def __init__(self, select_largest=False, keep_all=True):
self.pnet = PNet().to(device)
self.rnet = RNet().to(device)
self.onet = ONet().to(device)
self.pnet.eval()
self.rnet.eval()
self.onet.eval()
self.refrence = get_reference_facial_points(default_square=True)
def __init__(self):
self.pnet = PNet().to(device)
self.rnet = RNet().to(device)
self.onet = ONet().to(device)
self.onet.eval()
self.rnet.eval()
self.onet.eval()
self.refrence = get_reference_facial_points(default_square=True)
def __init__(self, device='cpu'):
self.device = torch.device("cuda:0" if device == 'cuda' else "cpu")
self.pnet = PNet().to(device)
self.rnet = RNet().to(device)
self.onet = ONet().to(device)
self.pnet.eval()
self.rnet.eval()
self.onet.eval()
self.refrence = get_reference_facial_points(default_square=True)
def __init__(self, weight_path='mtcnn_pytorch/src/weights'):
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
self.pnet = PNet(weight_path).to(self.device)
self.rnet = RNet(weight_path).to(self.device)
self.onet = ONet(weight_path).to(self.device)
self.pnet.eval()
self.rnet.eval()
self.onet.eval()
self.refrence = get_reference_facial_points(default_square=True)
def __init__(self,
device=devi,
thresholds=[0.3, 0.6, 0.8],
nms_thresholds=[0.6, 0.6, 0.6]):
self.pnet = PNet().to(device)
self.rnet = RNet().to(device)
self.onet = ONet().to(device)
self.onet.eval()
self.rnet.eval()
self.onet.eval()
self.refrence = get_reference_facial_points(default_square=True)
self.device = device
self.thresholds = thresholds
self.nms_thresholds = nms_thresholds
class MTCNN():
def __init__(self):
self.pnet = PNet().to(device)
self.rnet = RNet().to(device)
self.onet = ONet().to(device)
self.pnet.eval()
self.rnet.eval()
self.onet.eval()
self.refrence = get_reference_facial_points(default_square=True)
def align(self, img):
_, landmarks = self.detect_faces(img)
facial5points = [[landmarks[0][j], landmarks[0][j + 5]]
for j in range(5)]
warped_face = warp_and_crop_face(np.array(img),
facial5points,
self.refrence,
crop_size=(112, 112))
return Image.fromarray(warped_face)
def align_multi(self, img, limit=None, min_face_size=20.0):
boxes, landmarks = self.detect_faces(img, min_face_size)
if limit:
boxes = boxes[:limit]
landmarks = landmarks[:limit]
faces = []
for landmark in landmarks:
facial5points = [[landmark[j], landmark[j + 5]] for j in range(5)]
warped_face = warp_and_crop_face(np.array(img),
facial5points,
self.refrence,
crop_size=(112, 112))
faces.append(Image.fromarray(warped_face))
return boxes, landmarks, faces
def detect_faces(self,
image,
min_face_size=20.0,
thresholds=[0.6, 0.6, 0.7],
nms_thresholds=[0.7, 0.7, 0.7]):
"""
Arguments:
image: an instance of PIL.Image.
min_face_size: a float number.
thresholds: a list of length 3.
nms_thresholds: a list of length 3.
Returns:
two float numpy arrays of shapes [n_boxes, 4] and [n_boxes, 10],
bounding boxes and facial landmarks.
