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from config import DatasetName, AffectnetConf, InputDataSize, LearningConfig, ExpressionCodesAffectnet |
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from config import LearningConfig, InputDataSize, DatasetName, AffectnetConf, DatasetType |
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import numpy as np |
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import os |
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import matplotlib.pyplot as plt |
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import math |
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from datetime import datetime |
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from sklearn.utils import shuffle |
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from sklearn.model_selection import train_test_split |
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from numpy import save, load, asarray |
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import csv |
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from skimage.io import imread |
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import pickle |
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import csv |
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from tqdm import tqdm |
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from PIL import Image |
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from skimage.transform import resize |
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from skimage import transform |
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from skimage.transform import resize |
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import tensorflow as tf |
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import random |
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import cv2 |
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from skimage.feature import hog |
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from skimage import data, exposure |
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from matplotlib.path import Path |
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from scipy import ndimage, misc |
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from skimage.transform import SimilarityTransform, AffineTransform |
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from skimage.draw import rectangle |
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from skimage.draw import line, set_color |
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class DataHelper: |
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def create_generator_full_path(self, img_path, annotation_path, label=None): |
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img_filenames, exp_filenames = self._create_image_and_labels_name_full_path(img_path=img_path, |
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annotation_path=annotation_path, |
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label=label) |
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'''shuffle''' |
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img_filenames, exp_filenames = shuffle(img_filenames, exp_filenames) |
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return img_filenames, exp_filenames |
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def _create_image_and_labels_name_full_path(self, img_path, annotation_path, label): |
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img_filenames = [] |
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exp_filenames = [] |
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print('reading list -->') |
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file_names = tqdm(os.listdir(img_path)) |
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print('<-') |
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for file in file_names: |
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if file.endswith(".jpg") or file.endswith(".png"): |
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exp_lbl_file = str(file)[:-4] + "_exp.npy" |
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if os.path.exists(annotation_path + exp_lbl_file): |
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if label is not None: |
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exp = np.load(annotation_path + exp_lbl_file) |
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if label is not None and exp != label: |
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continue |
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img_filenames.append(img_path + str(file)) |
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exp_filenames.append(annotation_path + exp_lbl_file) |
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return np.array(img_filenames), np.array(exp_filenames) |
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def relabel_ds(self, labels): |
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new_labels = np.copy(labels) |
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index_src = [0, 1, 2, 3, 4, 5, 6, 7, 17, 18, 19, 20, 21, 31, 32, 36, 37, 38, 39, 40, 41, 48, 49, 50, |
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60, 61, 67, 59, 58] |
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index_dst = [16, 15, 14, 13, 12, 11, 10, 9, 26, 25, 24, 23, 22, 35, 34, 45, 44, 43, 42, 47, 46, 54, 53, 52, |
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64, 63, 65, 55, 56] |
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for i in range(len(index_src)): |
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new_labels[index_src[i] * 2] = labels[index_dst[i] * 2] |
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new_labels[index_src[i] * 2 + 1] = labels[index_dst[i] * 2 + 1] |
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new_labels[index_dst[i] * 2] = labels[index_src[i] * 2] |
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new_labels[index_dst[i] * 2 + 1] = labels[index_src[i] * 2 + 1] |
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return new_labels |
