def __getitem__(self, index):
# Select sample
if self.input_size == 3:
return ((np.asarray(self.data_im[index]) - self.mean_image) / self.std_image),self.exprs[index]
elif self.input_size == 68:
return np.asarray(heatmap_generator.getHeatMap(self.data_lm[index])), self.exprs[index]
else:
return np.vstack([((np.asarray(self.data_im[index]) - self.mean_image) / self.std_image),np.asarray(heatmap_generator.getHeatMap(self.data_lm[index]))]),self.exprs[index]
def __getitem__(self, index):
# Select sample
#image = 0.21*self.data_im[index][0,:,:]+0.72*self.data_im[index][1,:,:]+0.07*self.data_im[index][2,:,:]
#mean = 0.21*self.mean_image[0,:,:]+0.72*self.mean_image[1,:,:]+0.07*self.mean_image[2,:,:]
#std = 0.21*self.std_image[0,:,:]+0.72*self.std_image[1,:,:]+0.07*self.std_image[2,:,:]
#print(np.asarray([(image-mean)/std]).shape)
#print("*******",self.data_lm[index].shape)
return np.asarray(
(self.data_im[index] - self.mean_image) /
self.std_image), np.asarray(
heatmap_generator.getHeatMap(
self.data_lm[index])), (self.exprs[index]
if self.exprs[index] < 5 else 4)
def __getitem__(self, index): # Select sample return ((np.asarray(self.data_im[index]) - self.mean_image) / self.std_image), np.asarray(heatmap_generator.getHeatMap(self.data_lm[index])), self.exprs[index]