{
"cells": [
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"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": {
"execution": {
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"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": {
"execution": {
"iopub.execute_input": "2024-04-17T07:32:19.903534Z",
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{
"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": {
"execution": {
"iopub.execute_input": "2024-04-17T07:32:19.936889Z",
"iopub.status.busy": "2024-04-17T07:32:19.936784Z",
"iopub.status.idle": "2024-04-17T07:32:19.939294Z",
"shell.execute_reply": "2024-04-17T07:32:19.939120Z"
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"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": {
"execution": {
"iopub.execute_input": "2024-04-17T07:32:19.940532Z",
"iopub.status.busy": "2024-04-17T07:32:19.940367Z",
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"shell.execute_reply": "2024-04-17T07:32:19.944635Z"
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"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": {
"execution": {
"iopub.execute_input": "2024-04-17T07:32:19.945895Z",
"iopub.status.busy": "2024-04-17T07:32:19.945752Z",
"iopub.status.idle": "2024-04-17T07:32:19.948138Z",
"shell.execute_reply": "2024-04-17T07:32:19.947961Z"
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"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": {
"execution": {
"iopub.execute_input": "2024-04-17T07:32:19.949176Z",
"iopub.status.busy": "2024-04-17T07:32:19.949063Z",
"iopub.status.idle": "2024-04-17T07:32:19.951659Z",
"shell.execute_reply": "2024-04-17T07:32:19.951488Z"
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"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": {
"execution": {
"iopub.execute_input": "2024-04-17T07:32:19.952617Z",
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"iopub.status.idle": "2024-04-17T07:32:19.955230Z",
"shell.execute_reply": "2024-04-17T07:32:19.955060Z"
}
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"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.seed(42)\n",
"image = np.random.choice([0.0, 1.0], [10, 100, 3])\n",
"ggplot() + geom_imshow(image) + coord_cartesian()\n"
]
}
],
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