def main(data_dir, min_size):
with tf.Graph().as_default():
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.0)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options,
log_device_placement=False))
with sess.as_default():
pnet, rnet, onet = detect_face.create_mtcnn(sess, npy)
minsize = min_size # minimum size of face
threshold = [0.6, 0.7, 0.7] # three steps's threshold
factor = 0.709 # scale factor
dataset = face_net.get_dataset(data_dir)
number_sample = 0
number_face_detected = 0
for cls in dataset:
for image_path in cls.image_paths:
number_sample = number_sample + 1
img = cv2.imread(image_path, cv2.IMREAD_UNCHANGED)
if img.ndim == 2:
img = face_net.to_rgb(img)
img = img[:, :, 0:3]
bounding_boxes, _ = detect_face.detect_face(
img, minsize, pnet, rnet, onet, threshold, factor)
nrof_faces = bounding_boxes.shape[0]
if (nrof_faces == 0):
print('{0} face detected : {1}'.format(
nrof_faces, image_path))
elif (nrof_faces == 2):
print('{0} face detected : {1}'.format(
nrof_faces, image_path))
number_face_detected = number_face_detected + 1
else:
number_face_detected = number_face_detected + 1
print("Finish!!!!")
print('Number face detected {0}'.format(number_face_detected))
while True:
ret, frame = video_capture.read()
if ret == False:
break
# frame = cv2.resize(frame, (650, 650), fx=0.5, fy=0.5) # resize frame (optional)
number_frame += 1
# curTime = time.time() + 1 # calc fps
timeF = frame_interval
if (c % timeF == 0):
find_results = []
if frame.ndim == 2:
frame = face_net.to_rgb(frame)
frame = frame[:, :, 0:3]
bounding_boxes, _ = detect_face.detect_face(
frame, minsize, pnet, rnet, onet, threshold, factor)
nrof_faces = bounding_boxes.shape[0]
# if nrof_faces > 0:
# print('Detected_FaceNum: %d' % nrof_faces)
output_filename = os.path.expanduser(
output_dir + '/{0}_{1}_{2}.jpg'.format(
nrof_faces, number_frame,
datetime.strftime(datetime.now(), '%Y%m%d%H%M%S%f')))
cv2.imwrite(output_filename, frame)
if nrof_faces > 0:
det = bounding_boxes[:, 0:4]
det_arr = []
def main(data_dir, output_dir):
sleep(random.random())
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Store some git revision info in a text file in the log directory
src_path, _ = os.path.split(os.path.realpath(__file__))
# facenet.store_revision_info(src_path, output_dir, ' '.join('argument default'))
dataset = face_net.get_dataset(data_dir)
print('Creating networks and loading parameters')
with tf.Graph().as_default():
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=constants.GPU_MEMORY_FRACTION_DEFAULT)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
with sess.as_default():
pnet, rnet, onet = detect_face.create_mtcnn(sess, None)
minsize = constants.FACE_REG_MINSIZE # minimum size of face
threshold = constants.ALIGN_THRESHOLD # three steps's threshold
factor = constants.ALIGN_FACTOR # scale factor
# Add a random key to the filename to allow alignment using multiple processes
random_key = np.random.randint(0, high=99999)
face_detected_list = []
nrof_images_total = 0
nrof_successfully_aligned = 0
if constants.ALIGN_RANDOM_ORDER:
random.shuffle(dataset)
for cls in dataset:
output_class_dir = os.path.join(output_dir, cls.name)
if not os.path.exists(output_class_dir):
os.makedirs(output_class_dir)
if constants.ALIGN_RANDOM_ORDER:
random.shuffle(cls.image_paths)
for image_path in cls.image_paths:
nrof_images_total += 1
filename = os.path.splitext(os.path.split(image_path)[1])[0]
output_filename = os.path.join(output_class_dir, filename + '.png')
if not os.path.exists(output_filename):
try:
img = misc.imread(image_path)
except (IOError, ValueError, IndexError) as e:
error_message = '{}: {}'.format(image_path, e)
print(error_message)
else:
if img.ndim < 2:
print('Unable to align "%s"' % image_path)
face_detected_list.append('Unable to align {0}'.format(image_path))
# text_file.write('%s\n' % (output_filename))
continue
if img.ndim == 2:
img = face_net.to_rgb(img)
img = img[:, :, 0:3]
bounding_boxes, _ = detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)
nrof_faces = bounding_boxes.shape[0]
if nrof_faces > 0:
det = bounding_boxes[:, 0:4]
det_arr = []
img_size = np.asarray(img.shape)[0:2]
if nrof_faces > 1:
if constants.ALIGN_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)
bb[0] = np.maximum(det[0] - constants.COMPARE_MARGIN_DEFAULT / 2, 0)
bb[1] = np.maximum(det[1] - constants.COMPARE_MARGIN_DEFAULT / 2, 0)
bb[2] = np.minimum(det[2] + constants.COMPARE_MARGIN_DEFAULT / 2, img_size[1])
bb[3] = np.minimum(det[3] + constants.COMPARE_MARGIN_DEFAULT / 2, img_size[0])
