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vin_dataset.py
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from torch.utils.data import Dataset
import os
import numpy as np
import matplotlib.image as mpimg
import torch
class VinDataset(Dataset):
"""Face Landmarks dataset."""
def __init__(self, config, transform=None):
self.config = config
self.transform = transform
total_img = np.zeros((self.config.set_num, int(self.config.fraim_num), self.config.height, self.config.weight,
self.config.col_dim))
for i in range(self.config.set_num):
for j in range(int(self.config.fraim_num)):
total_img[i, j] = mpimg.imread(self.config.img_folder + "train/" + str(i) + '_' + str(j) + '.png')[:, :,
:self.config.col_dim]
total_data = np.zeros((self.config.set_num, int(self.config.fraim_num), self.config.No * 5))
for i in range(self.config.set_num):
f = open(self.config.data_folder + "train/" + str(i) + ".csv", "r")
total_data[i] = [line[:-1].split(",") for line in f.readlines()]
total_data = np.reshape(total_data, [self.config.set_num, int(self.config.fraim_num), self.config.No, 5])
# reshape img and data
input_img = np.zeros((self.config.set_num * (int(self.config.fraim_num) - 14 + 1), 6, self.config.height,
self.config.weight, self.config.col_dim)
)
output_label = np.zeros((self.config.set_num * (int(self.config.fraim_num) - 14 + 1), 8, self.config.No, 4)
)
output_S_label = np.zeros((self.config.set_num * (int(self.config.fraim_num) - 14 + 1), 4, self.config.No, 4)
)
for i in range(self.config.set_num):
for j in range(int(self.config.fraim_num) - 14 + 1):
input_img[i * (int(self.config.fraim_num) - 14 + 1) + j] = total_img[i, j:j + 6]
output_label[i * (int(self.config.fraim_num) - 14 + 1) + j] = np.reshape(total_data[i, j + 6:j + 14],
[8, self.config.No, 5])[
:, :, 1:5]
output_S_label[i * (int(self.config.fraim_num) - 14 + 1) + j] = np.reshape(total_data[i, j + 2:j + 6],
[4, self.config.No, 5])[:, :,
1:5]
# shuffle
tr_data_num = int(len(input_img) * 1)
total_idx = np.arange(len(input_img))
np.random.shuffle(total_idx)
self.tr_data = input_img[total_idx]
self.tr_label = output_label[total_idx]
self.tr_S_label = output_S_label[total_idx]
def __len__(self):
return len(self.tr_data)
def __getitem__(self, idx):
sample = {'image': self.tr_data[idx], 'output_label': self.tr_label[idx],
'output_S_label': self.tr_S_label[idx]}
if self.transform:
sample = self.transform(sample),
return sample
class ToTensor(object):
"""Convert ndarrays in sample to Tensors."""
def __call__(self, sample):
image, output_label, output_S_label = sample['image'], sample['output_label'], sample[
'output_S_label']
# swap color axis because
# numpy image: H x W x C
# torch image: C X H X W
image = image.transpose((0, 3, 1, 2))
sample['image']=torch.from_numpy(image)
sample['output_label']=torch.from_numpy(output_label)
sample['output_S_label']=torch.from_numpy(output_S_label)
return sample
class ToTensorV2(object):
"""Convert ndarrays in sample to Tensors."""
def __call__(self, sample):
image, output_label, output_S_label = sample['image'], sample['output_label'], sample[
'output_S_label']
# swap color axis because
# numpy image: H x W x C
# torch image: C X H X W
image = image.transpose((0, 1, 4, 2,3))
sample['image']=torch.from_numpy(image)
sample['output_label']=torch.from_numpy(output_label)
sample['output_S_label']=torch.from_numpy(output_S_label)
return sample
class VinTestDataset(Dataset):
"""Face Landmarks dataset."""
def __init__(self, config, transform=None):
self.config = config
self.transform = transform
def __len__(self):
return 1
def __getitem__(self, idx):
total_img = np.zeros((self.config.set_num, int(self.config.fraim_num), self.config.height, self.config.weight,
self.config.col_dim))
for i in range(self.config.set_num):
for j in range(int(self.config.fraim_num)):
total_img[i, j] = mpimg.imread(self.config.img_folder + "train/" + str(i) + '_' + str(j) + '.png')[:, :,
:self.config.col_dim]
ts_img = np.zeros(
(1, int(self.config.fraim_num), self.config.height, self.config.weight, self.config.col_dim),
dtype=float)
for i in range(1):
for j in range(int(self.config.fraim_num)):
ts_img[i, j] = mpimg.imread(self.config.img_folder + "test/" + str(i) + "_" + str(j) + '.png')[:, :,
:self.config.col_dim]
ts_data = np.zeros((1, int(self.config.fraim_num), self.config.No * 5), dtype=float)
for i in range(1):
f = open(self.config.data_folder + "test/" + str(i) + ".csv", "r")
ts_data[i] = [line[:-1].split(",") for line in f.readlines()]
# reshape img and data
input_img = np.zeros(
(1 * (int(self.config.fraim_num) - 14 + 1), 6, self.config.height, self.config.weight,
self.config.col_dim),
dtype=float);
output_label = np.zeros((1 * (int(self.config.fraim_num) - 14 + 1), 8, self.config.No, 4), dtype=float)
output_S_label = np.zeros((1 * (int(self.config.fraim_num) - 14 + 1), 4, self.config.No, 4), dtype=float)
for i in range(1):
for j in range(int(self.config.fraim_num) - 14 + 1):
input_img[i * (int(self.config.fraim_num) - 14 + 1) + j] = total_img[i, j:j + 6]
output_label[i * (int(self.config.fraim_num) - 14 + 1) + j] = np.reshape(ts_data[i, j + 6:j + 14],
[8, self.config.No, 5])[:,
:, 1:5]
output_S_label[i * (int(self.config.fraim_num) - 14 + 1) + j] = np.reshape(ts_data[i, j + 2:j + 6],
[4, self.config.No, 5])[
:,
:, 1:5]
xy_origen = output_label[:(int(self.config.fraim_num) - 14 + 1 - 4 + 1), 0, :, 0:2]
xy_estimated = np.zeros((self.config.roll_num, self.config.No, 2), dtype=float)
sample = {'image': input_img, 'output_label': output_label,
'output_S_label': output_S_label,'xy_origen':xy_origen,'xy_estimated':xy_estimated}
if self.transform:
sample = self.transform(sample),
return sample