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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "_vSR56u0u74i"
},
"source": [
"# 3D File to Lego\n",
"\n",
"First create 3D file using something like InstantMesh. Then we turn the 3D file into Lego build."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "M-2cYolwvEcp"
},
"source": [
"## Install Dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"id": "cuPmdwpq0txu"
},
"outputs": [],
"source": [
"%%capture\n",
"!pip install trimesh rtree PyQt5 colormath # xformers==0.0.22.post7 rembg"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IuNdIvDsvIgz"
},
"source": [
"## Load a 3D object\n",
"\n",
"And display our mesh"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 521
},
"id": "kyIn7pDq2h30",
"outputId": "98ab2ba7-eda8-471c-ee93-6eaba7b053a8"
},
"outputs": [],
"source": [
"import trimesh\n",
"import numpy as np\n",
"\n",
"mesh = trimesh.load('doll.obj')\n",
"mesh.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HE58Fe0xvOkD"
},
"source": [
"## Voxelize\n",
"\n",
"We can then voxelize using a custom resolution."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "i5el0q3H2xLr"
},
"outputs": [],
"source": [
"def voxelize(mesh, resolution: int = 64):\n",
" bounds = mesh.bounds\n",
" voxel_size = (bounds[1] - bounds[0]).max() / 64 # pitch\n",
"\n",
" return mesh.voxelized(pitch=voxel_size)\n",
"\n",
"def display_scene(mesh, voxels):\n",
" voxels_mesh = voxels.as_boxes().apply_translation((1.5,0,0))\n",
" scene = trimesh.Scene([mesh, voxels_mesh])\n",
"\n",
" return scene.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 521
},
"id": "hYzOb6Oq4z48",
"outputId": "3a84d86a-7184-44a3-a993-be3851e02e5c"
},
"outputs": [],
"source": [
"voxels = voxelize(mesh)\n",
"\n",
"display_scene(mesh, voxels)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5UOYrWRpvdaS"
},
"source": [
"## Voxelize with Color\n",
"\n",
"We need to colorize voxels by fetching the N nearest colors and taking the mean."
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {
"id": "eOW-K4PVDOlv"
},
"outputs": [],
"source": [
"import plotly.graph_objects as go\n",
"import plotly.express as px\n",
"import polars as pl\n",
"\n",
"def display_voxels_px(voxels, colors):\n",
" # Convert occupied_voxel_indices to a Polars DataFrame (if not already done)\n",
" df = pl.from_numpy(voxels.sparse_indices, schema=[\"x\", \"y\", \"z\"])\n",
" df = df.with_columns(color=pl.Series(colors))\n",
" px.scatter_3d(df, x=\"x\", y=\"y\", z=\"z\", color=\"color\",\n",
" color_discrete_map=\"identity\", symbol=[\"square\"]*len(df), symbol_map=\"identity\"\n",
").show()"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {
"id": "Iyz1dRYbHPg7"
},
"outputs": [],
"source": [
"from scipy.spatial import cKDTree\n",
"import numpy as np\n",
"\n",
"def tree_knearest_colors(k: int, mesh, voxels):\n",
" tree = cKDTree(mesh.vertices)\n",
" distances, vertex_indices = tree.query(voxels.points, k=k)\n",
" voxel_colors = []\n",
"\n",
" for nearest_indices in vertex_indices:\n",
" neighbor_colors = mesh.visual.vertex_colors[nearest_indices]\n",
" average_color = np.mean(neighbor_colors, axis=0).astype(np.uint8)\n",
