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convert_fer2013_to_images_and_landmarks.py
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convert_fer2013_to_images_and_landmarks.py
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import numpy as np
import pandas as pd
import os
import argparse
import errno
import scipy.misc
import dlib
import cv2
from skimage.feature import hog
# initialization
image_height = 48
image_width = 48
window_size = 24
window_step = 6
ONE_HOT_ENCODING = False
SAVE_IMAGES = False
GET_LANDMARKS = False
GET_HOG_FEATURES = False
GET_HOG_WINDOWS_FEATURES = False
SELECTED_LABELS = []
IMAGES_PER_LABEL = 500
OUTPUT_FOLDER_NAME = "fer2013_features"
# parse arguments and initialize variables:
parser = argparse.ArgumentParser()
parser.add_argument("-j", "--jpg", default="no", help="save images as .jpg files")
parser.add_argument("-l", "--landmarks", default="yes", help="extract Dlib Face landmarks")
parser.add_argument("-ho", "--hog", default="yes", help="extract HOG features")
parser.add_argument("-hw", "--hog_windows", default="yes", help="extract HOG features from a sliding window")
parser.add_argument("-o", "--onehot", default="no", help="one hot encoding")
parser.add_argument("-e", "--expressions", default="0,1,2,3,4,5,6", help="choose the faciale expression you want to use: 0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral")
args = parser.parse_args()
if args.jpg == "yes":
SAVE_IMAGES = True
if args.landmarks == "yes":
GET_LANDMARKS = True
if args.hog == "yes":
GET_HOG_FEATURES = True
if args.hog_windows == "yes":
GET_HOG_WINDOWS_FEATURES = True
if args.onehot == "yes":
ONE_HOT_ENCODING = True
if args.expressions != "":
expressions = args.expressions.split(",")
for i in range(0,len(expressions)):
label = int(expressions[i])
if (label >=0 and label<=6 ):
SELECTED_LABELS.append(label)
if SELECTED_LABELS == []:
SELECTED_LABELS = [0,1,2,3,4,5,6]
print( str(len(SELECTED_LABELS)) + " expressions")
# loading Dlib predictor and preparing arrays:
print( "preparing")
predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')
original_labels = [0, 1, 2, 3, 4, 5, 6]
new_labels = list(set(original_labels) & set(SELECTED_LABELS))
nb_images_per_label = list(np.zeros(len(new_labels), 'uint8'))
try:
os.makedirs(OUTPUT_FOLDER_NAME)
except OSError as e:
if e.errno == errno.EEXIST and os.path.isdir(OUTPUT_FOLDER_NAME):
pass
else:
raise
def get_landmarks(image, rects):
# this function have been copied from http://bit.ly/2cj7Fpq
if len(rects) > 1:
raise BaseException("TooManyFaces")
if len(rects) == 0:
raise BaseException("NoFaces")
return np.matrix([[p.x, p.y] for p in predictor(image, rects[0]).parts()])
def get_new_label(label, one_hot_encoding=False):
if one_hot_encoding:
new_label = new_labels.index(label)
label = list(np.zeros(len(new_labels), 'uint8'))
label[new_label] = 1
return label
else:
return new_labels.index(label)
def sliding_hog_windows(image):
hog_windows = []
for y in range(0, image_height, window_step):
for x in range(0, image_width, window_step):
window = image[y:y+window_size, x:x+window_size]
hog_windows.extend(hog(window, orientations=8, pixels_per_cell=(8, 8),
cells_per_block=(1, 1), visualise=False))
return hog_windows
print( "importing csv file")
data = pd.read_csv('fer2013.csv')
for category in data['Usage'].unique():
print( "converting set: " + category + "...")
# create folder
if not os.path.exists(category):
try:
os.makedirs(OUTPUT_FOLDER_NAME + '/' + category)
except OSError as e:
if e.errno == errno.EEXIST and os.path.isdir(OUTPUT_FOLDER_NAME):
pass
else:
raise
# get samples and labels of the actual category
category_data = data[data['Usage'] == category]
samples = category_data['pixels'].values
labels = category_data['emotion'].values
# get images and extract features
images = []
labels_list = []
landmarks = []
hog_features = []
hog_images = []
for i in range(len(samples)):
try:
if labels[i] in SELECTED_LABELS and nb_images_per_label[get_new_label(labels[i])] < IMAGES_PER_LABEL:
image = np.fromstring(samples[i], dtype=int, sep=" ").reshape((image_height, image_width))
images.append(image)
if SAVE_IMAGES:
scipy.misc.imsave(category + '/' + str(i) + '.jpg', image)
if GET_HOG_WINDOWS_FEATURES:
features = sliding_hog_windows(image)
f, hog_image = hog(image, orientations=8, pixels_per_cell=(16, 16),
cells_per_block=(1, 1), visualise=True)
hog_features.append(features)
hog_images.append(hog_image)
elif GET_HOG_FEATURES:
features, hog_image = hog(image, orientations=8, pixels_per_cell=(16, 16),
cells_per_block=(1, 1), visualise=True)
hog_features.append(features)
hog_images.append(hog_image)
if GET_LANDMARKS:
scipy.misc.imsave('temp.jpg', image)
image2 = cv2.imread('temp.jpg')
face_rects = [dlib.rectangle(left=1, top=1, right=47, bottom=47)]
face_landmarks = get_landmarks(image2, face_rects)
landmarks.append(face_landmarks)
labels_list.append(get_new_label(labels[i], one_hot_encoding=ONE_HOT_ENCODING))
nb_images_per_label[get_new_label(labels[i])] += 1
except Exception as e:
print( "error in image: " + str(i) + " - " + str(e))
np.save(OUTPUT_FOLDER_NAME + '/' + category + '/images.npy', images)
if ONE_HOT_ENCODING:
np.save(OUTPUT_FOLDER_NAME + '/' + category + '/labels.npy', labels_list)
else:
np.save(OUTPUT_FOLDER_NAME + '/' + category + '/labels.npy', labels_list)
if GET_LANDMARKS:
np.save(OUTPUT_FOLDER_NAME + '/' + category + '/landmarks.npy', landmarks)
if GET_HOG_FEATURES or GET_HOG_WINDOWS_FEATURES:
np.save(OUTPUT_FOLDER_NAME + '/' + category + '/hog_features.npy', hog_features)
np.save(OUTPUT_FOLDER_NAME + '/' + category + '/hog_images.npy', hog_images)