b374fdddb8f46b0ffeff2b98eb70f248e0c6d8f7,imgaug/augmenters/blur.py,AverageBlur,_augment_images,#AverageBlur#Any#Any#Any#Any#,200

Before Change

            raise Exception("Expected int, tuple/list with 2 entries or StochasticParameter. Got %s." % (type(k),))

    def _augment_images(self, images, random_state, parents, hooks):
        result = images
        nb_images = len(images)
        if self.mode == "single":
            samples = self.k.draw_samples((nb_images,), random_state=random_state)
            samples = (samples, samples)
        else:
            samples = (
                self.k[0].draw_samples((nb_images,), random_state=random_state),
                self.k[1].draw_samples((nb_images,), random_state=random_state),
            )
        for i in sm.xrange(nb_images):
            kh, kw = samples[0][i], samples[1][i]
            kernel_impossible = (kh == 0 or kw == 0)
            kernel_does_nothing = (kh == 1 and kw == 1)
            if not kernel_impossible and not kernel_does_nothing:
                image_aug = cv2.blur(result[i], (kh, kw))
                // cv2.blur() removes channel axis for single-channel images
                if image_aug.ndim == 2:
                    image_aug = image_aug[..., np.newaxis]
                result[i] = image_aug
        return result

    def _augment_heatmaps(self, heatmaps, random_state, parents, hooks):
        return heatmaps

After Change

                self.k[0].draw_samples((nb_images,), random_state=random_state),
                self.k[1].draw_samples((nb_images,), random_state=random_state),
            )
        for i, (image, kh, kw) in enumerate(zip(images, samples[0], samples[1])):
            kernel_impossible = (kh == 0 or kw == 0)
            kernel_does_nothing = (kh == 1 and kw == 1)
            if not kernel_impossible and not kernel_does_nothing:
                image_aug = cv2.blur(image, (kh, kw))
                // cv2.blur() removes channel axis for single-channel images
                if image_aug.ndim == 2:
                    image_aug = image_aug[..., np.newaxis]
                images[i] = image_aug
        return images

    def _augment_heatmaps(self, heatmaps, random_state, parents, hooks):
        return heatmaps

Instances