" voxel_colors.append(average_color)\n",
"\n",
" return voxel_colors\n",
"\n",
"def tree_knearest_color_mesh(k: int, mesh, voxels):\n",
" tree = cKDTree(mesh.vertices)\n",
" distances, vertex_indices = tree.query(voxels.points, k=k)\n",
" voxel_colors = []\n",
"\n",
" for nearest_indices in vertex_indices:\n",
" neighbor_colors = mesh.visual.vertex_colors[nearest_indices]\n",
" average_color = np.mean(neighbor_colors, axis=0).astype(np.uint8)\n",
" voxel_colors.append(average_color)\n",
"\n",
" # 2. Create a (X, Y, Z, 4) color matrix\n",
" color_matrix = np.zeros(voxels.shape + (4,), dtype=np.uint8) # Initialize with default color (e.g., transparent black)\n",
" color_matrix[voxels.sparse_indices[:, 0], voxels.sparse_indices[:, 1], voxels.sparse_indices[:, 2]] = voxel_colors\n",
"\n",
" # 3. Create a VoxelMesh using as_boxes() with the color matrix\n",
" voxel_mesh = voxels.as_boxes(colors=color_matrix)\n",
" return voxel_mesh"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DTphj_klvtv7"
},
"source": [
"### Display using scatter3d"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "a9dn8T6Xeq-i",
"outputId": "6264392d-fd82-454a-80a3-18810e746e57"
},
"outputs": [],
"source": [
"colors = tree_knearest_colors(5, mesh, voxels)\n",
"display_voxels_px(voxels, [f\"rgb{c[0],c[1],c[2]}\" for c in colors])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "s8Ta34iYvzTY"
},
"source": [
"### Display using Blocks in Plotly"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "B_1Z2bXIt5TP",
"outputId": "ca19a34d-98a6-4741-eebc-f490b16dd56d"
},
"outputs": [],
"source": [
"import plotly.graph_objects as go\n",
"import numpy as np\n",
"\n",
"# Assuming you have 'voxel_grid', 'colors_blocks', and 'voxels' from your previous code\n",
"\n",
"# Create Mesh3d traces for each occupied voxel\n",
"mesh_data = []\n",
"for i in range(voxels.sparse_indices.shape[0]):\n",
" x, y, z = voxels.sparse_indices[i]\n",
" color = colors[i] # Get color from colors_blocks\n",
" vertices = np.array([\n",
" [x, y, z], [x + 1, y, z], [x + 1, y + 1, z], [x, y + 1, z],\n",
" [x, y, z + 1], [x + 1, y, z + 1], [x + 1, y + 1, z + 1], [x, y + 1, z + 1]\n",
" ])\n",
" faces = np.array([\n",
" [0, 1, 2], [0, 2, 3], # Bottom face\n",
" [4, 5, 6], [4, 6, 7], # Top face\n",
" [0, 1, 5], [0, 5, 4], # Front face\n",
" [2, 3, 7], [2, 7, 6], # Back face\n",
" [0, 3, 7], [0, 7, 4], # Left face\n",
" [1, 2, 6], [1, 6, 5] # Right face\n",
" ])\n",
" mesh_data.append(go.Mesh3d(\n",
" x=vertices[:, 0],\n",
" y=vertices[:, 1],\n",
" z=vertices[:, 2],\n",
" i=faces[:, 0],\n",
" j=faces[:, 1],\n",
" k=faces[:, 2],\n",
" color=f'rgb({color[0]}, {color[1]}, {color[2]})', # Convert to rgb string\n",
" flatshading=True\n",
" ))\n",
"\n",
"# Create Plotly figure\n",
"fig = go.Figure(data=mesh_data)\n",
"fig.show(renderer=\"colab\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Jxvh2XwuwFgP"
},
"source": [
"## Routing Algorithm 'Merge Blocks'\n",
"\n",
"We need to merge blocks into LEGO pieces. Similar colors by threshold merges into uniblock. Prefer large?"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {
"id": "PimIkgBqs1fJ"
},
"outputs": [],
"source": [
"LEGO_COLORS_RGB = np.asarray([\n",
