吴沂钊
569 lines
18 KiB
Plaintext
569 lines
18 KiB
Plaintext
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import re\n",
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"import numpy as np\n",
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"import os\n",
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"import cv2\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"plt.rcParams['font.sans-serif'] = ['SimHei']\n",
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"plt.rcParams['axes.unicode_minus'] = False\n",
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"\n",
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"def getimgnames(path=None):\n",
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" \"\"\"\n",
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" 获取指定文件夹中的JPG图片名称（含路径）\n",
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" :param path: 指定文件夹\n",
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" :return: path中的所有JPG图片名称（含路径，例如：./path/image1.jpg）\n",
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" \"\"\"\n",
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" imgnames = []\n",
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" filenames = os.listdir(path) # 获取path中的所有文件名\n",
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" for i in filenames:\n",
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" if re.findall('\\.jpg$', i) != []: # 在所有文件名中找出JPG图片名称\n",
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" imgnames.append(os.path.join(path, i)) # 将图片名称和路径合并、保存\n",
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" return imgnames\n",
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"\n",
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"imglist = getimgnames('images/')\n",
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"imglist"
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]
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},
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"cell_type": "code",
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"id": "43738e00-e1fa-4d6f-b08c-1e64662a5ce0",
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"text/plain": [
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"'\\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'"
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],
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"source": [
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"\"\"\"\n",
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"def cut_image(img, imgFile):\n",
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" cx = int(np.size(img, 1))/2\n",
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" cy = int(np.size(img, 0))/2\n",
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" \n",
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" plt.figure(figsize=(8,8))\n",
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" plt.imshow(img)\n",
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"\n",
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" plt.plot([cx-50, cx+50], [cy+50, cy+50], 'r', linewidth=2)\n",
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" plt.plot([cx+50, cx+50], [cy-50, cy+50], 'r', linewidth=2)\n",
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" plt.plot([cx-50, cx+50], [cy-50, cy-50], 'r', linewidth=2)\n",
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" plt.plot([cx-50, cx-50], [cy-50, cy+50], 'r', linewidth=2)\n",
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" plt.annotate('选取的水样窗口', xy=(cx+50,cy-50), xytext=(cx+300, cy-300),\n",
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" arrowprops=dict(facecolor='black', shrink=0.1))\n",
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"\n",
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" plt.title('水色样本 '+imgFile+' 分辨率为'+str(img.size)+\" 类别标签 \"+str(imgFile[9]))\n",
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" plt.show()\n",
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" \n",
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"for i in range(len(imglist)):\n",
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" img = cv2.imread(imglist[i])\n",
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" cut_image(img, imglist[i])\n",
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"\"\"\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "65b2b8b6-ed00-47ff-84f1-e22f97d48cdb",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# 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",
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"from PIL import ImageStat,Image\n",
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"\n",
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"# 遍历全体图像进行快速检查\n",
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"size = 100\n",
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"imgPath = './images'\n",
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"imgWidth = [] # 图像宽度\n",
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"imgHeight = [] # 图像高度\n",
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"imgRrange = [] # 图像红色通道极差\n",
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"imgGrange = [] # 图像绿色通道极差\n",
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"imgBrange = [] # 图像蓝色通道极差\n",
