def preprocess(self, images):
processed_images = []
for img in images:
if img.shape[0] != self.image_size or img.shape[
1] != self.image_size:
print('Warning: custom resizing is used.')
img = misc.imresize(img, (self.image_size, self.image_size),
interp='bicubic')
if img.ndim == 2:
img = facenet2.to_rgb(img)
img = facenet2.prewhiten(img)
processed_images.append(img)
return processed_images
def detect(self, img):
with self.session.as_default():
if img.ndim < 2:
return [], []
if img.ndim == 2:
img = facenet2.to_rgb(img)
img = img[:, :, 0:3]
bounding_boxes, points = align.detect_face.detect_face(
img, self.minsize, self.pnet, self.rnet, self.onet,
self.threshold, self.factor)
nrof_faces = bounding_boxes.shape[0]
detected_faces = []
detected_bb = []
if nrof_faces > 0:
det = bounding_boxes[:, 0:4]
det_arr = []
img_size = np.asarray(img.shape)[0:2]
if nrof_faces > 1:
for i in range(nrof_faces):
det_arr.append(np.squeeze(det[i]))
for i, det in enumerate(det_arr):
det = np.squeeze(det)
bb = np.zeros(4, dtype=np.int32)
bb[0] = np.maximum(det[0] - self.margin / 2, 0)
bb[1] = np.maximum(det[1] - self.margin / 2, 0)
bb[2] = np.minimum(det[2] + self.margin / 2, img_size[1])
bb[3] = np.minimum(det[3] + self.margin / 2, img_size[0])
cropped = img[bb[1]:bb[3], bb[0]:bb[2], :]
scaled = misc.imresize(cropped,
(self.image_size, self.image_size),
interp='bilinear')
detected_faces.append(scaled)
detected_bb.append(bb)
return detected_faces, detected_bb
def detect(self, img):
with self.session.as_default():
if img.ndim < 2:
return [], []
if img.ndim == 2:
img = facenet2.to_rgb(img)
img = img[:, :, 0:3]
bounding_boxes, f_points = align.detect_face.detect_face(
img, self.minsize, self.pnet, self.rnet, self.onet,
self.threshold, self.factor)
#print('bounding_boxes', bounding_boxes)
nrof_faces = bounding_boxes.shape[0]
detected_faces = []
detected_bb = []
face_score = []
if nrof_faces > 0:
det = bounding_boxes[:, 0:4]
face_score = bounding_boxes[:, 4]
det_arr = []
img_size = np.asarray(img.shape)[0:2]
if nrof_faces > 1:
if self.detect_multiple_faces:
for i in range(nrof_faces):
det_arr.append(np.squeeze(det[i]))
else:
bounding_box_size = (det[:, 2] - det[:, 0]) * (
det[:, 3] - det[:, 1])
img_center = img_size / 2
offsets = np.vstack([
(det[:, 0] + det[:, 2]) / 2 - img_center[1],
(det[:, 1] + det[:, 3]) / 2 - img_center[0]
])
offset_dist_squared = np.sum(np.power(offsets, 2.0), 0)
index = np.argmax(
bounding_box_size - offset_dist_squared *
2.0) # some extra weight on the centering
det_arr.append(det[index, :])
else:
det_arr.append(np.squeeze(det))
for i, det in enumerate(det_arr):
det = np.squeeze(det)
bb = np.zeros(4, dtype=np.int32)
margin0 = (det[2] - det[0]) * self.margin_rate
margin1 = (det[3] - det[1]) * self.margin_rate
bb[0] = np.maximum(det[0] - margin0, 0)
bb[1] = np.maximum(det[1] - margin1, 0)
bb[2] = np.minimum(det[2] + margin0, img_size[1])
bb[3] = np.minimum(det[3] + margin1, img_size[0])
cropped = img[bb[1]:bb[3], bb[0]:bb[2], :]
scaled = misc.imresize(cropped,
(self.image_size, self.image_size),
interp='bicubic')
detected_faces.append(scaled)
detected_bb.append(bb)
for j in range(10):
if j < 5:
f_points[j, i] = (f_points[j, i] -
bb[0]) / (bb[2] - bb[0])
else:
f_points[j, i] = (f_points[j, i] -
bb[1]) / (bb[3] - bb[1])
return detected_faces, detected_bb, f_points, face_score
def main(args):
print('Creating networks and loading parameters')
with tf.Graph().as_default():
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=args.gpu_memory_fraction)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options,
