visualize the data

[mtk380]

Maybe the data is converted to bin files for faster dataloader, but I am confused about how to visualize the data such as the figure in your paper.

[zhongyy]

You can convert the bin to jpg.

[qqice]

import torch

from datetime import datetime
import matplotlib.pyplot as plt
plt.switch_backend('agg')
import numpy as np
from PIL import Image
import mxnet as mx
import io
import os, pickle, sklearn
import time
from IPython import embed

def load_bin(path, image_size=[112,112]):
    bins, issame_list = pickle.load(open(path, 'rb'), encoding='bytes')
    data_list = []
    for flip in [0,1]:
        data = torch.zeros((len(issame_list)*2, 3, image_size[0], image_size[1]))
        data_list.append(data)
    for i in range(len(issame_list)*2):
        _bin = bins[i]
        img = mx.image.imdecode(_bin)
        if img.shape[1]!=image_size[0]:
            img = mx.image.resize_short(img, image_size[0])
        img = mx.nd.transpose(img, axes=(2, 0, 1))
        for flip in [0,1]:
            if flip==1:
                img = mx.ndarray.flip(data=img, axis=2)
            img_pil = Image.fromarray(img.asnumpy().astype(np.uint8).transpose(1,2,0))
            img_pil.save('./lfw/'+str(i)+'_'+str(flip)+'.jpg')
            data_list[flip][i][:] = torch.tensor(img.asnumpy())
        if i%1000==0:
            print('loading bin', i)
    print(data_list[0].shape)
    return data_list, issame_list

datalist, issamelist = load_bin("eval/lfw.bin")
print(len(issamelist))

I've modified the code so that it can extract the bin file to thousands of jpgs. Hope that helps.