f98d7f30e941330e575412670a310a979bc21eba,imgaug/augmenters/geometric.py,PerspectiveTransform,_augment_heatmaps,#PerspectiveTransform#,2391

Before Change

                random_state.copy()
            )

        for i, (M, max_height, max_width, cval, mode) in enumerate(zip(matrices, max_heights, max_widths, cval_samples, mode_samples)):
            heatmaps_i = heatmaps[i]

            arr = heatmaps_i.arr_0to1

            nb_channels = arr.shape[2]

            warped = [cv2.warpPerspective(
                arr[..., c], M,
                (max_width, max_height),
                borderValue=0,
                borderMode=cv2.BORDER_CONSTANT) for c in sm.xrange(nb_channels)]
            warped = [warped_i[..., np.newaxis] for warped_i in warped]
            warped = np.dstack(warped)

            heatmaps_i_aug = ia.HeatmapsOnImage.from_0to1(warped, shape=heatmaps_i.shape,
                                                          min_value=heatmaps_i.min_value,
                                                          max_value=heatmaps_i.max_value)

            if self.keep_size:
                h, w = arr.shape[0:2]
                heatmaps_i_aug = heatmaps_i_aug.resize((h, w))
            else:
                heatmaps_i_aug.shape = (max_heights_imgs[i], max_widths_imgs[i]) + heatmaps_i_aug.shape[2:]

            result[i] = heatmaps_i_aug

        return result

After Change

                    random_state.copy()
                )

        gen = enumerate(
            zip(matrices, max_heights, max_widths, cval_samples, mode_samples)
        )

        for i, (M, max_height, max_width, cval, mode) in gen:
            heatmaps_i = heatmaps[i]

            arr = heatmaps_i.arr_0to1

            nb_channels = arr.shape[2]

            warped = [cv2.warpPerspective(
                arr[..., c], M,
                (max_width, max_height),
                borderValue=0,
                borderMode=cv2.BORDER_CONSTANT) for c in sm.xrange(nb_channels)]
            warped = [warped_i[..., np.newaxis] for warped_i in warped]
            warped = np.dstack(warped)

            heatmaps_i_aug = ia.HeatmapsOnImage.from_0to1(
                warped,
                shape=heatmaps_i.shape,
                min_value=heatmaps_i.min_value,
                max_value=heatmaps_i.max_value)

            if self.keep_size:
                h, w = arr.shape[0:2]
                heatmaps_i_aug = heatmaps_i_aug.resize((h, w))
            else:
                new_shape = (max_heights_imgs[i], max_widths_imgs[i]) \
                            + heatmaps_i_aug.shape[2:]
                heatmaps_i_aug.shape = new_shape

            result[i] = heatmaps_i_aug

        return result

Instances