mirror of
https://github.com/Priyatham-sai-chand/canvas-recognition.git
synced 2026-10-05 08:11:33 -07:00
2152 lines
67 KiB
Text
2152 lines
67 KiB
Text
|
{
|
||
|
|
"cells": [
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 56,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"import PIL\n",
|
||
|
|
"from PIL import Image, ImageEnhance, ImageDraw, ImageFont\n",
|
||
|
|
"from IPython.display import display\n",
|
||
|
|
"import numpy as np"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 57,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"img = Image.open('download.png')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 58,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=659x821 at 0x7FB7ED893E50>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"display(img)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 59,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"(659, 821)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 59,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"img.size"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 60,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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\n",
|
||
|
|
"text/plain": [
|
||
|
|
"<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=300x300 at 0x7FB7EED9A850>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"re_img = img.resize((300,300))\n",
|
||
|
|
"re_img.save('mo_img.png')\n",
|
||
|
|
"mo_img = Image.open('mo_img.png')\n",
|
||
|
|
"display(mo_img)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 61,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from numpy import asarray"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 62,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"data = asarray(mo_img)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 63,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"numpy.ndarray"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 63,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"type(data)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 64,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"(300, 300, 4)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 64,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"data.shape"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 65,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"array([[[0, 0, 0, 0],\n",
|
||
|
|
" [0, 0, 0, 0],\n",
|
||
|
|
" [0, 0, 0, 0],\n",
|
||
|
|
" ...,\n",
|
||
|
|
" [0, 0, 0, 0],\n",
|
||
|
|
" [0, 0, 0, 0],\n",
|
||
|
|
" [0, 0, 0, 0]],\n",
|
||
|
|
"\n",
|
||
|
|
" [[0, 0, 0, 0],\n",
|
||
|
|
" [0, 0, 0, 0],\n",
|
||
|
|
" [0, 0, 0, 0],\n",
|
||
|
|
" ...,\n",
|
||
|
|
" [0, 0, 0, 0],\n",
|
||
|
|
" [0, 0, 0, 0],\n",
|
||
|
|
" [0, 0, 0, 0]],\n",
|
||
|
|
"\n",
|
||
|
|
" [[0, 0, 0, 0],\n",
|
||
|
|
" [0, 0, 0, 0],\n",
|
||
|
|
" [0, 0, 0, 0],\n",
|
||
|
|
" ...,\n",
|
||
|
|
" [0, 0, 0, 0],\n",
|
||
|
|
" [0, 0, 0, 0],\n",
|
||
|
|
" [0, 0, 0, 0]],\n",
|
||
|
|
"\n",
|
||
|
|
" ...,\n",
|
||
|
|
"\n",
|
||
|
|
" [[0, 0, 0, 0],\n",
|
||
|
|
" [0, 0, 0, 0],\n",
|
||
|
|
" [0, 0, 0, 0],\n",
|
||
|
|
" ...,\n",
|
||
|
|
" [0, 0, 0, 0],\n",
|
||
|
|
" [0, 0, 0, 0],\n",
|
||
|
|
" [0, 0, 0, 0]],\n",
|
||
|
|
"\n",
|
||
|
|
" [[0, 0, 0, 0],\n",
|
||
|
|
" [0, 0, 0, 0],\n",
|
||
|
|
" [0, 0, 0, 0],\n",
|
||
|
|
" ...,\n",
