out[yy] = np.round(np.sum(img[ymin : ymin + ymax, 0:out.shape[1]] * k[:, np.newaxis], axis=0))
def imaging_resample(self, img, xsize, ysize):
- height, width, *args = img.shape
+ height, width = img.shape[0:2]
bounds_horiz, kk_horiz, ksize_horiz = self._precompute_coeffs(width, xsize)
bounds_vert, kk_vert, ksize_vert = self._precompute_coeffs(height, ysize)
return Li
def _prepare_to_transform(self, out_h=256, out_w=192, grid_size=5):
- grid = np.zeros([out_h, out_w, 3], dtype=np.float32)
grid_X, grid_Y = np.meshgrid(np.linspace(-1, 1, out_w), np.linspace(-1, 1, out_h))
grid_X = np.expand_dims(np.expand_dims(grid_X, axis=0), axis=3)
grid_Y = np.expand_dims(np.expand_dims(grid_Y, axis=0), axis=3)
def getMemoryShapes(self, inputs):
fetureAShape = inputs[0]
- b, c, h, w = fetureAShape
+ b, _, h, w = fetureAShape
return [[b, h * w, h, w]]
def forward(self, inputs):