def get_feature(self, img, state):
'''
利用图片和当前关键点计算特征，多次使用并且 直接用了类的参数所以 创建一个类方便修改
:param img:
:param state:
:return:
'''
return get_hog_feature(img, label_to_landmark(state),
self.feature_block,
self.block_size) # (4-1)*(4-1)*9*4*12 =3888
def compute_strid(self,img, state, decent_map):
'''
输入单张图片 和 标注 利用计算出来的每一步方向求出每一步的步伐
:param imgs:
:param state:
:param decent_map:
:return:
'''
phi = get_hog_feature(img, label_to_landmark(state),self.feature_block) # (4-1)*(4-1)*9*4*12 =3888
phi = np.array(phi)
phi_one = np.ones(phi.shape)
A = np.hstack((phi, phi_one))
A = np.mat(A)
return np.dot(A, decent_map)
def compute_strid(self,img, state, decent_map):
'''
输入单张图片 和 标注 利用计算出来的每一步方向求出每一步的步伐
:param imgs:
:param state:
:param decent_map:
:return:
'''
R = decent_map
phi = get_hog_feature(img, label_to_landmark(state)) # (4-1)*(4-1)*9*4*12 =3888
phi = np.array(phi)
A = phi-self.avg_phi_star
return np.dot(A, R)
def compute_strid_batch(self,imgs, states, decent_map):
'''
输入多张图片 和 标注 利用计算出来的每一步方向求出每一步的步伐
:param imgs:
:param state:
:param decent_map:
:return:
'''
R = decent_map
phi = [get_hog_feature(imgs[idx], label_to_landmark(states[idx]))for idx in range(len(imgs))] # (4-1)*(4-1)*9*4*12 =3888
phi = np.array(phi)
train_phi_star = [self.avg_phi_star for idx in range(len(imgs))]
train_phi_star = np.array(train_phi_star)
A = phi-train_phi_star
return np.dot(A, R)
def get_phis(self,imgs,label):
phi_star = [get_hog_feature(imgs[idx], label_to_landmark(label[idx]),self.feature_block) for idx in
range(len(imgs))] # (4-1)*(4-1)*9*4*12 =3888
phi_star = np.array(phi_star)
return phi_star