|
||
|
|
" [0, 0, 0, 0],\n",
|
||
|
|
" [0, 0, 0, 0],\n",
|
||
|
|
" [0, 0, 0, 0]],\n",
|
||
|
|
"\n",
|
||
|
|
" [[0, 0, 0, 0],\n",
|
||
|
|
" [0, 0, 0, 0],\n",
|
||
|
|
" [0, 0, 0, 0],\n",
|
||
|
|
" ...,\n",
|
||
|
|
" [0, 0, 0, 0],\n",
|
||
|
|
" [0, 0, 0, 0],\n",
|
||
|
|
" [0, 0, 0, 0]]], dtype=uint8)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 65,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"data"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 66,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"data_1 = data"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 67,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from numpy import sum"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 221,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"ename": "IndexError",
|
||
|
|
"evalue": "index 74 is out of bounds for axis 0 with size 74",
|
||
|
|
"output_type": "error",
|
||
|
|
"traceback": [
|
||
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||
|
|
"\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
|
||
|
|
"\u001b[0;32m<ipython-input-221-6b184cf9a78f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mmy_list\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmy_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
|
||
|
|
"\u001b[0;31mIndexError\u001b[0m: index 74 is out of bounds for axis 0 with size 74"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"my_list =[]\n",
|
||
|
|
"for i in range(300): \n",
|
||
|
|
" my_list.append((sum(data_1[i])))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 69,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"[0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.6509803921568628,\n",
|
||
|
|
" 2.411764705882353,\n",
|
||
|
|
" 2.556862745098039,\n",
|
||
|
|
" 2.396078431372549,\n",
|
||
|
|
" 2.2941176470588234,\n",
|
||
|
|
" 2.3490196078431373,\n",
|
||
|
|
" 2.447058823529412,\n",
|
||
|
|
" 2.2941176470588234,\n",
|
||
|
|
" 2.3607843137254902,\n",
|
||
|
|
" 2.4352941176470586,\n",
|
||
|
|
" 2.43921568627451,\n",
|
||
|
|
" 2.3372549019607844,\n",
|
||
|
|
" 2.407843137254902,\n",
|
||
|
|
" 2.4745098039215687,\n",
|
||
|
|
" 2.407843137254902,\n",
|
||
|
|
" 2.447058823529412,\n",
|
||
|
|
" 2.831372549019608,\n",
|
||
|
|
" 10.427450980392157,\n",
|
||
|
|
" 15.733333333333333,\n",
|
||
|
|
" 12.364705882352942,\n",
|
||
|
|
" 8.737254901960783,\n",
|
||
|
|
" 6.901960784313726,\n",
|
||
|
|
" 6.015686274509804,\n",
|
||
|
|
" 5.427450980392157,\n",
|
||
|
|
" 5.513725490196078,\n",
|
||
|
|
" 5.537254901960784,\n",
|
||
|
|
" 5.603921568627451,\n",
|
||
|
|
" 5.698039215686275,\n",
|
||
|
|
" 5.686274509803922,\n",
|
||
|
|
" 5.949019607843137,\n",
|
||
|
|
" 6.76078431372549,\n",
|
||
|
|
" 6.796078431372549,\n",
|
||
|
|
" 6.47843137254902,\n",
|
||
|
|
" 7.145098039215687,\n",
|
||
|
|
" 11.647058823529411,\n",
|
||
|
|
" 12.435294117647059,\n",
|
||
|
|
" 4.474509803921569,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0,\n",
|
||
|
|
" 0.0]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 69,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"my_list"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 70,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"import matplotlib.pyplot as plt"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 71,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"my_rows = []\n",
