Composite a number of RGBA images into one. The images are composited in the order in which they appear in the *images* list. Parameters ---------- images : list of Images Each must have a `!make_image` method. For each image, `!can_composite` should return `T
(images, renderer, magnification=1.0)
| 57 | |
| 58 | |
| 59 | def composite_images(images, renderer, magnification=1.0): |
| 60 | """ |
| 61 | Composite a number of RGBA images into one. The images are |
| 62 | composited in the order in which they appear in the *images* list. |
| 63 | |
| 64 | Parameters |
| 65 | ---------- |
| 66 | images : list of Images |
| 67 | Each must have a `!make_image` method. For each image, |
| 68 | `!can_composite` should return `True`, though this is not |
| 69 | enforced by this function. Each image must have a purely |
| 70 | affine transformation with no shear. |
| 71 | |
| 72 | renderer : `.RendererBase` |
| 73 | |
| 74 | magnification : float, default: 1 |
| 75 | The additional magnification to apply for the renderer in use. |
| 76 | |
| 77 | Returns |
| 78 | ------- |
| 79 | image : (M, N, 4) `numpy.uint8` array |
| 80 | The composited RGBA image. |
| 81 | offset_x, offset_y : float |
| 82 | The (left, bottom) offset where the composited image should be placed |
| 83 | in the output figure. |
| 84 | """ |
| 85 | if len(images) == 0: |
| 86 | return np.empty((0, 0, 4), dtype=np.uint8), 0, 0 |
| 87 | |
| 88 | parts = [] |
| 89 | bboxes = [] |
| 90 | for image in images: |
| 91 | data, x, y, trans = image.make_image(renderer, magnification) |
| 92 | if data is not None: |
| 93 | x *= magnification |
| 94 | y *= magnification |
| 95 | parts.append((data, x, y, image._get_scalar_alpha())) |
| 96 | bboxes.append( |
| 97 | Bbox([[x, y], [x + data.shape[1], y + data.shape[0]]])) |
| 98 | |
| 99 | if len(parts) == 0: |
| 100 | return np.empty((0, 0, 4), dtype=np.uint8), 0, 0 |
| 101 | |
| 102 | bbox = Bbox.union(bboxes) |
| 103 | |
| 104 | output = np.zeros( |
| 105 | (int(bbox.height), int(bbox.width), 4), dtype=np.uint8) |
| 106 | |
| 107 | for data, x, y, alpha in parts: |
| 108 | trans = Affine2D().translate(x - bbox.x0, y - bbox.y0) |
| 109 | _image.resample(data, output, trans, _image.NEAREST, |
| 110 | resample=False, alpha=alpha) |
| 111 | |
| 112 | return output, bbox.x0 / magnification, bbox.y0 / magnification |
| 113 | |
| 114 | |
| 115 | def _draw_list_compositing_images( |
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