121 lines
4.3 KiB
Rust
121 lines
4.3 KiB
Rust
use std::fs;
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use std::path::Path;
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use image::Rgb;
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use ddddocr_rs::{DdddOcr, DdddOcrBuilder}; // 假设你的包名是这个
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use ddddocr_rs::slide_model::Slide;
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fn load_image<P: AsRef<Path>>(path: P) -> anyhow::Result<image::DynamicImage> {
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// 1. 先将泛型转为具体的 &Path 引用
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let path_ref = path.as_ref();
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// 2. 调用 open 时传入引用（image::open 支持 AsRef<Path>）
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image::open(path_ref)
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.map_err(|e| {
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// 3. 此时 path_ref 依然有效，可以安全地在闭包中使用
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anyhow::anyhow!("无法加载图片 {:?}: {}", path_ref, e)
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})
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}
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/// 将检测结果绘制在图像上并保存
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fn save_debug_image( image_bytes: &[u8], bboxes: &Vec<Vec<i32>>, output_path: &str) -> anyhow::Result<()> {
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let dynamic_img = image::load_from_memory(image_bytes)?;
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let mut img = dynamic_img.to_rgb8();
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let (width, height) = img.dimensions();
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let red = Rgb([255u8, 0, 0]);
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for bbox in bboxes {
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// 基础边界检查
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let x1 = bbox[0].max(0).min(width as i32 - 1) as u32;
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let y1 = bbox[1].max(0).min(height as i32 - 1) as u32;
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let x2 = bbox[2].max(0).min(width as i32 - 1) as u32;
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let y2 = bbox[3].max(0).min(height as i32 - 1) as u32;
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// 绘制横向线条
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for x in x1..=x2 {
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img.put_pixel(x, y1, red);
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img.put_pixel(x, y2, red);
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// 如果要加粗，多画一行
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if y1 + 1 < height { img.put_pixel(x, y1 + 1, red); }
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if y2.saturating_sub(1) > 0 { img.put_pixel(x, y2 - 1, red); }
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}
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// 绘制纵向线条
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for y in y1..=y2 {
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img.put_pixel(x1, y, red);
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img.put_pixel(x2, y, red);
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// 如果要加粗，多画一列
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if x1 + 1 < width { img.put_pixel(x1 + 1, y, red); }
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if x2.saturating_sub(1) > 0 { img.put_pixel(x2 - 1, y, red); }
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}
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}
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img.save(output_path)?;
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Ok(())
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}
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#[test]
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fn test_full_classification() {
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// 1. 初始化模型
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let ocr = DdddOcrBuilder::new().build().expect("模型加载失败");
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// 2. 加载测试图片
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let img = image::open("samples/code3.png").expect("测试图片不存在");
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// 3. 执行识别
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let result = ocr.classification(&img).expect("识别过程出错");
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println!("识别结果: {}", result);
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assert!(!result.is_empty());
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}
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#[test]
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fn test_det_load()->anyhow::Result<()>{
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let det = DdddOcrBuilder::new().det().build()?;
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let image_path = "samples/det1.png";
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let image_bytes = fs::read(image_path)
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.map_err(|e| anyhow::anyhow!("无法读取图片 {}: {}", image_path, e))?;
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println!("图片读取成功，字节大小: {}", image_bytes.len());
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let bboxes =det.detection(&image_bytes)?;
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println!(":?{}",det);
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println!("检测到的目标数量: {}", bboxes.len());
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if bboxes.is_empty() {
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println!("未检测到任何目标。");
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} else {
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save_debug_image(&image_bytes, &bboxes, "samples/result.jpg")?;
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for (i, bbox) in bboxes.iter().enumerate() {
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println!("目标 [{}]: x1={}, y1={}, x2={}, y2={}", i, bbox[0], bbox[1], bbox[2], bbox[3]);
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}
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}
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Ok(())
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}
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#[test]
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fn test_real_slide_match() {
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let engine = Slide::new();
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// 1. 加载你准备好的测试图
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// 假设图片放在项目根目录下的 assets 文件夹
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let target_img = load_image("samples/hua.png")
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.expect("请确保 samples/hua.png 存在");
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let bg_img = load_image("samples/huatu.png")
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.expect("请确保 samples/huatu.png 存在");
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// 2. 执行匹配
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// 如果是那种带有明显阴影边缘的复杂滑块，建议 simple_target 传 false
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let start = std::time::Instant::now();
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let result = engine.slide_match(&target_img, &bg_img, false)
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.expect("Slide match 执行失败");
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let duration = start.elapsed();
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// 3. 打印结果
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println!("-------------------------------------------");
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println!("滑块匹配测试结果:");
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println!("检测坐标: [x: {}, y: {}]", result.target_x, result.target_y);
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println!("置信度: {:.4}", result.confidence);
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println!("耗时: {:?}", duration);
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println!("-------------------------------------------");
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// 验证基本逻辑：坐标不应为 0 (除非匹配失败)
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assert_eq!(result.target_x, 237);
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assert_eq!(result.target_y, 77);
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assert!(result.confidence > 0.0);
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} |