|
||
|
|
"for i in range(300):\n",
|
||
|
|
" my_rows.append(i)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 72,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"[0,\n",
|
||
|
|
" 1,\n",
|
||
|
|
" 2,\n",
|
||
|
|
" 3,\n",
|
||
|
|
" 4,\n",
|
||
|
|
" 5,\n",
|
||
|
|
" 6,\n",
|
||
|
|
" 7,\n",
|
||
|
|
" 8,\n",
|
||
|
|
" 9,\n",
|
||
|
|
" 10,\n",
|
||
|
|
" 11,\n",
|
||
|
|
" 12,\n",
|
||
|
|
" 13,\n",
|
||
|
|
" 14,\n",
|
||
|
|
" 15,\n",
|
||
|
|
" 16,\n",
|
||
|
|
" 17,\n",
|
||
|
|
" 18,\n",
|
||
|
|
" 19,\n",
|
||
|
|
" 20,\n",
|
||
|
|
" 21,\n",
|
||
|
|
" 22,\n",
|
||
|
|
" 23,\n",
|
||
|
|
" 24,\n",
|
||
|
|
" 25,\n",
|
||
|
|
" 26,\n",
|
||
|
|
" 27,\n",
|
||
|
|
" 28,\n",
|
||
|
|
" 29,\n",
|
||
|
|
" 30,\n",
|
||
|
|
" 31,\n",
|
||
|
|
" 32,\n",
|
||
|
|
" 33,\n",
|
||
|
|
" 34,\n",
|
||
|
|
" 35,\n",
|
||
|
|
" 36,\n",
|
||
|
|
" 37,\n",
|
||
|
|
" 38,\n",
|
||
|
|
" 39,\n",
|
||
|
|
" 40,\n",
|
||
|
|
" 41,\n",
|
||
|
|
" 42,\n",
|
||
|
|
" 43,\n",
|
||
|
|
" 44,\n",
|
||
|
|
" 45,\n",
|
||
|
|
" 46,\n",
|
||
|
|
" 47,\n",
|
||
|
|
" 48,\n",
|
||
|
|
" 49,\n",
|
||
|
|
" 50,\n",
|
||
|
|
" 51,\n",
|
||
|
|
" 52,\n",
|
||
|
|
" 53,\n",
|
||
|
|
" 54,\n",
|
||
|
|
" 55,\n",
|
||
|
|
" 56,\n",
|
||
|
|
" 57,\n",
|
||
|
|
" 58,\n",
|
||
|
|
" 59,\n",
|
||
|
|
" 60,\n",
|
||
|
|
" 61,\n",
|
||
|
|
" 62,\n",
|
||
|
|
" 63,\n",
|
||
|
|
" 64,\n",
|
||
|
|
" 65,\n",
|
||
|
|
" 66,\n",
|
||
|
|
" 67,\n",
|
||
|
|
" 68,\n",
|
||
|
|
" 69,\n",
|
||
|
|
" 70,\n",
|
||
|
|
" 71,\n",
|
||
|
|
" 72,\n",
|
||
|
|
" 73,\n",
|
||
|
|
" 74,\n",
|
||
|
|
" 75,\n",
|
||
|
|
" 76,\n",
|
||
|
|
" 77,\n",
|
||
|
|
" 78,\n",
|
||
|
|
" 79,\n",
|
||
|
|
" 80,\n",
|
||
|
|
" 81,\n",
|
||
|
|
" 82,\n",
|
||
|
|
" 83,\n",
|
||
|
|
" 84,\n",
|
||
|
|
" 85,\n",
|
||
|
|
" 86,\n",
|
||
|
|
" 87,\n",
|
||
|
|
" 88,\n",
|
||
|
|
" 89,\n",
|
||
|
|
" 90,\n",
|
||
|
|
" 91,\n",
|
||
|
|
" 92,\n",
|
||
|
|
" 93,\n",
|
||
|
|
" 94,\n",
|
||
|
|
" 95,\n",
|
||
|
|
" 96,\n",
|
||
|
|
" 97,\n",
|
||
|
|
" 98,\n",
|
||
|
|
" 99,\n",
|
||
|
|
" 100,\n",
|
||
|
|
" 101,\n",
|
||
|
|
" 102,\n",
|
||
|
|
" 103,\n",
|
||
|
|
" 104,\n",
|
||
|
|
" 105,\n",
|
||
|
|
" 106,\n",
|
||
|
|
" 107,\n",
|
||
|
|
" 108,\n",
|
||
|
|
" 109,\n",
|
||
|
|
" 110,\n",
|
||
|
|
" 111,\n",
|
||
|
|
" 112,\n",
|
||
|
|
" 113,\n",
|
||
|
|
" 114,\n",
|
||
|
|
" 115,\n",
|
||
|
|
" 116,\n",
|
||
|
|
" 117,\n",
|
||
|
|
" 118,\n",
|
||
|
|
" 119,\n",
|
||
|
|
" 120,\n",
|
||
|
|
" 121,\n",
|
||
|
|
" 122,\n",
|
||
|
|
" 123,\n",
|
||
|
|
" 124,\n",
|
||
|
|
" 125,\n",
|
||
|
|
" 126,\n",
|
||
|
|
" 127,\n",
|
||
|
|
" 128,\n",
|
||
|
|
" 129,\n",
|
||
|
|
" 130,\n",
|
||
|
|
" 131,\n",
|
||
|
|
" 132,\n",
|
||
|
|
" 133,\n",
|
||
|
|
" 134,\n",
|
||
|
|
" 135,\n",
|
||
|
|
" 136,\n",
|
||
|
|
" 137,\n",
|
||
|
|
" 138,\n",
|
||
|
|
" 139,\n",
|
||
|
|
" 140,\n",
|
||
|
|
" 141,\n",
|
||
|
|
" 142,\n",
|
||
|
|
" 143,\n",
|
||
|
|
" 144,\n",
|
||
|
|
" 145,\n",
|
||
|
|
" 146,\n",
|
||
|
|
" 147,\n",
|
||
|
|
" 148,\n",
|
||
|
|
" 149,\n",
|
||
|
|
" 150,\n",
|
||
|
|
" 151,\n",
|
||
|
|
" 152,\n",
|
||
|
|
" 153,\n",
|
||
|
|
" 154,\n",
|
||
|
|
" 155,\n",
|
||
|
|
" 156,\n",
|
||
|
|
" 157,\n",
|
||
|
|
" 158,\n",
|
||
|
|
" 159,\n",
|
||
|
|
" 160,\n",
|
||
|
|
" 161,\n",
|
||
|
|
" 162,\n",
|
||
|
|
" 163,\n",
|
||
|
|
