def main1(args):
capture_interval = args.capture_interval
capture_num = 100
capture_count = 0
frame_count = 0
file_paths = get_model_filenames(args.model_dir)
if not os.path.exists(args.save_path):
os.makedirs(args.save_path)
video = cv2.VideoCapture(args.video_path)
while (True):
ret, frame = video.read()
if not ret:
break
if (capture_count % capture_interval == 0):
l = detect_frame(capture_count, frame, file_paths, args.minsize,
args.threshold, args.factor, args.save_path)
ll = np.delete(l, 4, axis=1)
print(capture_count, ":", ll, '\n')
frame_count += 1
capture_count += 1
video.release()
cv2.destroyAllWindows()
def main(args):
detect_totalTime = 0.0
frameCount = 0
# Does there need store result images or not
# If yes, check the directory which store result is existed or not
# If the directory is existed, delete the directory recursively then recreate the directory.
if args.save_image:
output_directory = args.save_image
print(args.save_image)
if os.path.exists(output_directory):
shutil.rmtree(output_directory)
os.mkdir(output_directory)
fw = open(os.path.join(output_directory, args.save_bbox_coordinates + '_dets.txt'), 'w')
# Create
# The steps are similiar to "store result images" above.
if args.save_camera_images is not False:
source_directory = args.save_camera_images
if os.path.exists(source_directory):
shutil.rmtree(source_directory)
os.mkdir(source_directory)
with tf.device('/cpu:0'):
with tf.Graph().as_default():
config = tf.ConfigProto(allow_soft_placement=True)
with tf.Session(config=config) as sess:
file_paths = get_model_filenames(args.model_dir)
print(file_paths, len(file_paths))
# The if else statement is to check which type of model user used.
# if the if condition is true, which means user use separate P-Net, R-Net and O-Net models.
# In anaconda bash to type the command line which is "python test_camera.py --model_dir model/separate".
# And there are three folders which are P-Net, R-Net and O-Net in the named separate directory.
if len(file_paths) == 3:
image_pnet = tf.placeholder(
tf.float32, [None, None, None, 3])
pnet = PNet({'data': image_pnet}, mode='test')
out_tensor_pnet = pnet.get_all_output()
image_rnet = tf.placeholder(tf.float32, [None, 24, 24, 3])
rnet = RNet({'data': image_rnet}, mode='test')
out_tensor_rnet = rnet.get_all_output()
image_onet = tf.placeholder(tf.float32, [None, 48, 48, 3])
onet = ONet({'data': image_onet}, mode='test')
out_tensor_onet = onet.get_all_output()
saver_pnet = tf.train.Saver(
[v for v in tf.global_variables()
if v.name[0:5] == "pnet/"])
saver_rnet = tf.train.Saver(
[v for v in tf.global_variables()
if v.name[0:5] == "rnet/"])
saver_onet = tf.train.Saver(
[v for v in tf.global_variables()
if v.name[0:5] == "onet/"])
saver_pnet.restore(sess, file_paths[0])
def pnet_fun(img): return sess.run(
out_tensor_pnet, feed_dict={image_pnet: img})
saver_rnet.restore(sess, file_paths[1])
def rnet_fun(img): return sess.run(
out_tensor_rnet, feed_dict={image_rnet: img})
saver_onet.restore(sess, file_paths[2])
def onet_fun(img): return sess.run(
out_tensor_onet, feed_dict={image_onet: img})
else:
saver = tf.train.import_meta_graph(file_paths[0])
saver.restore(sess, file_paths[1])
def pnet_fun(img): return sess.run(
('softmax/Reshape_1:0',
'pnet/conv4-2/BiasAdd:0'),
feed_dict={
'Placeholder:0': img})
def rnet_fun(img): return sess.run(
('softmax_1/softmax:0',
'rnet/conv5-2/rnet/conv5-2:0'),
feed_dict={
'Placeholder_1:0': img})
def onet_fun(img): return sess.run(
('softmax_2/softmax:0',
'onet/conv6-2/onet/conv6-2:0',
'onet/conv6-3/onet/conv6-3:0'),
feed_dict={
'Placeholder_2:0': img})
video_capture = cv2.VideoCapture(0)
print(video_capture.get(cv2.CAP_PROP_FRAME_WIDTH), video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
if video_capture.isOpened() == False:
print("ERROR: NO VIDEO STREAM OR NO CAMERA DEVICE.")