"""
# BUILD AN IMAGE PYRAMID
width, height = image.size
#height, width, channel = image.shape
min_length = min(height, width)
min_detection_size = 12
factor = 0.707 # sqrt(0.5)
# scales for scaling the image
scales = []
# scales the image so that
# minimum size that we can detect equals to
# minimum face size that we want to detect
m = min_detection_size / min_face_size
min_length *= m
factor_count = 0
while min_length > min_detection_size:
scales.append(m * factor**factor_count)
min_length *= factor
factor_count += 1
# STAGE 1
# it will be returned
bounding_boxes = []
with torch.no_grad():
# run P-Net on different scales
for s in scales:
boxes = run_first_stage(image,
self.pnet,
scale=s,
threshold=thresholds[0])
bounding_boxes.append(boxes)
# collect boxes (and offsets, and scores) from different scales
bounding_boxes = [i for i in bounding_boxes if i is not None]
if len(bounding_boxes) == 0:
return [], []
bounding_boxes = np.vstack(bounding_boxes)
keep = nms(bounding_boxes[:, 0:5], nms_thresholds[0])
bounding_boxes = bounding_boxes[keep]
# use offsets predicted by pnet to transform bounding boxes
bounding_boxes = calibrate_box(bounding_boxes[:, 0:5],
bounding_boxes[:, 5:])
# shape [n_boxes, 5]
bounding_boxes = convert_to_square(bounding_boxes)
bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])
# STAGE 2
img_boxes = get_image_boxes(bounding_boxes, image, size=24)
img_boxes = torch.FloatTensor(img_boxes).to(device)
output = self.rnet(img_boxes)
offsets = output[0].cpu().data.numpy() # shape [n_boxes, 4]
probs = output[1].cpu().data.numpy() # shape [n_boxes, 2]
keep = np.where(probs[:, 1] > thresholds[1])[0]
bounding_boxes = bounding_boxes[keep]
bounding_boxes[:, 4] = probs[keep, 1].reshape((-1, ))
offsets = offsets[keep]
keep = nms(bounding_boxes, nms_thresholds[1])
bounding_boxes = bounding_boxes[keep]
bounding_boxes = calibrate_box(bounding_boxes, offsets[keep])
bounding_boxes = convert_to_square(bounding_boxes)
bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])
# STAGE 3
img_boxes = get_image_boxes(bounding_boxes, image, size=48)
if len(img_boxes) == 0:
return [], []
img_boxes = torch.FloatTensor(img_boxes).to(device)
output = self.onet(img_boxes)
landmarks = output[0].cpu().data.numpy() # shape [n_boxes, 10]
offsets = output[1].cpu().data.numpy() # shape [n_boxes, 4]
probs = output[2].cpu().data.numpy() # shape [n_boxes, 2]
keep = np.where(probs[:, 1] > thresholds[2])[0]
bounding_boxes = bounding_boxes[keep]
bounding_boxes[:, 4] = probs[keep, 1].reshape((-1, ))
offsets = offsets[keep]
landmarks = landmarks[keep]
# compute landmark points
width = bounding_boxes[:, 2] - bounding_boxes[:, 0] + 1.0
height = bounding_boxes[:, 3] - bounding_boxes[:, 1] + 1.0
xmin, ymin = bounding_boxes[:, 0], bounding_boxes[:, 1]
landmarks[:, 0:5] = np.expand_dims(
xmin, 1) + np.expand_dims(width, 1) * landmarks[:, 0:5]
landmarks[:, 5:10] = np.expand_dims(
ymin, 1) + np.expand_dims(height, 1) * landmarks[:, 5:10]
bounding_boxes = calibrate_box(bounding_boxes, offsets)
keep = nms(bounding_boxes, nms_thresholds[2], mode='min')
bounding_boxes = bounding_boxes[keep]
landmarks = landmarks[keep]
return bounding_boxes, landmarks
class MTCNN():
def __init__(self):
self.pnet = PNet().to(device)
self.rnet = RNet().to(device)
self.onet = ONet().to(device)
self.pnet.eval()
self.rnet.eval()
self.onet.eval()
self.refrence = get_reference_facial_points(default_square=True)
def align(self, img, crop_size=(112, 112), return_trans_inv=False):
_, landmarks = self.detect_faces(img)
if len(landmarks) == 0:
return None if not return_trans_inv else (None, None)
facial5points = [[landmarks[0][j], landmarks[0][j + 5]]
for j in range(5)]
warped_face = warp_and_crop_face(np.array(img),
facial5points,
self.refrence,
crop_size=crop_size,
