重命名 4. QuickDraw在线交互识别系统 为 4. QuickDraw(你画我猜)在线交互识别系统
This commit is contained in:
wty-yy
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
@@ -0,0 +1,564 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "78c67687",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"['images/1_1.jpg',\n",
|
||||
" 'images/1_10.jpg',\n",
|
||||
" 'images/1_11.jpg',\n",
|
||||
" 'images/1_12.jpg',\n",
|
||||
" 'images/1_13.jpg',\n",
|
||||
" 'images/1_14.jpg',\n",
|
||||
" 'images/1_15.jpg',\n",
|
||||
" 'images/1_16.jpg',\n",
|
||||
" 'images/1_17.jpg',\n",
|
||||
" 'images/1_18.jpg',\n",
|
||||
" 'images/1_19.jpg',\n",
|
||||
" 'images/1_2.jpg',\n",
|
||||
" 'images/1_20.jpg',\n",
|
||||
" 'images/1_21.jpg',\n",
|
||||
" 'images/1_22.jpg',\n",
|
||||
" 'images/1_23.jpg',\n",
|
||||
" 'images/1_24.jpg',\n",
|
||||
" 'images/1_25.jpg',\n",
|
||||
" 'images/1_26.jpg',\n",
|
||||
" 'images/1_27.jpg',\n",
|
||||
" 'images/1_28.jpg',\n",
|
||||
" 'images/1_3.jpg',\n",
|
||||
" 'images/1_30.jpg',\n",
|
||||
" 'images/1_31.jpg',\n",
|
||||
" 'images/1_32.jpg',\n",
|
||||
" 'images/1_33.jpg',\n",
|
||||
" 'images/1_34.jpg',\n",
|
||||
" 'images/1_35.jpg',\n",
|
||||
" 'images/1_36.jpg',\n",
|
||||
" 'images/1_37.jpg',\n",
|
||||
" 'images/1_38.jpg',\n",
|
||||
" 'images/1_39.jpg',\n",
|
||||
" 'images/1_4.jpg',\n",
|
||||
" 'images/1_40.jpg',\n",
|
||||
" 'images/1_41.jpg',\n",
|
||||
" 'images/1_42.jpg',\n",
|
||||
" 'images/1_43.jpg',\n",
|
||||
" 'images/1_44.jpg',\n",
|
||||
" 'images/1_45.jpg',\n",
|
||||
" 'images/1_46.jpg',\n",
|
||||
" 'images/1_47.jpg',\n",
|
||||
" 'images/1_48.jpg',\n",
|
||||
" 'images/1_49.jpg',\n",
|
||||
" 'images/1_5.jpg',\n",
|
||||
" 'images/1_51.jpg',\n",
|
||||
" 'images/1_6.jpg',\n",
|
||||
" 'images/1_7.jpg',\n",
|
||||
" 'images/1_8.jpg',\n",
|
||||
" 'images/1_9.jpg',\n",
|
||||
" 'images/2_1.jpg',\n",
|
||||
" 'images/2_10.jpg',\n",
|
||||
" 'images/2_12.jpg',\n",
|
||||
" 'images/2_13.jpg',\n",
|
||||
" 'images/2_14.jpg',\n",
|
||||
" 'images/2_15.jpg',\n",
|
||||
" 'images/2_17.jpg',\n",
|
||||
" 'images/2_18.jpg',\n",
|
||||
" 'images/2_19.jpg',\n",
|
||||
" 'images/2_2.jpg',\n",
|
||||
" 'images/2_20.jpg',\n",
|
||||
" 'images/2_21.jpg',\n",
|
||||
" 'images/2_22.jpg',\n",
|
||||
" 'images/2_23.jpg',\n",
|
||||
" 'images/2_24.jpg',\n",
|
||||
" 'images/2_26.jpg',\n",
|
||||
" 'images/2_27.jpg',\n",
|
||||
" 'images/2_28.jpg',\n",
|
||||
" 'images/2_29.jpg',\n",
|
||||
" 'images/2_3.jpg',\n",
|
||||
" 'images/2_30.jpg',\n",
|
||||
" 'images/2_31.jpg',\n",
|
||||
" 'images/2_32.jpg',\n",
|
||||
" 'images/2_33.jpg',\n",
