寒假PyTorch工具第四天

课程记录

从20种增强方法到椒盐噪声到逻辑直觉

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课程代码

无

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作业

1.     将介绍的transforms方法一一地，单独地实现对图片的变换，并且通过plt.savefig将图片保存下来

略

2.      自定义一个增加椒盐噪声的transforms方法，使得其能正确运行

见下面代码

3.      用手机将自己钱包中的100元人民币正面进行拍照，并且放到hello pytorch/lesson/lesson-09/test_data/100文件夹下，通过修改不同的数据增强方法，使得模型在10个epoch之后能正确区分你钱包中的100元

略

1. 椒盐噪声

peper noise

import torch 
import numpy as np 
from PIL import Image
from torchvision import datasets, models, transforms


np.random.seed(1234)

image_path = 'a.jpg'

class AddPepperNoise(object):
    def __init__(self, snr, p):
        self.snr = snr
        self.p = p 

    def __call__(self, img):
        p1 = np.random.uniform(low = 0.0, high = 1.0)
        if p1 > self.p:
            return img
        else:
            out_img = np.asarray(img).copy()
            h, w, c = out_img.shape
            for i in range(h):
                for j in range(w):
                    for k in range(c):
                        p2 = np.random.uniform(low = 0.0, high = 1.0)
                        if p2 < self.snr:
                            out_img[i][j][k] = out_img[i][j][k]
                        elif p2 < self.snr + (1 - self.snr) / 2:
                            out_img[i][j][k] = 0
                        else:
                            out_img[i][j][k] = 255
                        # print(out_img[i][j][k])
            print(out_img)
            tmp = Image.fromarray(out_img.astype('uint8')).convert('RGB')
            print('tmp:', tmp, np.asarray(tmp))
            return tmp


def main():
    img = Image.open(image_path)

    
    now_transforms = transforms.Compose([
        transforms.Resize((224, 224)),
        AddPepperNoise(snr = 0.9, p = 1.0),
        # transforms.ToTensor(),
    ])
    img_transformed = now_transforms(img)

    print(img_transformed, np.asarray(img_transformed))
    img_transformed.save('a_pepperNoise.jpg')



if __name__ == '__main__':
    main()