return_trans_inv=return_trans_inv)
if return_trans_inv:
return Image.fromarray(warped_face[0]), warped_face[1]
else:
return Image.fromarray(warped_face)
def align_fully(self,
img,
crop_size=(112, 112),
return_trans_inv=False,
ori=[0, 1, 3],
fast_mode=True):
ori_size = img.copy()
h = img.size[1]
w = img.size[0]
sw = 320. if fast_mode else w
scale = sw / w
img = img.resize((int(w * scale), int(h * scale)))
candi = []
for i in ori:
if len(candi) > 0:
break
if i > 0:
rimg = img.transpose(i + 1)
else:
rimg = img
box, landmarks = self.detect_faces(rimg,
min_face_size=sw / 10,
thresholds=[0.6, 0.7, 0.7])
landmarks /= scale
if len(landmarks) == 0:
continue
if i == 0:
f5p = [[landmarks[0][j], landmarks[0][j + 5]]
for j in range(5)]
elif i == 1:
f5p = [[w - 1 - landmarks[0][j + 5], landmarks[0][j]]
for j in range(5)]
elif i == 2:
f5p = [[w - 1 - landmarks[0][j], h - 1 - landmarks[0][j + 5]]
for j in range(5)]
elif i == 3:
f5p = [[landmarks[0][j + 5], h - 1 - landmarks[0][j]]
for j in range(5)]
candi.append((box[0][4], f5p))
if len(candi) == 0:
return None if not return_trans_inv else (None, None)
while len(candi) > 1:
if candi[0][0] > candi[1][0]:
del candi[1]
else:
del candi[0]
facial5points = candi[0][1]
warped_face = warp_and_crop_face(np.array(ori_size),
facial5points,
self.refrence,
crop_size=crop_size,
return_trans_inv=return_trans_inv)
if return_trans_inv:
return Image.fromarray(warped_face[0]), warped_face[1]
else:
return Image.fromarray(warped_face)
def align_multi(self,
img,
limit=None,
min_face_size=64.0,
crop_size=(112, 112)):
boxes, landmarks = self.detect_faces(img, min_face_size)
if len(landmarks) == 0:
return None
if limit:
boxes = boxes[:limit]
landmarks = landmarks[:limit]
faces = []
for landmark in landmarks:
facial5points = [[landmark[j], landmark[j + 5]] for j in range(5)]
warped_face = warp_and_crop_face(np.array(img),
facial5points,
self.refrence,
crop_size=crop_size)
faces.append(Image.fromarray(warped_face))
# return boxes, faces
return faces
def get_landmarks(self,
img,
min_face_size=32,
crop_size=(256, 256),
fast_mode=False,
ori=[0, 1, 3]):
ori_size = img.copy()
h = img.size[1]
w = img.size[0]
sw = 640. if fast_mode else w
scale = sw / w
img = img.resize((int(w * scale), int(h * scale)))
min_face_size = min_face_size if not fast_mode else sw / 20
candi = []
boxes = np.zeros([0, 5])
for i in ori:
if i > 0:
rimg = img.transpose(i + 1)
else:
rimg = img
box, landmarks = self.detect_faces(rimg,
min_face_size=min_face_size,
thresholds=[0.6, 0.7, 0.7])
landmarks /= scale
if len(landmarks) == 0:
continue
if i == 0:
f5p = [[landmarks[0][j], landmarks[0][j + 5]]
for j in range(5)]
elif i == 1:
f5p = [[w - 1 - landmarks[0][j + 5], landmarks[0][j]]
for j in range(5)]
x1 = w - 1 - box[:, 1]
y1 = box[:, 0]
x2 = w - 1 - box[:, 3]
y2 = box[:, 2]
box[:, :4] = np.stack((x2, y1, x1, y2), axis=1)
elif i == 2:
f5p = [[w - 1 - landmarks[0][j], h - 1 - landmarks[0][j + 5]]
for j in range(5)]
x1 = w - 1 - box[:, 0]
y1 = h - 1 - box[:, 1]
x2 = w - 1 - box[:, 2]
y2 = h - 1 - box[:, 3]
box[:, :4] = np.stack((x2, y2, x1, y1), axis=1)
elif i == 3:
f5p = [[landmarks[0][j + 5], h - 1 - landmarks[0][j]]
for j in range(5)]
x1 = box[:, 1]
y1 = h - 1 - box[:, 0]
x2 = box[:, 3]
y2 = h - 1 - box[:, 2]
box[:, :4] = np.stack((x1, y2, x2, y1), axis=1)
candi.append(f5p)
boxes = np.concatenate((boxes, box), axis=0)
# pick = nms(boxes)
faces = []
for idx, facial5points in enumerate(candi):
# if idx not in pick:
# continue
warped_face = warp_and_crop_face(np.array(ori_size),
facial5points,
self.refrence,
crop_size=crop_size,
return_trans_inv=False)
faces.append((warped_face, facial5points))
return faces
def detect_faces(self,
image,
min_face_size=64.0,
thresholds=[0.6, 0.7, 0.8],
nms_thresholds=[0.7, 0.7, 0.7]):
"""
Arguments:
image: an instance of PIL.Image.
