def mtcnn_localize_faces(image,
pnet,
rnet,
onet,
minsize=20,
threshold=[0.7, 0.8, 0.85],
factor=0.75):
"""
Localize faces & its landmarks in image using MTCNN
Params
:image
:minsize - min. face size
:threshold - a list/array with 3 values. The thresholds for pnet, rnet & onet, respectively
:factor - sclaing factor for image octave
Return
:bbs - list of bounding boxes
:lds - list of face landmarks
"""
image = image[:, :, 0:3]
bounding_boxes, landmarks = detect_face.detect_face(
image, minsize, pnet, rnet, onet, threshold, factor)
nrof_faces = bounding_boxes.shape[0]
bbs = list()
lds = list()
if nrof_faces > 0:
det = bounding_boxes[:, 0:4]
bb = np.zeros((nrof_faces, 4), dtype=np.int32)
lands = np.zeros((nrof_faces, 10), dtype=np.int32)
landmarks = np.reshape(landmarks, (nrof_faces, 10))
for i in range(nrof_faces):
## Convert to int32
lands[i] = np.ravel(landmarks[i])
bb[i] = np.ravel(det[i])
# inner exception
if bb[i][0] <= 0 or bb[i][1] <= 0 or bb[i][2] >= len(
image[0]) or bb[i][3] >= len(image):
print('face is inner of range!')
continue
else:
## get as top, right, bottom, left
bbs.append((bb[i][1], bb[i][2], bb[i][3], bb[i][0]))
lds.append(lands[i])
return bbs, lds
def detect_faces_and_keypoints(self, image):
"""
Detect faces from a image, return face bounding boxes and face key points.
face boxes maybe further filtered with scores and keypoints
"""
bounding_boxes, markers = detect_face.detect_face(
image, self.minsize, self.nets[0], self.nets[1], self.nets[2],
self.threshold, self.factor)
num_faces = bounding_boxes.shape[0]
bboxes = []
scores = []
keypoints = []
for i in range(num_faces):
bboxes.append(bounding_boxes[i, 0:4])
scores.append(bounding_boxes[i, 4])
pts = []
for k in range(5):
pts.append((int(markers[k, i]), int(markers[5 + k, i])))
keypoints.append(pts)
return bboxes, scores, keypoints
def main(args):
sleep(random.random())
output_dir = os.path.expanduser(args.output_dir)
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(sys.argv))
dataset = facenet.get_dataset(args.input_dir)
print('Creating networks and loading parameters')
with tf.Graph().as_default():
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_memory_fraction)
configs_ = tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False)
configs_.gpu_options.allow_growth = True
sess = tf.Session(config=configs_)
with sess.as_default():
pnet, rnet, onet = detect_face.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
# Add a random key to the filename to allow alignment using multiple processes
random_key = np.random.randint(0, high=99999)
bounding_boxes_filename = os.path.join(output_dir, 'bounding_boxes_%05d.txt' % random_key)
with open(bounding_boxes_filename, "w") as text_file:
nrof_images_total = 0
nrof_successfully_aligned = 0
if args.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 args.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')
print(image_path)
if not os.path.exists(output_filename):
try:
img = misc.imread(image_path)
except (IOError, ValueError, IndexError) as e:
errorMessage = '{}: {}'.format(image_path, e)
print(errorMessage)
else:
if img.ndim<2:
print('Unable to align "%s"' % image_path)
text_file.write('%s\n' % (output_filename))
continue
if img.ndim == 2:
img = facenet.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 args.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]-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 = misc.imresize(cropped, (args.image_size, args.image_size), interp='bilinear')
nrof_successfully_aligned += 1
filename_base, file_extension = os.path.splitext(output_filename)
if args.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)
text_file.write('%s %d %d %d %d\n' % (output_filename_n, bb[0], bb[1], bb[2], bb[3]))