else:
print(video_capture.get(cv2.CAP_PROP_FPS))
while True:
ret, frame = video_capture.read()
original_img = frame.copy()
if ret:
width = int(video_capture.get(cv2.CAP_PROP_FRAME_WIDTH)*args.resize)
height = int(video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT)*args.resize)
resized_image = cv2.resize(frame, (width, height))
start_time = time.time()*1000
# P-Net + R-Net + O-Net
if args.net == "ALL":
rectangles, points = detect_face(resized_image, args.minsize,
pnet_fun, rnet_fun, onet_fun,
args.threshold, args.factor)
# P-Net + R-Net without faces' landmarks
elif args.net == "PR":
rectangles = detect_face_24net(resized_image, args.minsize,
pnet_fun, rnet_fun,
args.threshold, args.factor)
# Only P-Net
elif args.net == "P":
rectangles = detect_face_12net(resized_image, args.minsize,
pnet_fun, args.threshold, args.factor)
else:
print("ERROR: WRONG NET INPUT")
end_time = time.time()*1000
detect_totalTime = detect_totalTime + (end_time - start_time)
if args.net == "ALL":
points = np.transpose(points) # The outputs of O-Net which are faces' landmarks
else:
points = None # the others
add_overlays(frame, rectangles, points, 1000/(end_time - start_time), 1/args.resize, 1/args.resize)
cv2.imshow("MTCNN-Tensorflow wangbm", frame)
print("ID: {:d}, cost time: {:.1f}ms".format(frameCount, (end_time - start_time))) s
if points is not None:
for point in points:
for i in range(0, 10, 2):
point[i] = point[i] * (1/args.resize)
point[i+1] = point[i+1] * (1/args.resize)
print("\tID: {:d}, face landmarks x = {:.1f}, y = {:.1f}".format(int(i/2+1), point[i], point[i+1]))
if args.save_image:
outputFilePath = os.path.join(output_directory, str(frameCount) + ".jpg")
cv2.imwrite(outputFilePath, frame)
for rectangle in rectangles:
fw.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.format(str(frameCount), rectangle[4], rectangle[0], rectangle[1], rectangle[2], rectangle[3]))
fw.close()
if args.save_camera_images:
sourceFilePath = os.path.join(source_directory, str(frameCount) + ".jpg")
cv2.imwrite(sourceFilePath, original_img)
frameCount = frameCount + 1
if cv2.waitKey(1) & 0xFF == ord('q'):
cv2.destroyAllWindows()
break
video_capture.release()
detect_average_time = detect_totalTime/frameCount
print("*" * 50)
print("detection average time: " + str(detect_average_time) + "ms" )
print("detection fps: " + str(1000/detect_average_time))
def main(args):
img = cv2.imread(args.image_path)
file_paths = get_model_filenames(args.model_dir)
count = 0
with tf.device('/gpu:0'):
with tf.Graph().as_default():
config = tf.ConfigProto(allow_soft_placement=True)
with tf.Session(config=config) as sess:
if len(file_paths) == 3:
image_pnet = tf.placeholder(
tf.float32, [None, None, None, 3])
pnet = PNet({'data': image_pnet}, mode='test')
out_tensor_pnet = pnet.get_all_output()
image_rnet = tf.placeholder(tf.float32, [None, 24, 24, 3])
rnet = RNet({'data': image_rnet}, mode='test')
out_tensor_rnet = rnet.get_all_output()
image_onet = tf.placeholder(tf.float32, [None, 48, 48, 3])
onet = ONet({'data': image_onet}, mode='test')
out_tensor_onet = onet.get_all_output()
saver_pnet = tf.train.Saver(
[v for v in tf.global_variables()
if v.name[0:5] == "pnet/"])
saver_rnet = tf.train.Saver(
[v for v in tf.global_variables()
if v.name[0:5] == "rnet/"])
saver_onet = tf.train.Saver(
[v for v in tf.global_variables()
if v.name[0:5] == "onet/"])
saver_pnet.restore(sess, file_paths[0])
