scaling-note.ipynb

{
	"cells": [
		{
			"cell_type": "code",
			"execution_count": 1,
			"metadata": {},
			"outputs": [],
			"source": [
				"import numpy as np\n",
				"import cv2\n",
				"import matplotlib.pyplot as plt"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 2,
			"metadata": {},
			"outputs": [
				{
					"data": {
						"text/plain": [
							"[[(3, 2), (1, 4)],\n",
							" [(2, 3), (5, 3)],\n",
							" [(1.5, 4.5), (4.5, 4.5)],\n",
							" [(4, 2), (3, 6)],\n",
							" [(1, 6), (6, 6)]]"
						]
					},
					"execution_count": 2,
					"metadata": {},
					"output_type": "execute_result"
				}
			],
			"source": [
				"strokes = [\n",
				"    [(3, 2), (1, 4)],\n",
				"    [(2, 3), (5, 3)],\n",
				"    [(1.5, 4.5), (4.5, 4.5)],\n",
				"    [(4, 2), (3, 6)],\n",
				"    [(1, 6), (6, 6)]\n",
				"]\n",
				"\n",
				"strokes"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 13,
			"metadata": {},
			"outputs": [
				{
					"data": {
						"text/plain": [
							"(512, 512)"
						]
					},
					"execution_count": 13,
					"metadata": {},
					"output_type": "execute_result"
				}
			],
			"source": [
				"img = np.zeros((512, 512), np.uint8)\n",
				"img.shape"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 4,
			"metadata": {},
			"outputs": [
				{
					"data": {
						"text/plain": [
							"64.0"
						]
					},
					"execution_count": 4,
					"metadata": {},
					"output_type": "execute_result"
				}
			],
			"source": [
				"512 / 8"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 5,
			"metadata": {},
			"outputs": [
				{
					"data": {
						"text/plain": [
							"(5, 2, 2)"
						]
					},
					"execution_count": 5,
					"metadata": {},
					"output_type": "execute_result"
				}
			],
			"source": [
				"scaled_strokes = np.array(strokes, dtype=np.uint8)\n",
				"scaled_strokes.shape"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 6,
			"metadata": {},
			"outputs": [],
			"source": [
				"scaled_strokes = scaled_strokes * 64"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 7,
			"metadata": {},
			"outputs": [
				{
					"data": {
						"text/plain": [
							"(5, 2, 2)"
						]
					},
					"execution_count": 7,
					"metadata": {},
					"output_type": "execute_result"
				}
			],
			"source": [
				"scaled_strokes.shape"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 8,
			"metadata": {},
			"outputs": [
				{
					"data": {
						"text/plain": [
							"dtype('uint8')"
						]
					},
					"execution_count": 8,
					"metadata": {},
					"output_type": "execute_result"
				}
			],
			"source": [
				"scaled_strokes.dtype"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 9,
			"metadata": {},
			"outputs": [
				{
					"data": {
						"text/plain": [
							"array([[[192, 128],\n",
							"        [ 64,   0]],\n",
							"\n",
							"       [[128, 192],\n",
							"        [ 64, 192]],\n",
							"\n",
							"       [[ 64,   0],\n",
							"        [  0,   0]],\n",
							"\n",
							"       [[  0, 128],\n",
							"        [192, 128]],\n",
							"\n",
							"       [[ 64, 128],\n",
							"        [128, 128]]], dtype=uint8)"
						]
					},
					"execution_count": 9,
					"metadata": {},
					"output_type": "execute_result"
				}
			],
			"source": [
				"scaled_strokes"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 14,
			"metadata": {},
			"outputs": [],
			"source": [
				"for pair_points in strokes:\n",
				"    p1 = pair_points[0]\n",
				"    p2 = pair_points[1]\n",
				"    # print(p1, p2)\n",
				"    x1 = int(p1[0] * 64)\n",
				"    y1 = int(p1[1] * 64)\n",
				"    x2 = int(p2[0] * 64)\n",
				"    y2 = int(p2[1] * 64)\n",
				"    cv2.line(img, (x1, y1), (x2, y2), (255,), 10)"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 15,
			"metadata": {},
			"outputs": [
				{
					"data": {
						"text/plain": [
							"<matplotlib.image.AxesImage at 0x10c09c82d48>"
						]
					},
					"execution_count": 15,
					"metadata": {},
					"output_type": "execute_result"