else:
print('Unable to align "%s"' % image_path)
text_file.write('%s\n' % (output_filename))
print('Total number of images: %d' % nrof_images_total)
print('Number of successfully aligned images: %d' % nrof_successfully_aligned)
def mtcnn(self,
image,
image_size=182,
margin=44,
gpu_memory_fraction=1.0,
detect_multiple_faces=False):
# output_dir = os.path.expanduser(output_dir)
# 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(sys.argv))
# dataset = facenet.get_dataset(input_dir)
# for f in glob.glob(os.path.join(input_dir,'*.jpg')):
# dataset.append(f)
# print(dataset)
# print('Creating networks and loading parameters')
self.detection = True
# with tf.Graph().as_default():
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction)
# sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
minsize = 20 # minimum size of face
threshold = [0.6, 0.7, 0.7] # three steps's threshold
factor = 0.709 # scale factor
# Add a random key to the filename to allow alignment using multiple processes
# random_key = np.random.randint(0, high=99999)
# bounding_boxes_filename = os.path.join(output_dir, 'bounding_boxes_temp.txt')
#
# with open(bounding_boxes_filename, "w") as text_file:
nrof_images_total = 0
nrof_successfully_aligned = 0
# if random_order:
# random.shuffle(dataset)
for i in range(0, 1):
# image_path = dataset
nrof_images_total += 1
# filename = os.path.splitext(os.path.split(image_path)[1])[0]
# print(filename)
# filename = 'temp'
# output_filename = os.path.join(output_dir, filename+'.jpg')
# print(image_path)
# if not os.path.exists(output_filename):
# try:
# img = misc.imread(image_path)
img = image
# except (IOError, ValueError, IndexError) as e:
# errorMessage = '{}: {}'.format(image_path, e)
# print(errorMessage)
# else:
# if img.ndim<2:
# print('Unable to align "%s"' % image_path)
# text_file.write('%s\n' % (output_filename))
# continue
if img.ndim == 2:
img = facenet.to_rgb(img)
img = img[:, :, 0:3]
bounding_boxes, _ = detect_face.detect_face(
img, minsize, self.pnet, self.rnet, self.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 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] - margin / 2, 0)
bb[1] = np.maximum(det[1] - margin / 2, 0)
bb[2] = np.minimum(det[2] + margin / 2, img_size[1])
bb[3] = np.minimum(det[3] + margin / 2, img_size[0])
cropped = img[bb[1]:bb[3], bb[0]:bb[2], :]
# scaled = misc.imresize(cropped, (image_size, image_size), interp='bilinear')
scaled = np.array(
Image.fromarray(cropped).resize(size=(image_size,
image_size)))
nrof_successfully_aligned += 1
# filename_base, file_extension = os.path.splitext(output_filename)
# if 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)
# text_file.write('%s %d %d %d %d\n' % (output_filename_n, bb[0], bb[1], bb[2], bb[3]))
else:
# print('Unable to align')
print()
# text_file.write('%s\n' % (output_filename))
# print('Total number of images: %d' % nrof_images_total)
# print('Number of successfully aligned images: %d' % nrof_successfully_aligned)
if nrof_successfully_aligned != 0:
self.detection = True
return scaled, bb
else:
return None, None
def main():
if ARGS.test == 'train':
train(ARGS)
elif ARGS.test == 'gen':
gen(ARGS)
elif ARGS.test == 'predict':
predict(ARGS)
elif ARGS.test == 'server':
server_start(ARGS)
elif ARGS.test == 'server_production':
server_start(ARGS, ARGS.port)
elif ARGS.test == 'hnm':
hnm(ARGS)
elif ARGS.test == 'val':
val(ARGS)
elif ARGS.test == 'fer':
fer(ARGS)
elif ARGS.test == 'profiling':
profiling(ARGS)
elif ARGS.test == 'facenet':
print(facenet)
mtcnn = tf.Session()
pnet, rnet, onet = FaceDetector.create_mtcnn(mtcnn, None)
# Load the model
t_ = time.time()
print('Loading model...')