def pnet_fun(img): return sess.run(
out_tensor_pnet, feed_dict={image_pnet: img})
saver_rnet.restore(sess, file_paths[1])
def rnet_fun(img): return sess.run(
out_tensor_rnet, feed_dict={image_rnet: img})
saver_onet.restore(sess, file_paths[2])
def onet_fun(img): return sess.run(
out_tensor_onet, feed_dict={image_onet: img})
else:
saver = tf.train.import_meta_graph(file_paths[0])
saver.restore(sess, file_paths[1])
def pnet_fun(img): return sess.run(
('softmax/Reshape_1:0',
'pnet/conv4-2/BiasAdd:0'),
feed_dict={
'Placeholder:0': img})
def rnet_fun(img): return sess.run(
('softmax_1/softmax:0',
'rnet/conv5-2/rnet/conv5-2:0'),
feed_dict={
'Placeholder_1:0': img})
def onet_fun(img): return sess.run(
('softmax_2/softmax:0',
'onet/conv6-2/onet/conv6-2:0',
'onet/conv6-3/onet/conv6-3:0'),
feed_dict={
'Placeholder_2:0': img})
start_time = time.time()
rectangles, points = detect_face(img, args.minsize,
pnet_fun, rnet_fun, onet_fun,
args.threshold, args.factor)
duration = time.time() - start_time
points = np.transpose(points)
for rectangle in rectangles:
cv2.putText(img, str(rectangle[4]),
(int(rectangle[0]), int(rectangle[1])),
cv2.FONT_HERSHEY_SIMPLEX,
0.5, (0, 255, 0))
cv2.rectangle(img, (int(rectangle[0]), int(rectangle[1])),
(int(rectangle[2]), int(rectangle[3])),
(255, 0, 0), 1)
count+=1
for point in points:
for i in range(0, 10, 2):
cv2.circle(img, (int(point[i]), int(
point[i + 1])), 2, (0, 255, 0))
print(duration)
print(type(rectangles))
print(args.image_path)
print(count)
print(np.int_(rectangles))
data = [args.image_path, "\n", str(count), "\n", str(np.int_(rectangles)), "\n"]
file = open(args.save_file,"a+")
file.writelines(data)
cv2.imshow("test", img)
if args.save_image:
cv2.imwrite(args.save_name, img)
if cv2.waitKey(0) & 0xFF == ord('q'):
cv2.destroyAllWindows()
def main(args):
img = cv2.imread(args.image_path)
print("\n"+"LOCATION!!!image get"+"\n")
file_paths = get_model_filenames(args.model_dir)
with tf.device('/gpu:0'):
with tf.Graph().as_default():
config = tf.ConfigProto(allow_soft_placement=True)
with tf.Session(config=config) as sess:
print("\n"+"LOCATION!!!tf config done"+"\n")
if len(file_paths) == 3:
print("\n"+"LOCATION!!!file_paths(model_dir)=3"+"\n")
image_pnet = tf.placeholder(
tf.float32, [None, None, None, 3])
pnet = PNet({'data': image_pnet}, mode='test')
out_tensor_pnet = pnet.get_all_output()
image_rnet = tf.placeholder(tf.float32, [None, 24, 24, 3])
rnet = RNet({'data': image_rnet}, mode='test')
out_tensor_rnet = rnet.get_all_output()
image_onet = tf.placeholder(tf.float32, [None, 48, 48, 3])
onet = ONet({'data': image_onet}, mode='test')
out_tensor_onet = onet.get_all_output()
print("\n"+"LOCATION!!!placeholder and out_tensor done"+"\n")
saver_pnet = tf.train.Saver(
[v for v in tf.global_variables()
if v.name[0:5] == "pnet/"])
saver_rnet = tf.train.Saver(
[v for v in tf.global_variables()
if v.name[0:5] == "rnet/"])
saver_onet = tf.train.Saver(
[v for v in tf.global_variables()
if v.name[0:5] == "onet/"])
print("\n"+"LOCATION!!!saver done"+"\n")
saver_pnet.restore(sess, file_paths[0])
def pnet_fun(img): return sess.run(
out_tensor_pnet, feed_dict={image_pnet: img})
saver_rnet.restore(sess, file_paths[1])
def rnet_fun(img): return sess.run(
out_tensor_rnet, feed_dict={image_rnet: img})
saver_onet.restore(sess, file_paths[2])
def onet_fun(img): return sess.run(
out_tensor_onet, feed_dict={image_onet: img})