" 164,\n",
|
||
|
|
" 165,\n",
|
||
|
|
" 166,\n",
|
||
|
|
" 167,\n",
|
||
|
|
" 168,\n",
|
||
|
|
" 169,\n",
|
||
|
|
" 170,\n",
|
||
|
|
" 171,\n",
|
||
|
|
" 172,\n",
|
||
|
|
" 173,\n",
|
||
|
|
" 174,\n",
|
||
|
|
" 175,\n",
|
||
|
|
" 176,\n",
|
||
|
|
" 177,\n",
|
||
|
|
" 178,\n",
|
||
|
|
" 179,\n",
|
||
|
|
" 180,\n",
|
||
|
|
" 181,\n",
|
||
|
|
" 182,\n",
|
||
|
|
" 183,\n",
|
||
|
|
" 184,\n",
|
||
|
|
" 185,\n",
|
||
|
|
" 186,\n",
|
||
|
|
" 187,\n",
|
||
|
|
" 188,\n",
|
||
|
|
" 189,\n",
|
||
|
|
" 190,\n",
|
||
|
|
" 191,\n",
|
||
|
|
" 192,\n",
|
||
|
|
" 193,\n",
|
||
|
|
" 194,\n",
|
||
|
|
" 195,\n",
|
||
|
|
" 196,\n",
|
||
|
|
" 197,\n",
|
||
|
|
" 198,\n",
|
||
|
|
" 199,\n",
|
||
|
|
" 200,\n",
|
||
|
|
" 201,\n",
|
||
|
|
" 202,\n",
|
||
|
|
" 203,\n",
|
||
|
|
" 204,\n",
|
||
|
|
" 205,\n",
|
||
|
|
" 206,\n",
|
||
|
|
" 207,\n",
|
||
|
|
" 208,\n",
|
||
|
|
" 209,\n",
|
||
|
|
" 210,\n",
|
||
|
|
" 211,\n",
|
||
|
|
" 212,\n",
|
||
|
|
" 213,\n",
|
||
|
|
" 214,\n",
|
||
|
|
" 215,\n",
|
||
|
|
" 216,\n",
|
||
|
|
" 217,\n",
|
||
|
|
" 218,\n",
|
||
|
|
" 219,\n",
|
||
|
|
" 220,\n",
|
||
|
|
" 221,\n",
|
||
|
|
" 222,\n",
|
||
|
|
" 223,\n",
|
||
|
|
" 224,\n",
|
||
|
|
" 225,\n",
|
||
|
|
" 226,\n",
|
||
|
|
" 227,\n",
|
||
|
|
" 228,\n",
|
||
|
|
" 229,\n",
|
||
|
|
" 230,\n",
|
||
|
|
" 231,\n",
|
||
|
|
" 232,\n",
|
||
|
|
" 233,\n",
|
||
|
|
" 234,\n",
|
||
|
|
" 235,\n",
|
||
|
|
" 236,\n",
|
||
|
|
" 237,\n",
|
||
|
|
" 238,\n",
|
||
|
|
" 239,\n",
|
||
|
|
" 240,\n",
|
||
|
|
" 241,\n",
|
||
|
|
" 242,\n",
|
||
|
|
" 243,\n",
|
||
|
|
" 244,\n",
|
||
|
|
" 245,\n",
|
||
|
|
" 246,\n",
|
||
|
|
" 247,\n",
|
||
|
|
" 248,\n",
|
||
|
|
" 249,\n",
|
||
|
|
" 250,\n",
|
||
|
|
" 251,\n",
|
||
|
|
" 252,\n",
|
||
|
|
" 253,\n",
|
||
|
|
" 254,\n",
|
||
|
|
" 255,\n",
|
||
|
|
" 256,\n",
|
||
|
|
" 257,\n",
|
||
|
|
" 258,\n",
|
||
|
|
" 259,\n",
|
||
|
|
" 260,\n",
|
||
|
|
" 261,\n",
|
||
|
|
" 262,\n",
|
||
|
|
" 263,\n",
|
||
|
|
" 264,\n",
|
||
|
|
" 265,\n",
|
||
|
|
" 266,\n",
|
||
|
|
" 267,\n",
|
||
|
|
" 268,\n",
|
||
|
|
" 269,\n",
|
||
|
|
" 270,\n",
|
||
|
|
" 271,\n",
|
||
|
|
" 272,\n",
|
||
|
|
" 273,\n",
|
||
|
|
" 274,\n",
|
||
|
|
" 275,\n",
|
||
|
|
" 276,\n",
|
||
|
|
" 277,\n",
|
||
|
|
" 278,\n",
|
||
|
|
" 279,\n",
|
||
|
|
" 280,\n",
|
||
|
|
" 281,\n",
|
||
|
|
" 282,\n",
|
||
|
|
" 283,\n",
|
||
|
|
" 284,\n",
|
||
|
|
" 285,\n",
|
||
|
|
" 286,\n",
|
||
|
|
" 287,\n",
|
||
|
|
" 288,\n",
|
||
|
|
" 289,\n",
|
||
|
|
" 290,\n",
|
||
|
|
" 291,\n",
|
||
|
|
" 292,\n",
|
||
|
|
" 293,\n",
|
||
|
|
" 294,\n",
|
||
|
|
" 295,\n",
|
||
|
|
" 296,\n",
|
||
|
|
" 297,\n",
|
||
|
|
" 298,\n",
|
||
|
|
" 299]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 72,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"my_rows"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 73,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"[<matplotlib.lines.Line2D at 0x7fb7eed9f340>]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 73,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.plot(my_rows,my_list)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 90,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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\n",
|
||
|
|
"text/plain": [
|
||
|
|
