deep_sort/deep/feature_extractor.py
def _preprocess(self, im_crops):
        """
        TODO:
            1. to float with scale from 0 to 1
            2. resize to (64, 128) as Market1501 dataset did
            3. concatenate to a numpy array
            3. to torch Tensor
            4. normalize
        """

        result = []
        for im in im_crops:
            im = F.interpolate(self.__norm(im).unsqueeze(0), size=(self.size[1], self.size[0]), mode="nearest")
            result.append(im)

        im_batch = torch.cat(result, dim=0)
        return im_batch

        TODO:
            1. to float with scale from 0 to 1
            2. resize to (64, 128) as Market1501 dataset did
            3. concatenate to a numpy array
            3. to torch Tensor
            4. normalize
        
1. to float with scale from 0 to 1
2. resize to (64, 128) as Market1501 dataset did
3. concatenate to a numpy array
3. to torch Tensor
4. normalize
1 . to float with scale from 0 to 1 2 . resize to ( 64 128 ) as Market1501 dataset did 3 . concatenate to a numpy array 3 . to torch Tensor 4 . normalize
def _preprocess(self, im_crops):
        result = []
        for im in im_crops:
            im = F.interpolate(self.__norm(im).unsqueeze(0), size=(self.size[1], self.size[0]), mode="nearest")
            result.append(im)
        im_batch = torch.cat(result, dim=0)
        return im_batch
def _preprocess ( self , im_crops ) : result = [ ] for im in im_crops : im = F . interpolate ( self . __norm ( im ) . unsqueeze ( 0 ) , size = ( self . size [ 1 ] , self . size [ 0 ] ) , mode = "nearest" ) result . append ( im ) im_batch = torch . cat ( result , dim = 0 ) return im_batch
TODO:
1. to float with scale from 0 to 1
2. resize to (64, 128) as Market1501 dataset did
3. concatenate to a numpy array
3. to torch Tensor
4. normalize
TODO : 1 . to float with scale from 0 to 1 2 . resize to ( 64 128 ) as Market1501 dataset did 3 . concatenate to a numpy array 3 . to torch Tensor 4 . normalize
"""
        TODO:
            1. to float with scale from 0 to 1
            2. resize to (64, 128) as Market1501 dataset did
            3. concatenate to a numpy array
            3. to torch Tensor
            4. normalize
        """
self
im_crops