print("\n"+"LOCATION!!!def net_fun done"+"\n")
else:
print("LOCATION!!!ifile_paths(model_dir)!=3"+"\n")
saver = tf.train.import_meta_graph(file_paths[0])
saver.restore(sess, file_paths[1])
def pnet_fun(img): return sess.run(
('softmax/Reshape_1:0',
'pnet/conv4-2/BiasAdd:0'),
feed_dict={
'Placeholder:0': img})
def rnet_fun(img): return sess.run(
('softmax_1/softmax:0',
'rnet/conv5-2/rnet/conv5-2:0'),
feed_dict={
'Placeholder_1:0': img})
def onet_fun(img): return sess.run(
('softmax_2/softmax:0',
'onet/conv6-2/onet/conv6-2:0',
'onet/conv6-3/onet/conv6-3:0'),
feed_dict={
'Placeholder_2:0': img})
def get_bounding_boxes(self, image=None):
with tf.device('/gpu:0'):
with tf.Graph().as_default():
config = tf.ConfigProto(allow_soft_placement=True)
with tf.Session(config=config) as sess:
nrof_images_total = 0
nrof_successfully_aligned = 0
file_paths = get_model_filenames(self.model_dir)
#if the Pnet, Rnet, and Onet trained separetely
if len(file_paths) == 3:
image_pnet = tf.placeholder(tf.float32,
[None, None, None, 3])
pnet = PNet({'data': image_pnet}, mode='test')
out_tensor_pnet = pnet.get_all_output()
image_rnet = tf.placeholder(tf.float32,
[None, 24, 24, 3])
rnet = RNet({'data': image_rnet}, mode='test')
out_tensor_rnet = rnet.get_all_output()
image_onet = tf.placeholder(tf.float32,
[None, 48, 48, 3])
onet = ONet({'data': image_onet}, mode='test')
out_tensor_onet = onet.get_all_output()
saver_pnet = tf.train.Saver([
v for v in tf.global_variables()
if v.name[0:5] == "pnet/"
])
saver_rnet = tf.train.Saver([
v for v in tf.global_variables()
if v.name[0:5] == "rnet/"
])
saver_onet = tf.train.Saver([
v for v in tf.global_variables()
if v.name[0:5] == "onet/"
])
saver_pnet.restore(sess, file_paths[0])
def pnet_fun(img):
return sess.run(out_tensor_pnet,
feed_dict={image_pnet: img})
saver_rnet.restore(sess, file_paths[1])
def rnet_fun(img):
return sess.run(out_tensor_rnet,
feed_dict={image_rnet: img})
saver_onet.restore(sess, file_paths[2])
def onet_fun(img):
return sess.run(out_tensor_onet,
feed_dict={image_onet: img})
else:
saver = tf.train.import_meta_graph(file_paths[0])
saver.restore(sess, file_paths[1])
def pnet_fun(img):
return sess.run(('softmax/Reshape_1:0',
'pnet/conv4-2/BiasAdd:0'),
feed_dict={'Placeholder:0': img})
def rnet_fun(img):
return sess.run(('softmax_1/softmax:0',
'rnet/conv5-2/rnet/conv5-2:0'),
feed_dict={'Placeholder_1:0': img})
def onet_fun(img):
return sess.run(('softmax_2/softmax:0',
'onet/conv6-2/onet/conv6-2:0',
'onet/conv6-3/onet/conv6-3:0'),
feed_dict={'Placeholder_2:0': img})
bounding_boxes, points = detect_face(
image, self.minsize, pnet_fun, rnet_fun, onet_fun,
self.threshold, self.factor)
return bounding_boxes, points
def main(args):
detect_totalTime = 0.0
frameCount = 0
# Does there need store result images or not
# If yes, check the directory which store result is existed or not
# If the directory is existed, delete the directory recursively then recreate the directory.
if args.save_image:
output_directory = args.save_image
print(args.save_image)
if os.path.exists(output_directory):
shutil.rmtree(output_directory)
os.mkdir(output_directory)
fw = open(
os.path.join(output_directory,
args.save_bbox_coordinates + '_dets.txt'), 'w')
with tf.device('/cpu:0'):
with tf.Graph().as_default():
config = tf.ConfigProto(allow_soft_placement=True)
with tf.Session(config=config) as sess:
file_paths = get_model_filenames(args.model_dir)
