//! OCR 识别与模型信息集成测试。

mod common;

use common::{model_path, sample_path};
use ddddocr_core::traits::{Info, Loader};
use ddddocr_core::{ModelMetadata, Normalization, Ocr, Resize};
use ddddocr_tract2::OcrRuntime;
use ddddocr_tract2::loader::ModelLoader;

/// 用官方 sml2h3 f32 模型识别验证码图片，结果不应为空。
#[test]
fn ocr_classification_recognizes_code_image() {
    let session = ModelLoader::default()
        .build_for_path(model_path("common_sml2h3_f32.onnx"))
        .expect("模型加载失败");
    let metadata = ModelMetadata::from_builtin_beta(
        false,
        Resize::DynamicWidth(64),
        1,
        Normalization::MinusOneToOne,
    );
    let ocr = OcrRuntime::new(session, metadata);

    let img = image::open(sample_path("code2.png")).expect("测试图片不存在");
    let text = Ocr::builder()
        .build_with(&ocr)
        .predict(&img)
        .expect("识别过程出错")
        .into_text();

    println!("识别结果: {text}");
    assert!(!text.is_empty(), "识别结果不应为空");
}

/// 真实模型应能通过 `Info` trait 返回输入/输出张量信息。
#[test]
fn model_info_lists_inputs_and_outputs() -> anyhow::Result<()> {
    let session = ModelLoader::default()
        .build_for_path(model_path("common_huashi666_i64.onnx"))
        .expect("建立测试模型图失败");
    let metadata = ModelMetadata::from_builtin_beta(
        false,
        Resize::DynamicWidth(64),
        1,
        Normalization::MinusOneToOne,
    );
    let ocr = OcrRuntime::new(session, metadata);

    let inputs = ocr.input_info()?;
    let outputs = ocr.output_info()?;
    assert!(!inputs.is_empty(), "模型应有输入张量信息");
    assert!(!outputs.is_empty(), "模型应有输出张量信息");
    Ok(())
}