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| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/hotchpotch/3b816ec1389d02aeb4de163171b3d684/image-similar.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
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| { | |
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| "execution_count": 1, | |
| "metadata": { | |
| "id": "soezzuBuDwEd" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from __future__ import annotations\n", | |
| "import tensorflow as tf\n", | |
| "import numpy as np\n", | |
| "import pandas as pd\n", | |
| "import matplotlib.pyplot as plt\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "tf.__version__" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 35 | |
| }, | |
| "id": "bjApdIrAMtzi", | |
| "outputId": "3e4704c8-b813-4227-db5c-48462517ca89" | |
| }, | |
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| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "string" | |
| }, | |
| "text/plain": [ | |
| "'2.7.0'" | |
| ] | |
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| "execution_count": 2 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "model = tf.keras.applications.EfficientNetB0(\n", | |
| " include_top=False,\n", | |
| " weights=\"imagenet\",\n", | |
| " pooling='avg',\n", | |
| ")" | |
| ], | |
| "metadata": { | |
| "id": "maR5vTY1MnW4" | |
| }, | |
| "execution_count": 3, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import hashlib\n", | |
| "import pathlib\n", | |
| "\n", | |
| "def download_image(url):\n", | |
| " sha1 = hashlib.sha1(url.encode('utf-8')).hexdigest()\n", | |
| " local_path = pathlib.Path('images').joinpath(f\"{sha1}.jpg\")\n", | |
| " local_path.parent.mkdir(exist_ok=True)\n", | |
| " !wget {url} -O {local_path}\n", | |
| " return local_path\n", | |
| "\n" | |
| ], | |
| "metadata": { | |
| "id": "XB_0zl9cM6zc" | |
| }, | |
| "execution_count": 4, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import IPython.display as display\n", | |
| "image_path = download_image('https://cdn-ak.f.st-hatena.com/images/fotolife/s/secondlife/20211213/20211213125617_120.jpg')\n", | |
| "display.display(display.Image(str(image_path)))\n" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 288 | |
| }, | |
| "id": "fpsOF-KnN1R7", | |
| "outputId": "cca9c8da-48bc-45d5-9966-76ec02ca6b39" | |
| }, | |
| "execution_count": 5, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "--2021-12-18 09:15:08-- https://cdn-ak.f.st-hatena.com/images/fotolife/s/secondlife/20211213/20211213125617_120.jpg\n", | |
| "Resolving cdn-ak.f.st-hatena.com (cdn-ak.f.st-hatena.com)... 23.62.165.20\n", | |
| "Connecting to cdn-ak.f.st-hatena.com (cdn-ak.f.st-hatena.com)|23.62.165.20|:443... connected.\n", | |
| "HTTP request sent, awaiting response... 200 OK\n", | |
| "Length: 61484 (60K) [image/jpeg]\n", | |
| "Saving to: ‘images/f0470c4f1571c078f1425a321645b782c1a27ac9.jpg’\n", | |
| "\n", | |
| "\r images/f0 0%[ ] 0 --.-KB/s \rimages/f0470c4f1571 100%[===================>] 60.04K --.-KB/s in 0.03s \n", | |
| "\n", | |
| "2021-12-18 09:15:08 (2.09 MB/s) - ‘images/f0470c4f1571c078f1425a321645b782c1a27ac9.jpg’ saved [61484/61484]\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
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mWMRZZDY+XrW3KNduC7EBU6Cp7ZuMPZ0IC57/AO5dZl15kagyFbSG2jZpOMKR7kdqfPMwS/hgHDlt6pnZgHnT88PJvl71La0lF9ert6EPxUb/ACm6Lm70XYGfN6p+iH9J189eTtgjxy/nfwf6ZLNAP5uef4INTkuYvLnlu9QvE6D1Lp46/CQhoqchuH5N/qZk6j8joiYCJz5ofzv7trGkz5xeTJUJfw4vT/x//O9KTeYv+cbdU1Wxu5v8ZXlzqaM5t0lA9NkH2UZUKHl/lfZ/ycsw+0uMSA8KMYdeD6m+HZOOG4A4v6Q4v9lJ5L+WXk270X829E03zNppW6W4DNZ3VApPBikgO6vRl5L/AJX+VnTdpayGTQzniltw/VFjAyGURIfZmpXUltxWFAoK/E1NgPozyLVZZQjcRbvtPjEzuk9yNO1yIadq+mw3tsFLPFKgkQg/DurCm4OUaftbJGfo4oSrilKDfk0kQLtX0fRfJ+gyNJplnb6czIPVWCNVJQD4a8BUqoG2ZeTtEz+ucp/xes8bUcM5ch/vVS4v/WjM9lcC4XkCETcEfLMSOp45DgIlEthwcAqYMS8W/wCcpPJ9nq0Xl/UY7mOLUXuVslioKus3U1Hxfu6cs7H2f1vhGfF9EY+J/pP+KdfkwmdAcyeH/TPRfy+8oaH5W8nWmi2lvBfXKw8rqdI1BlcirM4NT+Oc9re2hqchyRJPF6ow/mRcoaU49jtX8SFvLHyL5uV7TV9LiK2BMUUigALLxoeLLT4lrmH2d7RSEyImWP8ArfxtOXGDYkLDwVvy0/OHRdSRtC+sNpt1dyW9hctOD8AYlDMCS0a8R9rO5x9s6XICZGP9Xh/i/i4HBy9h4/5tTr68R4JcP9LheqaVafnzoNpBHe29nrkbqTNDBLR1CjYBpAgbl881+WfZ+Yn6sXm440uqxf3c+Ov4Mn/Fo/RPze8s3t/NpOsg6VrEX7uS1uKAI4/yh+vKc3ZOWI48R8SH8LUO0BH0aiHhn+LJH1Q/038P+czOzuLhbdrmCdZLdQCprUP8s1cslGsgr+k7PGOIcWGXGD/CjotYtZG9Of8AcTbbHpvkZaYSFxZw1VGpekooEEVqCD0I6ZiSiYmi5gIPJhnnzS7hFj1mwH+l2Z5kD9pP2lyqYbYlOvL2tQatpkN1E1eSio8DhjK2MhSaFh0yVopRkBpUYCoUlkI64LTT/9Y+/Lryw0dmdSvBW6uz6jV6ivQZpchvZ20Nt2dCxiUVpuMq4Qy4lCe3ULsNzkCGYQoslY1brkKZomGzTwwgIJVxbqDkmKLtdO9U8m+GMdTmfpNCcps7RcTU6uOMeacWyFUdbONWMFDLGTxcqe4zdejGOCH1fzXUcU8p4penH/PYx53/ADT8r+VuOlBm1XV72RUstIio83qvTipP7ILd2zY6PsrJmuczwY/50/p/zf5zi5NWPowi5D+L/i5fiX9FDr5H85eb7G2v/OmrS6RaSMWn8vWLemFhIoqTXA/eNIDu3plU/Z+L7WXy7RwaYmOCIn/t+T+d/Rh9PCzhojLfIeM/zf4fx/pkrX/nG/yS+oySJcXF9pjROPSMoHpSGnEh0Cs+38+DJ7T6mEBwCEZcXq2+qP8AnOfHBA7T3/m/w/7lmXky9uNC8ttoWv3ivNpxaC1ulSnq2o/uWNK/Hw+GT7Px5oe0+1dNPJ4kTweIOKUP5uT+P/jrl4OzspA4RxB1pqw1DUbePTJhEsDhpZ5F+0g6qB/lZo+zdVjOQ3dcP+mdnq9FOGO5cyknmHyvpusea117VmF5c2NI7BIqxpEqtzDGhq8nL9o/7HMvU+0OSEPCwHgj/lJfV4n/ABMHHwaONXIb/wBJlI1GV1ZJ7yKHkhZI3IJeg6DfNWNRknYlMR2+n+czGEDeMTLf+H+FJry5u0s5LmIPzDcSkQJLL2bbdetMwTxcPEOfk7HBjiZgHl/SW6Mb5irXMRjuEZZ1SepMkLjiR8YBVY6jlRsu04nY4h6vq9X8z/O/m/1meq4OUTcT6PR/Bkj/AFf56NSDVYrx5bS4MUE8hV4Ej2Wh+IqV/ZFTxJXMo4skZExlwgn6QP8ActBniMKkLlEfVbFvzH/LXUNe8z+W9VHoRWOkNJLeuSRJcM7K3xcKAemyfD/rZvYdqzwaaeOQMpZI+nJt6HWY8UDMTBrhP92zy2spoLVEtAY7u4U8ZG3CKo3O3jmjwYTCI4B65/7llPMJSPFvEJGNLOnRQQXSKEjkkkDR0UzzyftvT4uQ/lzEOI46BAqJMv6UptHBe4W3sWqQWjP6yzLNKKKtaI4/kB/HDnGSEOIG+I/6X+r/AL52Giyxl6ZCuEf7H+kitHu9dbUViEisiiksJ2I2rjo8+fxOEH/NbdRjw8BJH9WTxn/nKO7046holqloba8iWWaW44qFdCApXkNyVahz0f2fyyljnvz4Y8P810GfDRBO8d0k/LVPzpt7CLU9DtZL7Q2qUW4kUKyjqY+R5U98zNdn0pPBk+of6Z1J7M348JOCf/SuX+Z/xL1Dy/8Ambpusg6be27aZrDUNxDdKVkIG1UBpUfysM0ufs6UP3mEiQYjtDhPhauJA/hnH6Zf5zIl1bUdLT62ql9PL8Fic/GR4qMpx5oZfTMcEvP+JvnHLgAnD99j/ofVjiyK0v7DV7ItCwZXFHjPUVzF1OkljPk5+m1UcoBiXn9tcyeUPN31CU8dK1Ni1sx+ykp3Kf7LqMweRc7mHo4ZXUOpqDuMmWtsHkMVU2QDAr//1+uWVrHDCkaCioAAM0Tt1WQEdMBSEBcyVag7ZVItsQ1ErE1yKUYKKtclbFfbRiVi7bRLuxPTNh2fojmPEfo/3Th6zVDFHzTK1VriahXjaqKvGxZG4no6kCh/1c3eXNwyGOH1fxf0Y/0XSY8XixObIax/5P8ApS/pvLvzH/NjzDPe/wCCvy7hk1PW2ZoJ9RQBliNCxjEjUT1eKn7TfDm+7N7Jx4o+LnNR+r1/77+Jx8uaeplQ9GP+h/H/AFf99L/Sfzo8CutM/MHyN5w0/W9e02aPV4pfr0X1seoJvSYcyWUt0rua/BnUcWDVYTGEo/6n6P4eJlGIx0KoPrXyh+YumfmB5Nh1KKeC01FPiu9OD82icVHFqhWIbqp455h29oTpxKEia/hn/Od3oZ3IEbopb2a1D+m3CSdRGI0Pwn3p45x41uSMOAHm7nwIzlZ6IZIZFtpotYYFJd4o1+3T3OUAUKyfD+c5M8gEgcP8P1S/hbipbmOOztxGJN2A6hfFjkeIg+kVbEz4wTOXJIvM+r6ik0vCGUafEgDTrRUMhqAQV+LrTL5Hbfd1syg/LWv2CaadQmhnNipUTzyElfWUhAQCPsO9B+yv7OCJEZctnb6bF4uMCB9Uk5/x9bQvbz2OnveWUx4s1QFlL/CXoSx/dkcaZcNZES2Hp/i4v4myXZZIIlMRkP8AYf0f89Ei91O9uvrk9y8VwkEcMlvsIlWpaqbcuTVpu3+xyuWonkNk1Q/2DUMMMY4QLiZGX9L/ADmRNIzWqJCzPO8RCRszCMbbP8NeZNRmwB9Io7kcrPD/AFv6TgCIErP08X+d/V/osW0zVvNjXVzaX/wtEyPGgQSxcAQCpWledBy/yswceXNxcJv/AEvHF3GfTabhEodb68Mv+kWUeX7uW+kNxITzTkhFCoYVADCv82ZmlmZysnePpdPr8YxjhHL8elKvOzWtpHHdNeLFPbS/6NbvUiSV/hANPnlGriB/F6on6WjTk869NKl7qiobeEwCUmhWKvGRXAoTT+XK8mYAAVY/2XEungSdv85f5fuW/Stx6/IervzAICkihBPtTHRZP3pMv4nM1UP3YrohL3yxpet+abe51OzS/j0lWazZyCB6mx5A/aO2b3Q5skDMRl+7lX9ZxdUI+HH+f1btdfuFgu7KxSCzgtZHjhtgKmgPSg6csq1WulHJwgcX++YY9KDHiJYb+elppN55Lh1a4k+qeYLYxtp8MTCORJHPE8hszKorXOj7JnIZP6BHrdZqMcZxMZDiDF/y1/MtpzDpGuyj6x9mC/YA8jSlN/sv/wASzN1vZ4yx4wKmHTR49FP0mUtPL6/+JekSxy6bOl7pzMse1TIQDOx68EHfNZp9VX7rL/p5OVl02wzaf6ZfVhj/ALr+siPNGmW3nHyzKIT6eoQDnGR9pJE3Vh9OUazSGG45OdpNUMgQf5Y+cZdWsH06/wD3eq6exhuoz15LtX5N1zXxLmkWzlmAFRkixDTOSMbWn//Q7GCAPlmidupzyBY+Xc5GRZRCWNJV6fflJLaArI9OmR4k0qfE7KgPXL9LgObIID8Ras2UY4mRTC1X15xZKFMaEc1YlebeAI8M6zNKODGIwril9Ef6P/FPMYwdTlJlfh4/rlH+f/DH+qwr89PP1zoejp5d8uyldTvEPrXJYkwW7bVqf23Pwx/yr8WbXsLs4fXP1Rh/D/On/wATFdRLx519MP4uH0/jiZN+VHljStD/ACqtW0Ix3VzdxfX5bmcDm9045Hmy71Q/Av7XFcwe1dVPJqJGf8Po4Y/zf+POXhgAAEPJ5j1XTZ4Lqe4XzE9w0dvNZtCiyxpI9HMQRahUrykDhvgj/mzR6fXHJk8OUPB5y4o8X8I/j4/916fVJ2c9HER4gUls/wAv9L0vzfqGuxzKgnlZ7extl9OGJXA5V7vyarfy5ru3O2zqIxxD6cf1T/iySc3RacQjdeossvdRtrPTxqCxes2yQQhfiZ2NF38K5pDkjGPGA34sMpz4L4f50v6KHaD67CouXRNS3asZYqp6hCemVGIkNz6/x6WwyED6ReNKLjWru00tORW3u5WkQEsCWMZ9+1BkcUjw+bTriIyPD9LzbWPODXesR2C6oLfTrqZfXJdVCBVqR8X2RJx+E/ztmTDDIY7kPW4WmHi5AJGo29BliVgjs07RGINcWfJTG67Esy8Sz8f2FzHGY3vf4/3T1EQB6QIij6JV+OFhS2Poa3bQWWqUsru4WQaRTjEHqeCxuGDKsX7x2UquWcfpqt+/+FuymUd5fw+n+m9Vs7P1Bx4sqKoYKx5Ej36A/QMYw4vJ0+XLRvqi59SitbaHTraIT3Ev7whWo0K1pU06A5kyyiOMQA4pS/6VtEcRnI5JHhiNv+GJrY6a7LNcRu3qv8D1Y0qQOq7AUOZunxEgkHf6XFy6gCokbBZq17YSXkOmxSATRhJpippwU1AqR13Hf+XI6yUSYwH+mYYCY3I/xfStv9P0mS+it7yFbgx7pMO3IfPrkckYQyCMvV/SYiMpQ4h8kr1nyTardfpO2mcTE1lmZieEdPhAWv6sr1mhjw8Uf+kWzR6kxPCeX81imt+ZVs4ZbqynklhhJE8wVgrHoCWpx65r4QnKVR4vlJ244QPXw/N4r5h/Mf8ANHy95im1+0mJ0y6VI2jI5pwQ1+L+RmrTnnWdnaOEYcJPrvik6PW6gynYHoZjov8AzkhHfXVreR+XreCeFX+tr6tTIWAAIbh2p3zdjs+FVx3I/Ts4pJqxyTp/I3mD8z2bzbrl9BpdqITHp8Ea8nCgmgflSgLf8F/k5dj1kdKPDEeI3693HlDiZDb/AJNflvJpEOn27M+tmIst0ZnVvUoKyFagcFY/ZpmJ/K+WUiQRw39OzI6YAbhA+UdYnstSu/KWsTrJqennjFfQgO0sXUFOtJKH4hlutwDNj8QDh/3rrMPFpMwrfHkLITK+kahFdwRSRW8x4vFcMDKzd2ZR9kSYNDkGXHwH6o/7lGtidPqLH93l/m/THJ/x5iP5hwS+U/Mlj540oH9G3nGPU416cSdnP+r3zT6nD4cqd1gy8cbep6TqttqVjDdQOHimUMCPcZUC2EIytPhPbpir/9Hr5YAU7jNBbuKS69uavwHQdcqkW2IQ8ZLVOVEtiIQFR88CpjpsNS856Ivw/wCsc6jsbBw4+M88n+4i6HtXPvX8z/dJ2kr6XpklzMfSt4Y2kmLcdwoLFlYVNcyBxZs3l9Mf5rhiIxYAP45/VzjN4FYeV9e8/wCu3l6SsIuZWZrqcN6K9PTg5AGj8OPEZ1+XUQ02Phs3H6eD6/6U/wCqmEKAFX3pjYflT+cHljzqtx5TuLC4isrdJpYbmeRbSVpi6FfRANJBw+38P+t+zmuz6/Bmweu+Mnh+n1+n+m3ddh9qY2f5261F5suLLzVoVtodzCU0+eNZfUdrhjzWQHiAIyr/AA/E32vtZpe1OzuDT+LjJmOEzczR+s15sr0lLm9N1c3BDRPcMyUP2o1ACj6P2s84A493opHgoDmjtUkmEXGFazsP3YAJRTTYmmHLaMERe/0pR5ZW9Fkw1GMxXNpPK0b8v70PU8mH05XDl5xcjV8PF6fpmAkesavawTwQTypIt28npF6BKGvY7Nu3+yyMSSWOXScWKR6xea+YvIut6zLPfeX0hvfqLi5W3ZQomeN+RjRD8DCq8fi/ZzbafURiale/+x4v4v8ANdJHTS2mOV+n+k9Y1FnigjijRmuZuKrGpoxY0+Cn+tmpEd66vWaQ8RMpcgxa+/Lbzbba3pmtWjpb3trOJponcVEPIh04qGVmMW32l+L4szonwwYyHMf7L+Fr1OthmFRPpvu9Mv5zOtO1+5MMjxMnFAS4rxdHRv2uX7O/LMWGeVbOPqNNC6/EorLHU7PTra5d5Fe7n+P4CCzE/aI8eOHHkoH+cWGfGZEfzQidO/N3S5Lg6TBbvctxCSyR0FXb4V6V8Piza6fWER4TH0kfV/OdTq9MIeu97+lGeULTT45DcafLJfTX07/X7uRuTApVab0P7X2APgxxxAkODc36v+KcfLMzJMtv5o/3qcXV1Y6bfrpjyJ66qrlQKUU1AND408cjnrFk4DzcnBgnkx8YHp+lObGVbtQkg5xU+B+n0GnVczdPMZNj9Lg5YmB25qHmLU9Ki8r6lHfxq8SwPHPEV+E1FNxT7PvmV+cjGBv6otfgSMrHIvFrLQNOvtCi+rqbi0mAjnJAkTi22y/a2zUDtLJxXtYc44NtuTxjzh5WT8uvONnNbtJLZLJHcFZAtHX1eRjjFPs8Rx+POn0Gu/Mgg/X+PqcGUfC5/S+nIfPOk+bPKUOqaTd2tlJC6O9lcFfUPZY2VSOJdqcMfyk5DgnfqH1Y14xCdhvzRqFv5X+qa9rdxHdzQVii0e2YKzCWgL1Y/HwHUN8H+yyHZ+hFmMTvL+OX9Fc+cy6PEdf823Wp+bX8wQRi1dZFNvDCAvCNPsqSKcmI+0c6nDg4YiPQfzv4nXZ4CcJA/wAX+x/mvaIUTWtMiu7f0+V2iuzqHed9qrQmtB/NmhmPy2pHTiaYCWq0koysyxfzfR9P0+pFadZW+veXL3QtQQSK6sKHs32W/HfMrtPAJRsMOydSZRF/539Z59+Vmv3XlrzJdeQ9ZYgwOTp0rftRk/CN857zd95PaWrx916H2wlAf//S6lPdCNCx7frznDJ3YCV+pzJPdsqJbgEVCKDbIqrVFN/kMUJ3pqA2yL2eUCu5+yPb5Z3OOIx4gP5kHktSTknX8+aA/M+5Np5IvfSZCLsxwAxghSJJAG6714q2HsOAOQHbrP0/0XJ1t+IAeLYfxJb+S2q6PFot9pMt2LS8MpkZZTGAwZQA0ZYfFxpuGzM7XxzlKOQDijXn/sljQJCcz6RCtiTp8DG6YkRa3bsFV5OXwyvIKVAd96qyfa45oDDOMgPF6b+iZ/g/m+H/AFXNjkxmO4/H9ZgP56/lrretaVoEOl2EbanDL62uajCVSQwhVWSTk7eq7r9pVq/+tl2p15hGQFmP8EP4f+JZ6WFkD+cRxI7TNQKaLZpblooOPCIMTyMafDyNdzypyzzoyNPYSwREj1pXstV1VpJklEfo8lNq1TzIOx512GWGYAFXxfxOOcXqJPJOkkFxC8MlsFU1HNW612rt0rkh6hVNc40QRJ5h520vSrPWrD6/I62enKI47dQWMhWjItfo3yMTKJMQN3KgTKN/zmceQdZg1PSTwsv0etszBY2NfhO4YbfDWv7OWRO9f7Jw8+PhI82Q6TY20uuxSzQhnt3LRyhjTlQg8h0+GtVIy3SQHigkcmvUZZDCQDtJJb/WNZsNa9a4ZPVvJ3+rwEg8oVIHNa07EbZj5Mkxk4/5xc2GHDPFwgH0R9R/ppV+YvlL6/ZnVtFkeO7YercWsZ4q6/tkUpvTtlsxD6h/F9TVpM5B4J8htxPJ18xa8mp2VpFaGO0SQRPzqq1qFHeoXkeT5diw46MpG/6LPX6jwwBH6iRHiREOpX1nritalEk9V4IJ46lXqxVnalAHPFnXjy+19nDE1EUeX+weZ1+YzzGiNvT6f4n0BoCw6fpKXX91C0YuY6cdhUVHwiu/fll2OQiOLv8AW3cQOyYaxY2+szW2pWE4tbiKKSKQsC3OM78TTvyG3+yy3VCOepQl4ZjxR/nOVpNQcMTCY4oyMZf5y7RtXglvpbJE9OW2cQTGjKGPAPzUMBs32sjhzAT4Ko/T/W/pLq9ORETu+L1f8dTefT47iK5e9VXjnUxyREBgYyTQMO5bpmZLDfFKfX/c/wDFOCMtUI/wvO7u0m0zUecAH6OlCKCaA+qCfhoPs7ZzmWNGw7/HkE40eYYp+a+naLrOhxpqDpBJISkUjFfhIXl1PhTNn2RkmMwMXD1eGJxkH4SePflnoWq65qn6I0+MXl3wMsUZNAqpTkeTfZUErno2LUCGE78O7qc+GoxJHJ7tp/5ReX0sW/T1xe3HmCWLkbYE8YXI2YMtfUQHozPxbMPJ2mcZuPDGH87+e4vhce1J/L+V/kW98uy2+k2X+nWylfr0peOQTBa/vCftdeXCnDKIdo5JGzLihL8en+assIjtVEJf+U8k03loW7GVjBJLC6RsF2VgQN/8l8yO142LHP0/T9TgaOo6kg1wy/nfR/pf4k70cC38xyxqAoZyOCnkBzXpXv0zIieLDEnucLABHLMDlxsK/PrydczWUHm3RwU1bSGEjFBu0YNT08M5mcakQ9NE3G2Vflr54tfN3la21GJx9ajAjvIu6yLscgdkg2//0+hahKGKxDuatnMEu/iFOFQTXIskWgOxA26YEL3p0A6frwqE88vy+tZxOj0In2anKhIr0zvtQCIn+o8dsZC9/Wl35wr63lESqwf07mAyMAQOrL9k9PtY9hz4p1f8MnL1EOHLyr/ZMK8sfl7feZNLa6ju7S3Cq7RxuWMtVNByU/ZQ9pKtmxzaiOnyfxy+XCnjMxVBP7LQvM2n+Vedp5pikgjtyZ9K5KQi14tGjEv