#fnet = facenet.load_model('../models/facenet/20180204-160909') # squeezenet
fnet = facenet.load_model(
'../models/facenet/20170512-110547.pb') # InceptionResnet V1
t_ = time.time() - t_
print('done', t_ * 1000)
stats = {
'same': {
'd_avg': 0.,
'd_max': -9999.,
'd_min': 9999.,
'sqr_avg': 0.,
'count': 0,
},
'diff': {
'd_avg': 0.,
'd_max': -9999.,
'd_min': 9999.,
'sqr_avg': 0.,
'count': 0,
},
'precision': {},
'timing': {
'count': 0,
'forward': 0.
}
}
if True:
# Get input and output tensors
emb = None
names = []
for iteration in range(16):
# Load faces from LFW dataset and parse their names from path to group faces
images = []
batch_size = 128
fid = 0
for i in range(batch_size):
f = DirectoryWalker().get_a_file(
directory='../data/face/lfw', filters=['.jpg'])
if f is None or not f.path:
break
n = os.path.split(os.path.split(f.path)[0])[1]
#print('name', n)
n = abs(hash(n)) % (10**8)
img = cv2.imread(f.path, 1)
img = img
extents, landmarks = FaceDetector.detect_face(
img / 255.,
120,
pnet,
rnet,
onet,
threshold=[0.6, 0.7, 0.9],
factor=0.6,
interpolation=cv2.INTER_LINEAR)
for j, e in enumerate(extents):
x1, y1, x2, y2, confidence = e.astype(dtype=np.int)
#print(len(landmarks[j]))
#cropped = img[int(x1):int(x2), int(y1):int(y2), :]
aligned = FaceApplications.align_face(img,
landmarks[j],
intensity=1.,
sz=160,
ortho=True,
expand=1.5)
#cv2.imwrite('../data/face/mtcnn_cropped/'+str(fid).zfill(4)+'.jpg', aligned)
images.append(aligned / 255.)
names.append(n)
"""debug = aligned.astype(dtype=np.int)
print('debug', debug)
for p in debug:
cv2.circle(img, (p[0], p[1]), 2, (255, 0, 255))
for p in landmarks[j]:
cv2.circle(img, (p[0], p[1]), 2, (255, 255, 0))"""
fid += 1
#cv2.imwrite('../data/face/mtcnn_cropped/'+str(i).zfill(4)+'-annotated.jpg', img)
# Run forward pass to calculate embeddings
if len(images):
t_ = time.time()
if emb is None:
emb = fnet(images)
else:
emb = np.concatenate((emb, fnet(images)))
#emb = emb + sess.run(embeddings, feed_dict=feed_dict)
t_ = time.time() - t_
stats['timing']['count'] += len(images)
stats['timing']['forward'] += t_ * 1000
print('forward', emb.shape, t_ * 1000)
print()
print()
print('avg. forward time:',
stats['timing']['forward'] / stats['timing']['count'])
# Test distance
samples = sklearn.preprocessing.normalize(emb)
for i1, s1 in enumerate(samples):
for i2, s2 in enumerate(samples):
if i1 != i2:
d_ = scipy.spatial.distance.cosine(s1, s2)
if names[i1] == names[
i2]: # Same person as annotated by LFW
cate = 'same'
else: # Different person
cate = 'diff'
c_ = stats[cate]['count']
stats[cate]['d_avg'] = stats[cate]['d_avg'] * c_ / (
c_ + 1) + d_ / (c_ + 1)
d_sqr = d_ * d_
stats[cate]['sqr_avg'] = stats[cate][
'sqr_avg'] * c_ / (c_ + 1) + d_sqr / (c_ + 1)
if d_ > stats[cate]['d_max']: stats[cate]['d_max'] = d_
elif d_ < stats[cate]['d_min']:
stats[cate]['d_min'] = d_
stats[cate]['count'] += 1
# Get statistics of precision on different thresholds
increments = 64
for t_ in range(increments):
threshold = 0.2 + t_ * (0.6 / increments)
if threshold not in stats['precision']:
stats['precision'][threshold] = {
'correct': 0,
'total': 0,
'precision': 0.,
'true_pos': 0,
'total_pos': 0,
'recall': 0.,
}
if (cate == 'same' and d_ <= threshold) or (
cate == 'diff' and d_ > threshold):
stats['precision'][threshold]['correct'] += 1
if cate == 'same':
if d_ <= threshold:
stats['precision'][threshold][
'true_pos'] += 1
stats['precision'][threshold]['total_pos'] += 1
stats['precision'][threshold][
'recall'] = stats['precision'][threshold][
'true_pos'] / stats['precision'][