				},
				{
					"data": {
						"image/png": 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\n",
						"text/plain": [
							"<Figure size 432x288 with 1 Axes>"
						]
					},
					"metadata": {
						"needs_background": "light"
					},
					"output_type": "display_data"
				}
			],
			"source": [
				"plt.imshow(img)"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 19,
			"metadata": {},
			"outputs": [],
			"source": [
				"min_x = 9999\n",
				"max_x = 0\n",
				"min_y = 9999\n",
				"max_y = 0"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 18,
			"metadata": {},
			"outputs": [],
			"source": [
				"RenderedImageWidth = 64\n",
				"RenderedImageHeight = 64\n",
				"RenderedImagePaddingRatio = 0.125\n",
				"RenderedStrokeWidthRatio = 0.0625"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 24,
			"metadata": {},
			"outputs": [
				{
					"data": {
						"text/plain": [
							"(8.0, 8.0)"
						]
					},
					"execution_count": 24,
					"metadata": {},
					"output_type": "execute_result"
				}
			],
			"source": [
				"offset_left = RenderedImageWidth * RenderedImagePaddingRatio\n",
				"offset_top = RenderedImageHeight * RenderedImagePaddingRatio\n",
				"offset_left, offset_top"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 25,
			"metadata": {},
			"outputs": [
				{
					"data": {
						"text/plain": [
							"(48.0, 48.0)"
						]
					},
					"execution_count": 25,
					"metadata": {},
					"output_type": "execute_result"
				}
			],
			"source": [
				"render_width = RenderedImageWidth - (2 * offset_left)\n",
				"render_height = RenderedImageHeight - (2 * offset_top)\n",
				"render_width, render_height"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 20,
			"metadata": {},
			"outputs": [],
			"source": [
				"for stroke in strokes:\n",
				"    for point in stroke:\n",
				"        x = point[0]\n",
				"        y = point[1]\n",
				"        min_x = min(min_x, x)\n",
				"        max_x = max(max_x, x)\n",
				"        min_y = min(min_y, y)\n",
				"        max_y = max(max_y, y)"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 21,
			"metadata": {},
			"outputs": [
				{
					"data": {
						"text/plain": [
							"(1, 6)"
						]
					},
					"execution_count": 21,
					"metadata": {},
					"output_type": "execute_result"
				}
			],
			"source": [
				"min_x, max_x"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 22,
			"metadata": {},
			"outputs": [
				{
					"data": {
						"text/plain": [
							"(2, 6)"
						]
					},
					"execution_count": 22,
					"metadata": {},
					"output_type": "execute_result"
				}
			],
			"source": [
				"min_y, max_y"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 23,
			"metadata": {},
			"outputs": [
				{
					"data": {
						"text/plain": [
							"(5, 4)"
						]
					},
					"execution_count": 23,
					"metadata": {},
					"output_type": "execute_result"
				}
			],
			"source": [
				"char_width = max_x - min_x\n",
				"char_height = max_y - min_y\n",
				"char_width, char_height"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 26,
			"metadata": {},
			"outputs": [
				{
					"data": {
						"text/plain": [
							"(9.6, 12.0)"
						]
					},
					"execution_count": 26,
					"metadata": {},
					"output_type": "execute_result"
				}
			],
			"source": [
				"scale_ratio_x = render_width / char_width\n",
				"scale_ratio_y = render_height / char_height\n",
				"scale_ratio_x, scale_ratio_y"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 27,
			"metadata": {},
			"outputs": [
				{
					"data": {
						"text/plain": [
							"9.6"
						]
					},
					"execution_count": 27,
					"metadata": {},
					"output_type": "execute_result"
				}
			],
			"source": [
				"scale_ratio = min(scale_ratio_x, scale_ratio_y)\n",
				"scale_ratio"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 28,
			"metadata": {},
			"outputs": [
				{
					"data": {
						"text/plain": [
							"(8.0, 12.8)"
						]
					},
					"execution_count": 28,