# The if else statement is to check which type of model user used.
# if the if condition is true, which means user use separate P-Net, R-Net and O-Net models.
# In anaconda bash to type the command line which is "python test_camera.py --model_dir model/separate".
# And there are three folders which are P-Net, R-Net and O-Net in the named separate directory.
if len(file_paths) == 3:
image_pnet = tf.placeholder(tf.float32,
[None, None, None, 3])
pnet = PNet({'data': image_pnet}, mode='test')
out_tensor_pnet = pnet.get_all_output()
image_rnet = tf.placeholder(tf.float32, [None, 24, 24, 3])
rnet = RNet({'data': image_rnet}, mode='test')
out_tensor_rnet = rnet.get_all_output()
image_onet = tf.placeholder(tf.float32, [None, 48, 48, 3])
onet = ONet({'data': image_onet}, mode='test')
out_tensor_onet = onet.get_all_output()
saver_pnet = tf.train.Saver([
v for v in tf.global_variables()
if v.name[0:5] == "pnet/"
])
saver_rnet = tf.train.Saver([
v for v in tf.global_variables()
if v.name[0:5] == "rnet/"
])
saver_onet = tf.train.Saver([
v for v in tf.global_variables()
if v.name[0:5] == "onet/"
])
saver_pnet.restore(sess, file_paths[0])
def pnet_fun(img):
return sess.run(out_tensor_pnet,
feed_dict={image_pnet: img})
saver_rnet.restore(sess, file_paths[1])
def rnet_fun(img):
return sess.run(out_tensor_rnet,
feed_dict={image_rnet: img})
saver_onet.restore(sess, file_paths[2])
def onet_fun(img):
return sess.run(out_tensor_onet,
feed_dict={image_onet: img})
else:
saver = tf.train.import_meta_graph(file_paths[0])
saver.restore(sess, file_paths[1])
def pnet_fun(img):
return sess.run(
('softmax/Reshape_1:0', 'pnet/conv4-2/BiasAdd:0'),
feed_dict={'Placeholder:0': img})
def rnet_fun(img):
return sess.run(('softmax_1/softmax:0',
'rnet/conv5-2/rnet/conv5-2:0'),
feed_dict={'Placeholder_1:0': img})
def onet_fun(img):
return sess.run(('softmax_2/softmax:0',
'onet/conv6-2/onet/conv6-2:0',
'onet/conv6-3/onet/conv6-3:0'),
feed_dict={'Placeholder_2:0': img})
for filename in os.listdir(args.image_path):
img = cv2.imread(os.path.join(args.image_path, filename))
height, width, _ = img.shape
orginal_img = img.copy()
width = int(width * args.resize)
height = int(height * args.resize)
resized_image = cv2.resize(img, (width, height))
start_time = time.time() * 1000
# P-Net + R-Net + O-Net
if args.net == "ALL":
rectangles, points = detect_face(
resized_image, args.minsize, pnet_fun, rnet_fun,
onet_fun, args.threshold, args.factor)
# P-Net + R-Net without faces' landmarks
elif args.net == "PR":
rectangles = detect_face_24net(resized_image,
args.minsize, pnet_fun,
rnet_fun,
args.threshold,
args.factor)
# Only P-Net
elif args.net == "P":
rectangles = detect_face_12net(resized_image,
args.minsize, pnet_fun,
args.threshold,
args.factor)
else:
print("ERROR: WRONG NET INPUT")
end_time = time.time() * 1000
detect_totalTime = detect_totalTime + (end_time -
start_time)
print(
str(frameCount) + " time : " +
str(end_time - start_time) + "ms")
if args.net == "ALL":
points = np.transpose(
points
) # The outputs of O-Net which are faces' landmarks
else:
points = None # the others
add_overlays(img, rectangles, points,
1000 / (end_time - start_time),
1 / args.resize, 1 / args.resize)
cv2.imshow("MTCNN-Tenssorflow wangbm", img)
frameCount = frameCount + 1
if args.save_image:
outputFilePath = os.path.join(output_directory,
filename)
cv2.imwrite(outputFilePath, img)
for rectangle in rectangles:
fw.write(
'{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.
format(filename[:-4], rectangle[4],
rectangle[0], rectangle[1],
rectangle[2], rectangle[3]))
if cv2.waitKey(1) & 0xFF == ord('q'):
cv2.destroyAllWindows()
break
if args.save_image:
fw.close()
detect_average_time = detect_totalTime / frameCount
print("*" * 50)
print("detection average time: " + str(detect_average_time) + "ms")
print("detection fps: " + str(1000 / detect_average_time))
