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483 lines
1.1 MiB
483 lines
1.1 MiB
2 years ago
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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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"\n",
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"<a href='http://www.pieriandata.com'><img src='../Pierian_Data_Logo.png'/></a>\n",
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"___\n",
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"<center><em>Copyright by Pierian Data Inc.</em></center>\n",
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"<center><em>For more information, visit us at <a href='http://www.pieriandata.com'>www.pieriandata.com</a></em></center>"
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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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"# K Means Color Quantization"
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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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"### Imports\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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"outputs": [],
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"source": [
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"import numpy as np\n",
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"\n",
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"import matplotlib.image as mpimg\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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"### The Image"
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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": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"image_as_array = mpimg.imread('../DATA/palm_trees.jpg')"
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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": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[[ 25, 89, 127],\n",
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" [ 25, 89, 127],\n",
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" [ 25, 89, 127],\n",
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" ...,\n",
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" [ 23, 63, 98],\n",
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" [ 51, 91, 127],\n",
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" [ 50, 90, 126]],\n",
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"\n",
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" [[ 25, 89, 127],\n",
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" [ 25, 89, 127],\n",
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" [ 25, 89, 127],\n",
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" ...,\n",
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" [ 31, 71, 106],\n",
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" [ 48, 88, 124],\n",
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" [ 47, 90, 125]],\n",
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"\n",
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" [[ 25, 89, 127],\n",
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" [ 25, 89, 127],\n",
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" [ 25, 89, 127],\n",
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" ...,\n",
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" [ 39, 79, 114],\n",
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" [ 42, 85, 120],\n",
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" [ 44, 88, 123]],\n",
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"\n",
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" ...,\n",
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"\n",
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" [[ 4, 4, 6],\n",
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" [ 4, 4, 6],\n",
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" [ 4, 4, 6],\n",
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" ...,\n",
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" [ 9, 9, 11],\n",
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" [ 9, 9, 11],\n",
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" [ 9, 9, 11]],\n",
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"\n",
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" [[ 3, 3, 5],\n",
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" [ 3, 3, 5],\n",
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" [ 3, 3, 5],\n",
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" ...,\n",
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" [ 8, 8, 10],\n",
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" [ 8, 8, 10],\n",
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" [ 8, 8, 10]],\n",
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"\n",
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" [[ 4, 4, 6],\n",
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" [ 4, 4, 6],\n",
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" [ 4, 4, 6],\n",
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" ...,\n",
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" [ 9, 9, 11],\n",
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" [ 10, 10, 12],\n",
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" [ 10, 10, 12]]], dtype=uint8)"
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]
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},
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"execution_count": 8,
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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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"source": [
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"image_as_array # RGB CODES FOR EACH PIXEL"
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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": 12,
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"metadata": {},
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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 0x16d48ff0eb0>"
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]
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},
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"execution_count": 12,
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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 1200x1200 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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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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"plt.figure(figsize=(6,6),dpi=200)\n",
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"plt.imshow(image_as_array)"
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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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"## Using Kmeans to Quantize Colors\n",
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"\n",
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"Quantizing colors means we'll reduce the number of unique colors here to K unique colors. Let's try just 6 colors!"
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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": 42,
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"metadata": {},
|
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"outputs": [
|
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|
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{
|
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"data": {
|
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|
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"text/plain": [
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"(1401, 934, 3)"
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]
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},
|
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"execution_count": 42,
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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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"source": [
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"image_as_array.shape\n",
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"# (w,h,3 color channels)"
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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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|
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"### Convert from 3d to 2d\n",