8Qb4B8P2vs5harLCeT6DE39f++Z49g8M86eYPPMv5l6ToU2o31pskCI7GNxBPUsvxAclYLty5fZzUdpRxxxGWx/i/znaYN5QA6l7ZeaREkMDwrxtreNY0QdOCgLQAfKmefziTu9NizVt1XoPsqqqHNAakkivbKE+9M1ubaztlE8gSNm4uab0puemZEJCI3Ozj+HLJL0iyxjzPpN1fs99bR+ubeMHah2QVSld/wyqQJNuThnEDhOxKN8o/XrS0W91b04GuaBbeNuQjiI3LsQOTH2yYlGJu2jKDP0j+H/dMvt7VLZrm6Cmqx0Sh3c0PEjtWmZmOPDZ7h6XAnk4qj3lKHbTL61W3volnRSGhDqvqRtXpQ1+z7ZijJGqO4c0wnGXFH0/zv5slG9t9Qhuo6ShI0XiiMppQ9SKdRlc4yBFsoShKJ2eTeedD1KG/a8/diKYt9VgV+su1eoHFf2vi+HMrTyAibdd2sCYCQ5R/3X8KI/L2Z4xNb3lqkpdhIYWAdVlj6MoUc1cV+F1yjMY+ID/CQ6XAQZUXoj3VydFS1W5jd1cMUjaoKDdo6H9k5bchF2WKid09s7+R9HjuFQpzKL6dRQnuFP7X0YRKXBbl+FHjpkdreQ3VvHDSjlfhZ1CsNun+x5ZtonigIn/ZOFlwygSeii91PBK9tIspcgGgU8aA1NCMxjllAmJ4mQxiQ4hSE1uy06+0y8srpGtzdoyeojVAYiokArsyn4uS4MnBR4hwmX+l/rcLLEZggg8XC8v1/wDLaZo9PgF/LNbQ3PGV6hmaJ0ajgUbi9dsjoZeFkJH+a5moy+JEbMX8ka1Y+Svzc1O2AaHT3Y288kUatJHGUSQFQQaDk3x8fizrtHinnhflxep1+qH7u+56/pt1pVzLfa4vmVLbTrqQssd0IvWAjBUlUPH0xWnBSjM32v28Oq0PHKMZRPHEfwOvxZzEGmMah+ZWjJoTabY2X1u9mkkF1e3qh0atV9ZUBrycBeMfwLHmxxdnSxmxyj9MY+n/AGX/AElxNEsnFzR35VWqf4daQrGUlupSnrMUBChErUd6rke1gRHhPPhj/wAU4uCjqge6+nGyOyQSeZXZOBUOqgxii/Cvbv3y3DHhwRHk4glx55yu/UyC5toriGSCZQ8UilXU+BFDnPZ/rPvegxfSPc+alkuvyg/NV4JC3+GNZavfiquaBvnGT8X+R/q5DmPNPJ//1J6SHkZvHp8s5Z6BViAp8J6dcVVw1KCu/X7sVWcyT1oa4Eph5Pu0X6xYVAeGRmUdyCeY/A538JeJhhk/nQj/AMRJ4/PDhyTh3S4v9+n/AJusDrflW+sg8cl1JDyiCgqeaUdBQ/5S0zF7Pz+FnHEfpl/sXKzwBjGcRQLEvyx1yx1Ty8/lnULuO3mRZEtfUjBf0pKkhHkJTktSOHD7P82bbtTTyhk8WINfxb/8T6mMJA7FPbrQYbiKTTNNsbaaewVFW+tXVGiehZG2APrKq8uPL7TJ/NmhjiyRkJymeGX8Mr9bmeLGqp55+ev6I0yHRPNUk9pd3+nXEVlL64CXic2A5BvhesYJZ0kT7Oa7tHSZJxPDxRjXF6fo/H9J2HZ2phA+oA/73i9LIdO1myubCGeA+ukoEtF2J79M5IZK2d4cRvf0oNblY53Y8nZnDRoV2p3qcxzVebnmIMQOSfJcRtEJZo/hcgegy1XYdwcyPFrf/YuslEg0D/nMOu/M/wBR87GeVlttOht2F5IDRVqw4/AftdDtkIyuXFyN/wALleFeIDmnEi+T/NaM1nrCz+mR66WUhBX/AFlH923zy3LgANy2tx8WoyY+Uf8ATMgT9xBBaxFPqMCBU9acmWiigJZuRY/PDKV93D73HAskm+M/zY+lLdTsND1j4ZZVZfslopSrfCANmSmVTMTK3LxTyYxy+xG28OnRWyWZnlnjXZEdizAeHM/H+OSBjVEmTjyMyeKhFj/mDS9MvbOe2trU3c8VVkieUhweooTlJIB9PNyBHiFZK4Jf0WH+RtDuLrzPPbXMUlolrGZPqroF5uCOI5EfEqjlQrlksQnt1dVn7PjiFg2JS/0r1y50G11i2niVns5JyWSSHij8gvGjbHdffM7FiE76H8elx6MPOmI+cLvT9Mt7fT4ZZbia0FUHILUj4S1BSvhyzC1XAPSD9Lu+zBkviOwkmGj+bdFLQTW88ov+KpOk9erDcpX/AFcv/NwAEgT4n8fF/vGzV6TIYkVHg/h4WZRahN6UQYl/VIo/7QXvTMqGeW19XQRxAkn+axzUXnhCSRymcQSH63K1W2JI4H+XjXMDPY3+rh+r/iXbYRGW3K/pY95i1qHRobFbNWaPUWdoSOTgOu7LQb0y7SYbkCPp+phkkTYP1Mf0n8tbq/Yed9TvEiN6S8WmqOM08QerGJiSDI0Y/dJwztOzc4GOUSNpfx/w/wBF1Wsy7cA5s0ufy/8AIN3cW9txutPMknI3Vwzh5aV/dIGqgLdd15/yLy+xl6ftCQsRMZ7fT/N/pOBkxmtxTD/zD8taJ5e1WKDRzOqSwcjFMpLBwaAqzUZufhxzYYJzzgCW/q/2LVtGy9E8q6cmieXrS1njgeSCENMZTUrM9ZH4r1PxMc13aE/GzAAWCf8AY/T/ALlx9LLghPLZj/D6R/vv6yL8uwPJcS3kvUlpGNKCrf5jMzNIRjXQOLpIEiz9UvV/pk6br/knOZkbNvQgUGA/nF5Bh84eUp4YlH6UswZ7CTvyUbp8mG2AFS//1Z4g3H8vvnLPQIhahTSlT92KrySN+xG5+fTFCwHrTbAySue/k0fXLTURtby0huCOgYGqMfnuudl7OZxlxSwH6ofvMf8AV/j/AOKea7cxnHOOYfT/AHeT/p3L/e/6V6ZBKjGK4E8ogcBlSJQ6kYNVjMZcQ4tl0sokGBEal6oyk8q/MLyqmka29yoK6dfkyxMFPwStu8ZHav21zpdDq5ZMYr64/wC5/nNQAFxKC0Hy55rvLWa40NLoWwoJ5YZGhVt+wBUycepC8suzajECBl4b/h/i/wCkVEf5pSH86Pyg1WTRINQj1i01K9SWJBacmNzM17ItvGELk86yUX4uP+t8OYmm1UcsuEiWOFS4to8HDD1y/quXGuGikHlfXvNP5fX0XlXzpYHT5owz29/O1UkjairSVSyNuQv2v9b484HtnsXhkcuD14j/ADf4f60Xd6HtDiiMeQ7/AML01PM7G7hS2t4pY5QFa7D/AAhX+LkNiCP+GzmqIsVu7cYwY3aYnWZFRxKwcqSF9Pfp88qMq5oOIdHl4tLbzXr9017b3D6ckqhCjBEb0jUFjVWFG3GWxkcQBiRxf8U5FiQoh6vpbWVhZxxWkCw2aikNtCpLSP4n9psh4jjTiTzO/wDO/mL5dDvdQkS51Gb6lZA1ktxRpJB/ISPsD/V+LJjDvxT5LHVRgOGA45/zv4YqGrahBpGnCPSLCR4IiFkW1VWaJT1kYEgt/seTZA0do7MscJTlxZDz/nMfbSvOep+nfaRrc8cJPIQz26LU9h+8WvH5f8FjA7bx4m6c8Q2IEWbahJa2l1YpdenJfNbol6UopZ6dqHb/ACcv1HDGUdt+H1uBgjKcJV9PF6GodL+uQzPaXPqvbOArP/eIWHJQzD6MjHAZgyieRRLKIECY5oHT9U1FddtNKjBM10GkkrV1VFJDnoAK/wA2Qw8fGAOrkaiGPwzI8oozz7ax2mnW11bWsEt+eUVuJl5A8nr4jYAFsytbjjCMT73G7PkZyIJPC84t7TXrrWYYQ6XmsS7RQWoAigUEfFKacfTH7XL4v99/FmDHF4hoB3pz48eL1fT/ALp7HaRlIoFd1kMXGL1EFA0p+0FBr8K0zZQjVb/T6f8AP/468xPrQ+r1f5rEPMuvMNWbTrFCIY+Yump8MjSMTQeLb1b+XMLVTHFUe88XzdjpcI4Llz24WO+ZtSt7XTbO0I9WQMypFGR6pDkCRI2NOJZKx86/DyzZ9k6c5p8I2j/vWvKOcj/pmVWt55Uvo7XVv0pH5fksQEh0tCZmVU+H4kb4HkKbKY0/2b528tIRDwhHxIn+L6fx/nPMHKeLi6oc+ddJ+qLcyzXt5rJDrAiCO2itzyPFwaMCzLx5V9X/AGOW4ezBiO3BGH87fJKX4/zWvJnlJLvK2mar5l8w/pfWZmuUsyvKWXiA8qfYT4QF4p9o/wDN2