					"metadata": {},
					"output_type": "execute_result"
				}
			],
			"source": [
				"centering_offset_x = offset_left + ((render_width - (char_width * scale_ratio)) / 2)\n",
				"centering_offset_y = offset_top + ((render_height - (char_height * scale_ratio)) / 2)\n",
				"centering_offset_x, centering_offset_y"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 31,
			"metadata": {},
			"outputs": [],
			"source": [
				"scaled_strokes = []\n",
				"\n",
				"for stroke in strokes:\n",
				"    scaled_points = []\n",
				"    scaled_strokes.append(scaled_points)\n",
				"    for point in stroke:\n",
				"        x = point[0]\n",
				"        y = point[1]\n",
				"        scaled_x = centering_offset_x + ((x - min_x) * scale_ratio)\n",
				"        scaled_y = centering_offset_y + ((y - min_y) * scale_ratio)\n",
				"        \n",
				"        scaled_points.append((scaled_x, scaled_y))"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 32,
			"metadata": {},
			"outputs": [
				{
					"data": {
						"text/plain": [
							"[[(27.2, 12.8), (8.0, 32.0)],\n",
							" [(17.6, 22.4), (46.4, 22.4)],\n",
							" [(12.8, 36.8), (41.6, 36.8)],\n",
							" [(36.8, 12.8), (27.2, 51.2)],\n",
							" [(8.0, 51.2), (56.0, 51.2)]]"
						]
					},
					"execution_count": 32,
					"metadata": {},
					"output_type": "execute_result"
				}
			],
			"source": [
				"scaled_strokes"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 34,
			"metadata": {},
			"outputs": [
				{
					"data": {
						"text/plain": [
							"<matplotlib.image.AxesImage at 0x10c1706adc8>"
						]
					},
					"execution_count": 34,
					"metadata": {},
					"output_type": "execute_result"
				},
				{
					"data": {
						"image/png": "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\n",
						"text/plain": [
							"<Figure size 432x288 with 1 Axes>"
						]
					},
					"metadata": {
						"needs_background": "light"
					},
					"output_type": "display_data"
				}
			],
			"source": [
				"render_img = np.zeros((RenderedImageHeight, RenderedImageWidth), dtype=np.uint8)\n",
				"plt.imshow(render_img)"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 33,
			"metadata": {},
			"outputs": [
				{
					"data": {
						"text/plain": [
							"2"
						]
					},
					"execution_count": 33,
					"metadata": {},
					"output_type": "execute_result"
				}
			],
			"source": [
				"round(1.5)"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 35,
			"metadata": {},
			"outputs": [
				{
					"data": {
						"text/plain": [
							"4"
						]
					},
					"execution_count": 35,
					"metadata": {},
					"output_type": "execute_result"
				}
			],
			"source": [
				"stroke_width = round(RenderedImageWidth * RenderedStrokeWidthRatio)\n",
				"stroke_width"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 36,
			"metadata": {},
			"outputs": [],
			"source": [
				"for stroke in scaled_strokes:\n",
				"    p1 = stroke[0]\n",
				"    p2 = stroke[1]\n",
				"    x1 = round(p1[0])\n",
				"    y1 = round(p1[1])\n",
				"    x2 = round(p2[0])\n",
				"    y2 = round(p2[1])\n",
				"    cv2.line(render_img, (x1, y1), (x2, y2), (255,), stroke_width)"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 37,
			"metadata": {},
			"outputs": [
				{
					"data": {
						"text/plain": [
							"<matplotlib.image.AxesImage at 0x10c15b5ec88>"
						]
					},
					"execution_count": 37,
					"metadata": {},
					"output_type": "execute_result"
				},
				{
					"data": {
						"image/png": 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j/g+FZN0eXaz47gAAAABJRU5ErkJggg==\n",
						"text/plain": [
							"<Figure size 432x288 with 1 Axes>"
						]
					},
					"metadata": {
						"needs_background": "light"
					},
					"output_type": "display_data"
				}
			],
			"source": [
				"plt.imshow(render_img)"
			]
		}
	],
	"metadata": {
		"kernelspec": {
			"display_name": "Python 3",
			"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"
		}
	},
	"nbformat": 4,
	"nbformat_minor": 4
}