def cropFaces(imagePath):
#Loading the image to be tested
image_raw = ImageUtils.createImageFromPath(imagePath)
#Converting to grayscale
image_gray = cv.cvtColor(image_raw, cv.COLOR_BGR2GRAY)
haar_cascade_face = cv.CascadeClassifier(
Constants.DATA_ROOT_PATH + '/' + Constants.PROGRAM_DATA_PATH + '/' +
'haarcascade_frontalface_default.xml')
faces_rects = haar_cascade_face.detectMultiScale(image_gray,
scaleFactor=1.2,
minNeighbors=5)
# Let us print the no. of faces found
print('Faces found: ', len(faces_rects))
croppedImage_list = []
for (x, y, w, h) in faces_rects:
croppedImage = image_raw[y:(y + h), x:(x + w)]
croppedImage_list.append(croppedImage)
cv.rectangle(image_raw, (x, y), (x + w, y + h), (0, 255, 0), 2)
result_data = {}
result_data['croppedImageList'] = croppedImage_list
result_data['imageHighlighed'] = image_raw
return result_data
def cropFaces2(imagePath):
#Loading the image to be tested
result_data = None
image_raw = ImageUtils.createImageFromPath(imagePath)
# image_raw = resizeImage(image_raw)
if (image_raw is not None):
cloned_image = ImageUtils.cloneImage(image_raw)
predictor_path = Constants.DATA_ROOT_PATH + '/' + Constants.PROGRAM_DATA_PATH + '/' + 'shape_predictor_68_face_landmarks.dat'
face_rec_model_path = Constants.DATA_ROOT_PATH + '/' + Constants.PROGRAM_DATA_PATH + '/' + 'dlib_face_recognition_resnet_model_v1.dat'
# prepare predict
sp = dlib.shape_predictor(predictor_path)
facerec = dlib.face_recognition_model_v1(face_rec_model_path)
detector = dlib.get_frontal_face_detector()
croppedImage_list = []
feature_list = []
dets = detector(image_raw, 1)
for k, d in enumerate(dets):
x, y, w, h = d.left(), d.top(), d.width(), d.height()
croppedImage = image_raw[y:(y + h), x:(x + w)]
croppedImage_list.append(croppedImage)
cv.rectangle(cloned_image, (x, y), (x + w, y + h), (0, 255, 0), 2)
shape = sp(cloned_image, d)
face_descriptor = facerec.compute_face_descriptor(image_raw, shape)
v = np.array(face_descriptor)
print('--------------------------')
print(str(imagePath) + ':' + str(k))
print(v)
print('--------------------------')
feature_list.append(v)
result_data = {}
result_data['croppedImageList'] = croppedImage_list
result_data['croppedFeatureList'] = feature_list
result_data['imageHighlighed'] = cloned_image
return result_data