threshold]['total_pos']
stats['precision'][threshold]['total'] += 1
stats['precision'][threshold]['precision'] = stats[
'precision'][threshold]['correct'] / stats[
'precision'][threshold]['total']
"""tree = scipy.spatial.KDTree(samples)
for i, s in enumerate(samples):
print(i, tree.query(s))"""
for cate in ['same', 'diff']:
stats[cate]['stddev'] = stats[cate][
'sqr_avg'] - stats[cate]['d_avg'] * stats[cate]['d_avg']
print()
pp = pprint.PrettyPrinter(indent=4)
pp.pprint(stats)
# Print precision vs recall
print()
print('threshold,recall,precision')
for t in stats['precision']:
t_stat = stats['precision'][t]
print(
str(t) + ',' + str(t_stat['recall']) + ',' +
str(t_stat['precision']))
elif ARGS.test == 'align':
face_app = FaceApplications()
face_app.align_dataset()
elif ARGS.test == 'fxpress':
fxpress(ARGS)
elif ARGS.test == 'fxpress_train':
fxpress_train(ARGS)
elif ARGS.test == 'emoc':
classifier = EmotionClassifier()
classifier.build_network(ARGS)
classifier.val(ARGS)
elif ARGS.test == 'face_app':
face_app = FaceApplications()
face_app.detect()
elif ARGS.test == 'face_benchmark': # Test different parameters, resolutions, interpolation methods for MTCNN face detection time vs precision
interpolations = ['NEAREST', 'LINEAR', 'AREA']
resolutions = [256, 320, 384, 448, 512, 640, 1024, 1280]
factors = [0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95]
expectations = [
['0001.jpg', 4],
['0002.jpg', 1],
['0003.jpg', 1],
['0004.jpg', 0],
['0005.jpg', 1],
['0006.jpg', 1],
['0007.jpg', 59],
['0008.jpg', 6],
['0009.jpg', 1],
['0010.jpg', 5],
['0011.jpg', 4],
['0012.jpg', 17],
['0013.jpg', 20],
['0014.jpg', 48],
['0015.jpg', 22],
]
log_file = open('../data/face_benchmark.csv', 'w')
log_file.write('time,precision_index,cp,options\n')
for interp in interpolations:
for res_cap in resolutions:
for factor in factors:
#res_cap = resolutions[0]
#factor = factors[0]
#interp = interpolations[0]
options = {
'res_cap': res_cap,
'factor': factor,
'interp': interp,
}
if interp == 'NEAREST':
interpolation = cv2.INTER_NEAREST
elif interp == 'LINEAR':
interpolation = cv2.INTER_LINEAR
elif interp == 'AREA':
interpolation = cv2.INTER_AREA
iterations = 20
file_count = len(expectations)
time_sampling = np.zeros((
file_count,
iterations,
),
dtype=np.float)
pi_sampling = np.zeros((
file_count,
iterations,
),
dtype=np.float)
image_dir = '../data/face_benchmark/'
for k, item in enumerate(expectations):
filename, expected_faces = item
inpath = image_dir + filename
img = cv2.imread(inpath, 1)
if img is None:
break
img, scaling = ImageUtilities.fit_resize(
img,
maxsize=(res_cap, res_cap),
interpolation=interpolation)
retval, bindata = cv2.imencode('.jpg', img)
bindata_b64 = base64.b64encode(bindata).decode()
requests = {
'requests': [{
'requestId':
str(uuid.uuid1()),
'media': {
'content': bindata_b64
},
'services': [{
'type': 'face_',
'model': 'a-emoc',
'options': options
}]
}],
'timing': {
'client_sent': time.time()
}
}
for i in range(iterations):
#url = 'http://10.129.11.4/cgi/predict'
requests['timing']['client_sent'] = time.time()
url = 'http://192.168.41.41:8080/predict'
postdata = json.dumps(requests)
#print()
#print(postdata)
#print()
request = Request(url, data=postdata.encode())
response = json.loads(
urlopen(request).read().decode())
timing = response['timing']
server_time = timing['server_sent'] - timing[
'server_rcv']
#print('server time:', server_time)
total_time = (time.time() -
timing['client_sent']) * 1000
client_time = total_time - server_time
print('response time:', total_time)
pi = 0.