def main(args):
detect_totalTime = 0.0
totalTime = 0.0
frameCount = 0
if args.save_image:
output_directory = args.save_path
print(args.save_image)
if os.path.exists(output_directory):
shutil.rmtree(output_directory)
else:
os.mkdir(output_directory)
with tf.device('/cpu:0'):
with tf.Graph().as_default():
config = tf.ConfigProto(allow_soft_placement=True)
with tf.Session(config=config) as sess:
file_paths = get_model_filenames(args.model_dir)
if len(file_paths) == 3:
image_pnet = tf.placeholder(tf.float32,
[None, None, None, 3])
pnet = PNet({'data': image_pnet}, mode='test')
out_tensor_pnet = pnet.get_all_output()
image_rnet = tf.placeholder(tf.float32, [None, 24, 24, 3])
rnet = RNet({'data': image_rnet}, mode='test')
out_tensor_rnet = rnet.get_all_output()
image_onet = tf.placeholder(tf.float32, [None, 48, 48, 3])
onet = ONet({'data': image_onet}, mode='test')
out_tensor_onet = onet.get_all_output()
saver_pnet = tf.train.Saver([
v for v in tf.global_variables()
if v.name[0:5] == "pnet/"
])
saver_rnet = tf.train.Saver([
v for v in tf.global_variables()
if v.name[0:5] == "rnet/"
])
saver_onet = tf.train.Saver([
v for v in tf.global_variables()
if v.name[0:5] == "onet/"
])
saver_pnet.restore(sess, file_paths[0])
def pnet_fun(img):
return sess.run(out_tensor_pnet,
feed_dict={image_pnet: img})
saver_rnet.restore(sess, file_paths[1])
def rnet_fun(img):
return sess.run(out_tensor_rnet,
feed_dict={image_rnet: img})
saver_onet.restore(sess, file_paths[2])
def onet_fun(img):
return sess.run(out_tensor_onet,
feed_dict={image_onet: img})
else:
saver = tf.train.import_meta_graph(file_paths[0])
saver.restore(sess, file_paths[1])
def pnet_fun(img):
return sess.run(
('softmax/Reshape_1:0', 'pnet/conv4-2/BiasAdd:0'),
feed_dict={'Placeholder:0': img})
def rnet_fun(img):
return sess.run(('softmax_1/softmax:0',
'rnet/conv5-2/rnet/conv5-2:0'),
feed_dict={'Placeholder_1:0': img})
def onet_fun(img):
return sess.run(('softmax_2/softmax:0',
'onet/conv6-2/onet/conv6-2:0',
'onet/conv6-3/onet/conv6-3:0'),
feed_dict={'Placeholder_2:0': img})
# for filename in os.listdir(args.image_path):
video_capture = cv2.VideoCapture(0)
if video_capture.isOpened() == False:
print("ERROR: NO VIDEO STREAM OR NO CAMERA DEVICE.")
else:
video_capture.set(3, 1280)
video_capture.set(4, 720)
while True:
ret, frame = video_capture.read()
if ret:
resized_image = cv2.resize(frame, (640, 360))
# resized_image = cv2.resize(frame, (640, 480))
start_time = time.time() * 1000
# P-Net + R-Net + O-Net
if args.net == "ALL":
rectangles, points = detect_face(
resized_image, args.minsize, pnet_fun,
rnet_fun, onet_fun, args.threshold,
args.factor)
# P-Net + R-Net without faces' landmarks
elif args.net == "PR":
rectangles = detect_face_24net(
resized_image, args.minsize, pnet_fun,
rnet_fun, args.threshold, args.factor)
# Only P-Net
elif args.net == "P":
rectangles = detect_face_12net(
resized_image, args.minsize, pnet_fun,
args.threshold, args.factor)
else:
print("ERROR: WRONG NET INPUT")
end_time = time.time() * 1000
detect_totalTime = detect_totalTime + (end_time -
start_time)
print(
str(frameCount) + " time : " +
str(end_time - start_time) + "ms")
# print(type(rectangles))
if args.net == "ALL":
points = np.transpose(
points
) # The outputs of O-Net which are faces' landmarks
for rectangle in rectangles:
cv2.putText(
resized_image, str(rectangle[4]),
(int(rectangle[0]), int(rectangle[1])),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0))
cv2.rectangle(
resized_image,
(int(rectangle[0]), int(rectangle[1])),
(int(rectangle[2]), int(rectangle[3])),
(255, 0, 0), 1)
if args.net == "ALL":
for point in points:
for i in range(0, 10, 2):
cv2.circle(
resized_image,
(int(point[i]), int(point[i + 1])),
2, (0, 255, 0))
cv2.imshow("MTCNN-Tensorflow wangbm",
resized_image)
if args.save_image:
outputFilePath = os.path.join(
output_directory,
