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Fixed code errors and replaced deprecated imports. #28

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Fixed code errors and replaced deprecated imports. #28

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61 changes: 39 additions & 22 deletions convert_fer2013_to_images_and_landmarks.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,10 @@
import os
import argparse
import errno
import scipy.misc
# import scipy.misc
from skimage import img_as_ubyte

import imageio
import dlib
import cv2

Expand All @@ -26,13 +29,18 @@

# 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("-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("-hi", "--hog_images", default="no", help="extract HOG images")
parser.add_argument("-hw", "--hog_windows", default="yes",
help="extract HOG features from a sliding window")
parser.add_argument("-hi", "--hog_images", default="no",
help="extract HOG images")
parser.add_argument("-o", "--onehot", default="yes", 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")
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
Expand All @@ -47,17 +55,17 @@
if args.onehot == "yes":
ONE_HOT_ENCODING = True
if args.expressions != "":
expressions = args.expressions.split(",")
for i in range(0,len(expressions)):
expressions = args.expressions.split(",")
for i in range(0, len(expressions)):
label = int(expressions[i])
if (label >=0 and label<=6 ):
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")
SELECTED_LABELS = [0, 1, 2, 3, 4, 5, 6]
print(str(len(SELECTED_LABELS)) + " expressions")

# loading Dlib predictor and preparing arrays:
print( "preparing")
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))
Expand All @@ -70,6 +78,7 @@
else:
raise


def get_landmarks(image, rects):
# this function have been copied from http://bit.ly/2cj7Fpq
if len(rects) > 1:
Expand All @@ -78,6 +87,7 @@ def get_landmarks(image, rects):
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)
Expand All @@ -87,20 +97,22 @@ def get_new_label(label, one_hot_encoding=False):
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))
cells_per_block=(1, 1), visualize=False))
return hog_windows

print( "importing csv file")

print("importing csv file")
data = pd.read_csv('fer2013.csv')

for category in data['Usage'].unique():
print( "converting set: " + category + "...")
print("converting set: " + category + "...")
# create folder
if not os.path.exists(category):
try:
Expand All @@ -110,12 +122,12 @@ def sliding_hog_windows(image):
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 = []
Expand All @@ -125,25 +137,30 @@ def sliding_hog_windows(image):
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))
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)
# scipy.misc.imsave(category + '/' + str(i) + '.jpg', image)
#img_as_ubyte
imageio.imwrite(category + '/' + str(i) + '.jpg', img_as_ubyte(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)
cells_per_block=(1, 1), visualize=True)
hog_features.append(features)
if GET_HOG_IMAGES:
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)
cells_per_block=(1, 1), visualize=True)
hog_features.append(features)
if GET_HOG_IMAGES:
hog_images.append(hog_image)
if GET_LANDMARKS:
scipy.misc.imsave('temp.jpg', image)
# scipy.misc.imsave('temp.jpg', image)
#img_as_ubyte(img)
imageio.imwrite('temp.jpg', img_as_ubyte(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)
Expand Down