for r_ in response['requests']:
for s_ in r_['services']:
rects_ = s_['results']['rectangles']
if expected_faces:
pi = len(rects_) / expected_faces
elif len(rects_):
pi = expected_faces / len(rects_)
else:
pi = 1.0
#print('faces detected:', len(rects_), pi)
time_sampling[k][i] = total_time
pi_sampling[k][i] = pi
#time.sleep(0.5)
#print()
#print(response)
#print()
time_mean = np.mean(time_sampling)
pi_mean = np.mean(pi_sampling) * 100
cp = pi_mean * pi_mean / time_mean
print(time_mean, pi_mean)
print()
log_file.write(','.join([
str(time_mean),
str(pi_mean),
str(cp),
json.dumps(options)
]) + '\n')
log_file.flush()
log_file.close()
def age_gender():
sample_url = url_for('uploaded_file', filename="sample.jpeg")
if request.method == 'POST':
if 'file' not in request.files:
return render_template("face.html",
error_msg="No file has been selected!")
file = request.files['file']
if file and allowed_file(file.filename):
with tf.Graph().as_default():
sess = tf.Session()
with sess.as_default():
pnet, rnet, onet = mtcnn.create_mtcnn(
sess, MTCNN_MODEL_PATH)
filename = file.filename
file_name = os.path.join(app.config['UPLOAD_FOLDER'], filename)
file.save(file_name)
start = time.time()
# Resize the orginal image to 680-width and save as jpeg
cv_ori = cv2.imread(file_name)
cv_resize = cv2.resize(
cv_ori,
dsize=(680, int(cv_ori.shape[0] / cv_ori.shape[1] * 680)),
interpolation=cv2.INTER_CUBIC)
cv2.imwrite(file_name, cv_resize)
# Read the image for face detection
img = misc.imread(file_name)
cv_img = cv2.imread(file_name)
# Detect faces and landmarks by MTCNN
bounding_boxes, landmarks = mtcnn.detect_face(
img, 20, pnet, rnet, onet, [0.6, 0.7, 0.7], 0.709)
# Crop the aligned faces for age and gender classification
aligned_images = gender_age_predict.load_image(cv_img, landmarks)
# Estimate gender and age of each face using ResNet50
genders, ages = gender_age_predict.inception(
FROZEN_GRAPH_PATH, aligned_images)
# Draw boxes and labels for faces
gender_age_predict.draw_label(cv_img, bounding_boxes, genders,
ages)
save_path = os.path.join(app.config['NEW_FOLDER'], filename)
cv2.imwrite(save_path, cv_img)
end = time.time()
print('\n Evaluation time: {:.3f}s\n'.format(end - start))
file_url = url_for('uploaded_file', filename=filename)
return render_template("face.html",
user_image=file_url,
error_msg='')
else:
print('\n Incorrect upload image format.\n')
return render_template("face.html",
user_image=sample_url,
error_msg='Incorrect image format!')
