1

src/玉米病虫害识别系统/uploads/table_08477.jpg.html

@@ -0,0 +1 @@
+<table><tr><th>잚깎</th><th>零斤똑</th></tr><tr><td>瑨叉</td><td>95.4335</td></tr><tr><td>鬼뒈일빪</td><td>1.9126</td></tr><tr><td>圖쵠측</td><td>1.2005</td></tr><tr><td>겜懃빻쏜밴</td><td>1.0906</td></tr><tr><td>댕뒈일빪</td><td>0.2073</td></tr><tr><td>뼝뒈일빪</td><td>0.0621</td></tr><tr><td>譏녔</td><td>0.0485</td></tr><tr><td>쏜濾녔</td><td>0.0193</td></tr><tr><td>庚療측</td><td>0.0087</td></tr><tr><td>迭直</td><td>0.0066</td></tr></table>

src/玉米病虫害识别系统/【B】准备图像分类数据集.ipynb

@@ -0,0 +1,160 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "4a36abc9-47b6-4e9a-8d2c-330e64012db2",
+   "metadata": {},
+   "source": [
+    "# 准备图像分类数据集\n",
+    "\n",
+    "同济子豪兄：https://space.bilibili.com/1900783\n",
+    "\n",
+    "[代码运行云GPU环境](https://featurize.cn/?s=d7ce99f842414bfcaea5662a97581bd1)：GPU RTX 3060、CUDA v11.2\n",
+    "\n",
+    "## 构建自己的图像分类数据集\n",
+    "\n",
+    "https://www.bilibili.com/video/BV1Jd4y1T7rw"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e0f48f3b-5137-4b11-a529-4ae41c8806e4",
+   "metadata": {},
+   "source": [
+    "## 下载样例数据集"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "70a97135-b7a8-4817-a43d-742171ac5978",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--2023-03-26 17:13:25--  https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220716-mmclassification/dataset/fruit30/fruit30_split.zip\n",
+      "正在连接 172.16.0.13:5848... 已连接。\n",
+      "已发出 Proxy 请求，正在等待回应... 200 OK\n",
+      "长度： 226278151 (216M) [application/zip]\n",
+      "正在保存至: “fruit30_split.zip”\n",
+      "\n",
+      "fruit30_split.zip   100%[===================>] 215.79M  46.1MB/s    用时 4.9s    \n",
+      "\n",
+      "2023-03-26 17:13:30 (43.9 MB/s) - 已保存 “fruit30_split.zip” [226278151/226278151])\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 下载数据集压缩包\n",
+    "!wget https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220716-mmclassification/dataset/fruit30/fruit30_split.zip"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "21711cbb-685d-40ff-9ec9-2348d8d1a1a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 解压\n",
+    "!unzip fruit30_split.zip >> /dev/null"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b9324762-352f-4b9c-b9e8-2b299c0ef2fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 删除压缩包\n",
+    "!rm fruit30_split.zip"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55155fe0-2d99-458e-bdb2-ac894d7a6046",
+   "metadata": {},
+   "source": [
+    "## 查看数据集目录结构"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "f06d00df-aa4f-41fe-a2be-50a677bb5a3f",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-10-12T01:01:43.528374Z",
+     "start_time": "2024-10-12T01:01:43.488297Z"
+    }
+   },
+   "source": [
+    "!sudo snap install tree"
+   ],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "'sudo' 不是内部或外部命令，也不是可运行的程序\n",
+      "或批处理文件。\n"
+     ]
+    }
+   ],
+   "execution_count": 1
+  },
+  {
+   "cell_type": "code",
+   "id": "8f9f463f-ea2e-4d7b-b7b2-9f528afe987c",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-10-12T01:01:45.086148Z",
+     "start_time": "2024-10-12T01:01:45.035422Z"
+    }
+   },
+   "source": "!tree dataset -L 2",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "参数太多 - -L\n"
+     ]
+    }
+   ],
+   "execution_count": 2
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "139d2504-a78a-4145-8520-5206fb51b829",
+   "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": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

src/玉米病虫害识别系统/图像识别界面.html

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+<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>图像分类工具 - 结果页面</title>
+    <style>
+        body {
+            font-family: "SimHei", sans-serif;
+            display: flex;
+            flex-direction: column;
+            align-items: center;
+            justify-content: center;
+            height: 100vh;
+            background-color: #f6f6f6;
+        }
+
+        h1 {
+            font-size: 50px; /* 标题放大 */
+        }
+
+        button {
+            margin: 25px;
+            padding: 25px 45px;
+            font-size: 20px; /* 按钮文字放大 */
+        }
+
+        #result {
+            margin-top: 30px;
+            font-size: 24px; /* 结果文字放大 */
+        }
+
+        /* 样式用于显示后端返回的 HTML 结果 */
+        #safe-result {
+            margin-top: 30px;
+            font-size: 24px; /* 结果文字放大 */
+            white-space: pre-line; /* 保持换行 */
+        }
+    </style>
+</head>
+<body>
+<h1>图像分类工具</h1>
+<input type="file" id="imageInput" accept="image/*" />
+<button onclick="analyzeImage()">获取分析结果</button>
+<div id="result"></div>
+
+<!-- 结果页面中用于显示后端返回的 HTML 内容 -->
+{% if result %}
+<div id="safe-result">
+    <!-- 使用 |safe 过滤器来确保 HTML 标签不被转义 -->
+    {{ result|safe }}
+</div>
+{% endif %}
+
+<script>
+    async function analyzeImage() {
+        const input = document.getElementById('imageInput');
+        if (!input.files[0]) {
+            alert("请先插入一张图片。");
+            return;
+        }
+
+        // 读取图像
+        const file = input.files[0];
+        const img = new Image();
+        img.src = URL.createObjectURL(file);
+        await img.decode();
+
+        // 此处应调用后端接口进行预测
+        // 示例：假设有一个 API '/api/predict' 返回预测结果
+        const formData = new FormData();
+        formData.append("image", file);
+
+        try {
+            const response = await fetch('/api/predict', {
+                method: 'POST',
+                body: formData
+            });
+
+            if (!response.ok) {
+                throw new Error("网络错误");
+            }
+
+            const result = await response.json();
+            document.getElementById('result').innerText = `具有最大置信度的类别名称：${result.label}`;
+
+        } catch (error) {
+            alert("分析失败：" + error.message);
+        }
+    }
+</script>
+</body>
+</html>

