- 移除 核心层与 tract/Tensor 的强耦合,前/后处理全线转用标准 ndarray - 针对 OCR 与目标检测(Det)分别设计独立的强类型输出小枚举(OcrOutput/DetOutput) - 利用 Trait 关联类型(Associated Type)InferenceEngine,OcrEngine,DetEngine 统一接口,实现多后端解耦 - 引入 thiserror 库,建立完备的强类型错误处理机制(DdddError/Result) - 完成项目结构初拆,剥离为 ddddocr-core 和 ddddocr-tract
24 lines
697 B
TOML
24 lines
697 B
TOML
[package]
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name = "ddddocr-tract"
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version = { workspace = true }
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edition = { workspace = true }
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license = { workspace = true }
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[dependencies]
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ddddocr-core = { path = "../ddddocr-core" } # 引入兄弟库
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tract-onnx = "0.21.10"
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anyhow = "1.0.102"
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image = { workspace = true }
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base64 = "0.22.1"
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imageproc = { version = "0.26.2", default-features = true }
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serde = { workspace = true }
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serde_json = "1.0.150"
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ndarray = { workspace = true } # 继承自工作空间
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thiserror = { workspace = true } # 刚好可以开始接入你需要的标准库错误处理
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[features]
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default = []
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embed-models = [] # 这是一个留给有特殊需求、且自己下载了模型放入 models/ 目录的人的后门 |