Z2oy48OOoDhM/8AcODkMpHhZ1qkxmK2iQxKSB6rx/Exp0Bb3zU6TFuckh6v4U6mYIGKBJxx+tO7ayS105UIpLKOTDvTKNdm2py9Lj3tTDHjv2zUuwUnI6judsFpf//WncdWodwOwzlnoES1RGARTltQeGKHEkgbbHfAlZtXwHXFKDvbaG9spraYH051KmmzAnuPdeozI0mqnp8sckPqgfxFqz4I5YGEhcZBd+X3mqfTbxvLOsy0vIQDbzdBJH+y61/EZ6LmENViGoxfRL6o/wAyf8UHiYiWnyeBk5j+7n/Pg9Iv9PtNc06TTL8POkw5LMePwEbqyn2zS4pz08+KPJ2xkMg6RmP9kwry/dS+S9Sl0jWlnaynPKzuoZHVTWgpw5COjft/yn/Jzd6iI1UBkx8PFH6o/wDHvqascq2Keah5J0ltJlkk0ex1KCL1LiKbkvrqAeY/elWrJ/xYsi/8bZz2c5Zz9M5wl8fxwf0eF2WDKI7FIV8raNYfl0jedVa90uxWa5trtZmvVhtnLlBxkIaZPq7KhQRyep/r5nynOeSon94ajP0+Hx5P4v8AZtcqJ2Dy7yjH5BtlvtR0zUJG8vIzJdWUcU/rW7FgI5YlQGT6s/X96nKP4v3qceGVdodhxyV6RHJL+kOGf9b+m5OPVZcA3PoZpovkXzBFc3p1e0tbHQitbV47t55HUk/FKWREVSprnNanQ4RXCOKf9VzcetmefJAeYvKWg6NZxayl8LaxgdVjmicspaQ8FQqvIShi1FWjZqMugymREBxX/D/snPxa2IFyR2jXaWtzH9YuJLueVeSArwVEPQkUDUzVkGJ3DnGPHE8NUp3Nr5rnmkOr3Svp0rk282n8k9BVIK8wSS4P7f8AzTkslbHdOMwAqI9X9JmNjY2tpoqX91IZUPw29ujcfUp3J68fbMzDgiMfHI/1Y3wuJLJKeXgiKP8AHP8AmoSXzVPBcC3Sxe2jdQRNbxNIVLeJIOVfmpDYREP6oZS0UTuZcZ/pFjOr+WbvUR6On6tKl965meQCstG7ENUdf5sx41xfzy5cMwiPUOGH+xZ/oVhNZWcWntIsl/MRLqEyiigKKCvvtmy0+Phj4Y+uR4sn9CLqNTkEyZ/wD041unwRWdzPdPQ3dyWJbYCOGpKryPQ/tNkcVQJP8U/9jFcpMwB/DH/ZSYrrmu6b5pupobSskNkOEF8tfT5ts4XdQ42zG1ebjlXSP0ux0enlhF9ZfVFOPK2m6PoVp6MNt6Ec/wC8c7erO52rI/hl2KYjXHyr/Tf8dcbWceQ3fER/pYIrW9ZitpZJoaSTRoEt4I+nqtsKf6vc5LPlHHY6D08P85pwYSY0eX8UnndoPMup6/NZLBHpmjQMi3fmC5qY2kk3KRtQjlUjq32v5cyNL2SclE+on+Ftz6wRJrozNPIPlqK9g1GeK91LT0tnF1NNRCHDDiy8eBCcebNw/wAjOq0k8enjwxIjMfxOkz58uUUfp/mou/8Ayt8syxfXrW7Gn2npc0Uv6vIkVDFnIpt+yubTF2lk5H1l18sYYVofle51i59NE9K3j3nn/ZUe3i+bLLqPDFk/j/iXFlIdB6nphh07StNgsdNaKRVAHp8amvdmbNFIHUT4pfSzGTwQQL8Wf+xRei6aordXA+EGpr+0x7ZfmyCEWvBiso6af1WLnr2Ht4ZocmTiNu4hDhFIR2HIjKb3bKac4UP/1+gRLQhelN2+Z7Zyzv15G3UEdBTwG7HFWiRyI6HqTgSsdvhIB2O1cUrFIpX5ke2BUo8waDFq0Mbq5gv4CXs7xNmRj+tG/aXNz2N2zPRZP5+Gf97j/H8Tgdo9nQ1OPhO0h9E/5qc+S/OmrRQtp/mK1dPqxEb3yisRqPhYkfZDZ2ufT4c8RkwyE4z/AIHlcUs2E8GUcMof5T/ffj/jr0G8sdK1zThZ3UUbWhWscoJ5A9mVu2afGcmnncdh/NdiZjIPV9f854H+ankfzfoFpcXOnX93+hphxeSzllWJh/LcxIQvTb1Kf62dV2drcWooVCWT+bkr/pXP/et+HKMR4cg4f6TDPyq8hah591T9FXuqPDp2lxpcehcu7o0fML6MS8x6XJeXxxj4P5ct7WyRwRJkBxz9PDjHD/nSyV6/9i7IZ4443jHFxfxH/iHsXmXyJ5HTVbbQfKvliSDWbB7e7uL63kaCI24lUyxPOXDTetD6i/Dy4Px58M5eHaBwR4skvTkEowx8Pr+n6/6PBLhRj4p2ZHb+kzHTnTUbifQLyyl+pzRuJtL1R0nDwEcf3Eq8/Uj/AGZI5/s8s1kZY8g4oS9Uf8ycP63/ABUGvJjMB32g9T8jLp0ulWvlA2WhWlvd/WtS036uvG4hKlGAVSvF/wCWRf2v+BzGynn4nFP08MZX9KcZ/m7JT5oOjNqhhtJYG1uyUGWhBlgjl3UPTdVl4/tfy5zXaMZQiNvTM/7l3ehkJS33pBw6/b2xaK+jkiqBWRF2DdztWo/ys1YIc6WAneJDJbe50q+sY7eY8lXZmBoeI3VhTMqGSBiIlw5DJjmZRWSacIEBt/UuLdieKo9aD/KqchLFXL1RSM/EfVUZKkVlqDRiO0WO1VmBkWIcpD83Oy5OOOZFR2/3TA5YXcrn/W+j/Srb3W49Gsmit1M87yKkso3AJO9W8F/4bJHOMUTEfV/FJlHTnNKz6R/DFIdYuNSlna3kkK2nHlViAJCw6H2XMSc5d7mYIwAsD1L/AC35ftrW1RpGEOlWoLSyHqx6/wDDZPDj4rlI1AfUjPqyCQBeSfJFnUdR1eWV9HspLpyBGoQAJDH0ADMVQt/N8WZeOM85uMduX9SLjyjDEKnKv9/NhF7N5znt7u60/RrqWBJJLeS9EfKNXiYpIqqD6jhXUguq+m382brTdjDCRLL65fzIn6fx/pmjLroVwjl/NTfyb578snyeNI8wNfXuoQl3eEcVQnmWHp8eAXj/AMWfEudHkgcZ4oR4cf8Ap/8AinXTwyyyscMUZqf5seQ47GS1l1LULy+9MhIBBygWVt1jbkqJKF+weZbnmPi0sZyuMYy/oX/vWcNLluhw1/skT5buPOfnO2tX8wQx6RpUSAR2Vupj9cilJGV6sg2+BP2f+I7SUsOnOx4sh/nfwf8AFOBrRDeOH95/Oyf7xm8Nza6VbNZ2cEYp8K03A8a/zHMXgnllc+Tq+OMB6Dc/9yqWOju5N5e/u0Y1pSjN8h2yyeWOMUOiwxykbPMo24uPUHBV4RJ/dqOw6GuaTPnMz5O1xYhFC8iHK07ch8u+YzctkFAaCtegxVTVgQSevh74qX//0J/9hTT4u/zY7DOWd8vCgCg6KaA/LqcUrVoPiOxP6v8AM4ErJkJHEHv9O+2KWgFRCT36D5dcCrYiWblSi9SPDAldbXk1rctJFTeiOjDkjqeqsp2Zd8zdHrp4Dt9P838fxNOfBHIKKMttYbT3abS6JDX97pErEqfE28h6f8YmztdHrMWrjRPr/nfx/wDJSP8Av3mdVo54Nx9H4/0rLdJ81abq1ubYP6bUpLaygBx4ghhuMr1GhnA3/sosMOoiRVcX9GTCvNH5GaBqeo/pLy5I2j6tCwnhYKrWxkU8hWM7dftU+1+1mdh7dzRj4eT14/538Tk6Y+FvGp/7VL/ez/6SU7jz1510OW0t/O+gLdaZaOXk1rSAWJI+x+6UrwH+/eLfFmKdFgzRJxy4j/Mn+P8ASuxjLGfpl4Uj/Bl/4v6ZJdrv5+flXZtFqui2cmo6+rCIQ3MckbCB2BlHrSBgmwqAn2myvTdlTjIx+gH6pR/2LeNLlmRGR/qvPpdf1L80/wA0dMe0i/w9CAYo57MM88cYBdpJGj4FmJHH/fcfL4uWZebTjFjMDLi/ilH+d/Q/Hqc4YPAxmR9X9Z63qehab5U1awtr3VLnWW1guht/QgF88iqKSiaNIw0afZdZv5l+P9lue1mkwZ8RlOPhxxV6oylw+r+Hh9fql/RcfTZ5yBMYxjX+lRKaHZTzLHcPLpMMpEcMd+sb8pCK0DxNwX+Vfi5M2c4ey8Mzw45m/wClH/pFyzrJxF0J/wDC0H5usbfyfp5vZpJZrKoDzBKxREkBeTECnInbfB/IGUmoESbdNrfGNfSWG+WPP1nd6k8OmzymZjy9GISXBp3bhEJOKjKYdiawDijGh7+H/dOfmyYpRqVf7ln+py350uOW+1NYo7kVigWT05nrQbRqBJUV+Ifs5OfZ2qMTxHb+s67FmxCfpjy8uJvTPKt9ZQTG3vra4h+INalnlZ2A3VdieWQw9i5f50K/0zLP2ljmRcZA/wA76WF+e/zA1LyjLanVPKssUlxHW3mnkjaM8RUpVC9GH8pzZ4PZ2U9+KJP83f8A3zCGUSsQlY/0rJvy+12PzX5e/TF7e6YZ5OUUGlSAkQOhIXl8SiQt8Lf3ebD+SsOOQBEsnD/O/wCIcPUZZQNREo/0v5zXl+28w6BoAbzcLqOSOeZHvIZlis1W7n4r6aoyNFD8Sceac4lxzYs2TKY4eGOP+CMf6A/i4h9X+6axPGRueKXuTqDzHonknRkstf1ATSyyN9T0+CU3E4gY0RERaSSKg+01My8ODNlG44pfxy+mLj5RxEyj6Yj+KTCrnynJrvmCTU9MsF0TSZ19OD1V9PlUbyFP5mPbHJARl+8n/m2nBrdvTeX+r/xSZ+Xvy08seV9QM8kLapfvVjduVYhm3qifZTM6Wsy5fRCPhw/n/j1ScTV6qWT1Tnf/AEDw/wB8zCO7nEfoRuzqNwNqgf6wGOn0XCLkeI/zpODm1HFtEcEf5sEbp5tIQJSnqzk/DIw+BK+A7nHVZo4xRNf0f45MtPhMuQRU1zLMXVyWI3FfDNDmzmZrkHa48QioJKxYhhUrt165QC2kOZmAqRuvT/P5YbQt5llK9CNwcbRSHYcXrWleuBL/AP/RnwYqm1Cy/P7R/pnLO+XMGZeAG3Qnx7nFLaB9y1Kfs/LvgVY+8igfOuKXPzCANt2oPxwJUo2YKWbYsTQCvToMVc6At0+yS1e+2BKUMryXbOa8a0A6jY5ZDJKBBieGSmIIoqGqwahcyRNbXbW1xb/EjUqprtRv2vxzquzfafgHDnj4kf538Todf2EMnqxS8KX+xROl/mV5n0ekOtWpmtxt9Zj+NKePIbj/AGWdNDFpNWLwzF/zf4nn8stTpv76Nx/1SDN9L8++W9YtPq8VykJbcxmnX6c1+fsnJj5Dh/pQcrD2jDJzIyf0ZoDzN5B/LXXoBc3ejhr4Lx+s2tI3b3JQgH6cENRqcUeEHjH82Tk4cwExISlhr+b6sf8ApPp/2LGLL8mbfQNTg8weWteeyliYNbxXKiQA0oUYDixVv2lrgnrYgfvI/V9TtsHaeXMDEiOaH9H93P8A4n/Ys0jufOkcgufNGk22uem/PT5LMrCYgV4sQkpY8zX7Xq5jZIYJjhgeES+rxPVxMfHxj6ePF/O/j/2UP+JWW/nDTUlnbW/LOpQrBNz04yxi5BXiPiHBnVGDcqVyn+SYRowMJS9/0/6dlLPGVAZI7/Vzx/715d+dvn/zT5hifRdP028g8uOI2lE1ufVeVGLfaFaR/Z/2WbDR6aMT6vqdro8WDGOIzhx/0ZMJ/KHz1q3kPzK8ktpKNK1ApHqVbZnlCJy4tESVpRm+L/JzZajSw1ESBtKPqh/N4v4uJxdVLHP+OPze/wCr+afJh1zTdUtL1xc6uFhmkeFjEkKgvyeoHpNvxH8zNnK5eyJZru/3THGZQgRLh4f4fV/uUa+veSbXW7fUpJJNRvFV4YJLa3kb0kahaoRdw3EfFkdN2ZPHdD6vq4iINM85lHhuMR/WDzv86JNX/MMWWlaBot/wtJi5u7k+lE/JCvwxMeff7bKubvQYo4QZTlG6+n6lw6nFiB4pXxfzRL9TGvLf5Bed44IoJZ7SyvUJaOQs7vFX9peLIVbo1VyrJronISJf6X+Jtl2iIwEYwlIf0vTH/fMltfyouZ2k0bzh5y1K/sPtm0MriGR13HIsWYgU8cuh2iOeLF64/wAfD/xLqMuTNxAyljx45f6n/ef7L/iWW+V/Lvk7QYZkttOj9cURdRb45So6fG9W2zHzfms4qR4f6MFnkxg3csv/AA3/AHqbz6iZoltkZpYV6Vp/xLDg7LEQDP1H+k4+TWG/R6I/0G7SMvIqlPg/lX4V+/vmRmy48QslrxYZz5DZPoZFWP0hGiKdvhFPlvmhz9omR9P+mdpi0YA3UZlABDbHpmrmSTZ5udEdy+rcVkBBKij4obIUbnvtt4HCrdCVNDuo8OuFCkr/ABUr7qdunhgCStlWoqpqp7HCxf/SnwQqgr8XHdj4nOWd8vAJJBG6igOKr248BQ717+HXFWoV5E8hsB17nGkukValhTcVoffBSQpmPYsQPh+Kg9umNLbhHIIyzbEr2HfBS2l6w8W38eopviQztaITyqa1Y1322A2/XiVtbJCGjAps53HiOwyeOcoG4nhLGQB5pbqHlXR52HGIwXZAIlgPA1/mIHw50Oi9p9Vi2J8SH9N0+r7D02bfh4JfzoelRXTfNmllf0b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\n", | |
| "text/plain": [ | |
| "<IPython.core.display.Image object>" | |
| ] | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "raw = tf.io.read_file(\n", | |
| " str(image_path)\n", | |
| ")\n", | |
| "image_tensor = tf.io.decode_image(raw)\n", | |
| "image_tensor.shape" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "oRO5S_shOOR-", | |
| "outputId": "22ceb987-2738-44a0-c927-f5f551f14598" | |
| }, | |
| "execution_count": 6, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "TensorShape([80, 120, 3])" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 6 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "image_tensor = tf.expand_dims(image_tensor, 0)\n", | |
| "image_tensor.shape" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "xNsPknu6QmlK", | |
| "outputId": "8cea5106-002a-4b03-ee2d-52e580fd251f" | |
| }, | |
| "execution_count": 7, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "TensorShape([1, 80, 120, 3])" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 7 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "def image_files_to_tensor(pathes:list[pathlib.Path]):\n", | |
| " \"\"\"\n", | |
| " 224サイズにリサイズして tensor を返す\n", | |
| " \"\"\"\n", | |
| " l = [tf.image.resize(tf.io.decode_image(tf.io.read_file(str(path))), [224, 224]) for path in pathes]\n", | |
| " return tf.stack(l, 0)\n", | |
| "\n" | |
| ], | |
| "metadata": { | |
| "id": "MxCGOzmxVFzF" | |
| }, | |
| "execution_count": 8, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "images_tensor = image_files_to_tensor([image_path])\n", | |
| "print(images_tensor.shape)\n", | |
| "image_tensor_embedding = model.predict(images_tensor)\n", | |
| "print(image_tensor_embedding)\n", | |
| "print(image_tensor_embedding.shape)\n" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "CDdXkf7NPuDI", | |