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"\n",
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"Kmeans is designed to train on 2D data (data rows and feature columns), so we can reshape the above strip by using (w,h,c) ---> (w * h,c)"
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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": 43,
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"metadata": {},
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"outputs": [],
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"source": [
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"(w,h,c) = image_as_array.shape"
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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": 44,
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"metadata": {},
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"outputs": [],
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"source": [
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"image_as_array2d = image_as_array.reshape(w*h,c)"
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]
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},
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{
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"cell_type": "code",
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|
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"execution_count": 45,
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"metadata": {},
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"outputs": [],
|
||
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"source": [
|
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|
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"from sklearn.cluster import KMeans"
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]
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},
|
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{
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|
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"cell_type": "code",
|
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|
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"execution_count": 46,
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"metadata": {},
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"outputs": [],
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"source": [
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"model = KMeans(n_clusters=6)"
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]
|
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},
|
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{
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"cell_type": "code",
|
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|
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"execution_count": 47,
|
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|
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"metadata": {},
|
||
|
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"outputs": [
|
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|
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{
|
||
|
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"data": {
|
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|
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"text/plain": [
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|
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"KMeans(n_clusters=6)"
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]
|
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},
|
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|
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"execution_count": 47,
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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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|
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],
|
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"source": [
|
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|
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"model"
|
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|
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]
|
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|
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},
|
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|
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{
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"cell_type": "code",
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||
|
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"execution_count": 62,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"labels = model.fit_predict(image_as_array2d)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 63,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"array([3, 3, 3, ..., 0, 0, 0])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 63,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"labels"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 73,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"array([[ 2.78511649, 2.58768262, 3.70018933],\n",
|
||
|
|
" [138.48117295, 144.26702702, 143.36832053],\n",
|
||
|
|
" [193.42381114, 154.48097123, 107.32286981],\n",
|
||
|
|
" [ 71.66897867, 109.55660624, 137.71325107],\n",
|
||
|
|
" [219.38959087, 134.67545907, 45.42786667],\n",
|
||
|
|
" [ 67.69383456, 61.88876822, 62.08150368]])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 73,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# THESE ARE THE 6 RGB COLOR CODES!\n",
|
||
|
|
"model.cluster_centers_"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 74,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"rgb_codes = model.cluster_centers_.round(0).astype(int)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 75,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"array([[ 3, 3, 4],\n",
|
||
|
|
" [138, 144, 143],\n",
|
||
|
|
" [193, 154, 107],\n",
|
||
|
|
" [ 72, 110, 138],\n",
|
||
|
|
" [219, 135, 45],\n",
|
||
|
|
" [ 68, 62, 62]])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 75,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"rgb_codes"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 76,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"quantized_image = np.reshape(rgb_codes[labels], (w, h, c))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 77,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"array([[[ 72, 110, 138],\n",
|
||
|
|
" [ 72, 110, 138],\n",
|
||
|
|
" [ 72, 110, 138],\n",
|
||
|
|
" ...,\n",
|
||
|
|
" [ 68, 62, 62],\n",
|
||
|
|
" [ 72, 110, 138],\n",
|
||
|
|
" [ 72, 110, 138]],\n",
|
||
|
|
"\n",
|
||
|
|
" [[ 72, 110, 138],\n",
|
||
|
|
" [ 72, 110, 138],\n",
|
||
|
|
" [ 72, 110, 138],\n",
|
||
|
|
" ...,\n",
|
||
|
|
" [ 68, 62, 62],\n",
|
||
|
|
" [ 72, 110, 138],\n",
|
||
|
|
" [ 72, 110, 138]],\n",
|
||
|
|
"\n",
|
||
|
|
" [[ 72, 110, 138],\n",
|
||
|
|
" [ 72, 110, 138],\n",
|
||
|
|
" [ 72, 110, 138],\n",
|
||
|
|
" ...,\n",
|
||
|
|
" [ 72, 110, 138],\n",
|
||
|
|
" [ 72, 110, 138],\n",
|
||
|
|
" [ 72, 110, 138]],\n",
|
||
|
|
"\n",
|
||
|
|
" ...,\n",
|
||
|
|
"\n",
|
||
|
|
" [[ 3, 3, 4],\n",
|
||
|
|
" [ 3, 3, 4],\n",
|
||
|
|
" [ 3, 3, 4],\n",
|
||
|
|
" ...,\n",
|
||
|
|
" [ 3, 3, 4],\n",
|
||
|
|
" [ 3, 3, 4],\n",
|
||
|
|
" [ 3, 3, 4]],\n",
|
||
|
|
"\n",
|
||
|
|
" [[ 3, 3, 4],\n",
|
||
|
|
" [ 3, 3, 4],\n",
|
||
|
|
" [ 3, 3, 4],\n",
|
||
|
|
" ...,\n",
|
||
|
|
" [ 3, 3, 4],\n",
|
||
|
|
" [ 3, 3, 4],\n",
|
||
|
|
" [ 3, 3, 4]],\n",
|
||
|
|
"\n",
|
||
|
|
" [[ 3, 3, 4],\n",
|
||
|
|
" [ 3, 3, 4],\n",
|
||
|
|
" [ 3, 3, 4],\n",
|
||
|
|
" ...,\n",
|
||
|
|
" [ 3, 3, 4],\n",
|
||
|
|
" [ 3, 3, 4],\n",
|
||
|
|
" [ 3, 3, 4]]])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 77,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"quantized_image"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 79,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.image.AxesImage at 0x16d5bd928b0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 79,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1200x1200 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.figure(figsize=(6,6),dpi=200)\n",
|
||
|
|
"plt.imshow(quantized_image)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"anaconda-cloud": {},
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3",
|
||
|
|
"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": "3.8.5"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 1
|
||
|
|
}
|