return render_template("face.html", user_image=sample_url, error_msg='')
def detect_faces(self, image):
with self.graph.as_default():
with self.sess.as_default():
boxes, points = detect_face.detect_face(image, self.minsize, self.pnet, self.rnet, self.onet, self.threshold, self.factor)
return self._assemble_detect_face_result(boxes, points)
def cam_mtcnn(draw):
minsize = 40 # minimum size of face
threshold = [0.6, 0.7, 0.9] # three steps's threshold
factor = 0.709 # scale factor
#draw = cv2.resize(draw, (960, 540))
#img=cv2.cvtColor(draw,cv2.COLOR_BGR2GRAY)
original = draw.copy()
bounding_boxes, points = detect_face.detect_face(draw, minsize, pnet, rnet,
onet, threshold, factor)
nrof_faces = bounding_boxes.shape[0]
w, h, _ = draw.shape
face = []
for b in bounding_boxes:
cv2.rectangle(draw, (int(b[0]), int(b[1])), (int(b[2]), int(b[3])),
(0, 255, 0))
# [y1:y2, x1:x2]
zeros = np.zeros((w, h))
# face_w = int(b[2]) - int(b[0])
# face_h = int(b[3]) - int(b[1])
#
# if face_w > w:
# face_w = w
# if face_h > h:
# face_h = h
face_h, face_w = zeros[int(b[1]):int(b[3]), int(b[0]):int(b[2])].shape
ones = np.ones((face_h, face_w))
zeros[int(b[1]):int(b[3]), int(b[0]):int(b[2])] = ones
face_mask = zeros
face = original[int(b[1]):int(b[3]), int(b[0]):int(b[2])]
if len(points) != 0 and len(face) != 0:
for p in points.T:
for i in range(5):
cv2.circle(draw, (p[i], p[i + 5]), 1, (0, 0, 255), 2)
# print (points.shape)
size = 30
i = 0
cv2.rectangle(draw, (int(p[i] - size), int(p[i + 5] - size)),
(int(p[i] + size), int(p[i + 5] + size)),
(0, 255, 0))
left_eye = [p[i], p[i + 5]]
eye_left = original[int(p[i + 5] - size):int(p[i + 5] + size),
int(p[i] - size):int(p[i] + size)]
i = 1
cv2.rectangle(draw, (int(p[i] - size), int(p[i + 5] - size)),
(int(p[i] + size), int(p[i + 5] + size)),
(0, 255, 0))
right_eye = [p[i], p[i + 5]]
eye_right = original[int(p[i + 5] - size):int(p[i + 5] + size),
int(p[i] - size):int(p[i] + size)]
# cv2.namedWindow('Face Detection',cv2.WINDOW_NORMAL)
# cv2.resizeWindow('Face Detection', 1920, 1080)
# cv2.imshow('Face Detection',draw)
# disp_img("face", face)
# disp_img("eye_left", eye_left)
# disp_img("eye_right", eye_right)
# disp_img("face_mask", face_mask)
# disp_img("original", original)
#
# cv2.waitKey(0)
return [
original, draw, face, eye_left, eye_right, face_mask, left_eye,
right_eye
]
else:
return []
def convert_to_aligned_face(data_set, base_path, dataset_name):
file_name = data_set.file_name
genders = data_set.gender
ages = data_set.age
face_score = data_set.score
num_images = data_set.shape[0]
if dataset_name == "imdb":
data_base_dir = os.path.join(base_path, "imdb_crop")
elif dataset_name == "wiki":
data_base_dir = os.path.join(base_path, "wiki_crop")
else:
raise NameError
# load the mtcnn face detector
with tf.Graph().as_default():
sess = tf.Session()
with sess.as_default():