| "outputId": "51ab9377-d23d-4acf-c2b9-3f80a4ba3020" | |
| }, | |
| "execution_count": 9, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "(1, 224, 224, 3)\n", | |
| "[[-0.03652444 -0.05792176 -0.10945524 ... 0.11447129 -0.13936797\n", | |
| " 0.21209691]]\n", | |
| "(1, 1280)\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# 変更すれば、好きな画像で類似度を出せる\n", | |
| "images = [\n", | |
| " 'https://cdn-ak.f.st-hatena.com/images/fotolife/s/secondlife/20211213/20211213125626_120.jpg',\n", | |
| " 'https://cdn-ak.f.st-hatena.com/images/fotolife/s/secondlife/20211213/20211213125617_120.jpg',\n", | |
| " 'https://cdn-ak.f.st-hatena.com/images/fotolife/s/secondlife/20211124/20211124161056_120.jpg',\n", | |
| " 'https://cdn-ak.f.st-hatena.com/images/fotolife/s/secondlife/20211124/20211124161045_120.jpg',\n", | |
| " 'https://cdn-ak.f.st-hatena.com/images/fotolife/s/secondlife/20211201/20211201124940_120.jpg',\n", | |
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| " 'https://cdn-ak.f.st-hatena.com/images/fotolife/s/secondlife/20211117/20211117182553_120.jpg',\n", | |
| "]\n", | |
| "images_path = [download_image(url) for url in images]\n" | |
| ], | |
| "metadata": { | |
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| "base_uri": "https://localhost:8080/" | |
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| "execution_count": 10, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
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| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "\"\"\"\n", | |
| "base:\n", | |
| "https://colab.research.google.com/github/rom1504/image_embeddings/blob/master/notebooks/from_scratch.ipynb\n", | |
| "\"\"\"\n", | |
| "\n", | |
| "from IPython.display import Image, display\n", | |
| "from ipywidgets import widgets, HBox, VBox\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "import matplotlib.image as mpimg\n", | |
| "\n", | |
| "%matplotlib inline\n", | |
| "\n", | |
| "def display_with_label(data: list[tuple[pathlib.Path, str]]):\n", | |
| " hbox = HBox([VBox([widgets.Label(str(label)), widgets.Image(value=path.read_bytes())]) for path, label in data])\n", | |
| " display(hbox)" | |
| ], | |
| "metadata": { | |
| "id": "pHqO_pmMSmfl" | |
| }, | |
| "execution_count": 11, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "display_with_label([(path, str(idx)) for (idx, path) in list(enumerate(images_path))[0:5]])\n", | |
| "display_with_label([(path, str(idx)) for (idx, path) in list(enumerate(images_path))[5:]])\n" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 249, | |
| "referenced_widgets": [ | |
| "b516c2f64b0a4403ad5add0323dc4230", | |
| "f08e718b1f0e45d9b708090da5965fd3", | |
| "2d63bcc456bb4989a9b966808163a359", | |
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| "75487cf5095e4012819706f4e7127284", | |
| "7ff36ded7ad54abe8dcca10dad729d8a", | |
| "2672a59c1cff4ecb9ba3533916d1f3bf", | |
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| ] | |
| }, | |
| "id": "X7IZIdDwTww9", | |
| "outputId": "4f73ffa0-6fd3-49a8-831a-61f014e31370" | |
| }, | |
| "execution_count": 12, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "b516c2f64b0a4403ad5add0323dc4230", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "HBox(children=(VBox(children=(Label(value='0'), Image(value=b'\\xff\\xd8\\xff\\xe0\\x00\\x10JFIF\\x00\\x01\\x01\\x01\\x00…" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "714abff9bbb440bdbf4d0c4fd0a7dca3", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "HBox(children=(VBox(children=(Label(value='5'), Image(value=b'\\xff\\xd8\\xff\\xe0\\x00\\x10JFIF\\x00\\x01\\x01\\x01\\x00…" | |
| ] | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "images_tensor = image_files_to_tensor(images_path)\n", | |
| "images_tensor_embedding = model.predict(images_tensor)\n", | |
| "print(images_tensor_embedding)\n", | |
| "print(images_tensor_embedding.shape)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "R22qKLuhX534", | |
| "outputId": "98bedb3f-7393-412a-dd29-8938f636a791" | |
| }, | |
| "execution_count": 13, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "[[-0.10945629 -0.09343709 -0.08223398 ... 1.0024792 -0.1611993\n", | |
