{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## geom_imshow()\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",
"The array's `dtype` can be int, uint or float.\n",
"\n",
"By default, all values in the image array will be transformed to the range [0-255] using a linear scaler."
]
},
{
"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",
"from lets_plot import *\n",
"\n",
"LetsPlot.setup_html()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Grayscale image\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"A2x3 = np.array([\n",
" [50, 150 ,200],\n",
" [200,100,50]\n",
" ])\n",
"\n",
"ggplot() + geom_imshow(A2x3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Grayscale image without normalization"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ggplot() + geom_imshow(A2x3, norm=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Grayscale image in \"Viridis\" colors"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ggplot() + geom_imshow(A2x3, cmap=\"viridis\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### RGB image\n",
"\n",
"M x N x 3 array"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"A2x3x3 = np.array([\n",
" [[255, 0, 0], [0, 255, 0], [0, 0, 255]],\n",
" [[0, 255, 0], [0, 0, 255], [255, 0, 0]]\n",
" ])\n",
"ggplot() + geom_imshow(A2x3x3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### RGB image with alpha channel\n",
"\n",
"M x N x 4 array"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"A2x3x4 = np.array([\n",
" [[1, 0, 0, 1], [0, 1, 0, 1], [0, 0, 1, 1]],\n",
" [[0, 1, 0, 0.3], [0, 0, 1, 0.3], [1, 0, 0, 0.3]]\n",
" ])\n",
"\n",
"ggplot() + geom_imshow(A2x3x4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Just a random image"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"image = np.random.choice([0.0, 1.0], [10, 100, 3])\n",
"ggplot() + geom_imshow(image) + coord_cartesian()\n"
]
}
],
"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": "3.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 1
}