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前言
face_alignment是Python环境下一个用于检测人脸关键点（landmark）的常用的库。基于FAN方法实现，具有2D和3D关键点检测的能力。
方法示例
face-alignment库获取输入图片中的人脸关键点，图片输入格式为RGB通道顺序格式，因此如果用OpenCV读取图片后需要注意转换通道顺序，如果是用skimage.io读取则不用转换通道顺序。
2D test on CPU
import cv2
import face_alignment
def cv_draw_landmark(img_ori, pts, box=None, color=(0, 0, 255), size=2):
img = img_ori.copy()
n = pts.shape[0]
for i in range(n):
cv2.circle(img, (int(round(pts[i, 0])), int(round(pts[i, 1]))), size, color, -1)
# cv2.putText(img, str(i), (int(round(pts[i, 0])-3), int(round(pts[i, 1])-3)), cv2.FONT_HERSHEY_COMPLEX, 0.3, (0,255,0), 1)
return img
if __name__ == "__main__":
img_path = 'test.png'
fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=False, device='cpu')
input = cv2.imread(img_path)
preds = fa.get_landmarks(input[..., ::-1]) # 翻转BGR为RGB
print('landmark68 2D preds: ', preds)
output = cv_draw_landmark(input, preds[0])
cv2.imshow('result', output)
cv2.waitKey(0)
3D test on GPU
import face_alignment
from skimage import io
if __name__ == "__main__":
img_path = 'test.png'
fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._3D, flip_input=False, device='cuda:0')
input = io.imread(img_path)
preds = fa.get_landmarks(input)
print('landmark68 3D preds: ', preds)
源码简析
（1） get_landmarks方法已弃用，可改为调用get_landmarks_from_image方法。  （2）get_landmarks_from_image方法只传图片的情况下会先检测人脸，然后再检测关键点。  如果不需要使用face_alignment库的人脸检测，可以在调用get_landmarks_from_image方法时传入人脸bbox。
fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=False, device='cpu')
input = cv2.imread(path)
bbox = [[0,0,255,255]] # 传入的人脸bbox
preds = fa.get_landmarks(input[..., ::-1], bbox)
print(preds)
关键点图示
2D关键点位置
2D关键点编号
使用face_alignment库提取人脸嘴部区域
import os
from skimage import io
from skimage.transform import resize
import face_alignment
# use face_alignment to extract mouth area
fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=False, device='cuda:0')
img_path = 'test.png'
img = io.imread(img_path)
bbox = [[0,0,255,255]] # bbox for 256*256 img
preds = fa.get_landmarks_from_image(img, bbox)
mouth = np.zeros((64, 64, 3), dtype='float32')
mouth_center = np.zeros((2), dtype='int')
if preds is not None:
if len(preds) > 0:
ldmk68 = preds[0]
mouth_center = np.mean(ldmk68[48:], axis=0) # 48-67: mouth
mouth_center = mouth_center.astype(np.int)
pt1 = tuple(mouth_center - 32)
pt2 = tuple(mouth_center + 32)
mouth = img[pt1[1]:pt2[1],pt1[0]:pt2[0]]
if mouth.shape[0]!=0 and mouth.shape[1]!=0:
if mouth.shape[0] != 64 or mouth.shape[1] != 64:
mouth = resize(mouth, (64, 64), anti_aliasing=True)
else:
mouth = np.zeros((64, 64, 3), dtype='float32')
mouth_center = np.zeros((2), dtype='int')
mouth = mouth.transpose((2, 0, 1))
参考资料
[1] GitHub - 1adrianb / face-alignment [2] get_landmarks_from_image方法
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