图片处理完整流程（包含二值化处理、对黑白照片降噪、边缘去黑像素、三通道转为一通道、图片转array、图片转成任意像素等功能）——可满足一般图片处理要求

因为注释给的很详细，所以直接给代码：

from PIL import Image
import numpy as np
# 二值化处理


def 二值化处理(image):
    for i in range(1, 5):
        # 灰度图
        lim = image.convert('L')
        # 灰度阈值设为165，低于这个值的点全部填白色
        threshold = 165
        table = []
        for j in range(256):
            if j < threshold:
                table.append(0)
            else:
                table.append(1)
        bim = lim.point(table, '1')
    return bim
def 对黑白图片进行降噪(im):
    # 图像二值化
    data = im.getdata()
    w, h = im.size
    black_point = 0
    for x in range(1, w - 1):
        for y in range(1, h - 1):
            mid_pixel = data[w * y + x]  # 中央像素点像素值
            if mid_pixel < 50:  # 找出上下左右四个方向像素点像素值
                top_pixel = data[w * (y - 1) + x]
                left_pixel = data[w * y + (x - 1)]
                down_pixel = data[w * (y + 1) + x]
                right_pixel = data[w * y + (x + 1)]
                # 判断上下左右的黑色像素点总个数
                if top_pixel < 10:
                    black_point += 1
                if left_pixel < 10:
                    black_point += 1
                if down_pixel < 10:
                    black_point += 1
                if right_pixel < 10:
                    black_point += 1
                if black_point < 1:
                    im.putpixel((x, y), 255)
                # print(black_point)
                black_point = 0
    return im

def 边缘黑像素(im):
    # 去除干扰线

    # 图像二值化
    data = im.getdata()
    w, h = im.size
    black_point = 0
    for x in range(1, w - 1):
        for y in range(1, h - 1):
            if x < 2 or y < 2:
                im.putpixel((x - 1, y - 1), 255)
            if x > w - 3 or y > h - 3:
                im.putpixel((x + 1, y + 1), 255)
    return im
def 处理照片(image):
    im_1=二值化处理(image)
    im_2=对黑白图片进行降噪(im_1)
    im_3=边缘黑像素(im_2)
    return im_3

def 切图片(im):
    gray = im.convert('L')
    (x, y) = gray.size  # read image size
    x_s = 28  # define standard width
    y_s = 28  # calc height based on standard width
    out = gray.resize((x_s, y_s), Image.ANTIALIAS)  # resize image with high-quality
    return out
def 转array(im):
    im2 = 1 - np.array(im) / 255
    print(im2)
    print(im2.shape)
    return im2
def GET_true_img(url_name):
    image = Image.open('../data/test/'+url_name+'.png')
    im_1=处理照片(image)
    im_1.save("../data/test/去噪/"+url_name+"_out_处理.png")
    im_2=切图片(im_1)
    im_2.save("../data/test/去噪/"+url_name+"_out.png")
    im_narray=转array(im_2)

    return im_narray
if __name__ =='__main__':
    image = Image.open('../data/test/8.png')
    im_1=处理照片(image)
    im_1.save("../data/test/去噪/8_out_处理.png")
    im_2=切图片(im_1)
    im_2.save("../data/test/去噪/8_out.png")
    im_narray=转array(im_2)

处理前

 处理后：

转型后：