图像卷积实验，使用guass、soble、prewitt、 laplacian算子进行图像增强。

实现代码

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time    : 2017/9/18 16:57
# @Author  : Jasontang
# @Site    : 
# @File    : image_convolve.py
# @ToDo    :  图像卷积

import numpy as np
import os
from PIL import Image


def convolve(image, weight):
    height, width = image.shape
    h, w = weight.shape
    height_new = height - h + 1
    width_new = width - w + 1
    print image.shape
    image_new = np.zeros((height_new, width_new), dtype=np.float)
    for i in range(height_new):
        for j in range(width_new):
            image_new[i, j] = np.sum(image[i:i + h, j:j + w] * weight)
    image_new = image_new.clip(0, 255)
    image_new = np.rint(image_new).astype("uint8")
    print image_new.shape
    return image_new


# image_new = 255 * (image_new - image_new.min()) / (image_new.max() - image_new.min())

if __name__ == '__main__':
    image = Image.open("son.png", "r")
    output_path = ".\\ImageConvolve\\"
    if not os.path.exists(output_path):
        os.mkdir(output_path)
    a = np.array(image)
    avg3 = np.ones((3, 3))
    avg3 /= avg3.sum()
    avg5 = np.ones((5, 5))
    avg5 /= avg5.sum()

    gauss = np.array(([0.003, 0.013, 0.022, 0.013, 0.003],
                      [0.013, 0.059, 0.097, 0.059, 0.013],
                      [0.022, 0.097, 0.159, 0.097, 0.022],
                      [0.013, 0.059, 0.097, 0.059, 0.013],
                      [0.003, 0.013, 0.022, 0.013, 0.003]))

    soble_x = np.array(([-1, 0, 1], [-2, 0, 2], [-1, 0, 1]))
    soble_y = np.array(([-1, -2, -1], [0, 0, 0], [1, 2, 1]))
    soble = np.array(([-1, -1, 0], [-1, 0, 1], [0, 1, 1]))

    prewitt_x = np.array(([-1, 0, 1], [-1, 0, 1], [-1, 0, 1]))
    prewitt_y = np.array(([-1, -1, -1], [0, 0, 0], [1, 1, 1]))
    prewitt = np.array(([-2, -1, 0], [-1, 0, 1], [0, 1, 2]))

    laplacian4 = np.array(([0, -1, 0], [-1, 4, -1], [0, -1, 0]))
    laplacian8 = np.array(([-1, -1, -1], [-1, 8, -1], [-1, -1, -1]))
    weight_list = (
        'avg3', 'avg5', 'gauss', 'soble_x', 'soble_y', 'soble', 'prewitt_x', 'prewitt_y', 'prewitt', 'laplacian4',
        'laplacian8')

    print "梯度检测"
    for weight in weight_list:
        print weight, "R",
        R = convolve(a[:, :, 0], eval(weight))
        print "G",
        G = convolve(a[:, :, 1], eval(weight))
        print "B"
        B = convolve(a[:, :, 2], eval(weight))
        I = np.stack((R, G, B), 2)
    # Image.fromarray(I).save(output_path + weight + ".png")

实验结果

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