min_face_size: a float number.
thresholds: a list of length 3.
nms_thresholds: a list of length 3.
Returns:
two float numpy arrays of shapes [n_boxes, 4] and [n_boxes, 10],
bounding boxes and facial landmarks.
"""
# BUILD AN IMAGE PYRAMID
width, height = image.size
min_length = min(height, width)
min_detection_size = 12
factor = 0.707 # sqrt(0.5)
# scales for scaling the image
scales = []
# scales the image so that
# minimum size that we can detect equals to
# minimum face size that we want to detect
m = min_detection_size / min_face_size
min_length *= m
factor_count = 0
while min_length > min_detection_size:
scales.append(m * factor**factor_count)
min_length *= factor
factor_count += 1
# STAGE 1
# it will be returned
bounding_boxes = []
with torch.no_grad():
# run P-Net on different scales
for s in scales:
boxes = run_first_stage(image,
self.pnet,
scale=s,
threshold=thresholds[0])
bounding_boxes.append(boxes)
# collect boxes (and offsets, and scores) from different scales
bounding_boxes = [i for i in bounding_boxes if i is not None]
if len(bounding_boxes) == 0:
return np.zeros([0]), np.zeros([0])
bounding_boxes = np.vstack(bounding_boxes)
keep = nms(bounding_boxes[:, 0:5], nms_thresholds[0])
bounding_boxes = bounding_boxes[keep]
# use offsets predicted by pnet to transform bounding boxes
bounding_boxes = calibrate_box(bounding_boxes[:, 0:5],
bounding_boxes[:, 5:])
# shape [n_boxes, 5]
bounding_boxes = convert_to_square(bounding_boxes)
bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])
# STAGE 2
img_boxes = get_image_boxes(bounding_boxes, image, size=24)
img_boxes = torch.FloatTensor(img_boxes).to(device)
output = self.rnet(img_boxes)
offsets = output[0].cpu().data.numpy() # shape [n_boxes, 4]
probs = output[1].cpu().data.numpy() # shape [n_boxes, 2]
keep = np.where(probs[:, 1] > thresholds[1])[0]
bounding_boxes = bounding_boxes[keep]
bounding_boxes[:, 4] = probs[keep, 1].reshape((-1, ))
offsets = offsets[keep]
keep = nms(bounding_boxes, nms_thresholds[1])
bounding_boxes = bounding_boxes[keep]
bounding_boxes = calibrate_box(bounding_boxes, offsets[keep])
bounding_boxes = convert_to_square(bounding_boxes)
bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])
# STAGE 3
img_boxes = get_image_boxes(bounding_boxes, image, size=48)
if len(img_boxes) == 0:
return np.zeros([0]), np.zeros([0])
img_boxes = torch.FloatTensor(img_boxes).to(device)
output = self.onet(img_boxes)
landmarks = output[0].cpu().data.numpy() # shape [n_boxes, 10]
offsets = output[1].cpu().data.numpy() # shape [n_boxes, 4]
probs = output[2].cpu().data.numpy() # shape [n_boxes, 2]
keep = np.where(probs[:, 1] > thresholds[2])[0]
bounding_boxes = bounding_boxes[keep]
bounding_boxes[:, 4] = probs[keep, 1].reshape((-1, ))
offsets = offsets[keep]
landmarks = landmarks[keep]
# compute landmark points
width = bounding_boxes[:, 2] - bounding_boxes[:, 0] + 1.0
height = bounding_boxes[:, 3] - bounding_boxes[:, 1] + 1.0
xmin, ymin = bounding_boxes[:, 0], bounding_boxes[:, 1]
landmarks[:, 0:5] = np.expand_dims(