"<PIL.Image.Image image mode=RGBA size=300x37 at 0x7FB7EEF95850>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"display(mo_img.crop((0,39,300,76)))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 102,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"def getRow(mylist):\n",
|
||
|
|
" i=0\n",
|
||
|
|
" while(mylist[i]==0.0):\n",
|
||
|
|
" i=i+1\n",
|
||
|
|
" print(i)\n",
|
||
|
|
" j=i\n",
|
||
|
|
" while(mylist[j]!=0.0):\n",
|
||
|
|
" j=j+1\n",
|
||
|
|
" print(j)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 105,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"39\n",
|
||
|
|
"76\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"getRow(my_list)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 106,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"line_img = mo_img.crop((0,39,300,76))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 107,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"(300, 37)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 107,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"line_img.size"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 108,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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\n",
|
||
|
|
"text/plain": [
|
||
|
|
"<PIL.Image.Image image mode=RGBA size=300x37 at 0x7FB7EF1829D0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 108,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"line_img"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 140,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"line_img.save('line_img.png')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# word segmentation starts"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 143,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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\n",
|
||
|
|
"text/plain": [
|
||
|
|
"<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=300x37 at 0x7FB7EF182D90>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 143,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"line_img"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 144,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from numpy import asarray"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 145,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"data = asarray(line_img)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 146,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"(37, 300, 4)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 146,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"data.shape"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 166,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"[0,\n",
|
||
|
|
" 1,\n",
|
||
|
|
" 2,\n",
|
||
|
|
" 3,\n",
|
||
|
|
" 4,\n",
|
||
|
|
" 5,\n",
|
||
|
|
" 6,\n",
|
||
|
|
" 7,\n",
|
||
|
|
" 8,\n",
|
||
|
|
" 9,\n",
|
||
|
|
" 10,\n",
|
||
|
|
" 11,\n",
|
||
|
|
" 12,\n",
|
||
|
|
" 13,\n",
|
||
|
|
" 14,\n",
|
||
|
|
" 15,\n",
|
||
|
|
" 16,\n",
|
||
|
|
" 17,\n",
|
||
|
|
" 18,\n",
|
||
|
|
" 19,\n",
|
||
|
|
" 20,\n",
|
||
|
|
" 21,\n",
|
||
|
|
" 22,\n",
|
||
|
|
" 23,\n",
|
||
|
|
" 24,\n",
|
||
|
|
" 25,\n",
|
||
|
|
" 26,\n",
|
||
|
|
" 27,\n",
|
||
|
|
" 28,\n",
|
||
|
|
" 29,\n",
|
||
|
|
" 30,\n",
|
||
|
|
" 31,\n",
|
||
|
|
" 32,\n",
|
||
|
|
" 33,\n",
|
||
|
|
" 34,\n",
|
||
|
|
" 35,\n",
|
||
|
|
" 36,\n",
|
||
|
|
" 37,\n",
|
||
|
|
" 38,\n",