" (239, 239, 239), # White\n",
" (165, 165, 165), # Light Bluish Gray\n",
" (155, 155, 155), # Light Gray\n",
" (109, 110, 109), # Dark Bluish Gray\n",
" (88, 88, 88), # Dark Gray\n",
" (48, 48, 48), # Black\n",
" (196, 40, 28), # Red\n",
" (214, 0, 0), # Bright Red\n",
" (128, 0, 0), # Dark Red\n",
" (0, 85, 191), # Blue\n",
" (0, 51, 204), # Bright Blue\n",
" (0, 32, 96), # Dark Blue\n",
" (35, 122, 33), # Green\n",
" (0, 153, 0), # Bright Green\n",
" (0, 77, 0), # Dark Green\n",
" (247, 205, 24), # Yellow\n",
" (255, 204, 0), # Bright Yellow\n",
" (255, 153, 0), # Dark Yellow\n",
" (255, 102, 0), # Orange\n",
" (255, 128, 0), # Bright Orange\n",
" (124, 72, 36), # Brown\n",
" (160, 96, 53), # Light Brown\n",
" (215, 194, 149), # Tan\n",
" (144, 118, 72), # Dark Tan\n",
" (167, 205, 36), # Lime\n",
" (242, 176, 61), # Bright Light Orange\n",
" (247, 234, 142), # Bright Light Yellow\n",
" (115, 150, 200), # Medium Blue\n",
" (65, 165, 222), # Medium Azure\n",
" (137, 200, 240), # Light Azure\n",
" (144, 31, 118), # Magenta\n",
" (255, 153, 204), # Pink\n",
" (255, 189, 216) # Light Pink\n",
"])"
]
},
{
"cell_type": "code",
"execution_count": 194,
"metadata": {
"id": "DqA4M69ZwPAi"
},
"outputs": [],
"source": [
"from scipy.spatial.distance import cdist\n",
"from sklearn.cluster import KMeans\n",
"from scipy.spatial import cKDTree\n",
"from colormath.color_objects import sRGBColor, LabColor\n",
"from colormath.color_conversions import convert_color\n",
"from colormath.color_diff import delta_e_cie1976\n",
"\n",
"def map_colors_to_lego(model_colors, lego_palette):\n",
" \"\"\"\n",
" cdist is optimized broadcast OP.\n",
" \"\"\"\n",
" distances = cdist(model_colors, lego_palette, 'euclidean') # Calculate Euclidean distances\n",
" closest_indices = np.argmin(distances, axis=1) # Find indices of minimum distances\n",
" return lego_palette[closest_indices]\n",
"\n",
"\n",
"def convert_colors_max_diff(original_colors, predefined_colors):\n",
" \"\"\"\n",
" Converts colors by minimizing the maximum channel difference.\n",
"\n",
" Args:\n",
" original_colors: A NumPy array of shape (N, 3) representing original RGB colors.\n",
" predefined_colors: A NumPy array of shape (M, 3) representing predefined RGB colors.\n",
"\n",
" Returns:\n",
" A NumPy array of shape (N, 3) representing converted RGB colors.\n",
" \"\"\"\n",
" diffs = np.abs(original_colors[:, np.newaxis, :] - predefined_colors)\n",
" max_diffs = np.max(diffs, axis=2)\n",
" indices = np.argmin(max_diffs, axis=1)\n",
"\n",
" converted_colors = predefined_colors[indices]\n",
"\n",
" return converted_colors\n",
"\n",
"def quantize_colors(model_colors, k: int = 16):\n",
" \"\"\"\n",
" quantize colors by fitting into 16 unique colors.\n",
" \"\"\"\n",
" original_colors = np.array(colors)[:,:3]\n",
"\n",
" kmeans = KMeans(n_clusters=k, random_state=42)\n",
" kmeans.fit(original_colors)\n",
"\n",
" # Get the representative colors\n",
" representative_colors = kmeans.cluster_centers_.astype(int)\n",
"\n",
" # Transform the original colors to representative colors\n",
" transformed_colors = representative_colors[kmeans.labels_]\n",
" return transformed_colors\n",
"\n",
"\n",
