194 lines
546 KiB
Plaintext
194 lines
546 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n",
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"# Reading and writing an elephant\n",
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"\n",
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"Read and write images\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## original figure\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<matplotlib.image.AxesImage at 0x10b59b470>"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# 新建第一个图形窗口，用于显示原图\n",
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"plt.figure()\n",
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"\n",
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"# 从本地文件读取彩色图像，返回三维数组 (H, W, 3)，像素值归一化到 [0,1]\n",
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"img = plt.imread(\"elephant.png\")\n",
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"\n",
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"# 将原图直接绘制出来，默认使用 RGB 彩色映射\n",
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"plt.imshow(img)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## red channel displayed in grey\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<matplotlib.image.AxesImage at 0x10b7115e0>"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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|
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"image/png": "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"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# 新建第二个图形窗口，用于观察单通道\n",
|
||
|
|
"plt.figure()\n",
|
||
|
|
"\n",
|
||
|
|
"# 提取 RGB 图像的红色通道，得到二维数组 (H, W)\n",
|
||
|
|
"img_red = img[:, :, 0]\n",
|
||
|
|
"\n",
|
||
|
|
"# 以灰度色阶显示红色通道，黑色=低值，白色=高值\n",
|
||
|
|
"plt.imshow(img_red, cmap=\"gray\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## lower resolution\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 4,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"jupyter": {
|
||
|
|
"outputs_hidden": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# 新建第三个图形窗口，用于显示降采样后的图像\n",
|
||
|
|
"plt.figure()\n",
|
||
|
|
"\n",
|
||
|
|
"# 对原图每隔 6 个像素取一个点，实现 1/6 降采样，缩小图像尺寸\n",
|
||
|
|
"img_tiny = img[::6, ::6]\n",
|
||
|
|
"\n",
|
||
|
|
"# 用最邻近插值显示降采样图，避免平滑，保留块状像素效果\n",
|
||
|
|
"plt.imshow(img_tiny, interpolation=\"nearest\")\n",
|
||
|
|
"\n",
|
||
|
|
"# 一次性打开所有图形窗口\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"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": "3.12.11"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 4
|
||
|
|
}
|