xmin, 1) + np.expand_dims(width, 1) * landmarks[:, 0:5]
landmarks[:, 5:10] = np.expand_dims(
ymin, 1) + np.expand_dims(height, 1) * landmarks[:, 5:10]
bounding_boxes = calibrate_box(bounding_boxes, offsets)
keep = nms(bounding_boxes, nms_thresholds[2], mode='min')
bounding_boxes = bounding_boxes[keep]
landmarks = landmarks[keep]
return bounding_boxes, landmarks
class MTCNN_Alignment():
def __init__(self):
self.onet = ONet().to(device)
self.onet.eval()
self.refrence = get_reference_facial_points(default_square=True)
def align(self, img):
_, landmarks = self.detect_faces(img)
facial5points = [[landmarks[0][j], landmarks[0][j + 5]]
for j in range(5)]
warped_face = warp_and_crop_face(np.array(img),
facial5points,
self.refrence,
crop_size=(112, 112))
return Image.fromarray(warped_face)
def align_multi(self, img, limit=None, min_face_size=30.0):
boxes, landmarks = self.detect_faces(img, min_face_size)
if limit:
boxes = boxes[:limit]
landmarks = landmarks[:limit]
faces = []
for landmark in landmarks:
facial5points = [[landmark[j], landmark[j + 5]] for j in range(5)]
warped_face = warp_and_crop_face(np.array(img),
facial5points,
self.refrence,
crop_size=(112, 112))
faces.append(Image.fromarray(warped_face))
return boxes, faces
def detect_faces(self,
image,
min_face_size=20.0,
thresholds=[0.6, 0.7, 0.8],
nms_thresholds=[0.7, 0.7, 0.7]):
"""
Arguments:
image: an instance of PIL.Image.
min_face_size: a float number.
thresholds: a list of length 3.
nms_thresholds: a list of length 3.
Returns:
two float numpy arrays of shapes [n_boxes, 4] and [n_boxes, 10],
bounding boxes and facial landmarks.
"""
# BUILD AN IMAGE PYRAMID
width, height = image.size
# it will be returned
bounding_boxes = np.asarray([[0, 0, height, width, 1]])
with torch.no_grad():
img_boxes = get_image_boxes(bounding_boxes, image, size=48)
if len(img_boxes) == 0:
return [], []
img_boxes = torch.FloatTensor(img_boxes).to(device)
output = self.onet(img_boxes)
landmarks = output[0].cpu().data.numpy() # shape [n_boxes, 10]
offsets = output[1].cpu().data.numpy() # shape [n_boxes, 4]
probs = output[2].cpu().data.numpy() # shape [n_boxes, 2]
keep = np.where(probs[:, 1] > thresholds[2])[0]
bounding_boxes = bounding_boxes[keep]
bounding_boxes[:, 4] = probs[keep, 1].reshape((-1, ))
offsets = offsets[keep]
landmarks = landmarks[keep]
# compute landmark points
width = bounding_boxes[:, 2] - bounding_boxes[:, 0] + 1.0
height = bounding_boxes[:, 3] - bounding_boxes[:, 1] + 1.0
xmin, ymin = bounding_boxes[:, 0], bounding_boxes[:, 1]
landmarks[:, 0:5] = np.expand_dims(
xmin, 1) + np.expand_dims(width, 1) * landmarks[:, 0:5]
landmarks[:, 5:10] = np.expand_dims(
ymin, 1) + np.expand_dims(height, 1) * landmarks[:, 5:10]
bounding_boxes = calibrate_box(bounding_boxes, offsets)
keep = nms(bounding_boxes, nms_thresholds[2], mode='min')
bounding_boxes = bounding_boxes[keep]
landmarks = landmarks[keep]
return bounding_boxes, landmarks
class MTCNN():
def __init__(self):
self.pnet = PNet().to(device)
self.rnet = RNet().to(device)