"def map_color_cie(model_colors, lego_palette):\n",
" original_lab = np.array([convert_color(sRGBColor(*rgb, is_upscaled=True), LabColor).get_value_tuple()\n",
" for rgb in model_colors])\n",
" predefined_lab = np.array([convert_color(sRGBColor(*rgb, is_upscaled=True), LabColor).get_value_tuple()\n",
" for rgb in lego_palette])\n",
"\n",
" original_lab = original_lab[:, np.newaxis, :] # Reshape for broadcasting\n",
" predefined_lab = predefined_lab[np.newaxis, :, :] # Reshape for broadcasting\n",
" delta_e = np.sqrt(np.sum((original_lab - predefined_lab)**2, axis=2))\n",
"\n",
" # Find closest predefined color for each original color\n",
" indices = np.argmin(delta_e, axis=1)\n",
"\n",
" # Transform colors\n",
" transformed_colors = lego_palette[indices]\n",
"\n",
" return transformed_colors\n",
"\n",
"\n",
"def normalize_value_to_mid(rgb, target_v=0.7):\n",
" import colorsys\n",
" r, g, b, _ = rgb\n",
" # Scale to [0..1]\n",
" rr, gg, bb = (r/255, g/255, b/255)\n",
" h, s, v = colorsys.rgb_to_hsv(rr, gg, bb)\n",
" # Force to target_v\n",
" rr2, gg2, bb2 = colorsys.hsv_to_rgb(h, s, target_v)\n",
" # Scale back to [0..255]\n",
" return (int(rr2*255), int(gg2*255), int(bb2*255))\n",
"\n",
"\n",
"def lab_color_tfm(colors: np.ndarray, lego_palette: np.ndarray) -> np.ndarray:\n",
" from skimage import color\n",
"\n",
" scaled = rgb_array.astype(np.float32) / 255.0\n",
"\n",
" # 2) Reshape to (N,1,3) so that rgb2lab sees it as an image of height=N, width=1, channels=3\n",
" reshaped = scaled.reshape((-1, 1, 3))\n",
"\n",
" # 3) Convert to Lab\n",
" lab_reshaped = rgb2lab(reshaped) # shape: (N,1,3)\n",
"\n",
" # 4) Reshape back to (N,3)\n",
" lab_array = lab_reshaped.reshape((-1, 3))\n",
"\n",
"def find_nearest_lego_colors_lab_weighted(\n",
" lab_colors: np.ndarray,\n",
" lego_palette_lab: np.ndarray,\n",
" lego_palette_names: list,\n",
" lightness_weight: float = 0.2\n",
") -> np.ndarray:\n",
" \"\"\"\n",
" Find the nearest LEGO color in Lab space for each input Lab color,\n",
" reducing the influence of Lightness (L) in the distance calculation.\n",
"\n",
" Args:\n",
" lab_colors (np.ndarray): (N,3) array of Lab colors (input colors).\n",
" lego_palette_lab (np.ndarray): (M,3) array of Lab colors (LEGO palette colors).\n",
" lego_palette_names (list): List of M names corresponding to the LEGO colors.\n",
" lightness_weight (float): Weight for the L (lightness) component in the distance calculation.\n",
"\n",
" Returns:\n",
" np.ndarray: (N,) array of LEGO color names corresponding to the closest match for each input color.\n",
" \"\"\"\n",
" # Expand lab_colors to (N,1,3) and lego_palette_lab to (1,M,3)\n",
" lab_colors_exp = lab_colors[:, np.newaxis, :] # (N,1,3)\n",
" lego_palette_exp = lego_palette_lab[np.newaxis, :, :] # (1,M,3)\n",
"\n",
" # Apply weights: Scale L component\n",
" lab_colors_exp[:, :, 0] *= lightness_weight\n",
" lego_palette_exp[:, :, 0] *= lightness_weight\n",
"\n",
" # Compute weighted Euclidean distance in Lab space (L2 Norm) across the last axis\n",
" distances = np.linalg.norm(lab_colors_exp - lego_palette_exp, axis=2) # (N,M)\n",
"\n",
" # Find the index of the minimum distance for each row\n",
