def get_embeddings(filenames):
faces = [extract_faces(f) for f in filenames]
samples = asarray(faces, 'float32')
samples = preprocess_input(samples, version=2)
model = VGGFace(model = 'resnet50', include_top = False, input_shape = (224, 224, 3), pooling = 'avg')
yhat = model.predict(samples)
return yhat
def tensorflow_example():
"""This example uses TensorFlow instead of CoreML, and was found to give consistent numbers to CoreML"""
model = VGGFace(model="senet50", pooling="avg", include_top=False)
img = image.load_img('../image/ajb.jpg', target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = utils.preprocess_input(x, version=2)
embeddings = model.predict(x)[0]
print("TensorFlow embeddings: ", embeddings)
class face_analyzer:
def __init__(self):
self.face_not_found = 0
self.model = VGGFace(model='resnet50',
include_top=False,
input_shape=(224, 224, 3),
pooling='avg')
def get_face(self, img):
face_cascade = cv2.CascadeClassifier(
"cascades\\data\\haarcascade_frontalface_default.xml")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
coords = face_cascade.detectMultiScale(gray, 1.1, 4)
x, y, w, h = coords[0]
img = img[y:y + h, x:x + w]
img = cv2.resize(img, (224, 224))
return img
def find_best_match(self):
self.scores = {}
for person in os.listdir("userdata"):
self.get_cos_dif("userdata" + "\\" + person + "\\" +
"userface.jpg")
mini = 1
mini_link = ""
for person in self.scores:
prs = person.split('\\')[-2]
print(f"{prs} -> {self.scores[person]}")
if self.scores[person] < mini:
mini_link = person
mini = self.scores[person]
if mini < 0.4:
#print(f"User {mini_link} detected")
return "\\".join(mini_link.split("\\")[:-1]) + "\\" + "text.txt"
return None
def get_cos_dif(self, cnt_person):
self.face_not_found = 0
self.ppl = []
self.ppl.append(cv2.imread(cnt_person))
self.ppl.append(cv2.imread("auth_pers.jpg"))
faces = []
for img in self.ppl:
faces.append(self.get_face(img))
faces = asarray(faces, 'float32')
faces = preprocess_input(faces, version=2)
preds = self.model.predict(faces)
score = cosine(preds[0], preds[1])
#print( f"{cnt_person} --> {score}")
self.scores[cnt_person] = score
def testTHPrediction(self):
keras.backend.set_image_dim_ordering('th')
model = VGGFace()
img = image.load_img('image/ak.jpg', target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = utils.preprocess_input(x)
preds = model.predict(x)
print('Predicted:', utils.decode_predictions(preds))
self.assertIn(utils.decode_predictions(preds)[0][0][0], 'Aamir_Khan')
self.assertAlmostEqual(
utils.decode_predictions(preds)[0][0][1], 0.94938219)
def testRESNET50(self):
keras.backend.set_image_dim_ordering('tf')
model = VGGFace(model='resnet50')
img = image.load_img('image/ajb.jpg', target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = utils.preprocess_input(x, version=2)
preds = model.predict(x)
#print ('\n',"RESNET50")
#print('\n',preds)
#print('\n','Predicted:', utils.decode_predictions(preds))
self.assertIn('A._J._Buckley', utils.decode_predictions(preds)[0][0][0])
self.assertAlmostEqual(utils.decode_predictions(preds)[0][0][1], 0.91819614)
def testRESNET50(self):
keras.backend.set_image_dim_ordering('tf')
model = VGGFace(model='resnet50')
img = image.load_img('image/ajb.jpg', target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = utils.preprocess_input(x, version=2)
preds = model.predict(x)
#print ('\n',"RESNET50")
#print('\n',preds)
#print('\n','Predicted:', utils.decode_predictions(preds))
self.assertIn('A._J._Buckley', utils.decode_predictions(preds)[0][0][0])
self.assertAlmostEqual(utils.decode_predictions(preds)[0][0][1], 0.91819614,places=3)
def testSENET50(self):
image_data_format()
model = VGGFace(model='senet50')
img = image.load_img('image/ajb.jpg', target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = utils.preprocess_input(x, version=2)
preds = model.predict(x)
# print ('\n', "SENET50")
# print('\n',preds)
# print('\n','Predicted:', utils.decode_predictions(preds))
self.assertIn('A._J._Buckley', utils.decode_predictions(preds)[0][0][0])
self.assertAlmostEqual(utils.decode_predictions(preds)[0][0][1], 0.9993529, places=3)
