python 图片批量指定局部区域打码处理

(一)法. 推荐使用

1.看明白如下重复的for循环，就可以实现对同一张图片做任意位置的打码与任意数量的扩充了。

import os
from PIL import Image
import glob
import numpy as np

outdir = r'C:\Users\admin\Desktop\c1\dama'
count = 0
for i in glob.glob(r'C:\Users\admin\Desktop\c1\resize/*.jpg'):
    im1 = Image.open(i)
    arr = np.array(im1)
    h = arr.shape[0]
    w = arr.shape[1]

    a = 7
    for m in range(a): #每张图可以生成a张打码图
        im2 = im1.copy() #同一张每次取出都复制，以防上次操作影响到下次

        for j in range(int((m/a) * w),int(((m+1)/a) * w),1):#每个m对应一段x

            for k in range(int(h-(m/a)*h - 1),int(h-((m+1)/a)*h - 1),-1):

                im2.putpixel((j, k), (255,0,0))  # 这些像素点的颜色改成红色,改了图本身
        if m>0 and m 0 and m < a - 1:
            count += 1
            print(count)
            im3.save(os.path.join(outdir, '{}.jpg'.format(count)))

        im4 = im1.copy()
        for e in range(int((m / a) * w), int(((m + 1) / a) * w), 1):

            for f in range(int(h/2 - 1/2 * (1 /a) * w), int(h/2 + 1/2 * (1 /a) * w), 1):
                im4.putpixel((e, f), (255, 0, 0))
        if m > 0 and m < a - 1:
            count += 1
            print(count)
            im4.save(os.path.join(outdir, '{}.jpg'.format(count)))

2.按对应顺序生成打码图

import os
from PIL import Image
import numpy as np

outdir = r'C:\Users\admin\Desktop\c1\trainB'
count = 0
path = r'C:\Users\admin\Desktop\c1\trainA'
x_names = os.listdir(path)
x_names.sort(key=lambda i:int(i[:-4]))

for i in x_names:
    im1 = Image.open(os.path.join(path,i))
    arr = np.array(im1)
    h = arr.shape[0]
    w = arr.shape[1]

    for j in range(int((1 / 3) * w), int((2 / 3) * w), 1):
        for k in range(int((1 / 3) * h), int((2 / 3) * h), 1):
            im1.putpixel((j, k), (255, 0, 0))  
    count += 1
    print(count)
    im1.save(os.path.join(outdir, '{}.jpg'.format(count)))

(二)法.

1.只用PIL

import os
from PIL import Image
import glob
import numpy as np

outdir = r'C:\Users\admin\Desktop\c1\dama'

count = 0
for i in glob.glob(r'C:\Users\admin\Desktop\c1\img/*.jpg'):
    im1 = Image.open(i)
    arr = np.array(im1)
    '在此也能体会到：若是要读取图片的尺寸等属性，最好直接用cv2，而不是PIL或matplotlib'
    # h = im1.shape[0]#错,im1作为图片，本身没有shape等np矩阵属性,故需先图转矩阵
    # w = im1.shape[1]
    h = arr.shape[0]
    w = arr.shape[1]
    a = 9
    for j in range(int(1 / 4 * w), int(3 / 4 * w), a):
        for k in range(int(1 / 4 * w), int(3 / 4 * w), a):
            arr[j:j + a, k:k + a] = arr[j + (a // 2)][k + (a // 2)]

    count += 1
    print(count)
    im2 = Image.fromarray(arr)
    im2.save(os.path.join(outdir,os.path.basename(i)))

 

效果如下(顺序因其他操作给打乱了)

注：我是直接对celeba数据集人脸做了打码处理。其中第18、19行的系数和depts值，要根据具体图片尺寸以及你想要打码的区域，自行作出改动。

2. 结合cv2

1.cv2按BGR读取，以下是未做转RGB操作的效果

2.正确操作，效果如下