self.onet = ONet().to(device)
self.pnet.eval()
self.rnet.eval()
self.onet.eval()
self.refrence = get_reference_facial_points(default_square=True)
def share_memory(self):
self.pnet.share_memory()
self.rnet.share_memory()
self.onet.share_memory()
def align(self, img):
_, landmarks = self.detect_faces(img)
facial5points = [[landmarks[0][j], landmarks[0][j + 5]] for j in range(5)]
warped_face = warp_and_crop_face(np.array(img), facial5points, self.refrence, crop_size=(112, 112))
# cvb.show_img(warped_face)
return Image.fromarray(warped_face)
def align_best(self, img, limit=None, min_face_size=20., **kwargs):
try:
boxes, landmarks = self.detect_faces(img, min_face_size,)
img = to_numpy(img)
if limit:
boxes = boxes[:limit]
landmarks = landmarks[:limit]
nrof_faces = len(boxes)
boxes = np.asarray(boxes)
if nrof_faces > 0:
det = boxes[:, 0:4]
img_size = np.asarray(img.shape)[0:2]
bindex = 0
if nrof_faces > 1:
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)
bindex = np.argmax(
bounding_box_size - offset_dist_squared * 2.0) # some extra weight on the centering
boxes = boxes[bindex, 0:4]
landmarks = landmarks[bindex, :]
facial5points = [[landmarks[j], landmarks[j + 5]] for j in range(5)]
warped_face = warp_and_crop_face(np.array(img), facial5points, self.refrence, crop_size=(112, 112))
return to_image(warped_face)
else:
logging.warning(f'no face detected, {kwargs} ')
return to_image(img).resize((112, 112), Image.BILINEAR)
except Exception as e:
logging.warning(f'face detect fail, err {e}')
return to_image(img).resize((112, 112), Image.BILINEAR)
def detect_faces(self, image, min_face_size=20.,
# thresholds=[0.7, 0.7, 0.8],
thresholds=[0.1, 0.1, 0.9],
nms_thresholds=[0.7, 0.7, 0.7]):
"""
Arguments:
image: an instance of PIL.Image.
min_face_size: a float number.
thresholds: a list of length 3.
nms_thresholds: a list of length 3.
Returns:
two float numpy arrays of shapes [n_boxes, 4] and [n_boxes, 10],
bounding boxes and facial landmarks.
"""
image = to_image(image)
# BUILD AN IMAGE PYRAMID
width, height = image.size
min_length = min(height, width)
min_detection_size = 12
factor = 0.707 # sqrt(0.5)
# scales for scaling the image
scales = []
# scales the image so that
# minimum size that we can detect equals to
# minimum face size that we want to detect
m = min_detection_size / min_face_size
min_length *= m
factor_count = 0
while min_length > min_detection_size:
scales.append(m * factor ** factor_count)
min_length *= factor
factor_count += 1
# STAGE 1
# it will be returned
bounding_boxes = []
with torch.no_grad():
# run P-Net on different scales
for s in scales:
boxes = run_first_stage(image, self.pnet, scale=s, threshold=thresholds[0])
bounding_boxes.append(boxes)
# collect boxes (and offsets, and scores) from different scales
bounding_boxes = [i for i in bounding_boxes if i is not None]
bounding_boxes = np.vstack(bounding_boxes)
keep = nms(bounding_boxes[:, 0:5], nms_thresholds[0])
bounding_boxes = bounding_boxes[keep]
