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import cv2
import numpy as np
from skimage import transform as trans
# from mtcnn.mtcnn import MTCNN
def get_keypts(face):
# get key points from the results of mtcnn
if len(face['keypoints']) == 0:
return []
leye = np.array(face['keypoints']['left_eye'], dtype=np.int).reshape(-1, 2)
reye = np.array(face['keypoints']['right_eye'],
dtype=np.int).reshape(-1, 2)
nose = np.array(face['keypoints']['nose'], dtype=np.int).reshape(-1, 2)
lmouth = np.array(face['keypoints']['mouth_left'],
dtype=np.int).reshape(-1, 2)
rmouth = np.array(face['keypoints']['mouth_right'],
dtype=np.int).reshape(-1, 2)
pts = np.concatenate([leye, reye, nose, lmouth, rmouth], axis=0)
return pts
def img_align_crop(img, landmark=None, outsize=None, scale=1.3, mask=None):
""" align and crop the face according to the given bbox and landmarks
landmark: 5 key points
"""
M = None
target_size = [112, 112]
dst = np.array([
[30.2946, 51.6963],
[65.5318, 51.5014],
[48.0252, 71.7366],
[33.5493, 92.3655],
[62.7299, 92.2041]], dtype=np.float32)
if target_size[1] == 112:
dst[:, 0] += 8.0
dst[:, 0] = dst[:, 0] * outsize[0] / target_size[0]
dst[:, 1] = dst[:, 1] * outsize[1] / target_size[1]
target_size = outsize
margin_rate = scale - 1
x_margin = target_size[0] * margin_rate / 2.
y_margin = target_size[1] * margin_rate / 2.
# move
dst[:, 0] += x_margin
dst[:, 1] += y_margin
# resize
dst[:, 0] *= target_size[0] / (target_size[0] + 2 * x_margin)
dst[:, 1] *= target_size[1] / (target_size[1] + 2 * y_margin)
src = landmark.astype(np.float32)
# use skimage tranformation
tform = trans.SimilarityTransform()
tform.estimate(src, dst)
M = tform.params[0:2, :]
# M: use opencv
# M = cv2.getAffineTransform(src[[0,1,2],:],dst[[0,1,2],:])
img = cv2.warpAffine(img, M, (target_size[1], target_size[0]))
if outsize is not None:
img = cv2.resize(img, (outsize[1], outsize[0]))
if mask is not None:
mask = cv2.warpAffine(mask, M, (target_size[1], target_size[0]))
mask = cv2.resize(mask, (outsize[1], outsize[0]))
return img, mask
else:
return img
def expand_bbox(bbox, width, height, scale=1.3, minsize=None):
"""
Expand original boundingbox by scale.
:param bbx: original boundingbox
:param width: frame width
:param height: frame height
:param scale: bounding box size multiplier to get a bigger face region
:param minsize: set minimum bounding box size
:return: expanded bbox
"""
x, y, w, h = bbox
# box center
cx = int(x + w / 2)
cy = int(y + h / 2)
# expand by scale factor
new_size = max(int(w * scale), int(h * scale))
new_x = max(0, int(cx - new_size / 2))
new_y = max(0, int(cy - new_size / 2))
# Check for too big bbox for given x, y
new_size = min(width - new_x, new_size)
new_size = min(height - new_size, new_size)
return new_x, new_y, new_size, new_size
def extract_face_MTCNN(face_detector, image, expand_scale=1.3, res=256):
# Image size
height, width = image.shape[:2]
# Convert to rgb
rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Detect with dlib
faces = face_detector.detect_faces(rgb)
if len(faces):
# For now only take biggest face
face = None
bbox = None
max_region = 0
for ff in faces:
if max_region == 0:
face = ff
bbox = face['box']
max_region = bbox[2]*bbox[3]
else:
bb = ff['box']
region = bb[2]*bb[3]
if region > max_rigion:
max_rigion = region
face = ff
bbox = face['box']
print(max_region)
#face = faces[0]
#bbox = face['box']
# --- Prediction ---------------------------------------------------
# Face crop with MTCNN and bounding box scale enlargement
x, y, w, h = expand_bbox(bbox, width, height, scale=expand_scale)
cropped_face = rgb[y:y+h, x:x+w]
cropped_face = cv2.resize(
cropped_face, (res, res), interpolation=cv2.INTER_CUBIC)
cropped_face = cv2.cvtColor(cropped_face, cv2.COLOR_RGB2BGR)
return cropped_face
return None
def extract_aligned_face_MTCNN(face_detector, image, expand_scale=1.3, res=256, mask=None):
# Image size
height, width = image.shape[:2]
# Convert to rgb
rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Detect with dlib
faces = face_detector.detect_faces(rgb)
if len(faces):
# For now only take biggest face
face = None
bbox = None
max_region = 0
for i, ff in enumerate(faces):
if max_region == 0:
face = ff
bbox = face['box']
max_region = bbox[2]*bbox[3]
else:
bb = ff['box']
region = bb[2]*bb[3]
if region > max_region:
max_region = region
face = ff
bbox = face['box']
#print('face {}: {}'.format(i, max_region))
#face = faces[0]
landmarks = get_keypts(face)
# --- Prediction ---------------------------------------------------
# Face aligned crop with MTCNN and bounding box scale enlargement
if mask is not None:
cropped_face, cropped_mask = img_align_crop(rgb, landmarks, outsize=[
res, res], scale=expand_scale, mask=mask)
cropped_face = cv2.cvtColor(cropped_face, cv2.COLOR_RGB2BGR)
cropped_mask = cv2.cvtColor(cropped_mask, cv2.COLOR_RGB2GRAY)
return cropped_face, cropped_mask
else:
cropped_face = img_align_crop(rgb, landmarks, outsize=[
res, res], scale=expand_scale)
cropped_face = cv2.cvtColor(cropped_face, cv2.COLOR_RGB2BGR)
return cropped_face
return None
def extract_face_DLIB(face_detector, image, expand_scale=1.3, res=256):
# Image size
height, width = image.shape[:2]
# Convert to gray
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Detect with dlib
faces = face_detector(gray, 1)
if len(faces):
# For now only take biggest face
face = faces[0]
x1 = face.left()
y1 = face.top()
x2 = face.right()
y2 = face.bottom()
bbox = (x1, y1, x2-x1, y2-y1)
# --- Prediction ---------------------------------------------------
# Face crop with dlib and bounding box scale enlargement
x, y, w, h = expand_bbox(bbox, width, height, scale=expand_scale)
cropped_face = image[y:y+h, x:x+w]
cropped_face = cv2.resize(
cropped_face, (res, res), interpolation=cv2.INTER_CUBIC)
return cropped_face
return None
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