str(frameCount) + ".jpg")
cv2.imwrite(outputFilePath, resized_image)
if cv2.waitKey(1) & 0xFF == ord('q'):
cv2.destroyAllWindows()
break
frameCount = frameCount + 1
video_capture.release()
detect_average_time = detect_totalTime / frameCount
print("detection average time: " +
str(detect_average_time) + "ms")
print("detection fps: " +
str(1 / (detect_average_time / 1000)))
def main(args):
file_paths = get_model_filenames(args.model_dir)
#get image lise
jpg_list = glob.glob(r'mAP/images/*.jpg')
if len(jpg_list) == 0:
print("Error: no .jpg files found in ground-truth")
with tf.device('/gpu:2'):
with tf.Graph().as_default():
config = tf.ConfigProto(allow_soft_placement=True)
with tf.Session(config=config) as sess:
print("LOCATION!!!tf config done" + "\n")
if len(file_paths) == 3:
print("LOCATION!!!file_paths(model_dir)=3" + "\n")
image_pnet = tf.placeholder(tf.float32,
[None, None, None, 3])
pnet = PNet({'data': image_pnet}, mode='test')
out_tensor_pnet = pnet.get_all_output()
image_rnet = tf.placeholder(tf.float32, [None, 24, 24, 3])
rnet = RNet({'data': image_rnet}, mode='test')
out_tensor_rnet = rnet.get_all_output()
image_onet = tf.placeholder(tf.float32, [None, 48, 48, 3])
onet = ONet({'data': image_onet}, mode='test')
out_tensor_onet = onet.get_all_output()
print("LOCATION!!!placeholder and out_tensor done" + "\n")
saver_pnet = tf.train.Saver([
v for v in tf.global_variables()
if v.name[0:5] == "pnet/"
])
saver_rnet = tf.train.Saver([
v for v in tf.global_variables()
if v.name[0:5] == "rnet/"
])
saver_onet = tf.train.Saver([
v for v in tf.global_variables()
if v.name[0:5] == "onet/"
])
saver_pnet.restore(sess, file_paths[0])
print("LOCATION!!!saver done" + "\n")
def pnet_fun(img):
return sess.run(out_tensor_pnet,
feed_dict={image_pnet: img})
saver_rnet.restore(sess, file_paths[1])
def rnet_fun(img):
return sess.run(out_tensor_rnet,
feed_dict={image_rnet: img})
saver_onet.restore(sess, file_paths[2])
def onet_fun(img):
return sess.run(out_tensor_onet,
feed_dict={image_onet: img})
print("LOCATION!!!def net_fun done" + "\n")
else:
print("LOCATION!!!ifile_paths(model_dir)!=3" + "\n")
saver = tf.train.import_meta_graph(file_paths[0])
saver.restore(sess, file_paths[1])
def pnet_fun(img):
return sess.run(
('softmax/Reshape_1:0', 'pnet/conv4-2/BiasAdd:0'),
feed_dict={'Placeholder:0': img})
def rnet_fun(img):
return sess.run(('softmax_1/softmax:0',
'rnet/conv5-2/rnet/conv5-2:0'),
feed_dict={'Placeholder_1:0': img})
def onet_fun(img):
return sess.run(('softmax_2/softmax:0',
'onet/conv6-2/onet/conv6-2:0',
'onet/conv6-3/onet/conv6-3:0'),
feed_dict={'Placeholder_2:0': img})
# third_idxtry=[110,120]
# for third_idx in third_idxtry:
ROI_idx = [0, 300, 40, 310]
for tmp_file in jpg_list:
img = cv2.imread(tmp_file)
# add ROI region
ROI = img[ROI_idx[0]:ROI_idx[1], ROI_idx[2]:ROI_idx[3]]
ROI_temp = ROI.copy()
img[:, :, :] = 0
img[ROI_idx[0]:ROI_idx[1],
ROI_idx[2]:ROI_idx[3]] = ROI_temp
#create txt file
tmp_file = tmp_file.replace("jpg", "txt")
txt_filename = tmp_file.replace("images", "predicted")
print("LOACTION!!!predict:" + tmp_file)
# start_time = time.time()
#print("LOCATION!!!detect_face function start"+"\n")
rectangles, points = detect_face(img, args.minsize,
pnet_fun, rnet_fun,
onet_fun, args.threshold,
args.factor)
#print("LOCATION!!!idetect_face function done"+"\n")
# duration = time.time() - start_time
# print("duration:"+str(duration))
#print(type(rectangles))
points = np.transpose(points)
#print("LOCATION!!!loop rectangles"+"\n")
with open(txt_filename, 'w') as result_file:
for rectangle in rectangles:
result_file.write("head" + " " +
str(rectangle[4]) + " " +
str(rectangle[0]) + " " +
str(rectangle[1]) + " " +
str(rectangle[2]) + " " +
str(rectangle[3]) + "\n")
#print("LOCATION!!!Write done!"+"\n")
print(ROI_idx)
os.chdir("mAP/")
os.system("python main.py -na")
def bounding_box_face(img, gpu_mem):