" closest_indices = np.argmin(distances, axis=1) # (N,)\n",
"\n",
" # Map indices to LEGO color names\n",
" closest_colors = np.array([lego_palette_names[i] for i in closest_indices])\n",
"\n",
" return closest_colors\n",
"\n",
"\n",
"# Should I merge colors first or colors and bits at the same time?\n",
"def to_df(voxels, colors) -> pl.DataFrame:\n",
" df = pl.from_numpy(voxels.sparse_indices, schema=[\"x\", \"z\", \"y\"])\n",
" df = df.with_columns(color=pl.Series(colors))\n",
" return df\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 443
},
"id": "whURTjlRbog1",
"outputId": "2e575ee3-08be-4443-f034-4a02ea9901e8"
},
"outputs": [],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "R37iNUS_FOgd",
"outputId": "e01e300c-8f66-4d7e-a626-82f5ba4c0481"
},
"outputs": [],
"source": [
"np.unique(np.asarray(mid_colors), axis=0).shape,np.unique(np.asarray(colors)[:,:3], axis=0).shape"
]
},
{
"cell_type": "code",
"execution_count": 197,
"metadata": {
"id": "QtaCdCb20tUQ"
},
"outputs": [],
"source": [
"# This merges color while walking along neighbors. It's suboptimal in that it might override a previous group with a new neighbour. Should include visited.\n",
"if False:\n",
" import pandas as pd\n",
" from scipy.spatial.distance import cdist\n",
" def get_neighbors(x, y, z):\n",
" \"\"\"6-connected neighbors in 3D.\"\"\"\n",
" return [\n",
" (x+1, y, z), (x-1, y, z),\n",
" (x, y+1, z), (x, y-1, z),\n",
" (x, y, z+1), (x, y, z-1)\n",
" ]\n",
"\n",
" def coord_to_idx(df: pl.DataFrame) -> dict[tuple[int,int,int], int]:\n",
" return dict(zip(zip(df[\"x\"], df[\"y\"], df[\"z\"]), range(len(df))))\n",
"\n",
" df = df.drop(\"r\", \"b\", \"g\", strict=False).with_columns(colors=pl.Series(colors))\n",
" coord_to_idx = coord_to_idx(df)\n",
" groups = {}\n",
" df = df.with_columns(group=pl.arange(pl.len()))\n",
" color_np = df[\"color\"].to_numpy()\n",
" color_diff = cdist(color_np, color_np, 'euclidean') # indexed diff\n",
"\n",
" for idx in range(len(df)):\n",
" neighbors = get_neighbors(df[idx, \"x\"], df[idx, \"y\"], df[idx, \"z\"])\n",
" for neighbor in neighbors:\n",
" if neighbor in coord_to_idx:\n",
" neighbor_idx = coord_to_idx[neighbor]\n",
" if neighbor_idx < idx:\n",
" continue\n",
" if (color_diff[idx, neighbor_idx] < 50):\n",
" df[neighbor_idx, \"group\"] = df[idx, \"group\"] # bad mutation...\n",
"\n",
" color_list = pl.col(\"color\").arr\n",
" df_group_color = df.with_columns(r=color_list.get(0), b=color_list.get(1), g=color_list.get(2)).group_by(\"group\").agg(pl.mean(\"r\", \"g\", \"b\"))\n",
" df = df.join(df_group_color, on=\"group\")\n",
" df.head()\n",
" df = df.with_columns(color_rgb=pl.concat_str(\"r\", \"b\", \"g\", separator=\",\")).with_columns(\n",
" color_rgb = \"rgb(\"+pl.col(\"color_rgb\")+\")\"\n",
" )\n",
" display_voxels_px(voxels, df[\"color_rgb\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "EzdZxLoREyMc",
"outputId": "46a654f0-436d-455c-ee81-dd0f8c9a20b0"
},
"outputs": [],
"source": [
"mid_colors = [normalize_value_to_mid(x, 0.8) for x in colors]\n",
"display_voxels_px(voxels, [f\"rgb{c[0],c[1],c[2]}\" for c in mid_colors])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "31jo_asa2uW8",
