refactor(errors): 重构错误处理，支持强类型匹配并剥离 base64 依赖
- 新增 Other变体以及构造函数new - 剥离图像预处理中的 Base64 相关错误至业务层处理 - 引入强类型 `LogitsDimensionMismatch` 替代不便匹配的字符串错误 - 优化 `normalize_ocr_logits` 的转换流程，兼顾零拷贝性能与精细化报错 - 优化 全库错误处理
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ddddocr-core/src/utils/image_convert.rs
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180
ddddocr-core/src/utils/image_convert.rs
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use crate::error::{DdddError, ImagePreprocessReason, Result};
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use image::{DynamicImage, GenericImageView, ImageBuffer, Luma, Rgb, Rgba};
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use ndarray::{Array3, ArrayViewD};
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#[derive(Debug)]
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pub enum ColorMode {
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RGB,
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RGBA,
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L,
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}
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/// 封装数组转图像的逻辑，
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// 对应 Python 版 _numpy_to_pil_image
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pub fn ndarray_to_hwc_image(array: ArrayViewD<u8>) -> Result<DynamicImage> {
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let shape = array.shape();
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let dim = shape.len();
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// 1. 确保数据在内存中是连续的 (C order / Standard Layout)
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// 如果 arr 是经过切片或转置的，这一步会进行必要的内存拷贝
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// let standard = array.as_standard_layout();
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// let (raw_data, _offset) = standard.to_owned().into_raw_vec_and_offset();
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let color_mode = match dim {
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// 对应 Python: len(array.shape) == 2 (灰度图 H, W)
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2 => ColorMode::L,
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// 对应 Python: len(array.shape) == 3 (H, W, C)
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3 => {
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let (_h, _w, c) = (shape[0], shape[1], shape[2]);
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match c {
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// 对应 Python: array.shape[2] == 1 (单通道 H, W, 1)
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1 => ColorMode::L,
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// 对应 Python: array.shape[2] == 3 (RGB H, W, 3)
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3 => ColorMode::RGB,
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// 对应 Python: array.shape[2] == 4 (RGBA H, W, 4)
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4 => ColorMode::RGBA,
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_ => {
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return Err(DdddError::Preprocess(
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ImagePreprocessReason::UnsupportedChannels(c),
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));
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}
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}
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}
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_ => {
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return Err(DdddError::Preprocess(
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ImagePreprocessReason::InvalidImageDimensions {
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expected: "2D (H,W) 或 3D (H,W,C)".to_string(),
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actual: shape.to_vec(),
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},
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));
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}
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};
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from_ndarray(array, color_mode)
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}
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/// 处理PNG图片的RGBA透明背景，将透明部分设置为白色背景
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// 对应 Python 的 png_rgba_black_preprocess
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pub fn png_rgba_white_preprocess(img: &DynamicImage) -> DynamicImage {
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// 1. 检查是否包含透明通道，如果没有，直接克隆并返回
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if !img.color().has_alpha() {
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return DynamicImage::ImageRgb8(img.to_rgb8());
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}
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let (width, height) = img.dimensions();
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// 2. 创建一个新的 RGB 图像缓冲，默认填充为白色 (255, 255, 255)
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let mut background = ImageBuffer::from_pixel(width, height, Rgb([255u8, 255u8, 255u8]));
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// 3. 获取原图的 RGBA 视图
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let rgba_img = img.to_rgba8();
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// 4. 遍历像素并手动进行 Alpha 混合
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// 对应 Python 的 utils.paste(img, ..., mask=img)
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// 使用 enumerate_pixels_mut 同时获取坐标和背景像素的可变引用，减少查找开销
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for (x, y, bg_pixel) in background.enumerate_pixels_mut() {
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// 安全性说明：x, y 源自 background 尺寸，与 rgba_img 一致，get_pixel 是安全的
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let src_pixel = rgba_img.get_pixel(x, y);
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let alpha_u8 = src_pixel[3];
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match alpha_u8 {
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// 情况 A：完全不透明，直接覆盖背景色
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255 => {
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bg_pixel.0 = [src_pixel[0], src_pixel[1], src_pixel[2]];
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}
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// 情况 B：完全透明，保持背景色（白色），无需操作
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0 => {
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continue;
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}
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// 情况 C：半透明，进行 Alpha 混合计算