### Model parameters
model_dir = 'assets/save_model/'
minsize = 20
# factor = 0.7
# threshold = [0.8, 0.8, 0.8]
factor = 0.709
threshold = [0.6, 0.7, 0.7]
file_paths = get_model_filenames(model_dir)
with tf.device('/gpu:0'):
with tf.Graph().as_default():
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.per_process_gpu_memory_fraction = gpu_mem
with tf.Session(config=config) as sess:
if len(file_paths) == 3:
image_pnet = tf.placeholder(tf.float32,
[None, None, None, 3])
pnet = PNet({'data': image_pnet}, mode='test')
out_tensor_pnet = pnet.get_all_output()
image_rnet = tf.placeholder(tf.float32, [None, 24, 24, 3])
rnet = RNet({'data': image_rnet}, mode='test')
out_tensor_rnet = rnet.get_all_output()
image_onet = tf.placeholder(tf.float32, [None, 48, 48, 3])
onet = ONet({'data': image_onet}, mode='test')
out_tensor_onet = onet.get_all_output()
saver_pnet = tf.train.Saver([
v for v in tf.global_variables()
if v.name[0:5] == "pnet/"
])
saver_rnet = tf.train.Saver([
v for v in tf.global_variables()
if v.name[0:5] == "rnet/"
])
saver_onet = tf.train.Saver([
v for v in tf.global_variables()
if v.name[0:5] == "onet/"
])
saver_pnet.restore(sess, file_paths[0])
def pnet_fun(img):
return sess.run(out_tensor_pnet,
feed_dict={image_pnet: img})
saver_rnet.restore(sess, file_paths[1])
def rnet_fun(img):
return sess.run(out_tensor_rnet,
feed_dict={image_rnet: img})
saver_onet.restore(sess, file_paths[2])
def onet_fun(img):
return sess.run(out_tensor_onet,
feed_dict={image_onet: img})
else:
saver = tf.train.import_meta_graph(file_paths[0])
saver.restore(sess, file_paths[1])
def pnet_fun(img):
return sess.run(
('softmax/Reshape_1:0', 'pnet/conv4-2/BiasAdd:0'),
feed_dict={'Placeholder:0': img})
def rnet_fun(img):
return sess.run(('softmax_1/softmax:0',
'rnet/conv5-2/rnet/conv5-2:0'),
feed_dict={'Placeholder_1:0': img})
def onet_fun(img):
return sess.run(('softmax_2/softmax:0',
'onet/conv6-2/onet/conv6-2:0',
'onet/conv6-3/onet/conv6-3:0'),
feed_dict={'Placeholder_2:0': img})
rectangles, points = detect_face(img, minsize, pnet_fun,
rnet_fun, onet_fun, threshold,
factor)
tf.reset_default_graph()
return rectangles, points
def __init__(self):
file_paths = get_model_filenames('save_model/all_in_one')
with tf.Session() as sess:
if len(file_paths) == 3:
image_pnet = tf.placeholder(tf.float32, [None, None, None, 3])
pnet = PNet({'data': image_pnet}, mode='test')
out_tensor_pnet = pnet.get_all_output()
image_rnet = tf.placeholder(tf.float32, [None, 24, 24, 3])
rnet = RNet({'data': image_rnet}, mode='test')
out_tensor_rnet = rnet.get_all_output()
image_onet = tf.placeholder(tf.float32, [None, 48, 48, 3])
onet = ONet({'data': image_onet}, mode='test')
out_tensor_onet = onet.get_all_output()
saver_pnet = tf.train.Saver([
v for v in tf.global_variables() if v.name[0:5] == "pnet/"
])
saver_rnet = tf.train.Saver([
v for v in tf.global_variables() if v.name[0:5] == "rnet/"
])
saver_onet = tf.train.Saver([
v for v in tf.global_variables() if v.name[0:5] == "onet/"
])
saver_pnet.restore(sess, file_paths[0])
def pnet_fun(img):
return sess.run(out_tensor_pnet,
feed_dict={image_pnet: img})
saver_rnet.restore(sess, file_paths[1])
def rnet_fun(img):
return sess.run(out_tensor_rnet,
feed_dict={image_rnet: img})
saver_onet.restore(sess, file_paths[2])
def onet_fun(img):
return sess.run(out_tensor_onet,
feed_dict={image_onet: img})
else:
saver = tf.train.import_meta_graph(file_paths[0])
saver.restore(sess, file_paths[1])
def pnet_fun(img):
return sess.run(
('softmax/Reshape_1:0', 'pnet/conv4-2/BiasAdd:0'),
feed_dict={'Placeholder:0': img})
def rnet_fun(img):
return sess.run(
('softmax_1/softmax:0', 'rnet/conv5-2/rnet/conv5-2:0'),
feed_dict={'Placeholder_1:0': img})
def onet_fun(img):
return sess.run(
('softmax_2/softmax:0', 'onet/conv6-2/onet/conv6-2:0',
'onet/conv6-3/onet/conv6-3:0'),
feed_dict={'Placeholder_2:0': img})