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"\n",
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"newImgs = [] # 获得选取后的图像作为模型训练和验证数据\n",
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"\n",
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"imgFiles = os.listdir(imgPath)\n",
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"for imgFile in imgFiles:\n",
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" img = Image.open(os.path.join(imgPath,imgFile))\n",
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" imgWidth.append(img.size[0])\n",
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" imgHeight.append(img.size[1])\n",
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" \n",
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" # 获得图像中心区域大小为size的图像块\n",
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" cx, cy = (int(i/2) for i in img.size)\n",
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" box = (cx-50, cy-50, cx+50, cy+50)\n",
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" region = img.crop(box)\n",
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" \n",
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" # 计算选取图像块的标准差 分为红绿蓝三种\n",
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" stat = ImageStat.Stat(region)\n",
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" imgRrange.append(stat.extrema[0][1]-stat.extrema[0][0])\n",
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" imgGrange.append(stat.extrema[1][1]-stat.extrema[1][0])\n",
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" imgBrange.append(stat.extrema[2][1]-stat.extrema[2][0])\n",
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" \n",
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" newImgs.append(region)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "527d76c9-7201-4fe6-a835-3a329fb90bc7",
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"--- ------ -------------- ----- \n",
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"dtypes: float64(9)\n",
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" <th></th>\n",
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||
" <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",
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||
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|
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" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>148.6037</td>\n",
|
||
" <td>138.6396</td>\n",
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||
" <td>64.3687</td>\n",
|
||
" <td>3.633077</td>\n",
|
||
" <td>4.128330</td>\n",
|
||
" <td>10.499046</td>\n",
|
||
" <td>4.254340</td>\n",
|
||
" <td>4.802914</td>\n",
|
||
" <td>12.057685</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>163.6769</td>\n",
|
||
" <td>145.5470</td>\n",
|
||
" <td>54.4608</td>\n",
|
||
" <td>3.934350</td>\n",
|
||
" <td>2.840174</td>\n",
|
||
" <td>3.502551</td>\n",
|
||
" <td>4.453499</td>\n",
|
||
" <td>3.245141</td>\n",
|
||
" <td>4.065518</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>153.9430</td>\n",
|
||
" <td>147.0834</td>\n",
|
||
" <td>71.9601</td>\n",
|
||
" <td>2.225657</td>\n",
|
||
" <td>1.810979</td>\n",
|
||
" <td>3.136608</td>\n",
|
||
" <td>2.644340</td>\n",
|
||
" <td>2.148726</td>\n",
|
||
" <td>3.651915</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>150.3724</td>\n",
|
||
" <td>151.3982</td>\n",
|
||
" <td>64.3164</td>\n",
|
||
" <td>2.037380</td>\n",
|
||
" <td>1.521590</td>\n",
|
||
" <td>2.728093</td>\n",
|
||
" <td>2.375780</td>\n",
|
||
" <td>1.819692</td>\n",
|
||
" <td>3.173392</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>150.7381</td>\n",
|
||
" <td>150.9774</td>\n",
|
||
" <td>64.6204</td>\n",
|
||
" <td>1.918674</td>\n",
|
||
" <td>1.665260</td>\n",
|
||
" <td>3.110901</td>\n",
|
||
" <td>2.259915</td>\n",
|
||
" <td>1.958815</td>\n",
|
||
" <td>3.619921</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" 0 1 2 3 4 5 6 \\\n",
|
||
"0 148.6037 138.6396 64.3687 3.633077 4.128330 10.499046 4.254340 \n",
|
||
"1 163.6769 145.5470 54.4608 3.934350 2.840174 3.502551 4.453499 \n",
|
||
"2 153.9430 147.0834 71.9601 2.225657 1.810979 3.136608 2.644340 \n",
|
||
"3 150.3724 151.3982 64.3164 2.037380 1.521590 2.728093 2.375780 \n",
|
||
"4 150.7381 150.9774 64.6204 1.918674 1.665260 3.110901 2.259915 \n",
|
||
"\n",
|
||
" 7 8 \n",
|
||
"0 4.802914 12.057685 \n",
|
||
"1 3.245141 4.065518 \n",
|
||
"2 2.148726 3.651915 \n",
|
||
"3 1.819692 3.173392 \n",
|
||
"4 1.958815 3.619921 "
|
||
]
|
||
},
|
||
"execution_count": 13,
|
||
"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": 14,
|
||
"id": "208d30d2-1850-45eb-a68d-94e44a5f59eb",
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"((128, 9), (33, 9), (128,))"
|
||
]
|
||
},
|
||
"execution_count": 14,
|
||
"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": 15,
|
||
"id": "726c20cb-1065-4f7e-9d71-39e261178fd6",
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"0.3939393939393939\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": "51804510-c0a3-4a51-9795-0e38a7c9a8f4",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "419b9b44-bcd3-499f-8307-cb9f72ef52b3",
|
||
"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": "3.11.3"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|