log_device_placement=False))
with sess.as_default():
pnet, rnet, onet = align.detect_face2.create_mtcnn(sess, None)
minsize = 20 # minimum size of face
threshold = [0.6, 0.7, 0.7] # three steps's threshold
factor = 0.709 # scale factor
try:
img = cv2.imread(args.input_image)
except (IOError, ValueError, IndexError) as e:
errorMessage = '{}: {}'.format(args.input_image, e)
print(errorMessage)
return
if img.ndim < 2:
print('Unable to align "%s"' % image_path)
text_file.write('%s\n' % (output_filename))
return
if img.ndim == 2:
img = facenet2.to_rgb(img)
img = img[:, :, 0:3]
bounding_boxes, _ = align.detect_face2.detect_face(
img, minsize, pnet, rnet, onet, threshold, factor)
nrof_faces = bounding_boxes.shape[0]
detected_faces = []
detected_bb = []
if nrof_faces > 0:
det = bounding_boxes[:, 0:4]
det_arr = []
img_size = np.asarray(img.shape)[0:2]
if nrof_faces > 1:
for i in range(nrof_faces):
det_arr.append(np.squeeze(det[i]))
for i, det in enumerate(det_arr):
det = np.squeeze(det)
bb = np.zeros(4, dtype=np.int32)
bb[0] = np.maximum(det[0] - args.margin / 2, 0)
bb[1] = np.maximum(det[1] - args.margin / 2, 0)
bb[2] = np.minimum(det[2] + args.margin / 2, img_size[1])
bb[3] = np.minimum(det[3] + args.margin / 2, img_size[0])
cropped = img[bb[1]:bb[3], bb[0]:bb[2], :]
#scaled = scipy.misc.imresize(cropped, (args.image_size, args.image_size), interp='bilinear')
scaled = cv2.resize(cropped,
(args.image_size, args.image_size))
detected_faces.append(scaled)
detected_bb.append(bb)
print(nrof_faces, 'faces are detected')
print(detected_bb)
source_image = img
print('Loading feature extraction model')
facenet2.load_model(args.model)
# Get input and output tensors
images_placeholder = tf.get_default_graph().get_tensor_by_name(
"input:0")
embeddings = tf.get_default_graph().get_tensor_by_name(
"embeddings:0")
phase_train_placeholder = tf.get_default_graph(
).get_tensor_by_name("phase_train:0")
embedding_size = embeddings.get_shape()[1]
# Run forward pass to calculate embeddings
print('Calculating features for images')
# preprocessing
images = []
for img in detected_faces:
if img.ndim == 2:
img = facenet2.to_rgb(img)
img = facenet2.prewhiten(img)
images.append(img)
feed_dict = {
images_placeholder: images,
phase_train_placeholder: False
}
emb_array = sess.run(embeddings, feed_dict=feed_dict)
print('Testing classifier')
classifier_filename_exp = os.path.expanduser(
args.classifier_filename)
with open(classifier_filename_exp, 'rb') as infile:
(model, class_names) = pickle.load(infile)
print('Loaded classifier model from file "%s"' %
classifier_filename_exp)
predictions = model.predict_proba(emb_array)
best_class_indices = np.argmax(predictions, axis=1)
best_class_probabilities = predictions[
np.arange(len(best_class_indices)), best_class_indices]
for i in range(len(best_class_indices)):
print('%4d %s: %.3f' % (i, class_names[best_class_indices[i]],
best_class_probabilities[i]))
source_image = np.array(source_image)
source_image = cv2.cvtColor(source_image, cv2.COLOR_BGR2RGB)
for i in range(len(detected_bb)):
bb = detected_bb[i]
cv2.rectangle(source_image, (bb[0], bb[1]), (bb[2], bb[3]),
(0, 255, 0), 3)
cv2.putText(source_image, class_names[best_class_indices[i]],
(bb[0], bb[1] - 20), cv2.FONT_HERSHEY_SIMPLEX, 1,
(0, 255, 0), 2)
cv2.putText(source_image, '%.3f' % best_class_probabilities[i],
(bb[0], bb[3] + 40), cv2.FONT_HERSHEY_SIMPLEX, 1,
(0, 255, 0), 2)
cv2.imwrite('result.png', source_image)