class BlackboxModel(metaclass=Singleton):
"""
Singleton class representing blackbox model
"""
def __init__(self, architecture):
self.model = VGGFace(model=architecture)
def predict(self, batch):
preprocessed_batch = utils.preprocess_input(batch, version=2)
preds = self.model.predict(preprocessed_batch)
return preds
def __call__(self, batch):
return self.model(batch)
def testVGG16(self):
keras.backend.image_data_format()
model = VGGFace(model='vgg16')
img = image.load_img('image/ajb.jpg', target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = utils.preprocess_input(x, version=1)
preds = model.predict(x)
# print ('\n', "VGG16")
# print('\n',preds)
# print('\n','Predicted:', utils.decode_predictions(preds))
self.assertIn('A.J._Buckley', utils.decode_predictions(preds)[0][0][0])
self.assertAlmostEqual(utils.decode_predictions(preds)[0][0][1],
0.9790116,
places=3)
def get_predictions_from_png_image_example():
"""Example usage to get predictions (human identity) from image"""
from tensorflow.keras.preprocessing import image
import numpy as np
import keras_vggface.utils as libutils
image_preprocessor = create_preprocessing_model()
model = VGGFace(model='senet50')
img = image.load_img('image/ajb-resized.jpg',
target_size=(224, 224),
interpolation="bilinear")
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
preprocessed = image_preprocessor.predict(x)
predictions = model.predict(preprocessed)
print('Predicted:', libutils.decode_predictions(predictions))
def tensorflow_custom_preprocessing_example():
"""Example usage to get face embeddings from cropped image of human face"""
import numpy as np
from tensorflow.keras.preprocessing import image
image_preprocessor = create_preprocessing_model()
embeddings_model = VGGFace(model="senet50",
pooling="avg",
include_top=False,
input_shape=(224, 224, 3))
img = image.load_img('../image/ajb.jpg', target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
preprocessed = image_preprocessor.predict(x)
embeddings = embeddings_model.predict(preprocessed)
print("TensorFlow embeddings: ", embeddings)
def testVGG16(self):
keras.backend.set_image_dim_ordering('tf')
model = VGGFace(include_top=False,
input_shape=(224, 224, 3),
pooling='avg') # pooling: None, avg or max
#model = VGGFace(model='vgg16')
img = image.load_img('image/ajb.jpg', target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = utils.preprocess_input(x, version=2)
preds = model.predict(x)
print('\n', "VGG16")
print('\n', preds)
print('\n', 'Predicted:', utils.decode_predictions(preds))
self.assertIn('A.J._Buckley', utils.decode_predictions(preds)[0][0][0])
self.assertAlmostEqual(utils.decode_predictions(preds)[0][0][1],
0.9790116,
places=3)
def double_faces_extraction(double_face_video_path, detections,
temp_dir_double_extraction):
list_det_final = []
frame_counter = 0
face_model = VGGFace(model='resnet50',
include_top=False,
input_shape=(224, 224, 3),
pooling='avg')
cv2video = cv2.VideoCapture(double_face_video_path)
video_double = os.path.basename(double_face_video_path)
video_name = video_double[:-4]
video_frames_extraction_folder = os.path.join(temp_dir_double_extraction,
video_name)
# #print("Processing video")
analysis_step = True
right_face = {
'best_img': None,
'features': [],
'avg_feature': None,
'rois': []
}
left_face = {
'best_img': None,
'features': [],
'avg_feature': None,
'rois': []
}
current_annotation = 'right'
video_frames_extraction_folder_annotation_left = video_frames_extraction_folder + "_left"
Path(video_frames_extraction_folder_annotation_left).mkdir(parents=True,
exist_ok=True)
video_frames_extraction_folder_annotation_right = video_frames_extraction_folder + "_right"
Path(video_frames_extraction_folder_annotation_right).mkdir(parents=True,
exist_ok=True)
counter_for_analysis = 0
counter_maximum_frame_analysis = 20
# #print("Processing: ", video_name)
pbar = tqdm(total=len(detections))
total = len(detections)
previous_f = {}
previous_f['roi'] = None
detections_final = {}
roi_right = None
roi_left = None
while (frame_counter < total):
# Capture frame-by-frame
ret, frame = cv2video.read()
if ret is True:
if analysis_step and frame_counter % 30 == 0:
faces = detections[frame_counter]
if len(faces) == 2:
extraction_condition = check_left_right_from_center(
faces, frame.shape[1])
if extraction_condition:
left_face_from_frame, right_face_from_frame = extract_left_right_faces(
faces)
if current_annotation == 'right':
f = right_face_from_frame
else:
f = left_face_from_frame
resized_left_face = cv2.resize(
left_face_from_frame['img'], (224, 224))
y_left = face_model.predict(
np.expand_dims(resized_left_face, axis=0))
left_face['features'].append(y_left)
resized_right_face = cv2.resize(
right_face_from_frame['img'], (224, 224))
y_right = face_model.predict(
np.expand_dims(resized_right_face, axis=0))
right_face['features'].append(y_right)
elif not analysis_step:
faces = detections[frame_counter]
if len(faces) > 1:
if current_annotation == 'right':
f, pred = findFaceOnSide(face_model, faces,
(left_face, right_face), True,
frame.shape[1],
previous_f['roi'])
else:
f, pred = findFaceOnSide(face_model, faces,
(left_face, right_face),
False, frame.shape[1],
previous_f['roi'])
elif len(faces) == 1:
# print("only one face")
checked_similarity, pred = check_face_similarity(
face_model, faces[0], (left_face, right_face))
if (checked_similarity == 0 and current_annotation
== 'left') or (checked_similarity == 1
and current_annotation == 'right'):
f = faces[0]
# just a not verified condition
##in this case is a face B
else:
f = None
# detection fails --> return map_if_error error
else:
# print("detection problem: ", frame_counter)
f = None
if (f is not None) and (f['img'].size != 0):
detections_final[frame_counter] = [f]
previous_f = f
f['roi'] = add_margin(f['roi'], 0.9 * f['roi'][2])
f['roi'] = enclosing_square(f['roi'])
img = cut(frame, f['roi'])
cv2.imwrite(
video_frames_extraction_folder + "_" +
current_annotation +
"/frame-{:06}.png".format(frame_counter), img)
else:
if previous_f['roi'] is None:
roi_prov = (100, 100, 100, 100)
img_recover = cut(frame, roi_prov)
detections_final[frame_counter] = [{'roi': roi_prov}]
else:
img_recover = cut(frame, previous_f['roi'])
detections_final[frame_counter] = [previous_f]
cv2.imwrite(
video_frames_extraction_folder + "_" +
current_annotation +
"/frame-{:06}.png".format(frame_counter), img_recover)
# else:
# if not analysis_step:
# print("ret False")
# in this case just append a None object (interpolation to do after this stage), cv2 video fails
frame_counter += 1
if frame_counter == total and analysis_step or counter_for_analysis > counter_maximum_frame_analysis:
frame_counter = 0
cv2video.set(cv2.CAP_PROP_POS_FRAMES, 0)
analysis_step = False
left_face['avg_feature'] = np.mean(left_face['features'], axis=0)
right_face['avg_feature'] = np.mean(right_face['features'], axis=0)
counter_for_analysis = 0
pbar = tqdm(total=total)
if frame_counter == total and not analysis_step and current_annotation == 'right':
cv2video.set(cv2.CAP_PROP_POS_FRAMES, 0)
frame_counter = 0
current_annotation = 'left'
pbar = tqdm(total=total)
list_det_final.append(detections_final)
detections_final = {}
previous_f = {}
previous_f['roi'] = None
pbar.update(1)
pbar.close()
# When everything done, release the video capture object
cv2video.release()
# Closes all the frames
cv2.destroyAllWindows()
list_det_final.append(detections_final)
return list_det_final
args = parser.parse_args()
dir_path = args.images_dir
output_filename = 'pubfig_feats_vgg_512_test.npy'
# LOAD MODEL
vgg_features = VGGFace(include_top=False,
input_shape=(224, 224, 3),
pooling='avg') # pooling: None, avg or max
# LIST FILES
files = sorted(glob.glob(dir_path + '*.jpg'))
feats_list = []
# CALCULATE FEATURES
for i, image_path in enumerate(files):
print("[INFO]: Extracting {0} ...".format(image_path))
image_name = os.path.basename(image_path)[:-4]
im = image.load_img(image_path)
im = cv2.resize(np.uint8(im), (224, 224))
im_np = np.expand_dims(im, axis=0)
im_np = im_np.astype('float32')
im_preproc = utils.preprocess_input(im_np, version=1)
feats = vgg_features.predict(im_preproc)
feats_list.append(feats[0])
if len(feats_list) % 1000 == 0:
np.save(output_filename, feats_list)
np.save(output_filename, feats_list)