"outputId": "4173b76f-9a8e-4091-8769-6f9adcb36ae7"
},
"outputs": [],
"source": [
"# map_colors_to_lego, map_color_cie, quantize_colors, convert_colors_max_diff\n",
"color_lego = map_color_cie(np.asarray(mid_colors)[:,:3], np.asarray(LEGO_COLORS_RGB))\n",
"display_voxels_px(voxels, [f\"rgb{c[0],c[1],c[2]}\" for c in color_lego])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "VgXRAunt5dKW",
"outputId": "e6b06c30-8683-4173-a4e4-5f9ec3facdfe"
},
"outputs": [],
"source": [
"color_lego = quantize_colors(np.asarray(mid_colors)[:,:3], k=16)\n",
"display_voxels_px(voxels, [f\"rgb{c[0],c[1],c[2]}\" for c in color_lego])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "t1QvgKbC3lsL",
"outputId": "7558cb5c-de8b-4bb5-f0b2-f8685c2ad3b4"
},
"outputs": [],
"source": [
"display_voxels_px(voxels, [f\"rgb{c[0],c[1],c[2]}\" for c in colors])"
]
},
{
"cell_type": "code",
"execution_count": 202,
"metadata": {
"id": "aEgErcK3lCxq"
},
"outputs": [],
"source": [
"df = to_df(voxels, color_lego)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 443
},
"id": "kAGJZc_9f-3e",
"outputId": "bd950ea5-6832-4813-97f1-2ccd1b741685"
},
"outputs": [],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 204,
"metadata": {
"id": "Meq7cqqhiis1"
},
"outputs": [],
"source": [
"BLOCK_SIZES = np.asarray([\n",
" [1,1],[1,2],[1,3],[1,4],[1,6],[1,8],\n",
" [2,2],[2,3],[2,4],[2,6],[2,8]\n",
"])\n",
"coords = {(x,y,z) for x,y,z in df.select(\"x\", \"y\", \"z\").to_numpy()}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "VMqkFCJdg-NZ",
"outputId": "f5725408-f8ad-49ed-e2dc-9f05d44d720a"
},
"outputs": [],
"source": [
"# TODO: make sure user flips figure to stand on z = 0, z being height axis.\n",
"\n",
"def get_xy_neighbors(x, y, z):\n",
" return [(x-1,y,z), (x+1,y,z), (x, y-1,z), (x,y+1,z)]\n",
"\n",
"y_group_1 = df.filter((pl.col(\"z\") == 16) & (pl.col(\"color\") == [44, 94, 130]))\n",
"group_coords = {(x,y,z) for x,y,z in y_group_1[[\"x\", \"y\", \"z\"]].to_numpy()}\n",
"\n",
"for row in range(len(y_group_1)):\n",
" for neighbor in get_xy_neighbors(y_group_1[row, \"x\"], y_group_1[row, \"y\"], y_group_1[row, \"z\"]):\n",
" if neighbor in group_coords:\n",
" \"found\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "_ZuNj25io0OK"
},
"outputs": [],
"source": [
"def merge_into_bricks(grouped_df: pl.DataFrame) -> pl.DataFrame:\n",
" color_str = grouped_df[0,\"color_str\"]\n",
" z_val = grouped_df[0, \"z\"]\n",
"\n",
" xy_grid = np.zeros((grouped_df[\"x\"].max() + 1, grouped_df[\"y\"].max() +1), dtype=bool)\n",
" xy_grid[grouped_df[\"x\"], grouped_df[\"y\"]] = 1\n",
" out_rows = []\n",
" grouped_df = grouped_df.sort(by=[\"x\", \"y\"])\n",
" coords = {(x,y) for x,y in grouped_df[[\"x\", \"y\"]].to_numpy()}\n",
"\n",
" while coords:\n",
" (x0, y0) = coords.pop()\n",
" coords.add((x0, y0)) # reinsert until placed\n",
"\n",
" placed = False\n",
" for (width, height) in BLOCK_SIZES:\n",
" if x0+width > xy_grid.shape[0] or y0+height > xy_grid.shape[1]:\n",
" continue\n",
" if np.all(xy_grid[x0:x0+width, y0:y0+height] == 1):\n",
" place_block(x0, y0, width, height, coords)\n",
" out_rows.append((color_str, z_val, x0, y0, width, height))\n",
" placed = True\n",
" break\n",
"\n",
" if not placed:\n",