# use offsets predicted by pnet to transform bounding boxes
bounding_boxes = calibrate_box(bounding_boxes[:, 0:5], bounding_boxes[:, 5:])
# shape [n_boxes, 5]
bounding_boxes = convert_to_square(bounding_boxes)
bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])
# STAGE 2
img_boxes = get_image_boxes(bounding_boxes, image, size=24)
img_boxes = torch.FloatTensor(img_boxes).to(device)
output = self.rnet(img_boxes)
offsets = output[0].cpu().data.numpy() # shape [n_boxes, 4]
probs = output[1].cpu().data.numpy() # shape [n_boxes, 2]
thresh = thresholds[1]
keep = np.where(probs[:, 1] > thresh)[0]
# while keep.shape[0] == 0:
# thresh -= 0.01
# keep = np.where(probs[:, 1] > thresh)[0]
# print('2 stage thresh', thresh)
bounding_boxes = bounding_boxes[keep]
bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,))
offsets = offsets[keep]
keep = nms(bounding_boxes, nms_thresholds[1])
bounding_boxes = bounding_boxes[keep]
bounding_boxes = calibrate_box(bounding_boxes, offsets[keep])
bounding_boxes = convert_to_square(bounding_boxes)
bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])
# STAGE 3
img_boxes = get_image_boxes(bounding_boxes, image, size=48)
if len(img_boxes) == 0:
return [], []
img_boxes = torch.FloatTensor(img_boxes).to(device)
output = self.onet(img_boxes)
landmarks = output[0].cpu().data.numpy() # shape [n_boxes, 10]
offsets = output[1].cpu().data.numpy() # shape [n_boxes, 4]
probs = output[2].cpu().data.numpy() # shape [n_boxes, 2]
thresh = thresholds[2]
keep = np.where(probs[:, 1] > thresh)[0]
if len(keep) == 0:
return [], []
# while keep.shape[0] == 0:
# thresh -= 0.01
# keep = np.where(probs[:, 1] > thresh)[0]
# print('3 stage one thresh', thresh)
bounding_boxes = bounding_boxes[keep]
bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,))
offsets = offsets[keep]
landmarks = landmarks[keep]
# compute landmark points
width = bounding_boxes[:, 2] - bounding_boxes[:, 0] + 1.0
height = bounding_boxes[:, 3] - bounding_boxes[:, 1] + 1.0
xmin, ymin = bounding_boxes[:, 0], bounding_boxes[:, 1]
landmarks[:, 0:5] = np.expand_dims(xmin, 1) + np.expand_dims(width, 1) * landmarks[:, 0:5]
landmarks[:, 5:10] = np.expand_dims(ymin, 1) + np.expand_dims(height, 1) * landmarks[:, 5:10]
bounding_boxes = calibrate_box(bounding_boxes, offsets)
keep = nms(bounding_boxes, nms_thresholds[2], mode='min')
bounding_boxes = bounding_boxes[keep]
landmarks = landmarks[keep]
return bounding_boxes, landmarks
def __init__(self):
self.pnet = PNet()
self.rnet = RNet()
self.onet = ONet()
self.onet.eval()
class MTCNN():
def __init__(self):
self.pnet = PNet()
self.rnet = RNet()
self.onet = ONet()
self.onet.eval()
# self.nets = (self.pnet,self.rnet,self.onet)
def detect_faces(self,
image,
min_face_size=180.0,
thresholds=[0.6, 0.7, 0.8],
nms_thresholds=[0.7, 0.7, 0.7]):
"""
Arguments:
image: an instance of PIL.Image.
min_face_size: a float number.
thresholds: a list of length 3.
nms_thresholds: a list of length 3.
Returns:
two float numpy arrays of shapes [n_boxes, 4] and [n_boxes, 10],
bounding boxes and facial landmarks.