pnet, rnet, onet = mtcnn.create_mtcnn(sess, MTCNN_MODEL_PATH)
error_count = 0
write_count = 0
for index in range(num_images):
if face_score[index] < 0.75:
continue
if ~(0 <= ages[index] <= 100):
continue
if np.isnan(genders[index]):
continue
try:
# Read the image for face detection
img = misc.imread(
os.path.join(data_base_dir, str(file_name[index][0])))
cv_img = cv2.imread(
os.path.join(data_base_dir, str(file_name[index][0])),
cv2.IMREAD_COLOR)
# Detect faces for age and gender classification
bounding_boxes, landmarks = mtcnn.detect_face(
img, 20, pnet, rnet, onet, [0.6, 0.7, 0.7], 0.709)
if bounding_boxes.shape[0] != 1:
continue
else:
# Crop aligned faces from image
aligned_faces = load_image(cv_img, landmarks)
face = aligned_faces[0]
# Resize and write image to path
output_dir = os.getcwd() + '/faces/' + dataset_name + '/'
if os.path.isdir(output_dir):
pass
else:
os.mkdir(output_dir)
image_name = 'image{}_{}_{}.jpg'.format(
index + 70000, int(genders[index]), ages[index])
output_path = output_dir + image_name
cv2.imwrite(output_path, face)
except Exception: # some files seem not exist in face_data dir
error_count = error_count + 1
print("read {} error".format(index + 1))
pass
write_count = write_count + 1
print("There are ", error_count, " missing pictures")
print("Found", write_count, "valid faces")
def detect(self, media):
timing = dict()
result = dict({
'mtcnn': list(),
'mtcnn_5p': list(),
'emotions': list(),
})
#print(media)
src_img = None
if 'content' in media:
bindata = base64.b64decode(media['content'].encode())
src_img = cv2.imdecode(np.frombuffer(bindata, np.uint8), 1)
if src_img is not None:
#print(img.shape)
src_shape = src_img.shape
time_start = time.time()
gray = ImageUtilities.preprocess(src_img,
convert_gray=cv2.COLOR_RGB2YCrCb,
equalize=False,
denoise=False,
maxsize=384)
time_diff = time.time() - time_start
timing['preprocess'] = time_diff * 1000
#print('preprocess', time_diff)
processed_shape = gray.shape
mrate = [
processed_shape[0] / src_shape[0],
processed_shape[1] / src_shape[1]
]
time_start = time.time()
rects, landmarks = FaceDetector().detect(gray)
time_diff = time.time() - time_start
timing['detect'] = time_diff * 1000
#print('hog+svm detect', time_diff)
time_start = time.time()
facelist = list()
rects_ = list()
predictions_ = list()
# Crop faces from source image
for rect in rects:
face = None
(x, y, w, h) = ImageUtilities.rect_to_bb(rect, mrate=mrate)
height, width, *rest = src_img.shape
(x, y, w,
h) = ImageUtilities.rect_fit_ar([x, y, w, h],
[0, 0, width, height],
1.,
mrate=1.)
if w > 0 and h > 0:
face = ImageUtilities.transform_crop((x, y, w, h),
src_img,
r_intensity=0.,
p_intensity=0.)
face = imresize(face, shape_raw[0:2])
#face = ImageUtilities.preprocess(face, convert_gray=None)
if face is not None:
facelist.append(face)
rects_.append([x, y, w, h])
predictions_.append([0., 0.])