|
||
|
|
" 39,\n",
|
||
|
|
" 40,\n",
|
||
|
|
" 41,\n",
|
||
|
|
" 42,\n",
|
||
|
|
" 43,\n",
|
||
|
|
" 44,\n",
|
||
|
|
" 45,\n",
|
||
|
|
" 46,\n",
|
||
|
|
" 47,\n",
|
||
|
|
" 48,\n",
|
||
|
|
" 49,\n",
|
||
|
|
" 50,\n",
|
||
|
|
" 51,\n",
|
||
|
|
" 52,\n",
|
||
|
|
" 53,\n",
|
||
|
|
" 54,\n",
|
||
|
|
" 55,\n",
|
||
|
|
" 56,\n",
|
||
|
|
" 57,\n",
|
||
|
|
" 58,\n",
|
||
|
|
" 59,\n",
|
||
|
|
" 60,\n",
|
||
|
|
" 61,\n",
|
||
|
|
" 62,\n",
|
||
|
|
" 63,\n",
|
||
|
|
" 64,\n",
|
||
|
|
" 65,\n",
|
||
|
|
" 66,\n",
|
||
|
|
" 67,\n",
|
||
|
|
" 68,\n",
|
||
|
|
" 69,\n",
|
||
|
|
" 70,\n",
|
||
|
|
" 71,\n",
|
||
|
|
" 72,\n",
|
||
|
|
" 73,\n",
|
||
|
|
" 74,\n",
|
||
|
|
" 75,\n",
|
||
|
|
" 76,\n",
|
||
|
|
" 77,\n",
|
||
|
|
" 78,\n",
|
||
|
|
" 79,\n",
|
||
|
|
" 80,\n",
|
||
|
|
" 81,\n",
|
||
|
|
" 82,\n",
|
||
|
|
" 83,\n",
|
||
|
|
" 84,\n",
|
||
|
|
" 85,\n",
|
||
|
|
" 86,\n",
|
||
|
|
" 87,\n",
|
||
|
|
" 88,\n",
|
||
|
|
" 89,\n",
|
||
|
|
" 90,\n",
|
||
|
|
" 91,\n",
|
||
|
|
" 92,\n",
|
||
|
|
" 93,\n",
|
||
|
|
" 94,\n",
|
||
|
|
" 95,\n",
|
||
|
|
" 96,\n",
|
||
|
|
" 97,\n",
|
||
|
|
" 98,\n",
|
||
|
|
" 99,\n",
|
||
|
|
" 100,\n",
|
||
|
|
" 101,\n",
|
||
|
|
" 102,\n",
|
||
|
|
" 103,\n",
|
||
|
|
" 104,\n",
|
||
|
|
" 105,\n",
|
||
|
|
" 106,\n",
|
||
|
|
" 107,\n",
|
||
|
|
" 108,\n",
|
||
|
|
" 109,\n",
|
||
|
|
" 110,\n",
|
||
|
|
" 111,\n",
|
||
|
|
" 112,\n",
|
||
|
|
" 113,\n",
|
||
|
|
" 114,\n",
|
||
|
|
" 115,\n",
|
||
|
|
" 116,\n",
|
||
|
|
" 117,\n",
|
||
|
|
" 118,\n",
|
||
|
|
" 119,\n",
|
||
|
|
" 120,\n",
|
||
|
|
" 121,\n",
|
||
|
|
" 122,\n",
|
||
|
|
" 123,\n",
|
||
|
|
" 124,\n",
|
||
|
|
" 125,\n",
|
||
|
|
" 126,\n",
|
||
|
|
" 127,\n",
|
||
|
|
" 128,\n",
|
||
|
|
" 129,\n",
|
||
|
|
" 130,\n",
|
||
|
|
" 131,\n",
|
||
|
|
" 132,\n",
|
||
|
|
" 133,\n",
|
||
|
|
" 134,\n",
|
||
|
|
" 135,\n",
|
||
|
|
" 136,\n",
|
||
|
|
" 137,\n",
|
||
|
|
" 138,\n",
|
||
|
|
" 139,\n",
|
||
|
|
" 140,\n",
|
||
|
|
" 141,\n",
|
||
|
|
" 142,\n",
|
||
|
|
" 143,\n",
|
||
|
|
" 144,\n",
|
||
|
|
" 145,\n",
|
||
|
|
" 146,\n",
|
||
|
|
" 147,\n",
|
||
|
|
" 148,\n",
|
||
|
|
" 149,\n",
|
||
|
|
" 150,\n",
|
||
|
|
" 151,\n",
|
||
|
|
" 152,\n",
|
||
|
|
" 153,\n",
|
||
|
|
" 154,\n",
|
||
|
|
" 155,\n",
|
||
|
|
" 156,\n",
|
||
|
|
" 157,\n",
|
||
|
|
" 158,\n",
|
||
|
|
" 159,\n",
|
||
|
|
" 160,\n",
|
||
|
|
" 161,\n",
|
||
|
|
" 162,\n",
|
||
|
|
" 163,\n",
|
||
|
|
" 164,\n",
|
||
|
|
" 165,\n",
|
||
|
|
" 166,\n",
|
||
|
|
" 167,\n",
|
||
|
|
" 168,\n",
|
||
|
|
" 169,\n",
|
||
|
|
" 170,\n",
|
||
|
|
" 171,\n",
|
||
|
|
" 172,\n",
|
||
|
|
" 173,\n",
|
||
|
|
" 174,\n",
|
||
|
|
" 175,\n",
|
||
|
|
" 176,\n",
|
||
|
|
" 177,\n",
|
||
|
|
" 178,\n",
|
||
|
|
" 179,\n",
|
||
|
|
" 180,\n",
|
||
|
|
" 181,\n",
|
||
|
|
" 182,\n",
|
||
|
|
" 183,\n",
|
||
|
|
" 184,\n",
|
||
|
|
" 185,\n",
|
||
|
|
" 186,\n",
|
||
|
|
" 187,\n",
|
||
|
|
" 188,\n",
|
||
|
|
" 189,\n",
|
||
|
|
" 190,\n",
|
||
|
|
" 191,\n",
|
||
|
|
" 192,\n",
|
||
|
|
" 193,\n",
|
||
|
|
" 194,\n",
|
||
|
|
" 195,\n",
|
||
|
|
" 196,\n",
|
||
|
|
" 197,\n",
|
||
|
|
" 198,\n",
|
||
|
|
" 199,\n",
|
||
|
|
" 200,\n",
|
||
|
|
" 201,\n",
|
||
|
|
" 202,\n",
|
||
|
|
" 203,\n",
|
||
|
|
" 204,\n",
|
||
|
|
" 205,\n",
|
||
|
|
" 206,\n",
|
||
|
|
" 207,\n",
|
||
|
|
" 208,\n",
|
||
|
|
" 209,\n",
|
||
|
|
" 210,\n",
|
