def prepare_input(img, inp_dim):
orig_im = img
dim = orig_im.shape[1], orig_im.shape[0]
img = (custom_resize(orig_im, (inp_dim, inp_dim)))
img_ = img[:, :, ::-1].transpose((2, 0, 1)).copy()
img_ = torch.from_numpy(img_).float().div(255.0).unsqueeze(0)
return img_, orig_im, dim
def prepare_input(img, inp_dim):
"""
Prepare image for inputting to the neural network.
Perform tranpose and return Tensor
"""
orig_im = img
dim = orig_im.shape[1], orig_im.shape[0]
img = (custom_resize(orig_im, (inp_dim, inp_dim)))
img_ = img[:,:,::-1].transpose((2,0,1)).copy()
img_ = torch.from_numpy(img_).float().div(255.0).unsqueeze(0)
return img_, orig_im, dim
def prepare_input(img, inp_dim):
"""
Prepare image for inputting to the neural network.
Perform tranpose and return Tensor
"""
orig_im = img
dim = orig_im.shape[1], orig_im.shape[0]
img = (custom_resize(orig_im, (inp_dim, inp_dim)))
# Opencv image format,[Channels * Width * Height]. The opencv channles are BGR,
# img[:,:,::-1] is convert to RGB. transpose(2,0,1) is to change them to [C * W * H].
# then creates a copy
img_ = img[:, :, ::-1].transpose((2, 0, 1)).copy()
# unsqueeze returns new tensor with a dimension of 0
img_ = torch.from_numpy(img_).float().div(255.0).unsqueeze(0)
return img_, orig_im, dim