|
||||
" 'images/2_34.jpg',\n",
|
||||
" 'images/2_35.jpg',\n",
|
||||
" 'images/2_36.jpg',\n",
|
||||
" 'images/2_37.jpg',\n",
|
||||
" 'images/2_38.jpg',\n",
|
||||
" 'images/2_39.jpg',\n",
|
||||
" 'images/2_4.jpg',\n",
|
||||
" 'images/2_40.jpg',\n",
|
||||
" 'images/2_41.jpg',\n",
|
||||
" 'images/2_42.jpg',\n",
|
||||
" 'images/2_43.jpg',\n",
|
||||
" 'images/2_44.jpg',\n",
|
||||
" 'images/2_5.jpg',\n",
|
||||
" 'images/2_6.jpg',\n",
|
||||
" 'images/2_7.jpg',\n",
|
||||
" 'images/2_8.jpg',\n",
|
||||
" 'images/2_9.jpg',\n",
|
||||
" 'images/3_10.jpg',\n",
|
||||
" 'images/3_11.jpg',\n",
|
||||
" 'images/3_12.jpg',\n",
|
||||
" 'images/3_13.jpg',\n",
|
||||
" 'images/3_14.jpg',\n",
|
||||
" 'images/3_15.jpg',\n",
|
||||
" 'images/3_16.jpg',\n",
|
||||
" 'images/3_17.jpg',\n",
|
||||
" 'images/3_18.jpg',\n",
|
||||
" 'images/3_19.jpg',\n",
|
||||
" 'images/3_20.jpg',\n",
|
||||
" 'images/3_21.jpg',\n",
|
||||
" 'images/3_22.jpg',\n",
|
||||
" 'images/3_23.jpg',\n",
|
||||
" 'images/3_25.jpg',\n",
|
||||
" 'images/3_26.jpg',\n",
|
||||
" 'images/3_27.jpg',\n",
|
||||
" 'images/3_28.jpg',\n",
|
||||
" 'images/3_29.jpg',\n",
|
||||
" 'images/3_3.jpg',\n",
|
||||
" 'images/3_30.jpg',\n",
|
||||
" 'images/3_31.jpg',\n",
|
||||
" 'images/3_32.jpg',\n",
|
||||
" 'images/3_34.jpg',\n",
|
||||
" 'images/3_38.jpg',\n",
|
||||
" 'images/3_40.jpg',\n",
|
||||
" 'images/3_42.jpg',\n",
|
||||
" 'images/3_43.jpg',\n",
|
||||
" 'images/3_44.jpg',\n",
|
||||
" 'images/3_45.jpg',\n",
|
||||
" 'images/3_46.jpg',\n",
|
||||
" 'images/3_47.jpg',\n",
|
||||
" 'images/3_48.jpg',\n",
|
||||
" 'images/3_49.jpg',\n",
|
||||
" 'images/3_5.jpg',\n",
|
||||
" 'images/3_50.jpg',\n",
|
||||
" 'images/3_51.jpg',\n",
|
||||
" 'images/3_52.jpg',\n",
|
||||
" 'images/3_53.jpg',\n",
|
||||
" 'images/3_55.jpg',\n",
|
||||
" 'images/3_56.jpg',\n",
|
||||
" 'images/3_57.jpg',\n",
|
||||
" 'images/3_58.jpg',\n",
|
||||
" 'images/3_59.jpg',\n",
|
||||
" 'images/3_65.jpg',\n",
|
||||
" 'images/3_66.jpg',\n",
|
||||
" 'images/3_7.jpg',\n",
|
||||
" 'images/3_71.jpg',\n",
|
||||
" 'images/3_72.jpg',\n",
|
||||
" 'images/3_73.jpg',\n",
|
||||
" 'images/3_74.jpg',\n",
|
||||
" 'images/3_75.jpg',\n",
|
||||
" 'images/3_76.jpg',\n",
|
||||
" 'images/3_77.jpg',\n",
|
||||
" 'images/3_78.jpg',\n",
|
||||
" 'images/3_8.jpg',\n",
|
||||
" 'images/3_9.jpg',\n",
|
||||
" 'images/4_10.jpg',\n",
|
||||
" 'images/4_11.jpg',\n",
|
||||
" 'images/4_13.jpg',\n",
|
||||
" 'images/4_14.jpg',\n",
|
||||
" 'images/4_21.jpg',\n",
|
||||
" 'images/4_6.jpg',\n",
|
||||
" 'images/4_7.jpg',\n",
|
||||