cropped = img[bb[1]:bb[3], bb[0]:bb[2], :]
scaled = misc.imresize(cropped, (constants.ALIGN_IMAGE_SIZE, constants.ALIGN_IMAGE_SIZE), interp='bilinear')
nrof_successfully_aligned += 1
filename_base, file_extension = os.path.splitext(output_filename)
if constants.ALIGN_DETECT_MULTIPLE_FACES:
output_filename_n = "{}_{}{}".format(filename_base, i, file_extension)
else:
output_filename_n = "{}{}".format(filename_base, file_extension)
misc.imsave(output_filename_n, scaled)
face_detected_list.append('%s --- BOX [%d , %d , %d , %d]\n' % (output_filename_n, bb[0], bb[1], bb[2], bb[3]))
else:
face_detected_list.append('Unable to align {0}'.format(image_path))
print('Total number of images: %d' % nrof_images_total)
print('Number of successfully aligned images: %d' % nrof_successfully_aligned)
return nrof_images_total, nrof_successfully_aligned, face_detected_list
def main(image_path, data_dir, model_dir, classifier_file):
npy = ''
with tf.Graph().as_default():
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=constants.
GPU_MEMORY_FRACTION_DEFAULT)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options,
log_device_placement=False))
with sess.as_default():
pnet, rnet, onet = detect_face.create_mtcnn(sess, None)
minsize = constants.FACE_REG_MINSIZE # minimum size of face
threshold = constants.ALIGN_THRESHOLD # three steps's threshold
factor = constants.ALIGN_FACTOR # scale factor
frame_interval = 3
image_size = 160
input_image_size = 160
human_names = os.listdir(data_dir)
human_names.sort()
print('Loading feature extraction model')
face_net.load_model(model_dir)
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]
classifier_filename_exp = os.path.expanduser(classifier_file)
with open(classifier_filename_exp, 'rb') as infile:
(model, class_names) = pickle.load(infile)
c = 0
print('Start Recognition!')
frame = cv2.imread(image_path, 0)
time_f = frame_interval
if c % time_f == 0:
if frame.ndim == 2:
frame = face_net.to_rgb(frame)
frame = frame[:, :, 0:3]
bounding_boxes, _ = detect_face.detect_face(
frame, minsize, pnet, rnet, onet, threshold, factor)
nrof_faces = bounding_boxes.shape[0]
print('Face Detected: %d' % nrof_faces)
if nrof_faces > 0:
det = bounding_boxes[:, 0:4]
cropped = []
scaled = []
scaled_reshape = []
bb = np.zeros((nrof_faces, 4), dtype=np.int32)
for i in range(nrof_faces):
emb_array = np.zeros((1, embedding_size))
bb[i][0] = det[i][0]
bb[i][1] = det[i][1]
bb[i][2] = det[i][2]
bb[i][3] = det[i][3]
# inner exception
if bb[i][0] <= 0 or bb[i][1] <= 0 or bb[i][2] >= len(
frame[0]) or bb[i][3] >= len(frame):
print('face is too close')
break
cropped.append(frame[bb[i][1]:bb[i][3],
bb[i][0]:bb[i][2], :])
cropped[i] = face_net.flip(cropped[i], False)
scaled.append(
misc.imresize(cropped[i], (image_size, image_size),
interp='bilinear'))
scaled[i] = cv2.resize(
scaled[i], (input_image_size, input_image_size),
interpolation=cv2.INTER_CUBIC)
scaled[i] = face_net.prewhiten(scaled[i])
scaled_reshape.append(scaled[i].reshape(
-1, input_image_size, input_image_size, 3))
feed_dict = {
images_placeholder: scaled_reshape[i],
phase_train_placeholder: False
}
emb_array[0, :] = sess.run(embeddings,
feed_dict=feed_dict)
predictions = model.predict_proba(emb_array)
print(predictions)
best_class_indices = np.argmax(predictions, axis=1)
# print(best_class_indices)
best_class_probabilities = predictions[
np.arange(len(best_class_indices)),
best_class_indices]
print(best_class_probabilities)
cv2.rectangle(frame, (bb[i][0], bb[i][1]),
(bb[i][2], bb[i][3]), (0, 255, 0),
2) # boxing face
# plot result idx under box
text_x = bb[i][0]
text_y = bb[i][3] - 10
print(
i, 'Result Indices: ', best_class_indices[0],
' : ', 'Face detected of : {0}'.format(
human_names[best_class_indices[0]]))
print(human_names)
for H_i in human_names:
# print(H_i)
if human_names[best_class_indices[
0]] == H_i and best_class_probabilities >= constants.FACE_REG_POSSIBILITY:
result_names = human_names[
best_class_indices[0]]
cv2.putText(frame,
str(i) + ': ' + result_names,
(text_x, text_y),
cv2.FONT_HERSHEY_PLAIN,
1, (0, 0, 255),
thickness=1,
lineType=1)
print('------------------')
else:
print('Unable to align')
frame = cv2.resize(frame, (0, 0), fx=0.5,
fy=0.5) # resize frame (optional)
cv2.imshow('Image', frame)
cv2.imwrite('output/' + image_path.split('/')[-1], frame)
if cv2.waitKey(1000000) & 0xFF == ord('q'):
sys.exit("Thanks")
def main(test_dir, data_dir, model_dir, classifier_file):
with tf.Graph().as_default():
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=constants.