" # fallback to 1x1\n",
" coords.remove((x0, y0))\n",
" out_rows.append((color_str, z_val, x0, y0, 1, 1))\n",
"\n",
" return pl.DataFrame(\n",
" {\n",
" \"color_str\": [row[0] for row in out_rows],\n",
" \"z\": [row[1] for row in out_rows],\n",
" \"x\": [row[2] for row in out_rows],\n",
" \"y\": [row[3] for row in out_rows],\n",
" \"width\": [row[4] for row in out_rows],\n",
" \"height\": [row[5] for row in out_rows],\n",
" }\n",
" )\n",
"\n",
"def can_place_block(x0, y0, w, h, coords):\n",
" for xx in range(x0, x0 + w):\n",
" for yy in range(y0, y0 + h):\n",
" if (xx, yy) not in coords:\n",
" return False\n",
" return True\n",
"\n",
"def place_block(x0, y0, w, h, coords):\n",
" for xx in range(x0, x0 + w):\n",
" for yy in range(y0, y0 + h):\n",
" coords.remove((xx, yy))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "edX0wBA7_O-g"
},
"outputs": [],
"source": [
"def merge_cells_recursive(df_group: pl.DataFrame, coords: set[tuple[int, int, int]]) -> pl.DataFrame:\n",
" # Merge by xy_grid as above.\n",
"\n",
" pass\n",
"\n",
"\n",
"def merge_cells(df_group: pl.DataFrame, coords: set[tuple[int, int, int]]) -> pl.DataFrame:\n",
" # df [x, y, z, x2, y2, z2, color] (merged)\n",
" # df [x, y, z, color] (unmerged)\n",
" pass\n",
"\n",
"BLOCK_SIZES = [\n",
" [1,1],[1,2],[1,3],[1,4],[1,6],[1,8],\n",
" [2,1],[3,1],[4,1],[6,1],[8,1],\n",
" [2,2],[2,3],[2,4],[2,6],[2,8],\n",
" [3,2],[4,2],[6,2],[8,2]\n",
"]\n",
"# Sort array by area, largest first.\n",
"BLOCK_SIZES.sort(key=lambda x: x[0]*x[1], reverse=True)\n",
"\n",
"coords = {(x,y,z) for x,y,z in df.select(\"x\", \"y\", \"z\").to_numpy()}\n",
"# Colors already merged.\n",
"df = df.with_columns(color_str = pl.col(\"color\").cast(pl.List(pl.String)).list.join(\"_\"))\n",
"merge_into_bricks(df.filter(pl.col(\"z\") == 17))\n",
"(df\n",
" .group_by(\"color_str\", \"z\")\n",
" .map_groups(merge_into_bricks)\n",
" .select(w_h=pl.struct(\"width\", \"height\").value_counts()).unnest(\"w_h\")\n",
")\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8BUgE9SYv3Y2"
},
"source": [
"## Utils"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pSnD2PwLv4wV"
},
"source": [
"### Enhance Brightness, Gamma and Saturation (color)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 521
},
"collapsed": true,
"id": "DulCp6to6adh",
"outputId": "6b64e715-80b0-43a2-ed74-c171e5664431"
},
"outputs": [],
"source": [
"def enhance_mesh_colors_vectorized(mesh, saturation_boost=1.5, brightness_factor=1.2, gamma=1.8):\n",
" \"\"\"\n",
" Enhances the saturation, brightness, and optionally applies gamma correction to mesh colors (vectorized).\n",
"\n",
" Args:\n",
" mesh: The trimesh mesh object.\n",
" saturation_boost: Factor to boost saturation ( > 1 increases saturation).\n",
" brightness_factor: Factor to adjust brightness ( > 1 increases brightness).\n",
" gamma: Gamma value for gamma correction (typically between 1.8 and 2.2).\n",
" \"\"\"\n",
" # Convert RGB to HSV (vectorized)\n",
" colors = mesh.visual.vertex_colors.astype(np.float32) / 255.0 # Normalize to 0-1\n",
" hsv_colors = np.array([colorsys.rgb_to_hsv(r, g, b) for r, g, b, a in colors])\n",
"\n",
" # Boost saturation (vectorized)\n",