" 'images/4_9.jpg',\n",
|
||||
" 'images/5_1.jpg',\n",
|
||||
" 'images/5_2.jpg',\n",
|
||||
" 'images/5_3.jpg',\n",
|
||||
" 'images/5_4.jpg',\n",
|
||||
" 'images/5_5.jpg',\n",
|
||||
" 'images/5_6.jpg']"
|
||||
]
|
||||
},
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import re\n",
|
||||
"import numpy as np\n",
|
||||
"import os\n",
|
||||
"import cv2\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
"plt.rcParams['font.sans-serif'] = ['SimHei']\n",
|
||||
"plt.rcParams['axes.unicode_minus'] = False\n",
|
||||
"\n",
|
||||
"def getimgnames(path=None):\n",
|
||||
" \"\"\"\n",
|
||||
" 获取指定文件夹中的JPG图片名称（含路径）\n",
|
||||
" :param path: 指定文件夹\n",
|
||||
" :return: path中的所有JPG图片名称（含路径，例如：./path/image1.jpg）\n",
|
||||
" \"\"\"\n",
|
||||
" imgnames = []\n",
|
||||
" filenames = os.listdir(path) # 获取path中的所有文件名\n",
|
||||
" for i in filenames:\n",
|
||||
" if re.findall('\\.jpg$', i) != []: # 在所有文件名中找出JPG图片名称\n",
|
||||
" imgnames.append(os.path.join(path, i)) # 将图片名称和路径合并、保存\n",
|
||||
" return imgnames\n",
|
||||
"\n",
|
||||
"imglist = getimgnames('images/')\n",
|
||||
"imglist"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "356b558c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'\\ndef cut_image(img, imgFile):\\n cx = int(np.size(img, 1))/2\\n cy = int(np.size(img, 0))/2\\n \\n plt.figure(figsize=(8,8))\\n plt.imshow(img)\\n\\n plt.plot([cx-50, cx+50], [cy+50, cy+50], \\'r\\', linewidth=2)\\n plt.plot([cx+50, cx+50], [cy-50, cy+50], \\'r\\', linewidth=2)\\n plt.plot([cx-50, cx+50], [cy-50, cy-50], \\'r\\', linewidth=2)\\n plt.plot([cx-50, cx-50], [cy-50, cy+50], \\'r\\', linewidth=2)\\n plt.annotate(\\'选取的水样窗口\\', xy=(cx+50,cy-50), xytext=(cx+300, cy-300),\\n arrowprops=dict(facecolor=\\'black\\', shrink=0.1))\\n\\n plt.title(\\'水色样本 \\'+imgFile+\\' 分辨率为\\'+str(img.size)+\" 类别标签 \"+str(imgFile[9]))\\n plt.show()\\n \\nfor i in range(len(imglist)):\\n img = cv2.imread(imglist[i])\\n cut_image(img, imglist[i])\\n'"
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"\"\"\"\n",
|
||||
"def cut_image(img, imgFile):\n",
|
||||
" cx = int(np.size(img, 1))/2\n",
|
||||
" cy = int(np.size(img, 0))/2\n",
|
||||
" \n",
|
||||
" plt.figure(figsize=(8,8))\n",
|
||||
" plt.imshow(img)\n",
|
||||
"\n",
|
||||
" plt.plot([cx-50, cx+50], [cy+50, cy+50], 'r', linewidth=2)\n",
|
||||
" plt.plot([cx+50, cx+50], [cy-50, cy+50], 'r', linewidth=2)\n",
|
||||
" plt.plot([cx-50, cx+50], [cy-50, cy-50], 'r', linewidth=2)\n",
|
||||
" plt.plot([cx-50, cx-50], [cy-50, cy+50], 'r', linewidth=2)\n",
|
||||
" plt.annotate('选取的水样窗口', xy=(cx+50,cy-50), xytext=(cx+300, cy-300),\n",