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_ => {
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let alpha = alpha_u8 as f32 / 255.0;
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let inv_alpha = 1.0 - alpha;
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bg_pixel[0] = (src_pixel[0] as f32 * alpha + 255.0 * inv_alpha).round() as u8;
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bg_pixel[1] = (src_pixel[1] as f32 * alpha + 255.0 * inv_alpha).round() as u8;
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bg_pixel[2] = (src_pixel[2] as f32 * alpha + 255.0 * inv_alpha).round() as u8;
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}
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}
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}
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DynamicImage::ImageRgb8(background)
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}
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/// 将 DynamicImage 转换为 array 数组
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pub fn image_to_ndarray(image: &DynamicImage, mode: ColorMode) -> Result<Array3<u8>> {
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// 1. 模式转换 (对应 utils.convert(target_mode)),此函数在时保留看后续优化是否需要替代image_to_ndarray
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// Rust utils 库通过 to_rgb8, to_luma8 等方法实现转换
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let (width, height) = image.dimensions();
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let (channels, raw) = match mode {
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ColorMode::L => (1, image.to_luma8().into_raw()),
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ColorMode::RGB => (3, image.to_rgb8().into_raw()),
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ColorMode::RGBA => (4, image.to_rgba8().into_raw()),
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};
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let array = Array3::from_shape_vec((height as usize, width as usize, channels), raw)
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.map_err(ImagePreprocessReason::from)?;
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Ok(array)
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}
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/// 将 array 数组转换为 DynamicImage
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pub fn ndarray_to_image(array: ArrayViewD<u8>, mode: ColorMode) -> Result<DynamicImage> {
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let shape = array.shape();
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// 基础边界检查：至少要有 H 和 W 两个维度
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if shape.len() < 2 {
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return Err(DdddError::Preprocess(
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ImagePreprocessReason::InvalidImageDimensions {
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expected: "至少为 2D array [H, W]".to_string(),
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actual: shape.to_vec(),
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},
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));
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}
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from_ndarray(array, mode)
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}
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fn from_ndarray(array: ArrayViewD<u8>, mode: ColorMode) -> Result<DynamicImage> {
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let shape = array.shape();
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// 映射：ndarray 的 shape 默认是 [Height, Width, (Channels)]
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// image 库的 from_raw 接收 (width, height)
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let height = shape[0] as u32;
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let width = shape[1] as u32;
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// 1. 确保数据在内存中是连续的 (C order)
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let standard = array.as_standard_layout();
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let (raw_data, _) = standard.to_owned().into_raw_vec_and_offset();
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let raw_len = raw_data.len();
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// 获取当前模式对应的通道数
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let channels = match mode {
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ColorMode::L => 1,
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ColorMode::RGB => 3,
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ColorMode::RGBA => 4,
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};
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let expected_len = (width * height) as usize * channels;
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// 构造通用错误闭包，避免 match 分支中重复编写冗长的错误对象
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let make_err = || {
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DdddError::Preprocess(ImagePreprocessReason::BufferLengthMismatch {
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expected: expected_len,
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actual: raw_len,
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width,
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height,
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channels,
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})
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};
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// 2. 重新解释内存并构建 ImageBuffer
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match mode {
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ColorMode::L => ImageBuffer::<Luma<u8>, _>::from_raw(width, height, raw_data)
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.map(DynamicImage::ImageLuma8)
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.ok_or_else(make_err),
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ColorMode::RGB => ImageBuffer::<Rgb<u8>, _>::from_raw(width, height, raw_data)
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.map(DynamicImage::ImageRgb8)
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.ok_or_else(make_err),
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ColorMode::RGBA => ImageBuffer::<Rgba<u8>, _>::from_raw(width, height, raw_data)
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.map(DynamicImage::ImageRgba8)
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.ok_or_else(make_err),
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}
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}
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