" hsv_colors[:, 1] = np.minimum(hsv_colors[:, 1] * saturation_boost, 1.0)\n",
"\n",
" # Adjust brightness (vectorized)\n",
" hsv_colors[:, 2] = np.minimum(hsv_colors[:, 2] * brightness_factor, 1.0)\n",
"\n",
" # Gamma correction (vectorized)\n",
" hsv_colors[:, 2] = hsv_colors[:, 2]**(1/gamma)\n",
"\n",
" # Convert back to RGB (vectorized)\n",
" rgb_colors = np.array([colorsys.hsv_to_rgb(h, s, v) for h, s, v in hsv_colors])\n",
"\n",
" # Add alpha channel back\n",
" rgb_colors = np.concatenate((rgb_colors, colors[:, 3:]), axis=1)\n",
"\n",
" # Denormalize and set back to mesh\n",
" mesh = mesh.copy()\n",
" mesh.visual.vertex_colors = (rgb_colors * 255).astype(np.uint8)\n",
" return mesh\n",
"\n",
"# ... (load mesh)\n",
"\n",
"# Enhance colors (vectorized)\n",
"enhance_mesh_colors_vectorized(mesh, saturation_boost=1.8, brightness_factor=1.2, gamma=2.0).show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AHpn3zunv8IC"
},
"source": [
"### Show RGB"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "LYD6Bxf_jvd2"
},
"outputs": [],
"source": [
"from IPython.display import display, HTML\n",
"def show_rgb(rgb_color):\n",
" html_code = f'<div style=\"background-color: rgb{rgb_color}; width: 100px; height: 100px;\"></div>'\n",
"\n",
" display(HTML(html_code))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DOFUBRglv9X6"
},
"source": [
"### Validate Points similar"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "aQOHdarW-Dt-"
},
"outputs": [],
"source": [
"import plotly.graph_objects as go\n",
"import numpy as np\n",
"def validate_points_similar(index: int):\n",
" \"\"\"N.B. Uses attributes available in notebook!\"\"\"\n",
" # Create Plotly figure\n",
" fig = go.Figure(data=[\n",
" go.Mesh3d(\n",
" x=mesh.vertices[:, 0],\n",
" y=mesh.vertices[:, 1],\n",
" z=mesh.vertices[:, 2],\n",
" i=mesh.faces[:, 0],\n",
" j=mesh.faces[:, 1],\n",
" k=mesh.faces[:, 2],\n",
" color='lightgray',\n",
" opacity=0.8\n",
" ),\n",
" go.Scatter3d(\n",
" x=mesh.vertices[vertex_indices[index:index+1], 0],\n",
" y=mesh.vertices[vertex_indices[index:index+1], 1],\n",
" z=mesh.vertices[vertex_indices[index:index+1], 2],\n",
" mode='markers',\n",
" marker=dict(size=5, color='red'),\n",
" name='Mesh Vertex'\n",
" ),\n",
" go.Scatter3d(\n",
" x=[voxels.points[index, 0]],\n",
" y=[voxels.points[index, 1]],\n",
" z=[voxels.points[index, 2]],\n",
" mode='markers',\n",
" marker=dict(size=5, color='blue'),\n",
" name='Voxel Point'\n",
" )\n",
" ])\n",
"\n",
" # Set layout (axis labels, title)\n",
" fig.update_layout(\n",
" scene=dict(\n",
" xaxis_title='X',\n",
" yaxis_title='Y',\n",
" zaxis_title='Z'\n",
" ),\n",
" title='Mesh with Two Corresponding Points'\n",
" )\n",
"\n",
" # Enable interactive mode for Colab\n",
" fig.show(renderer=\"colab\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "DBPOtFzAhtVJ",
"outputId": "c8b39ebf-c8c6-4966-95ab-54fe3484a4ae"
},
"outputs": [],
"source": [
"validate_points_similar(200)"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [
"pSnD2PwLv4wV"
],
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"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": "3.13.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|