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douboer
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run.sh
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269
run.sh
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#!/bin/bash
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# ============================================================================
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# YOLO数字识别系统 - 快速运行脚本
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# ============================================================================
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#
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# 功能: 提供交互式菜单，简化常用操作
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# 作者: Gavin Chan
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# 日期: 2025-10-30
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#
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# 使用方法:
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# ./run.sh # 交互式菜单
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#
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# 注意事项:
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# - 需要先激活虚拟环境（脚本会自动尝试）
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# - 确保最佳模型文件存在
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# - 识别结果保存在 results/ 目录
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# ============================================================================
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set -e # 遇到错误立即退出
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# ----------------------------------------------------------------------------
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# 颜色定义（用于美化输出）
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# ----------------------------------------------------------------------------
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GREEN='\033[0;32m' # 绿色：成功信息
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YELLOW='\033[1;33m' # 黄色：警告或进行中
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RED='\033[0;31m' # 红色：错误信息
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NC='\033[0m' # 无颜色：重置
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# ----------------------------------------------------------------------------
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# 标题
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# ----------------------------------------------------------------------------
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echo "=================================================="
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echo "🚀 YOLO数字识别系统 - 快速运行脚本"
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echo "=================================================="
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# ----------------------------------------------------------------------------
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# 1. 激活虚拟环境
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# 尝试多个可能的虚拟环境位置，找到第一个存在的就使用
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# ----------------------------------------------------------------------------
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echo -e "\n${GREEN}1. 激活虚拟环境${NC}"
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if [ -d ~/venv ]; then
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# 方式1: 用户主目录下的 venv
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source ~/venv/bin/activate
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echo "✓ 虚拟环境已激活: ~/venv"
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elif [ -d venv ]; then
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# 方式2: 当前目录下的 venv
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source venv/bin/activate
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echo "✓ 虚拟环境已激活: ./venv"
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else
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# 未找到虚拟环境，使用系统Python（可能缺少依赖）
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echo "⚠️ 未找到虚拟环境，使用系统Python"
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fi
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# 显示当前工作目录
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echo "✓ 工作目录: $(pwd)"
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# ----------------------------------------------------------------------------
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# 2. 检查依赖包
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# 验证必需的Python包是否已安装，如果缺少则自动安装
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# ----------------------------------------------------------------------------
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echo -e "\n${GREEN}2. 检查依赖包${NC}"
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if python -c "import ultralytics, cv2, numpy" 2>/dev/null; then
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echo "✓ 所有依赖包已安装"
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else
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echo -e "${RED}✗ 缺少依赖包，正在安装...${NC}"
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pip install ultralytics opencv-python numpy
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fi
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# ----------------------------------------------------------------------------
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# 3. 显示操作菜单
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# 提供常用功能的快捷入口
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# ----------------------------------------------------------------------------
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echo -e "\n${GREEN}3. 请选择操作${NC}"
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echo "=================================="
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echo "1) 识别数字 (使用最佳模型) ⭐推荐"
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echo "2) 低阈值识别 (conf=0.15，更多检测)"
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echo "3) 高清识别 (imgsz=640，更精确)"
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echo "4) 查看已有结果和模型信息"
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echo "0) 退出"
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echo "=================================="
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read -p "请输入选项 [0-4]: " choice
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# ----------------------------------------------------------------------------
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# 配置：最佳模型路径
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# 这是训练好的最佳模型，使用CLAHE预处理 + 150轮训练
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# mAP50 = 0.995，在训练集上接近完美
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# ----------------------------------------------------------------------------
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BEST_MODEL="runs/digit_yolo/exp_preprocessed_color_150/weights/best.pt"
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# ----------------------------------------------------------------------------
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# 4. 执行选择的操作
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# ----------------------------------------------------------------------------
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case $choice in
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# ------------------------------------------------------------------------
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# 选项1: 标准识别（最常用）
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# 使用最佳模型，置信度0.2，保存可视化结果
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# 适合大多数场景，速度和准确率平衡
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# ------------------------------------------------------------------------
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1)
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echo -e "\n${YELLOW}运行改进版识别（使用最佳模型）...${NC}"
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# 检查模型文件是否存在
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if [ ! -f "$BEST_MODEL" ]; then
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echo -e "${RED}❌ 错误: 模型文件不存在${NC}"
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echo " 路径: $BEST_MODEL"
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echo " 请先训练模型或检查路径"
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exit 1
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fi
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# 执行识别
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python scripts/predict_digits_improved.py \
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--model "$BEST_MODEL" \
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--source valid \
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--conf 0.2 \
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--save-vis
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# 显示结果位置
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echo -e "\n${GREEN}✓ 识别完成！${NC}"
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echo "📄 结果文件: results/predictions_improved.txt"
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echo "🖼️ 可视化: results/visualizations_improved/"
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;;
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# ------------------------------------------------------------------------
