{
"cells": [
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"cell_type": "markdown",
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"source": [
"## geom_imshow() and geom_raster()\n",
"\n",
"`geom_imshow()` displays an image specified by 2D or 3D Numpy array.\n",
"\n",
"Whether the image is grayscale or color depends on the shape of the image array:\n",
"- (M, N) - grey-scale image\n",
"- (M, N, 3) - color RGB image\n",
"- (M, N, 4) - color RGB image with alpha channel\n",
"\n",
"For example, you can create an RGB image array from a PNG file using `PIL` and `io` packages.\n",
"\n",
"`geom_raster()` on the other hand is a regular `ggplot` graphic layer wich takes a data-frame as the input \n",
"and applies the aesthetic mappings as specified by the `aes()` function.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from lets_plot import *\n",
"\n",
"LetsPlot.setup_html()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(225, 225, 3)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from PIL import Image\n",
"import requests\n",
"from io import BytesIO\n",
"\n",
"response = requests.get('https://github.com/JetBrains/lets-plot-docs/raw/master/source/examples/cookbook/images/fisher_boat.png')\n",
"\n",
"image = Image.open(BytesIO(response.content))\n",
"img = np.asarray(image)\n",
"img.shape\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### RGB image\n",
"\n",
"M x N x 3 `ndarray` specifies a color RGB image.\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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