{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Mosaic Art\n",
"\n",
"We would like to use images as mosaic pieces to build a bigger image.\n",
"\n",
"Data originally comes from [here](https://www.kaggle.com/thedownhill/art-images-drawings-painting-sculpture-engraving)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"execution": {
"iopub.execute_input": "2024-04-17T07:38:47.101204Z",
"iopub.status.busy": "2024-04-17T07:38:47.101000Z",
"iopub.status.idle": "2024-04-17T07:38:47.415427Z",
"shell.execute_reply": "2024-04-17T07:38:47.415108Z"
}
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"from lets_plot import *"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2024-04-17T07:38:47.417119Z",
"iopub.status.busy": "2024-04-17T07:38:47.416982Z",
"iopub.status.idle": "2024-04-17T07:38:47.419198Z",
"shell.execute_reply": "2024-04-17T07:38:47.419021Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"LetsPlot.setup_html()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2024-04-17T07:38:47.432396Z",
"iopub.status.busy": "2024-04-17T07:38:47.432297Z",
"iopub.status.idle": "2024-04-17T07:38:47.434623Z",
"shell.execute_reply": "2024-04-17T07:38:47.434444Z"
}
},
"outputs": [],
"source": [
"def get_img_or_none_by_index(i):\n",
" import io\n",
" from os.path import isfile, join\n",
" import requests\n",
" from PIL import Image\n",
"\n",
" TIMEOUT = 5\n",
" IMAGES_PATH = \"images/sculpture\"\n",
" IMAGES_URL = \"https://github.com/JetBrains/lets-plot-docs/raw/master/source/examples/demo/images/sculpture\"\n",
"\n",
" filepath = join(IMAGES_PATH, \"{0}.jpg\".format(i))\n",
" if isfile(filepath):\n",
" return Image.open(filepath)\n",
" filepath = join(IMAGES_PATH, \"{0}.jpeg\".format(i))\n",
" if isfile(filepath):\n",
" return Image.open(filepath)\n",
" r = requests.get(\"{0}/{1}.jpg\".format(IMAGES_URL, i), timeout=TIMEOUT)\n",
" if r.status_code == requests.codes.ok:\n",
" return Image.open(io.BytesIO(r.content))\n",
" r = requests.get(\"{0}/{1}.jpeg\".format(IMAGES_URL, i), timeout=TIMEOUT)\n",
" if r.status_code == requests.codes.ok:\n",
" return Image.open(io.BytesIO(r.content))\n",
" return None"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2024-04-17T07:38:47.435643Z",
"iopub.status.busy": "2024-04-17T07:38:47.435498Z",
"iopub.status.idle": "2024-04-17T07:38:47.436995Z",
"shell.execute_reply": "2024-04-17T07:38:47.436819Z"
}
},
"outputs": [],
"source": [
"def get_grayscale_img_or_none(img):\n",
" if img is None:\n",
" return None\n",
" if img.mode not in ['RGB', 'L']:\n",
" return None\n",
" return img.convert('L')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2024-04-17T07:38:47.437848Z",
"iopub.status.busy": "2024-04-17T07:38:47.437773Z",
"iopub.status.idle": "2024-04-17T07:38:47.439116Z",
"shell.execute_reply": "2024-04-17T07:38:47.438943Z"
}
},
"outputs": [],
"source": [
"WIDTH, HEIGHT = 20, 20\n",
"IMAGES_COUNT = 1_732\n",
"TARGET_IMAGE = 1_086"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2024-04-17T07:38:47.439960Z",
"iopub.status.busy": "2024-04-17T07:38:47.439881Z",
"iopub.status.idle": "2024-04-17T07:38:52.196355Z",
"shell.execute_reply": "2024-04-17T07:38:52.195995Z"
}
},
"outputs": [],
"source": [
"data = []\n",
"for i in range(IMAGES_COUNT):\n",
" img = get_grayscale_img_or_none(get_img_or_none_by_index(i))\n",
" if img is None:\n",
" continue\n",
" img = img.resize((WIDTH, HEIGHT))\n",
" image_data = np.asarray(img).reshape(WIDTH * HEIGHT)\n",
" image_agg_data = np.array([np.round(image_data.mean()), np.round(image_data.std())])\n",
" data.append(np.concatenate((image_agg_data, image_data), axis=0).astype(int))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2024-04-17T07:38:52.197757Z",
"iopub.status.busy": "2024-04-17T07:38:52.197665Z",
"iopub.status.idle": "2024-04-17T07:38:52.618680Z",
"shell.execute_reply": "2024-04-17T07:38:52.618428Z"
}
},
"outputs": [],
"source": [
"df = pd.DataFrame(data)\n",
"df = df.rename(columns={0: 'color', 1: 'std'})\n",
"df = df.sort_values(by=['color', 'std']).reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2024-04-17T07:38:52.619986Z",
"iopub.status.busy": "2024-04-17T07:38:52.619817Z",
"iopub.status.idle": "2024-04-17T07:38:52.993096Z",
"shell.execute_reply": "2024-04-17T07:38:52.992788Z"
}
},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ggplot() + \\\n",
" geom_bar(aes(x='color', fill='color'), data=df, sampling=sampling_pick(n=256), size=0) + \\\n",
" scale_fill_gradient(low='black', high='white') + \\\n",
" ggtitle('Gray Color Distribution')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2024-04-17T07:38:52.994271Z",
"iopub.status.busy": "2024-04-17T07:38:52.994193Z",
"iopub.status.idle": "2024-04-17T07:38:53.034363Z",
"shell.execute_reply": "2024-04-17T07:38:53.034065Z"
}
},
"outputs": [],
"source": [
"for missing_color in set(range(256)) - set(df.color.unique()):\n",
" df = pd.concat([df, pd.DataFrame.from_records([dict(color=missing_color)])])\n",
"df = df.sort_values(by='color').reset_index(drop=True)\n",
"df = df.ffill().bfill()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"execution": {
"iopub.execute_input": "2024-04-17T07:38:53.035662Z",
"iopub.status.busy": "2024-04-17T07:38:53.035566Z",
"iopub.status.idle": "2024-04-17T07:38:54.062463Z",
"shell.execute_reply": "2024-04-17T07:38:54.062203Z"
}
},
"outputs": [],
"source": [
"img = get_grayscale_img_or_none(get_img_or_none_by_index(TARGET_IMAGE))\n",
"img = img.resize((80, 80))\n",
"rows = ()\n",
"for row in np.asarray(img):\n",
" cols = ()\n",
" for color in row:\n",
" subimg = df[df.color == color].iloc[0, 2:].to_numpy().reshape(WIDTH, HEIGHT)\n",
" cols = cols + (subimg,)\n",
" rows = rows + (np.hstack(cols),)\n",
"image_data = np.vstack(rows)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"execution": {
"iopub.execute_input": "2024-04-17T07:38:54.063763Z",
"iopub.status.busy": "2024-04-17T07:38:54.063687Z",
"iopub.status.idle": "2024-04-17T07:38:54.179176Z",
"shell.execute_reply": "2024-04-17T07:38:54.178909Z"
}
},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ggplot() + \\\n",
" geom_imshow(image_data=image_data) + \\\n",
" ggsize(800, 800) + \\\n",
" ggtitle('Mosaic Image') + \\\n",
" theme_void() + theme(legend_position='none')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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"
}
},
"nbformat": 4,
"nbformat_minor": 4
}