|
||||
" arrowprops=dict(facecolor='black', shrink=0.1))\n",
|
||||
"\n",
|
||||
" plt.title('水色样本 '+imgFile+' 分辨率为'+str(img.size)+\" 类别标签 \"+str(imgFile[9]))\n",
|
||||
" plt.show()\n",
|
||||
" \n",
|
||||
"for i in range(len(imglist)):\n",
|
||||
" img = cv2.imread(imglist[i])\n",
|
||||
" cut_image(img, imglist[i])\n",
|
||||
"\"\"\"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "85e7fe5e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 加载图像统计信息模块（注：也可以直接通过颜色通道来计算）\n",
|
||||
"from PIL import ImageStat,Image\n",
|
||||
"\n",
|
||||
"# 遍历全体图像进行快速检查\n",
|
||||
"size = 100\n",
|
||||
"imgPath = './images'\n",
|
||||
"imgWidth = [] # 图像宽度\n",
|
||||
"imgHeight = [] # 图像高度\n",
|
||||
"imgRrange = [] # 图像红色通道极差\n",
|
||||
"imgGrange = [] # 图像绿色通道极差\n",
|
||||
"imgBrange = [] # 图像蓝色通道极差\n",
|
||||
"\n",
|
||||
"newImgs = [] # 获得选取后的图像作为模型训练和验证数据\n",
|
||||
"\n",
|
||||
"imgFiles = os.listdir(imgPath)\n",
|
||||
"for imgFile in imgFiles:\n",
|
||||
" img = Image.open(os.path.join(imgPath,imgFile))\n",
|
||||
" imgWidth.append(img.size[0])\n",
|
||||
" imgHeight.append(img.size[1])\n",
|
||||
" \n",
|
||||
" # 获得图像中心区域大小为size的图像块\n",
|
||||
" cx, cy = (int(i/2) for i in img.size)\n",
|
||||
" box = (cx-50, cy-50, cx+50, cy+50)\n",
|
||||
" region = img.crop(box)\n",
|
||||
" \n",
|
||||
" # 计算选取图像块的标准差 分为红绿蓝三种\n",
|
||||
" stat = ImageStat.Stat(region)\n",
|
||||
" imgRrange.append(stat.extrema[0][1]-stat.extrema[0][0])\n",
|
||||
" imgGrange.append(stat.extrema[1][1]-stat.extrema[1][0])\n",
|
||||
" imgBrange.append(stat.extrema[2][1]-stat.extrema[2][0])\n",
|
||||
" \n",
|
||||
" newImgs.append(region)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "b5ad4f91",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||||
"RangeIndex: 161 entries, 0 to 160\n",
|
||||
"Data columns (total 9 columns):\n",
|
||||
" # Column Non-Null Count Dtype \n",
|
||||
"--- ------ -------------- ----- \n",
|
||||
" 0 0 161 non-null float64\n",
|
||||
" 1 1 161 non-null float64\n",
|
||||
" 2 2 161 non-null float64\n",
|
||||
" 3 3 161 non-null float64\n",
|
||||
" 4 4 161 non-null float64\n",
|
||||
" 5 5 161 non-null float64\n",
|
||||
" 6 6 161 non-null float64\n",
|
||||
" 7 7 161 non-null float64\n",
|
||||
" 8 8 161 non-null float64\n",
|
||||
"dtypes: float64(9)\n",
|
||||
"memory usage: 11.4 KB\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>0</th>\n",
|
||||
" <th>1</th>\n",
|
||||
" <th>2</th>\n",
|
||||
" <th>3</th>\n",
|
||||
" <th>4</th>\n",
|
||||
" <th>5</th>\n",
|
||||
" <th>6</th>\n",
|
||||
" <th>7</th>\n",
