def get_faces_dimens(image_string, bounds):
try:
result = indicoio.facial_localization(image_string)
faces = []
for face in result:
x1, y1 = face["top_left_corner"]
x2, y2 = face["bottom_right_corner"]
faces.append((x1, y1, x2, y2))
return faces
except Exception as e:
logger.error(e)
def _get_single_face_corners(self, img):
"""
returns x and y coords of upper and lower left pixel of face in img (a PIL Image object)
"""
try:
face_corners = indicoio.facial_localization(img)[0]
x0, y0 = face_corners['top_left_corner']
x1, y1 = face_corners['bottom_right_corner']
return (x0, y0, x1, y1)
except:
return ()
def extractFaces(imgPath):
faceArray = []
faces = indicoio.facial_localization(imgPath)
img = cv2.imread(imgPath)
count = 0
for face in faces:
if (face['bottom_right_corner'][1] - face['top_left_corner'][1]) > (len(img[0]) * 0.2):
count += 1
faceImg = img[
face['top_left_corner'][1] : face['bottom_right_corner'][1],
face['top_left_corner'][0] : face['bottom_right_corner'][0]
]
storageLocation = faceImgStorageDir + str(int(time.time())) + str(count) + '.jpg'
cv2.imwrite(storageLocation, faceImg)
faceArray.append(storageLocation)
return faceArray
def recordFaceData(directory, nickname):
allImages = []
count = 0
for file in os.listdir(directory):
if file.endswith('.jpg'):
allImages.append(file)
currentRecordedFeatures = {}
currentRecordedFeatures['names'] = []
currentRecordedFeatures['features'] = []
print 'Caching...'
for image in allImages:
print 'Collecting info ... ', count
count += 1
face = indicoio.facial_localization(directory + '/' + image)
if len(face) > 0:
face = face[0]
img = cv2.imread(directory + '/' + image)
faceImg = img[
face['top_left_corner'][1] : face['bottom_right_corner'][1],
face['top_left_corner'][0] : face['bottom_right_corner'][0]
]
facialFeatures = indicoio.facial_features(faceImg)
if (count % 50) == 0:
cv2.imwrite('images/faces/' + str(int(time.time())) + '.jpg', faceImg)
currentRecordedFeatures['names'].append(nickname)
currentRecordedFeatures['features'].append(facialFeatures)
np.save(featuresCacheFile + nickname + '.npy', [currentRecordedFeatures])
print 'Done'
return currentRecordedFeatures
def test_facial_localization_crop(self):
test_face = os.path.normpath(os.path.join(DIR, "data/happy.png"))
res = facial_localization(test_face, crop=True)[0]
self.assertTrue(res.get("image"))
def test_facial_localization_sensitivity(self):
test_face = os.path.normpath(os.path.join(DIR, "data/happy.png"))
low_sens = facial_localization(test_face, sensitivity=0.1)
high_sens = facial_localization(test_face, sensitivity=0.9)
self.assertEqual(len(low_sens), 1)
self.assertTrue(len(high_sens) > 1)
def test_facial_localization(self):
test_face = os.path.normpath(os.path.join(DIR, "data/happy.png"))
res = facial_localization(test_face)[0]
self.assertTrue(res["top_left_corner"][0] < res["bottom_right_corner"][0])
self.assertTrue(res["top_left_corner"][1] < res["bottom_right_corner"][1])
def test_facial_localization_crop(self):
test_face = os.path.normpath(os.path.join(DIR, "data/happy.png"))
res = facial_localization(test_face, crop=True)[0]
self.assertTrue(res.get("image"))
def capture():
try:
global foo
img = cam.get_image()
pygame.image.save(img, "test" + str(foo) + ".jpg")
os.rename(
"/home/lx_user/Documents/programming/web/uanimate/test" +
str(foo) + ".jpg",
"/home/lx_user/Documents/programming/web/uanimate/test/test" +
str(foo) + ".jpg")
dict = indicoio.facial_localization(
"/home/lx_user/Documents/programming/web/uanimate/test/test" +
str(foo) + ".jpg")
image = pygame.image.load(
"/home/lx_user/Documents/programming/web/uanimate/test/test" +
str(foo) + ".jpg")
c_id = 0
#fear is disgust
happy = 0.0
sad = 0.0
angry = 0.0
fear = 0.0
surprise = 0.0
neutral = 0.0
total = 0.0
for i in range(0, int(len(dict))):
if (dict[i]['bottom_right_corner'][0] -
dict[i]['top_left_corner'][0] > 60000
or dict[i]['bottom_right_corner'][1] -
dict[i]['top_left_corner'][1] > 60000
or dict[i]['top_left_corner'][0] > 60000
or dict[i]['top_left_corner'][1] > 60000):
continue
crop_rect = (dict[i]['top_left_corner'][0],
dict[i]['top_left_corner'][1],
abs(dict[i]['bottom_right_corner'][0] -
dict[i]['top_left_corner'][0]),
abs(dict[i]['bottom_right_corner'][1] -
dict[i]['top_left_corner'][1]))
cropped = image.subsurface(crop_rect)
pygame.image.save(cropped,
str(foo) + "cropped" + str(c_id) + ".jpg")
# code for the emotion
results = indicoio.fer(str(foo) + "cropped" + str(c_id) + ".jpg")
# print(results)
if ('Happy' in results):
happy += results['Happy']
if ('Sad' in results):
sad += results['Sad']
if ('Angry' in results):
angry += results['Angry']
if ('Fear' in results):
fear += results['Fear']
if ('Surprise' in results):
surprise += results['Surprise']
total += (happy + sad + angry + fear + surprise)
os.rename(
"/home/lx_user/Documents/programming/web/uanimate/" +
str(foo) + "cropped" + str(c_id) + ".jpg",
"/home/lx_user/Documents/programming/web/uanimate/test/" +
str(foo) + "cropped" + str(c_id) + ".jpg")
c_id += 1
#open the file for reading:
file = open(
'/home/lx_user/Documents/programming/web/uanimate/results.txt',
'w')
#convert to string:
file.write(
str(happy) + " " + str(sad) + " " + str(angry) + " " + str(fear) +
" " + str(surprise) + " " + str(total))
file.close()
foo += 1
except (RuntimeError, TypeError, NameError, ValueError, IndexError,
IOError, KeyError):
print("wtf")