| " 0.01493335]\n", | |
| " [-0.03652436 -0.05792165 -0.10945524 ... 0.11447226 -0.1393679\n", | |
| " 0.21209645]\n", | |
| " [-0.10479932 -0.10463432 -0.08078936 ... -0.023954 -0.03211505\n", | |
| " -0.13908449]\n", | |
| " ...\n", | |
| " [-0.19969083 -0.1597161 -0.175438 ... -0.2206025 -0.04785771\n", | |
| " 0.03392322]\n", | |
| " [ 0.1287042 -0.07741662 -0.10888506 ... 0.8499286 -0.20345287\n", | |
| " 0.06938681]\n", | |
| " [-0.01477872 -0.09264231 -0.15021208 ... 0.30814508 -0.19284736\n", | |
| " 0.28705594]]\n", | |
| "(10, 1280)\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from sklearn.metrics.pairwise import cosine_similarity\n", | |
| "\n", | |
| "sim_res = cosine_similarity(images_tensor_embedding, images_tensor_embedding)\n", | |
| "sim_res" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "Q537zwdqZPH_", | |
| "outputId": "6f4775ed-f707-4752-ecbd-145049864e4e" | |
| }, | |
| "execution_count": 14, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "array([[ 1. , 0.44505394, -0.04161356, 0.01666282, 0.04543769,\n", | |
| " 0.24226916, 0.06981121, 0.00515541, 0.01611625, 0.06467595],\n", | |
| " [ 0.44505394, 1.0000002 , 0.00313169, 0.06676795, 0.07920375,\n", | |
| " 0.25869107, 0.05456577, 0.07960878, -0.0146864 , 0.02522465],\n", | |
| " [-0.04161356, 0.00313169, 0.9999999 , 0.5507376 , 0.4239326 ,\n", | |
| " -0.02977732, 0.3225319 , 0.62029684, 0.07330316, 0.03695932],\n", | |
| " [ 0.01666282, 0.06676795, 0.5507376 , 1.0000001 , 0.4721935 ,\n", | |
| " 0.05535758, 0.43453646, 0.6822141 , 0.17620735, 0.12906513],\n", | |
| " [ 0.04543769, 0.07920375, 0.4239326 , 0.4721935 , 0.9999998 ,\n", | |
| " 0.08597542, 0.17120099, 0.40617043, 0.0032471 , 0.02258891],\n", | |
| " [ 0.24226916, 0.25869107, -0.02977732, 0.05535758, 0.08597542,\n", | |
| " 0.99999976, 0.06380752, 0.07490596, 0.08929685, 0.13794066],\n", | |
| " [ 0.06981121, 0.05456577, 0.3225319 , 0.43453646, 0.17120099,\n", | |
| " 0.06380752, 1. , 0.49228954, 0.22836211, 0.29995295],\n", | |
| " [ 0.00515541, 0.07960878, 0.62029684, 0.6822141 , 0.40617043,\n", | |
| " 0.07490596, 0.49228954, 1.0000004 , 0.18286222, 0.11434785],\n", | |
| " [ 0.01611625, -0.0146864 , 0.07330316, 0.17620735, 0.0032471 ,\n", | |
| " 0.08929685, 0.22836211, 0.18286222, 0.9999999 , 0.60561 ],\n", | |
| " [ 0.06467595, 0.02522465, 0.03695932, 0.12906513, 0.02258891,\n", | |
| " 0.13794066, 0.29995295, 0.11434785, 0.60561 , 1.0000004 ]],\n", | |
| " dtype=float32)" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 14 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# 対角成分を 0 に\n", | |
| "sim_res_diag = np.diag(sim_res)\n", | |
| "sim_res_diag.flags.writeable = True\n", | |
| "sim_res_diag.fill(0)\n", | |
| "sim_res_diag.flags.writeable = False\n", | |
| "\n", | |
| "# スコア順に index を取得\n", | |
| "max_score_indexes = (-sim_res).argsort(axis=1)\n", | |
| "max_score_indexes" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "I9y7C-C0aj8N", | |
| "outputId": "5921e822-21a3-48b3-a456-cedee9d5577b" | |
| }, | |
| "execution_count": 15, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "array([[1, 5, 6, 9, 4, 3, 8, 7, 0, 2],\n", | |
| " [0, 5, 7, 4, 3, 6, 9, 2, 1, 8],\n", | |
| " [7, 3, 4, 6, 8, 9, 1, 2, 5, 0],\n", | |
| " [7, 2, 4, 6, 8, 9, 1, 5, 0, 3],\n", | |
| " [3, 2, 7, 6, 5, 1, 0, 9, 8, 4],\n", | |
| " [1, 0, 9, 8, 4, 7, 6, 3, 5, 2],\n", | |
| " [7, 3, 2, 9, 8, 4, 0, 5, 1, 6],\n", | |
| " [3, 2, 6, 4, 8, 9, 1, 5, 0, 7],\n", | |
| " [9, 6, 7, 3, 5, 2, 0, 4, 8, 1],\n", | |
| " [8, 6, 5, 3, 7, 0, 2, 1, 4, 9]])" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 15 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "for (idx, max_score_index) in enumerate(max_score_indexes):\n", | |
| " indexes = max_score_index[:3]\n", | |
| " labels = [f'[{idx}]'] + [f\"[{similar_idx}] {sim_res[idx][similar_idx]:.3f}\" for similar_idx in indexes]\n", | |
| " target_images = [images_path[idx]] + [images_path[i] for i in indexes]\n", | |
| " display_with_label(zip(target_images, labels))\n" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 1000, | |
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