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# 选项2: 低阈值识别
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# 降低置信度到0.15，可以检测出更多数字
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# 适合图片模糊或数字不清晰的情况
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# 可能会有更多误检
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# ------------------------------------------------------------------------
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2)
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echo -e "\n${YELLOW}运行低阈值识别（更敏感）...${NC}"
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python scripts/predict_digits_improved.py \
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--model "$BEST_MODEL" \
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--source valid \
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--conf 0.15 \
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--output results/predictions_conf015.txt \
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--save-vis
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echo -e "\n${GREEN}✓ 识别完成！${NC}"
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echo "📄 结果文件: results/predictions_conf015.txt"
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;;
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# ------------------------------------------------------------------------
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# 选项3: 高清识别
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# 使用更大的输入尺寸(640)，提高识别精度
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# 适合需要高精度的场景
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# 处理速度会变慢（约2倍时间）
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# ------------------------------------------------------------------------
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3)
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echo -e "\n${YELLOW}运行高清识别（更精确）...${NC}"
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python scripts/predict_digits_improved.py \
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--model "$BEST_MODEL" \
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--source valid \
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--imgsz 640 \
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--output results/predictions_640.txt \
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--save-vis
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echo -e "\n${GREEN}✓ 识别完成！${NC}"
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echo "📄 结果文件: results/predictions_640.txt"
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;;
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# ------------------------------------------------------------------------
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# 选项4: 查看结果和模型信息
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# 显示已有的模型、识别结果和可视化文件
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# 不执行任何识别操作
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# ------------------------------------------------------------------------
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4)
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echo -e "\n${YELLOW}查看已有结果和模型信息...${NC}"
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echo "=================================="
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# 显示最佳模型信息
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echo "📦 最佳模型:"
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if [ -f "$BEST_MODEL" ]; then
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ls -lh "$BEST_MODEL" | awk '{printf " 大小: %s\n", $5}'
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echo " 路径: $BEST_MODEL"
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else
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echo " ⚠️ 模型不存在"
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fi
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echo ""
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# 显示识别结果文件
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echo "📄 识别结果文件:"
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if ls results/*.txt 1> /dev/null 2>&1; then
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ls -lh results/*.txt | awk '{printf " %s (%s)\n", $9, $5}'
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else
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echo " 暂无结果文件"
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fi
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echo ""
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# 显示可视化目录
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echo "🖼️ 可视化结果:"
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if ls -d results/visualizations* 1> /dev/null 2>&1; then
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ls -d results/visualizations* | sed 's/^/ /'
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else
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echo " 暂无可视化结果"
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fi
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echo ""
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# 显示所有训练模型
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echo "🎯 所有训练模型:"
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if ls runs/digit_yolo/*/weights/best.pt 1> /dev/null 2>&1; then
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for model in runs/digit_yolo/*/weights/best.pt; do
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exp_name=$(dirname $(dirname "$model"))
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exp_name=$(basename "$exp_name")
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size=$(ls -lh "$model" | awk '{print $5}')
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echo " [$exp_name] $size"
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done
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else
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echo " 暂无模型文件"
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fi
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echo "=================================="
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;;
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# ------------------------------------------------------------------------
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# 选项0: 退出程序
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# ------------------------------------------------------------------------
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0)
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echo -e "\n${GREEN}👋 再见！${NC}"
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exit 0
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;;
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# ------------------------------------------------------------------------
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# 无效选项处理
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# ------------------------------------------------------------------------
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*)
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echo -e "\n${RED}❌ 无效选项: $choice${NC}"
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echo "请输入 0-4 之间的数字"
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exit 1
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;;
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esac
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# ----------------------------------------------------------------------------
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# 5. 显示后续操作提示
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# ----------------------------------------------------------------------------
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echo ""
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echo "=================================================="
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echo -e "${GREEN}✓ 操作完成！${NC}"
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echo "=================================================="
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echo ""
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# 常用命令提示
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echo "📊 查看结果的命令:"
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echo " cat results/predictions*.txt # 查看识别结果"
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echo " open results/visualizations*/ # 打开可视化图片（macOS）"
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echo ""
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# 更多操作提示
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echo "🔧 更多操作:"
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echo " python scripts/predict_digits_improved.py --help # 查看完整参数说明"
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echo " python scripts/preprocess_images.py --help # 图片预处理帮助"
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echo ""
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# 文档链接
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echo "📚 项目文档:"
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echo " cat README.md # 完整文档"
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echo " cat QUICKSTART.md # 快速开始指南"
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echo " cat FINAL_REPORT.md # 项目完成报告"
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echo ""
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