"""
# BUILD AN IMAGE PYRAMID
width, height = image.size
min_length = min(height, width)
min_detection_size = 12
factor = 0.707 # sqrt(0.5)
# scales for scaling the image
scales = []
# scales the image so that
# minimum size that we can detect equals to
# minimum face size that we want to detect
m = min_detection_size / min_face_size
min_length *= m
factor_count = 0
torch.set_grad_enabled(False)
while min_length > min_detection_size:
scales.append(m * factor**factor_count)
min_length *= factor
factor_count += 1
# STAGE 1
# it will be returned
bounding_boxes = []
# run P-Net on different scales
for s in scales:
boxes = run_first_stage(image,
self.pnet,
scale=s,
threshold=thresholds[0])
bounding_boxes.append(boxes)
# collect boxes (and offsets, and scores) from different scales
bounding_boxes = [i for i in bounding_boxes if i is not None]
if bounding_boxes == []:
return []
bounding_boxes = np.vstack(bounding_boxes)
keep = nms(bounding_boxes[:, 0:5], nms_thresholds[0])
bounding_boxes = bounding_boxes[keep]
# use offsets predicted by self.pnet to transform bounding boxes
bounding_boxes = calibrate_box(bounding_boxes[:, 0:5],
bounding_boxes[:, 5:])
# shape [n_boxes, 5]
bounding_boxes = convert_to_square(bounding_boxes)
bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])
# STAGE 2
img_boxes = get_image_boxes(bounding_boxes, image, size=24)
img_boxes = Variable(torch.FloatTensor(img_boxes))
output = self.rnet(img_boxes)
offsets = output[0].data.numpy() # shape [n_boxes, 4]
probs = output[1].data.numpy() # shape [n_boxes, 2]
keep = np.where(probs[:, 1] > thresholds[1])[0]
bounding_boxes = bounding_boxes[keep]
bounding_boxes[:, 4] = probs[keep, 1].reshape((-1, ))
offsets = offsets[keep]
keep = nms(bounding_boxes, nms_thresholds[1])
bounding_boxes = bounding_boxes[keep]
bounding_boxes = calibrate_box(bounding_boxes, offsets[keep])
bounding_boxes = convert_to_square(bounding_boxes)
bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])
# STAGE 3
img_boxes = get_image_boxes(bounding_boxes, image, size=48)
if len(img_boxes) == 0:
return []
img_boxes = Variable(torch.FloatTensor(img_boxes))
output = self.onet(img_boxes)
offsets = output[1].data.numpy() # shape [n_boxes, 4]
probs = output[2].data.numpy() # shape [n_boxes, 2]
keep = np.where(probs[:, 1] > thresholds[2])[0]
bounding_boxes = bounding_boxes[keep]
bounding_boxes[:, 4] = probs[keep, 1].reshape((-1, ))
offsets = offsets[keep]
bounding_boxes = calibrate_box(bounding_boxes, offsets)
keep = nms(bounding_boxes, nms_thresholds[2], mode='min')
bounding_boxes = bounding_boxes[keep]
# img = image.crop(bounding_boxes[0][:-1].tolist())
return bounding_boxes
print('A size {}'.format(a.size())) # torch.Size([1, 4, 51, 51])
print('B size {}'.format(b.size())) # torch.Size([1, 2, 51, 51])
# R-Net
model = RNet().to(device) # to device
model.eval() # to eval mode
example = torch.ones([1, 3, 24, 24]).to(device)
traced = torch.jit.trace(model, example)
traced.save(str(conf.save_path / 'rnet-gpu.pt'))
a, b = model(example)
print('R-Net')
print('Input size {}'.format(example.size())) # torch.Size([1, 3, 24, 24])
print('A size {}'.format(a.size())) # torch.Size([1, 4])
print('B size {}'.format(b.size())) # torch.Size([1, 2])
# O-Net
model = ONet().to(device) # to device
model.eval() # to eval mode
example = torch.ones([1, 3, 48, 48]).to(device)
traced = torch.jit.trace(model, example)
traced.save(str(conf.save_path / 'onet-gpu.pt'))
a, b, c = model(example)
print('O-Net')
print('Input size {}'.format(example.size())) # torch.Size([1, 3, 48, 48])
print('A size {}'.format(a.size())) # torch.Size([1, 10])
print('B size {}'.format(b.size())) # torch.Size([1, 4])
print('C size {}'.format(c.size())) # torch.Size([1, 2])