val_data = np.array(facelist, dtype=np.float32) / 255
reshaped = val_data.reshape((-1, ) + shape_flat)
time_diff = time.time() - time_start
timing['crop'] = time_diff * 1000
#print('prepare data for cnn', time_diff)
# MTCNN
img = ImageUtilities.preprocess(src_img,
convert_gray=None,
equalize=False,
denoise=False,
maxsize=384)
mrate_ = src_shape[0] / img.shape[0]
time_start = time.time()
minsize = 40 # minimum size of face
threshold = [0.6, 0.7, 0.9] # three steps's threshold
factor = 0.709 # scale factor
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
bounding_boxes, points = mtcnn_detect.detect_face(
img, minsize, self.pnet, self.rnet, self.onet, threshold,
factor)
if len(bounding_boxes):
points = np.array(points) * mrate_
points = points.reshape(2, -1)
points = np.transpose(points)
points = points.reshape((len(bounding_boxes), -1, 2))
time_diff = time.time() - time_start
timing['mtcnn'] = time_diff * 1000
timing['emotion'] = 0
print()
print()
print('result len', len(bounding_boxes), len(points))
nrof_faces = bounding_boxes.shape[0]
for i, b in enumerate(bounding_boxes):
r_ = (np.array([b[0], b[1], b[2] - b[0], b[3] - b[1]]) *
mrate_).astype(dtype=np.int).tolist()
result['mtcnn'].append(r_ + [
int(b[4] * 1000),
])
result['mtcnn_5p'].append(
points[i].astype(dtype=np.int).tolist())
#rects_.append(r_)
#predictions_.append([0., 2.])
# Facial Expression
time_start = time.time()
(x, y, w, h) = ImageUtilities.rect_fit_ar(
r_, [0, 0, src_shape[1], src_shape[0]], 1., crop=False)
if w > 0 and h > 0:
face = ImageUtilities.transform_crop((x, y, w, h),
src_img,
r_intensity=0.,
p_intensity=0.)
#face = imresize(face, shape_raw[0:2])
#face = ImageUtilities.preprocess(face, convert_gray=cv2.COLOR_RGB2YCrCb, equalize=False, denoise=False)
face = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)
cv2.imwrite('./face.jpg', face)
#face = np.array(face, dtype=np.float32)/255
face = cv2.resize(face, (48, 48),
interpolation=cv2.INTER_CUBIC) / 255.
if face is not None:
#emotions = self.fxpress.predict(face)[0]
emotions = self.emoc.predict(face)
print('emotion', face.shape, emotions)
result['emotions'].append(
(np.array(emotions) *
1000).astype(dtype=np.int).tolist())
time_diff = time.time() - time_start
timing['emotion'] += time_diff * 1000
print('rect', b, r_)
print('emotions', result['emotions'])
print('mtcnn_5p', result['mtcnn_5p'])
print()
print()
# Self-trained cascade face detection
img = ImageUtilities.preprocess(src_img,
convert_gray=None,
equalize=False,
denoise=False,
maxsize=384)
ms_rects, ms_predictions, ms_timing = self.multi_scale_detection(
img, expanding_rate=1.2, stride=12)
mrate_ = src_shape[0] / img.shape[0]
timing['cnn'] = ms_timing['cnn']
timing['window_count'] = ms_timing['window_count']
use_nms = True
if len(ms_predictions):
if use_nms:
# Apply non-maximum-supression
scores = np.array(
ms_predictions)[:,
target_class:target_class + 1].reshape(
(-1, ))
nms = tf.image.non_max_suppression(np.array(ms_rects),
scores,
iou_threshold=0.5,
max_output_size=99999)
for index, value in enumerate(nms.eval()):
r_ = (np.array(ms_rects[value]) *
mrate_).astype(dtype=np.int).tolist()
p_ = ms_predictions[value]
rects_.append(r_)
predictions_.append(p_)
else:
for index, p_ in enumerate(ms_predictions):
r_ = (np.array(ms_rects[index]) *
mrate_).astype(dtype=np.int).tolist()
rects_.append(r_)
predictions_.append(p_)
"""time_start = time.time()
feed_dict = {self.model.x: val_data.reshape((-1,)+shape_flat)}
predictions = self.model.sess.run(self.model.y, feed_dict)
time_diff = time.time() - time_start
timing['cnn'] = time_diff*1000
#print('cnn classify', time_diff, len(facelist))
#print('predictions', predictions)"""
return (rects_, predictions_, timing, result)