f5ba97d8316e20f445eac8a47e1cdeae3d2f38ca,imgaug/augmenters/geometric.py,PerspectiveTransform,_draw_samples,#PerspectiveTransform#Any#Any#,2834

Before Change

        // cv2 perspectiveTransform doesn"t accept numpy arrays as cval
        cval_samples_cv2 = cval_samples.tolist()

        for i in sm.xrange(nb_images):
            h, w = shapes[i][0:2]

            points = self.jitter.draw_samples((4, 2), random_state=rngs[2+i])
            points = np.mod(np.abs(points), 1)

            // modify jitter to the four corner point coordinates
            // some x/y values have to be modified from `jitter` to `1-jtter`
            // for that

            // top left -- no changes needed, just use jitter
            // top right
            points[2, 0] = 1.0 - points[2, 0]  // h = 1.0 - jitter
            // bottom right
            points[1, 0] = 1.0 - points[1, 0]  // w = 1.0 - jitter
            points[1, 1] = 1.0 - points[1, 1]  // h = 1.0 - jitter
            // bottom left
            points[0, 1] = 1.0 - points[0, 1]  // h = 1.0 - jitter

            points[:, 0] = points[:, 0] * w
            points[:, 1] = points[:, 1] * h

            // obtain a consistent order of the points and unpack them
            // individually
            points = self._order_points(points)
            (tl, tr, br, bl) = points

            // TODO remove these loops
            // compute the width of the new image, which will be the
            // maximum distance between bottom-right and bottom-left
            // x-coordiates or the top-right and top-left x-coordinates
            min_width = None
            while min_width is None or min_width < self.min_width:
                width_a = np.sqrt(((br[0]-bl[0])**2) + ((br[1]-bl[1])**2))
                width_b = np.sqrt(((tr[0]-tl[0])**2) + ((tr[1]-tl[1])**2))
                max_width = max(int(width_a), int(width_b))
                min_width = min(int(width_a), int(width_b))
                if min_width < self.min_width:
                    tl[0] -= self.shift_step_size
                    tr[0] += self.shift_step_size
                    bl[0] -= self.shift_step_size
                    br[0] += self.shift_step_size

            // compute the height of the new image, which will be the
            // maximum distance between the top-right and bottom-right
            // y-coordinates or the top-left and bottom-left y-coordinates
            min_height = None
            while min_height is None or min_height < self.min_height:
                height_a = np.sqrt(((tr[0]-br[0])**2) + ((tr[1]-br[1])**2))
                height_b = np.sqrt(((tl[0]-bl[0])**2) + ((tl[1]-bl[1])**2))
                max_height = max(int(height_a), int(height_b))
                min_height = min(int(height_a), int(height_b))
                if min_height < self.min_height:
                    tl[1] -= self.shift_step_size
                    tr[1] -= self.shift_step_size
                    bl[1] += self.shift_step_size
                    br[1] += self.shift_step_size

            // now that we have the dimensions of the new image, construct
            // the set of destination points to obtain a "birds eye view",
            // (i.e. top-down view) of the image, again specifying points
            // in the top-left, top-right, bottom-right, and bottom-left
            // order
            dst = np.array([
                [0, 0],
                [max_width - 1, 0],
                [max_width - 1, max_height - 1],
                [0, max_height - 1]
            ], dtype="float32")

            // compute the perspective transform matrix and then apply it
            m = cv2.getPerspectiveTransform(points, dst)

            if self.fit_output:
                m, max_width, max_height = self._expand_transform(m, (h, w))

            matrices.append(m)
            max_heights.append(max_height)
            max_widths.append(max_width)

        mode_samples = mode_samples.astype(np.int32)
        return _PerspectiveTransformSamplingResult(
            matrices, max_heights, max_widths, cval_samples_cv2,
            mode_samples)

After Change

        // cv2 perspectiveTransform doesn"t accept numpy arrays as cval
        cval_samples_cv2 = cval_samples.tolist()

        for shape, rng in zip(shapes, rngs[2:]):
            h, w = shape[0:2]

            points = self.jitter.draw_samples((4, 2), random_state=rng)

Instances