||
|
|
" 211,\n",
|
||
|
|
" 212,\n",
|
||
|
|
" 213,\n",
|
||
|
|
" 214,\n",
|
||
|
|
" 215,\n",
|
||
|
|
" 216,\n",
|
||
|
|
" 217,\n",
|
||
|
|
" 218,\n",
|
||
|
|
" 219,\n",
|
||
|
|
" 220,\n",
|
||
|
|
" 221,\n",
|
||
|
|
" 222,\n",
|
||
|
|
" 223,\n",
|
||
|
|
" 224,\n",
|
||
|
|
" 225,\n",
|
||
|
|
" 226,\n",
|
||
|
|
" 227,\n",
|
||
|
|
" 228,\n",
|
||
|
|
" 229,\n",
|
||
|
|
" 230,\n",
|
||
|
|
" 231,\n",
|
||
|
|
" 232,\n",
|
||
|
|
" 233,\n",
|
||
|
|
" 234,\n",
|
||
|
|
" 235,\n",
|
||
|
|
" 236,\n",
|
||
|
|
" 237,\n",
|
||
|
|
" 238,\n",
|
||
|
|
" 239,\n",
|
||
|
|
" 240,\n",
|
||
|
|
" 241,\n",
|
||
|
|
" 242,\n",
|
||
|
|
" 243,\n",
|
||
|
|
" 244,\n",
|
||
|
|
" 245,\n",
|
||
|
|
" 246,\n",
|
||
|
|
" 247,\n",
|
||
|
|
" 248,\n",
|
||
|
|
" 249,\n",
|
||
|
|
" 250,\n",
|
||
|
|
" 251,\n",
|
||
|
|
" 252,\n",
|
||
|
|
" 253,\n",
|
||
|
|
" 254,\n",
|
||
|
|
" 255,\n",
|
||
|
|
" 256,\n",
|
||
|
|
" 257,\n",
|
||
|
|
" 258,\n",
|
||
|
|
" 259,\n",
|
||
|
|
" 260,\n",
|
||
|
|
" 261,\n",
|
||
|
|
" 262,\n",
|
||
|
|
" 263,\n",
|
||
|
|
" 264,\n",
|
||
|
|
" 265,\n",
|
||
|
|
" 266,\n",
|
||
|
|
" 267,\n",
|
||
|
|
" 268,\n",
|
||
|
|
" 269,\n",
|
||
|
|
" 270,\n",
|
||
|
|
" 271,\n",
|
||
|
|
" 272,\n",
|
||
|
|
" 273,\n",
|
||
|
|
" 274,\n",
|
||
|
|
" 275,\n",
|
||
|
|
" 276,\n",
|
||
|
|
" 277,\n",
|
||
|
|
" 278,\n",
|
||
|
|
" 279,\n",
|
||
|
|
" 280,\n",
|
||
|
|
" 281,\n",
|
||
|
|
" 282,\n",
|
||
|
|
" 283,\n",
|
||
|
|
" 284,\n",
|
||
|
|
" 285,\n",
|
||
|
|
" 286,\n",
|
||
|
|
" 287,\n",
|
||
|
|
" 288,\n",
|
||
|
|
" 289,\n",
|
||
|
|
" 290,\n",
|
||
|
|
" 291,\n",
|
||
|
|
" 292,\n",
|
||
|
|
" 293,\n",
|
||
|
|
" 294,\n",
|
||
|
|
" 295,\n",
|
||
|
|
" 296,\n",
|
||
|
|
" 297,\n",
|
||
|
|
" 298,\n",
|
||
|
|
" 299]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 166,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"my_cols = []\n",
|
||
|
|
"for i in range(300):\n",
|
||
|
|
" my_cols.append(i)\n",
|
||
|
|
"my_cols\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 148,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"(37, 300, 4)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 148,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"data.shape"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 149,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"data_1=data.reshape(74,600)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 150,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"array([[0, 0, 0, ..., 0, 0, 0],\n",
|
||
|
|
" [0, 0, 0, ..., 0, 0, 0],\n",
|
||
|
|
" [0, 0, 0, ..., 0, 0, 0],\n",
|
||
|
|
" ...,\n",
|
||
|
|
" [0, 0, 0, ..., 0, 0, 0],\n",
|
||
|
|
" [0, 0, 0, ..., 0, 0, 0],\n",
|
||
|
|
" [0, 0, 0, ..., 0, 0, 0]], dtype=uint8)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"display(data_1)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 196,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"array([ 0, 0, 0, 34], dtype=uint8)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 196,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"data[10][98]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 219,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"my_col_list =[]\n",
|
||
|
|
"for j in range(300):\n",
|
||
|
|
" for i in range(37):\n",
|
||
|
|
" col_list_1=[]\n",
|
||
|
|
" col_list_1.append(data[i][j][3])\n",
|
||
|
|
