

 

    return out_v, out_h, out_d

# Read image

img = cv2.imread("../gezi.jpg").astype(np.float)

# grayscale

gray = BGR2GRAY(img)

# different filtering

out_v, out_h,out_d = different_filter(gray, K_size=3)

# Save result

cv2.imwrite("out_v.jpg", out_v)

cv2.imshow("result_v", out_v)

cv2.imwrite("out_h.jpg", out_h)

cv2.imshow("result_h", out_h)

cv2.imwrite("out_d.jpg", out_d)

cv2.imshow("result_d", out_d)

cv2.waitKey(0)

cv2.destroyAllWindows()