|
||||
" <th>8</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>148.6041</td>\n",
|
||||
" <td>138.6381</td>\n",
|
||||
" <td>64.3694</td>\n",
|
||||
" <td>3.625902</td>\n",
|
||||
" <td>4.125497</td>\n",
|
||||
" <td>10.485931</td>\n",
|
||||
" <td>4.242425</td>\n",
|
||||
" <td>4.798916</td>\n",
|
||||
" <td>12.044228</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>163.6788</td>\n",
|
||||
" <td>145.5487</td>\n",
|
||||
" <td>54.4581</td>\n",
|
||||
" <td>3.923370</td>\n",
|
||||
" <td>2.835001</td>\n",
|
||||
" <td>3.478972</td>\n",
|
||||
" <td>4.439465</td>\n",
|
||||
" <td>3.239916</td>\n",
|
||||
" <td>4.039823</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>153.9485</td>\n",
|
||||
" <td>147.0810</td>\n",
|
||||
" <td>71.9576</td>\n",
|
||||
" <td>2.208766</td>\n",
|
||||
" <td>1.803397</td>\n",
|
||||
" <td>3.115478</td>\n",
|
||||
" <td>2.623796</td>\n",
|
||||
" <td>2.139289</td>\n",
|
||||
" <td>3.621357</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>150.3755</td>\n",
|
||||
" <td>151.3985</td>\n",
|
||||
" <td>64.3118</td>\n",
|
||||
" <td>2.015167</td>\n",
|
||||
" <td>1.514034</td>\n",
|
||||
" <td>2.698922</td>\n",
|
||||
" <td>2.344658</td>\n",
|
||||
" <td>1.809065</td>\n",
|
||||
" <td>3.136727</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>150.7380</td>\n",
|
||||
" <td>150.9738</td>\n",
|
||||
" <td>64.6246</td>\n",
|
||||
" <td>1.902934</td>\n",
|
||||
" <td>1.658045</td>\n",
|
||||
" <td>3.098044</td>\n",
|
||||
" <td>2.242270</td>\n",
|
||||
" <td>1.952067</td>\n",
|
||||
" <td>3.593836</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" 0 1 2 3 4 5 6 \\\n",
|
||||
"0 148.6041 138.6381 64.3694 3.625902 4.125497 10.485931 4.242425 \n",
|
||||
"1 163.6788 145.5487 54.4581 3.923370 2.835001 3.478972 4.439465 \n",
|
||||
"2 153.9485 147.0810 71.9576 2.208766 1.803397 3.115478 2.623796 \n",
|
||||
"3 150.3755 151.3985 64.3118 2.015167 1.514034 2.698922 2.344658 \n",
|
||||
"4 150.7380 150.9738 64.6246 1.902934 1.658045 3.098044 2.242270 \n",
|
||||
"\n",
|
||||
" 7 8 \n",
|
||||
"0 4.798916 12.044228 \n",
|
||||
"1 3.239916 4.039823 \n",
|
||||
"2 2.139289 3.621357 \n",
|
||||
"3 1.809065 3.136727 \n",
|
||||
"4 1.952067 3.593836 "
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"# 构建训练数据集和分类标签\n",
|
||||
"data = []\n",
|
||||
"dy = []\n",
|
||||
"for i, img in enumerate(newImgs):\n",
|
||||
" r, g, b = np.split(np.array(img), 3, axis = 2)\n",
|
||||
" \n",
|
||||
" #计算一阶矩\n",
|
||||
" r_m1 = np.mean(r)\n",
|
||||
" g_m1 = np.mean(g)\n",
|
||||
" b_m1 = np.mean(b)\n",
|
||||
" \n",
