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= 255 - (255 - img[:, :, :3]) * img[:, :, [3]] / 255\n", + "\n", + "img = img.dot([0.07, 0.72, 0.21]) / 255\n", + "\n", + "paper_aspect = PAPER.content_width / PAPER.content_height\n", + "img_aspect = img.shape[1] / img.shape[0]\n", + "if img_aspect < paper_aspect:\n", + " new_height = round(img.shape[1] / paper_aspect)\n", + " y_offset = (img.shape[0] - new_height) // 2\n", + " img = img[y_offset:y_offset + new_height, :]\n", + "else:\n", + " new_width = round(img.shape[0] * paper_aspect)\n", + " x_offset = (img.shape[1] - new_width) // 2\n", + " img = img[:, x_offset:new_width + x_offset]\n", + "\n", + "img = img - np.min(img)\n", + "img = img / np.max(img)\n", + "if not INVERT:\n", + " img = 1 - img\n", + "\n", + "plt.imshow(img[:, :])\n", + "\n", + "xs = np.linspace(PAPER.left(), PAPER.right(), img.shape[1])\n", + "ys = np.linspace(PAPER.top(), PAPER.bottom(), img.shape[0])\n", + "img_interp = RegularGridInterpolator((xs, ys), img.T, bounds_error=False, fill_value=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([104098., 76074., 77126., 140403., 64943., 50219., 132457.,\n", + " 151751., 109284., 50045.]),\n", + " array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n", + " )" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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"\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "paper = deepcopy(PAPER)\n", + "\n", + "pen_colour = [0, 0, 0]\n", + "if INVERT:\n", + " paper.background_colour = [0, 0, 0]\n", + " pen_colour = [1, 1, 1]\n", + "\n", + "p_svg = plot.SVGPlotter(f'plots/hatch_{NAME}.svg', paper, colour=pen_colour)\n", + "p_hpgl = plot.HPGLPlotter(paper, f'plots/hatch_{NAME}_{{index}}.hpgl')\n", + "plotter = plot.MultiPlotter()\n", + "plotter.register_plotter(p_svg)\n", + "plotter.register_plotter(p_hpgl)\n", + "\n", + "plotter.move_to(paper.bottom_left())\n", + "plotter.line_to(paper.bottom_right())\n", + "plotter.line_to(paper.top_right())\n", + "plotter.line_to(paper.top_left())\n", + "plotter.line_to(paper.bottom_left())\n", + "\n", + "if TEXT_TOP:\n", + " plotter.draw_text(TEXT_TOP, (paper.centre()[0], paper.top() - 5), x_scale=0.75, spacing=TEXT_TOP_SPACE, fit_width=paper.content_width if TEXT_TOP_FIT else None, h_align='centre', v_align='bottom')\n", + "if TEXT_BOTTOM:\n", + " plotter.draw_text(TEXT_BOTTOM, (paper.centre()[0], paper.bottom() + 5), x_scale=0.75, spacing=TEXT_BOTTOM_SPACE, fit_width=paper.content_width if TEXT_BOTTOM_FIT else None, h_align='centre', v_align='top')\n", + "\n", + "for k in paper.margins.keys():\n", + " paper.margins[k] += 2\n", + "\n", + "if INVERT:\n", + " plotter.add_layer([0.9, 0.75, 0.3, 0.5])\n", + "else:\n", + " plotter.add_layer([0.3, 0.25, 0, 0.5])\n", + "\n", + "def filter_points(points):\n", + " return points[\n", + " np.logical_and(\n", + " np.logical_and(\n", + " paper.left() <= points[:, 0],\n", + " points[:, 0] <= paper.right(),\n", + " ),\n", + " np.logical_and(\n", + " paper.top() <= points[:, 1],\n", + " points[:, 1] <= paper.bottom()\n", + " )\n", + " )\n", + " ]\n", + "\n", + "def x_line_points(start_x, angle, count):\n", + " points = np.linspace([start_x, paper.top()], [start_x + np.tan(angle / 360 * 2 * np.pi) * paper.content_height, paper.bottom()], count)\n", + " return filter_points(points)\n", + "\n", + "def y_line_points(start_y, angle, count):\n", + " points = np.linspace([paper.left(), start_y], [paper.right(), start_y + np.tan((90 - angle) / 360 * 2 * np.pi) * paper.content_width], count)\n", + " return filter_points(points)\n", + "\n", + "def hatch(phase, threshold, angle=45, resolution=1, distance=2, noise=0, reverse_order=False):\n", + " while angle > 180:\n", + " angle -= 180\n", + " while angle < 0:\n", + " angle += 180\n", + "\n", + " if 45 < angle < 135:\n", + " # Shallow lines\n", + " points = int(paper.content_height / np.sin(angle / 360 * 2 * np.pi) / resolution)\n", + " line_starts = np.arange(-paper.top() - paper.content_width - phase * distance, paper.bottom() + paper.content_width, distance / np.sin(angle / 360 * 2 * np.pi))\n", + " lines = [y_line_points(start, angle, points) for start in line_starts]\n", + " else:\n", + " # Steep lines\n", + " points = abs(int(paper.content_height / np.cos(angle / 360 * 2 * np.pi) / resolution))\n", + " line_starts = np.arange(-paper.left() - paper.content_height - phase * distance, paper.right() + paper.content_height, abs(distance / np.cos(angle / 360 * 2 * np.pi)))\n", + " lines = [x_line_points(start, angle, points) for start in line_starts]\n", + "\n", + " if reverse_order:\n", + " lines = lines[::-1]\n", + "\n", + " for (i, line) in enumerate(lines):\n", + " if line.size == 0:\n", + " continue\n", + "\n", + " if noise:\n", + " line += np.random.normal(scale=noise, size=line.shape)\n", + "\n", + " # Mirror every second line for quicker plotting\n", + " if i % 2 == 0:\n", + " line = line[::-1, :]\n", + "\n", + " values = img_interp(line) # swap x and y\n", + " pen_down = False\n", + " for v, p in zip(values, line):\n", + " if v > threshold:\n", + " if not pen_down:\n", + " plotter.move_to(p)\n", + " else:\n", + " plotter.line_to(p)\n", + "\n", + " pen_down = True\n", + " else:\n", + " pen_down = False\n", + " \n", + " last_point = p\n", + "\n", + "thresholds = np.quantile(img[img > 0.001].ravel(), [0.001, 0.4, 0.6, 0.85, 0.9])\n", + "print('Thresholds:', thresholds)\n", + "phases = np.linspace(0, 1, len(thresholds), endpoint=False)\n", + "\n", + "for i in range(len(thresholds)):\n", + " angle = 10\n", + " if i > len(thresholds) / 2:\n", + " if ANGLE_FLIP:\n", + " angle *= -1\n", + " if ANGLE_ROTATE:\n", + " angle += 90\n", + " hatch(phases[i], thresholds[i], angle, distance=1.5, resolution=0.5, reverse_order=i % 2, noise=0.0)\n", + "\n", + "plotter.finalise()\n", + "IPython.display.SVG(filename=p_svg.file_name)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.8 ('venv': venv)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "d5a30a48a9b1be837de7de70323f2cebb904030d8b19bf7af129191d6d192ba3" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}