" my_col_list.append((sum(col_list_1)))\n",
|
||
|
|
" "
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 220,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"[0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 1,\n",
|
||
|
|
" 76,\n",
|
||
|
|
" 154,\n",
|
||
|
|
" 154,\n",
|
||
|
|
" 147,\n",
|
||
|
|
" 140,\n",
|
||
|
|
" 144,\n",
|
||
|
|
" 128,\n",
|
||
|
|
" 56,\n",
|
||
|
|
" 50,\n",
|
||
|
|
" 60,\n",
|
||
|
|
" 28,\n",
|
||
|
|
" 3,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0,\n",
|
||
|
|
" 0]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 220,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"my_col_list"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 202,
|
||
|
|
"metadata": {
|
||
|
|
"scrolled": true
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"[<matplotlib.lines.Line2D at 0x7fb7ef112490>]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 202,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.plot(my_cols,my_col_list)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 203,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"def getCol(mylist):\n",
|
||
|
|
" i=0\n",
|
||
|
|
" while(mylist[i]==0.0):\n",
|
||
|
|
" i=i+1\n",
|
||
|
|
" print(i)\n",
|
||
|
|
" j=i\n",
|
||
|
|
" while(mylist[j]!=0.0):\n",
|
||
|
|
" j=j+1\n",
|
||
|
|
" print(j)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 206,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"97\n",
|
||
|
|
"110\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"getCol(my_col_list)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 208,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"(300, 37)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 208,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"line_img.size"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"display(line_img.crop())"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 222,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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\n",
|
||
|
|
"text/plain": [
|
||
|
|
"<PIL.Image.Image image mode=RGBA size=13x37 at 0x7FB7EFD7D100>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"display(mo_img.crop((97,39,110,76)))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 235,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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\n",
|
||
|
|
"text/plain": [
|
||
|
|
"<PIL.Image.Image image mode=RGBA size=26x37 at 0x7FB7EFC8CE20>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"display(mo_img.crop((97,39,123,76)))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 236,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"final_img = mo_img.crop((97,39,123,76))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 237,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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\n",
|
||
|
|
"text/plain": [
|
||
|
|
"<PIL.Image.Image image mode=RGBA size=26x37 at 0x7FB7EF9BBAF0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 237,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"final_img"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 238,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"final_img.save('final_img.png')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"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": 4
|
||
|
|
}
|