|
||||
" #二阶矩\n",
|
||||
" r_m2 = np.std(r)\n",
|
||||
" g_m2 = np.std(g)\n",
|
||||
" b_m2 = np.std(b)\n",
|
||||
" \n",
|
||||
" #三阶矩\n",
|
||||
" r_m3 = np.mean(abs(r - r.mean())**3)**(1/3)\n",
|
||||
" g_m3 = np.mean(abs(g - g.mean())**3)**(1/3)\n",
|
||||
" b_m3 = np.mean(abs(b - b.mean())**3)**(1/3)\n",
|
||||
" \n",
|
||||
" # 构造新数据集\n",
|
||||
" df = np.array([r_m1,g_m1,b_m1,r_m2,g_m2,b_m2,r_m3,g_m3,b_m3])\n",
|
||||
" data.append(df)\n",
|
||||
" \n",
|
||||
" # 保存对应的分类标签\n",
|
||||
" dy.append(int(imgFiles[i][0]))\n",
|
||||
"\n",
|
||||
"dy = np.array(dy)\n",
|
||||
"data = pd.DataFrame(np.array(data))\n",
|
||||
"data.info()\n",
|
||||
"data.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "da56ab61",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"D:\\anaconda\\lib\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n",
|
||||
" warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"((128, 9), (33, 9), (128,))"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"x_train,x_test,y_train,y_test=train_test_split(data,dy,test_size=0.2,random_state=0)\n",
|
||||
"x_train.shape,x_test.shape,y_train.shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "8c6be264",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"0.7575757575757576\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"D:\\anaconda\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
|
||||
" warnings.warn(\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from sklearn.metrics import accuracy_score\n",
|
||||
"from sklearn.neural_network import MLPClassifier\n",
|
||||
"mlp=MLPClassifier()\n",
|
||||
"mlp.fit(x_train,y_train)\n",
|
||||
"y_pred=mlp.predict(x_test)\n",
|
||||
"print(accuracy_score(y_pred,y_test))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "d7c33fc8",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because it is too large
Load Diff
File diff suppressed because one or more lines are too long
@@ -0,0 +1,11 @@
|
||||
## 你画我猜
|
||||
|
||||
### 项目目标
|
||||
|
||||
搭建网页，通过网页上进行绘画，实时返回绘制图像的预测结果。
|
||||
|
||||
### 数据集
|
||||
|
||||
- [345个类别的npy二进制编码完整数据集 39.5GB - Google Cloud](https://console.cloud.google.com/storage/browser/quickdraw_dataset/full/numpy_bitmap;tab=objects?prefix=&forceOnObjectsSortingFiltering=false)
|
||||
|
||||
- [quickdraw-dataset - GitHub](https://github.com/googlecreativelab/quickdraw-dataset#get-the-data) GitHub上的quickdraw数据集介绍
|
||||
@@ -0,0 +1,10 @@
|
||||
组长：吴天阳
|
||||
|
||||
组员：
|
||||
- 张政
|
||||
- 师梓豪
|
||||
- 苏渝钦
|
||||
- 王承杰
|
||||
- 杨涵
|
||||
- 孙一珺
|
||||
- 程思诚
|
||||
Reference in New Issue
Block a user