src/玉米病虫害识别系统/界面.py

@@ -0,0 +1,109 @@
+import torch
+import torch.nn.functional as F
+import torchvision.transforms as transforms
+from PIL import Image
+import tkinter as tk
+from tkinter import filedialog, messagebox
+import numpy as np
+
+# 有 GPU 就用 GPU，没有就用 CPU
+device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+
+# 载入模型
+model_path = 'checkpoint/dataset_pytorch_C1.pth'  # 确保路径正确
+try:
+    model = torch.load(model_path)
+    model = model.eval().to(device)
+except FileNotFoundError:
+    print(f"错误：无法找到模型文件 '{model_path}'")
+    exit()
+
+# 测试集图像预处理-RCTN：缩放、裁剪、转 Tensor、归一化
+test_transform = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+# 类别映射
+idx_to_labels = np.load('idx_to_labels.npy', allow_pickle=True).item()
+
+# 全局变量保存图片和分析结果
+img_path = None
+pred_result = None
+
+
+def insert_image():
+    global img_path
+    root = tk.Tk()
+    root.withdraw()
+    file_path = filedialog.askopenfilename(
+        title="选择一张图片",
+        filetypes=[("Image files", "*.jpg *.jpeg *.png *.bmp *.gif")]
+    )
+
+    if file_path:
+        img_path = file_path
+        messagebox.showinfo("成功", "图片插入成功！")
+    else:
+        messagebox.showwarning("警告", "未选择任何图片。")
+
+
+def analyze_image():
+    global img_path, pred_result
+    if img_path is None:
+        messagebox.showwarning("警告", "请先插入一张图片。")
+        return
+
+    # 打开并预处理图像
+    img_pil = Image.open(img_path)
+    input_img = test_transform(img_pil).unsqueeze(0).to(device)
+
+    # 前向预测
+    with torch.no_grad():
+        pred_logits = model(input_img)
+
+    # 计算 softmax
+    pred_softmax = F.softmax(pred_logits, dim=1)
+
+    # 找到置信度最大的前 n 个结果
+    n = 10
+    top_n = torch.topk(pred_softmax, n)
+    pred_ids = top_n[1].cpu().numpy().squeeze()
+    confs = top_n[0].cpu().numpy().squeeze()
+
+    # 找到最大置信度的类别名称
+    max_conf_index = np.argmax(confs)
+    max_conf_class_name = idx_to_labels[pred_ids[max_conf_index]]
+
+    # 保存结果
+    pred_result = f"具有最大置信度的类别名称：{max_conf_class_name}"
+
+    # 打开分析结果窗口
+    analysis_window = tk.Toplevel()
+    analysis_window.title("分析结果")
+    analysis_window.geometry("400x300")
+
+    result_label = tk.Label(analysis_window, text=pred_result, font=("SimHei", 12), wraplength=350, justify="left")
+    result_label.grid(row=0, column=0, padx=20, pady=20)
+
+    # 使用 grid() 将按钮放在右下角
+    ok_button = tk.Button(analysis_window, text="确定", command=analysis_window.destroy)
+    ok_button.grid(row=1, column=0, sticky="ew", padx=20, pady=10)  # sticky="ew" 使按钮占据整行
+
+
+# 创建主窗口
+main_window = tk.Tk()
+main_window.title("图像分类工具")
+main_window.geometry("300x200")
+
+# 创建按钮
+insert_button = tk.Button(main_window, text="插入图片", command=insert_image)
+insert_button.pack(pady=20)
+
+analyze_button = tk.Button(main_window, text="获取分析结果", command=analyze_image)
+analyze_button.pack(pady=20)
+
+# 运行主循环
+main_window.mainloop()

src/玉米病虫害识别系统/训练日志-测试集.csv

@@ -0,0 +1,31 @@
+epoch,test_loss,test_accuracy,test_precision,test_recall,test_f1-score
+1,2.0930123,0.38472418670438474,0.1691631623197644,0.21857142857142858,0.18289168026241523
+2,1.9176342,0.4045261669024045,0.3099095071675483,0.2430026455026455,0.22423302592957986
+3,1.8613107,0.4144271570014144,0.27610813015358454,0.25837301587301587,0.24874843872971764
+4,1.8651094,0.4214992927864215,0.33039935968860973,0.2672883597883598,0.24876040306538025
+5,1.8502045,0.4314002828854314,0.31955499557865813,0.2849338624338625,0.26468437342725254
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+7,1.7824266,0.43705799151343705,0.3353262740983161,0.28779100529100526,0.27932666603867806
+8,1.8227451,0.4314002828854314,0.29637810366383366,0.2860846560846561,0.26648188873060297
+9,1.7772177,0.44271570014144274,0.3099393993772887,0.29665343915343917,0.2819555068621611
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+27,1.7535378,0.44130127298444133,0.29291407487928234,0.29400793650793655,0.287903699063399
+28,1.7552626,0.43847241867043846,0.29508124173100647,0.29215608465608467,0.2885014907300926
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+30,1.7565756,0.42998585572843,0.28064225400986786,0.2828703703703704,0.27678367056270065