import os
import numpy as np
import pandas as pd
import torch
from skimage import io
from torch.utils.data import Dataset
from ida_lib.core.pipeline_geometric_ops import RandomScalePipeline, HflipPipeline
from ida_lib.core.pipeline_pixel_ops import RandomContrastPipeline
from ida_lib.image_augmentation.augment_to_disk import AugmentToDisk
# Create custom dataset to read the input data to be augmented
class FaceLandmarksDataset(Dataset):
"""Face Landmarks dataset."""
def __init__(self, csv_file, root_dir, transform=None):
"""
Args:
csv_file (string): Path to the csv file with annotations.
root_dir (string): Directory with all the images.
transform (callable, optional): Optional transform to be applied
on a sample.
"""
self.landmarks_frame = pd.read_csv(csv_file)
self.root_dir = root_dir
self.transform = transform
def __len__(self):
return len(self.landmarks_frame)
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
img_name = os.path.join(self.root_dir,
self.landmarks_frame.iloc[idx, 0])
item_id = (self.landmarks_frame.iloc[idx, 0]).split('.')[0]
image = io.imread(img_name)
landmarks = self.landmarks_frame.iloc[idx, 1:]
landmarks = np.array([landmarks])
landmarks = landmarks.astype('float').reshape(-1, 2)
sample = {'id': item_id, 'image': image, 'landmarks': landmarks}
return sample
# Inicialize the custom datset
face_dataset = FaceLandmarksDataset(csv_file='faces/face_landmarks.csv',
root_dir='faces/')
# parameter setting and initialization
augmentor = AugmentToDisk(dataset=face_dataset, # custom dataset that provides the input data
samples_per_item=5, # number of samples per imput item
operations=(RandomScalePipeline(probability=0.6, scale_range=(0.8, 1.2), center_deviation=20),
HflipPipeline(probability=0.5),
RandomContrastPipeline(probability=0.5, contrast_range=(1, 1.5))),
interpolation='nearest',
padding_mode='zeros',
resize=(250, 250), # Here resizing is necessary because the input images have different sizes
output_extension='.jpg',
output_csv_path='anotations.csv',
output_path='./augmented_custom')
augmentor() # Run the augmentation