{ "cells": [ { "cell_type": "code", "execution_count": 46, "id": "c523f7aa", "metadata": {}, "outputs": [], "source": [ "from PIL import Image\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 47, "id": "fc13ad60", "metadata": {}, "outputs": [], "source": [ "img = Image.open('dataset/2.bmp')" ] }, { "cell_type": "code", "execution_count": 48, "id": "34b0f0da", "metadata": {}, "outputs": [ { "data": { "image/jpeg": 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"image/png": 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"text/plain": [ "" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "img" ] }, { "cell_type": "code", "execution_count": 49, "id": "3872b3f7", "metadata": {}, "outputs": [], "source": [ "img2 = np.array(img)" ] }, { "cell_type": "code", "execution_count": 50, "id": "57f8d39a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[[255, 255, 255],\n", " [255, 255, 255],\n", " [255, 255, 255],\n", " ...,\n", " [255, 255, 255],\n", " [255, 255, 255],\n", " [255, 255, 255]],\n", "\n", " [[255, 255, 255],\n", " [255, 255, 255],\n", " [255, 255, 255],\n", " ...,\n", " [255, 255, 255],\n", " [255, 255, 255],\n", " [255, 255, 255]],\n", "\n", " [[255, 255, 255],\n", " [255, 255, 255],\n", " [255, 255, 255],\n", " ...,\n", " [255, 255, 255],\n", " [255, 255, 255],\n", " [255, 255, 255]],\n", "\n", " ...,\n", "\n", " [[255, 255, 255],\n", " [255, 255, 255],\n", " [255, 255, 255],\n", " ...,\n", " [255, 255, 255],\n", " [255, 255, 255],\n", " [255, 255, 255]],\n", "\n", " [[255, 255, 255],\n", " [255, 255, 255],\n", " [255, 255, 255],\n", " ...,\n", " [255, 255, 255],\n", " [255, 255, 255],\n", " [255, 255, 255]],\n", "\n", " [[255, 255, 255],\n", " [255, 255, 255],\n", " [255, 255, 255],\n", " ...,\n", " [255, 255, 255],\n", " [255, 255, 255],\n", " [255, 255, 255]]], shape=(64, 64, 3), dtype=uint8)" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "img2" ] }, { "cell_type": "code", "execution_count": 51, "id": "a92b4af3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(64, 64, 3)" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "img2.shape" ] }, { "cell_type": "code", "execution_count": 52, "id": "d2efa037", "metadata": {}, "outputs": [], "source": [ "img3 = img2[:, :, 0]" ] }, { "cell_type": "code", "execution_count": 53, "id": "9bdb5f41", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(64, 64)" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "img3.shape" ] }, { "cell_type": "code", "execution_count": 54, "id": "3414910c", "metadata": {}, "outputs": [], "source": [ "img4 = img3.flatten()" ] }, { "cell_type": "code", "execution_count": 55, "id": "e79c1aa9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(4096,)" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "img4.shape" ] }, { "cell_type": "code", "execution_count": 76, "id": "04db882e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(30, 4096)\n", "(30,)\n" ] } ], "source": [ "X = []\n", "\n", "for i in range(1, 31):\n", " img = Image.open(f'dataset/{i}.bmp')\n", " img2 = np.array(img)\n", " img3 = img2[:, :, 0]\n", " img4 = img3.flatten()\n", " img5 = img4 / 255\n", " X.append(img5)\n", "\n", "X = np.array(X)\n", "y = np.array([0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2])\n", "\n", "\n", "print(X.shape)\n", "print(y.shape)\n" ] }, { "cell_type": "code", "execution_count": 77, "id": "5d8d8c21", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
LogisticRegression(max_iter=1000)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "LogisticRegression(max_iter=1000)" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.linear_model import LogisticRegression\n", "\n", "model = LogisticRegression(max_iter=1000)\n", "\n", "model.fit(X, y)" ] }, { "cell_type": "code", "execution_count": 78, "id": "3b06bff1", "metadata": {}, "outputs": [], "source": [ "X_test = []\n", "\n", "for i in range(1, 4):\n", " img = Image.open(f'dataset/t{i}.bmp')\n", " img2 = np.array(img)\n", " img3 = img2[:, :, 0]\n", " img4 = img3.flatten()\n", " img5 = img4 / 255\n", " X_test.append(img5)\n", "\n", "X_test = np.array(X_test)" ] }, { "cell_type": "code", "execution_count": 79, "id": "9e6f2366", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0, 1, 2])" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.predict(X_test)" ] }, { "cell_type": "code", "execution_count": null, "id": "3a15c2dd", "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": "3.11.7" } }, "nbformat": 4, "nbformat_minor": 5 }