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import cv2
import numpy as np
import torch
from face_landmark.MobileFaceNet import MobileFaceNet
model_landmark = MobileFaceNet(input_size=64, embedding_size=136)
model_landmark.load_state_dict(torch.load("./face_recognition/face_landmark/vfl_1.02_578_6.734591484069824.pth.tar", map_location=torch.device('cpu'))['state_dict'])
model_landmark.eval()
def get_face_landmark(gray_img, bounding_box):
image = gray_img
box = bounding_box
nHeight, nWidth = image.shape
rLeftMargin = 0.05
rTopMargin = 0.00
rRightMargin = 0.05
rBottomMargin = 0.10
rW = box[2] - box[0]
rH = box[3] - box[1]
cx = (box[0] + box[2]) / 2
cy = (box[1] + box[3]) / 2
sz = pow(rW * rH, 0.5)
rX = cx - sz / 2
rY = cy - sz / 2
rW = sz
rH = sz
#get image range to get face landmark from face rect
iExFaceX = int(rX - rLeftMargin * rW)
iExFaceY = int(rY - rTopMargin * rH)
iExFaceW = int((1 + (rLeftMargin + rRightMargin)) * rW)
iExFaceH = int((1 + (rTopMargin + rBottomMargin)) * rH)
iExFaceX = np.clip(iExFaceX, 0, nWidth - 1)
iExFaceY = np.clip(iExFaceY, 0, nHeight - 1)
iExFaceW = np.clip(iExFaceX + iExFaceW, 0, nWidth - 1) - iExFaceX
iExFaceH = np.clip(iExFaceY + iExFaceH, 0, nHeight - 1) - iExFaceY
#crop face image in range to face landmark
image = image[iExFaceY:iExFaceY+iExFaceH, iExFaceX:iExFaceX+iExFaceW]
#normalize croped face image
image = cv2.resize(image, (64, 64), cv2.INTER_LINEAR)
# cv2.imwrite("D:/crop.png", image)
image = image / 256
image = torch.from_numpy(image.astype(np.float32))
#convert mask_align_image from type [n,n] to [1,1,n,n]
image = image.unsqueeze(0).unsqueeze(0)
#get landmark fron croped face image
landmark = model_landmark(image)
#reshape face landmark and convert to image coordinates
landmark = landmark.reshape(68, 2)
landmark[:,0] = landmark[:,0] * iExFaceW + iExFaceX
landmark[:,1] = landmark[:,1] * iExFaceH + iExFaceY
landmark = landmark.reshape(-1)
return landmark |