ddddocr-rs/tests/ocr_test.rs

use std::fs;
use std::path::Path;
use image::Rgb;
use ddddocr_rs::{DdddOcr, DdddOcrBuilder}; // 假设你的包名是这个
use ddddocr_rs::slide_model::Slide;
fn load_image<P: AsRef<Path>>(path: P) -> anyhow::Result<image::DynamicImage> {
    // 1. 先将泛型转为具体的 &Path 引用
    let path_ref = path.as_ref();

    // 2. 调用 open 时传入引用（image::open 支持 AsRef<Path>）
    image::open(path_ref)
        .map_err(|e| {
            // 3. 此时 path_ref 依然有效，可以安全地在闭包中使用
            anyhow::anyhow!("无法加载图片 {:?}: {}", path_ref, e)
        })
}
/// 将检测结果绘制在图像上并保存
fn save_debug_image( image_bytes: &[u8], bboxes: &Vec<Vec<i32>>, output_path: &str) -> anyhow::Result<()> {


    let dynamic_img = image::load_from_memory(image_bytes)?;
    let mut img = dynamic_img.to_rgb8();
    let (width, height) = img.dimensions();
    let red = Rgb([255u8, 0, 0]);

    for bbox in bboxes {
        // 基础边界检查
        let x1 = bbox[0].max(0).min(width as i32 - 1) as u32;
        let y1 = bbox[1].max(0).min(height as i32 - 1) as u32;
        let x2 = bbox[2].max(0).min(width as i32 - 1) as u32;
        let y2 = bbox[3].max(0).min(height as i32 - 1) as u32;

        // 绘制横向线条
        for x in x1..=x2 {
            img.put_pixel(x, y1, red);
            img.put_pixel(x, y2, red);
            // 如果要加粗，多画一行
            if y1 + 1 < height { img.put_pixel(x, y1 + 1, red); }
            if y2.saturating_sub(1) > 0 { img.put_pixel(x, y2 - 1, red); }
        }
        // 绘制纵向线条
        for y in y1..=y2 {
            img.put_pixel(x1, y, red);
            img.put_pixel(x2, y, red);
            // 如果要加粗，多画一列
            if x1 + 1 < width { img.put_pixel(x1 + 1, y, red); }
            if x2.saturating_sub(1) > 0 { img.put_pixel(x2 - 1, y, red); }
        }
    }

    img.save(output_path)?;
    Ok(())
}
#[test]
fn test_full_classification() {
    // 1. 初始化模型
    let ocr = DdddOcrBuilder::new().build().expect("模型加载失败");

    // 2. 加载测试图片
    let img = image::open("samples/code3.png").expect("测试图片不存在");

    // 3. 执行识别
    let result = ocr.classification(&img).expect("识别过程出错");

    println!("识别结果: {}", result);
    assert!(!result.is_empty());
}
#[test]
fn test_det_load()->anyhow::Result<()>{
    let det = DdddOcrBuilder::new().det().build()?;
    let image_path = "samples/det1.png";
    let image_bytes = fs::read(image_path)
        .map_err(|e| anyhow::anyhow!("无法读取图片 {}: {}", image_path, e))?;

    println!("图片读取成功，字节大小: {}", image_bytes.len());
    let bboxes =det.detection(&image_bytes)?;
    println!(":?{}",det);
    println!("检测到的目标数量: {}", bboxes.len());
    if bboxes.is_empty() {
        println!("未检测到任何目标。");
    } else {
        save_debug_image(&image_bytes, &bboxes, "samples/result.jpg")?;
        for (i, bbox) in bboxes.iter().enumerate() {
            println!("目标 [{}]: x1={}, y1={}, x2={}, y2={}", i, bbox[0], bbox[1], bbox[2], bbox[3]);
        }
    }
    Ok(())
}


#[test]
fn test_real_slide_match() {
    let engine = Slide::new();

    // 1. 加载你准备好的测试图
    // 假设图片放在项目根目录下的 assets 文件夹
    let target_img = load_image("samples/hua.png")
        .expect("请确保 samples/hua.png 存在");
    let bg_img = load_image("samples/huatu.png")
        .expect("请确保 samples/huatu.png 存在");

    // 2. 执行匹配
    // 如果是那种带有明显阴影边缘的复杂滑块，建议 simple_target 传 false
    let start = std::time::Instant::now();
    let result = engine.slide_match(&target_img, &bg_img, false)
        .expect("Slide match 执行失败");
    let duration = start.elapsed();

    // 3. 打印结果
    println!("-------------------------------------------");
    println!("滑块匹配测试结果:");
    println!("检测坐标: [x: {}, y: {}]", result.target_x, result.target_y);
    println!("置信度: {:.4}", result.confidence);
    println!("耗时: {:?}", duration);
    println!("-------------------------------------------");

    // 验证基本逻辑：坐标不应为 0 (除非匹配失败)
    assert_eq!(result.target_x, 237);
    assert_eq!(result.target_y, 77);
    assert!(result.confidence > 0.0);
}

#[test]
fn test_real_slide_comparison() {
    let engine = Slide::new();

    // 1. 加载你准备好的测试图
    // 假设图片放在项目根目录下的 assets 文件夹
    let target_img = load_image("samples/ken.jpg")
        .expect("请确保 samples/ken.jpg 存在");
    let bg_img = load_image("samples/kenyuan.jpg")
        .expect("请确保 samples/kenyuan.jpg 存在");

    // 2. 执行匹配
    // 如果是那种带有明显阴影边缘的复杂滑块，建议 simple_target 传 false
    let start = std::time::Instant::now();
    let result = engine.slide_comparison(&target_img, &bg_img)
        .expect("Slide match 执行失败");
    let duration = start.elapsed();

    // 3. 打印结果
    println!("-------------------------------------------");
    println!("滑块匹配测试结果:");
    println!("检测坐标: [x: {}, y: {}]", result.target_x, result.target_y);
    println!("置信度: {:.4}", result.confidence);
    println!("耗时: {:?}", duration);
    println!("-------------------------------------------");

    // 验证基本逻辑：坐标不应为 0 (除非匹配失败)
    assert_eq!(result.target_x, 171);
    assert_eq!(result.target_y, 91);
    assert!(result.confidence > 0.0);
}