GPU_MEMORY_FRACTION_DEFAULT)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options,
log_device_placement=False))
with sess.as_default():
pnet, rnet, onet = detect_face.create_mtcnn(sess, None)
minsize = constants.FACE_REG_MINSIZE # minimum size of face
threshold = constants.ALIGN_THRESHOLD # three steps's threshold
factor = constants.ALIGN_FACTOR # scale factor
image_size = 160
input_image_size = 160
human_names = os.listdir(data_dir)
human_names.sort()
print('Loading feature extraction model')
face_net.load_model(model_dir)
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]
classifier_filename_exp = os.path.expanduser(classifier_file)
with open(classifier_filename_exp, 'rb') as infile:
(model, class_names) = pickle.load(infile)
c = 0
print('Start Recognition!')
dataset = face_net.get_dataset(test_dir)
number_of_face_recognition = 0
for cls in dataset:
for image_path in cls.image_paths:
frame = cv2.imread(image_path, 0)
if frame.ndim == 2:
frame = face_net.to_rgb(frame)
frame = frame[:, :, 0:3]
bounding_boxes, _ = detect_face.detect_face(
frame, minsize, pnet, rnet, onet, threshold, factor)
nrof_faces = bounding_boxes.shape[0]
if nrof_faces > 0:
det = bounding_boxes[:, 0:4]
cropped = []
scaled = []
scaled_reshape = []
bb = np.zeros((nrof_faces, 4), dtype=np.int32)
for i in range(nrof_faces):
emb_array = np.zeros((1, embedding_size))
bb[i][0] = det[i][0]
bb[i][1] = det[i][1]
bb[i][2] = det[i][2]
bb[i][3] = det[i][3]
# inner exception
if bb[i][0] <= 0 or bb[i][1] <= 0 or bb[i][
2] >= len(
frame[0]) or bb[i][3] >= len(frame):
print(
'Face is too close {0}'.format(image_path))
break
cropped.append(frame[bb[i][1]:bb[i][3],
bb[i][0]:bb[i][2], :])
cropped[i] = face_net.flip(cropped[i], False)
scaled.append(
misc.imresize(cropped[i],
(image_size, image_size),
interp='bilinear'))
scaled[i] = cv2.resize(
scaled[i],
(input_image_size, input_image_size),
interpolation=cv2.INTER_CUBIC)
scaled[i] = face_net.prewhiten(scaled[i])
scaled_reshape.append(scaled[i].reshape(
-1, input_image_size, input_image_size, 3))
feed_dict = {
images_placeholder: scaled_reshape[i],
phase_train_placeholder: False
}
emb_array[0, :] = sess.run(embeddings,
feed_dict=feed_dict)
predictions = model.predict_proba(emb_array)
best_class_indices = np.argmax(predictions, axis=1)
# print(best_class_indices)
best_class_probabilities = predictions[
np.arange(len(best_class_indices)),
best_class_indices]
# plot result idx under box
for H_i in human_names:
# print(H_i)
if human_names[best_class_indices[0]] == H_i \
and H_i in image_path:
print('{0} : {1}'.format(
best_class_probabilities, image_path))
number_of_face_recognition = number_of_face_recognition + 1
else:
print('Unable to recognition {0}'.format(image_path))
print("Finish!!!!")
print('Number face detected {0}'.format(number_of_face_recognition))