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refactor: 优化 slide_model.rs

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- 新增 cv2.rs 模拟 opencv
This commit is contained in:
CNWei
2026-05-09 17:52:34 +08:00
parent e8b365dced
commit a51147c888
4 changed files with 188 additions and 181 deletions
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46
src/cv2.rs
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@@ -1,4 +1,17 @@
use tract_onnx::prelude::tract_ndarray::{Array2, ArrayView3}; use image::{ImageBuffer, Luma};
use tract_onnx::prelude::tract_ndarray::{azip, Array2, Array3, ArrayView2, ArrayView3};
/// 1. 计算两个数组的绝对差值 (对应 cv2.absdiff)
pub fn abs_diff(a: &ArrayView3<u8>, b: &ArrayView3<u8>) -> Array3<u8> {
// 利用 ndarray 的 map_collect，生成差值的绝对值数组
// 或者直接使用 zip_mut_with 处理以减少内存分配
let mut diff = Array3::zeros(a.dim());
azip!((res in &mut diff, &va in a, &vb in b) {
*res = (va as i16 - vb as i16).abs() as u8;
});
diff
}
/// RGB 到灰度转换 /// RGB 到灰度转换
pub fn rgb_to_gray(rgb: ArrayView3<u8>) -> Array2<u8> { pub fn rgb_to_gray(rgb: ArrayView3<u8>) -> Array2<u8> {
@@ -11,3 +24,34 @@ pub fn rgb_to_gray(rgb: ArrayView3<u8>) -> Array2<u8> {
(0.299 * r + 0.587 * g + 0.114 * b) as u8 (0.299 * r + 0.587 * g + 0.114 * b) as u8
}) })
} }
/// 寻找匹配结果图中的最大值及其坐标 (模拟 cv2.minMaxLoc 的一部分)
pub fn min_max_loc(result_map: &ImageBuffer<Luma<f32>, Vec<f32>>) -> (f32, (u32, u32)) {
// 4. 找到最佳匹配位置 (对齐 cv2.minMaxLoc)
let mut max_val: f32 = -1.0;
let mut max_loc = (0, 0);
// 遍历匹配得分图
for (x, y, score) in result_map.enumerate_pixels() {
let s = score.0[0];
// 可以在此处加入你之前验证过的起始位过滤
// if x < 15 { continue; }
if s > max_val {
max_val = s;
max_loc = (x, y);
}
}
(max_val, max_loc)
}
pub fn ndarray_to_luma8(array: ArrayView2<u8>) -> ImageBuffer<Luma<u8>, Vec<u8>> {
let (height, width) = array.dim();
let mut buffer = ImageBuffer::new(width as u32, height as u32);
for y in 0..height {
for x in 0..width {
buffer.put_pixel(x as u32, y as u32, Luma([array[[y, x]]]));
}
}
buffer
}

169
src/image_io.rs
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@@ -1,9 +1,15 @@
use anyhow::{Context, Result, anyhow}; use anyhow::{Context, Result, anyhow, bail};
use base64::{Engine as _, engine::general_purpose}; use base64::{Engine as _, engine::general_purpose};
use image::{DynamicImage, GenericImageView, ImageBuffer, ImageFormat, Luma, Rgb, RgbImage, Rgba}; use image::{DynamicImage, GenericImageView, ImageBuffer, ImageFormat, Luma, Rgb, RgbImage, Rgba};
use std::fs; use std::fs;
use std::path::{Path, PathBuf}; use std::path::{Path, PathBuf};
use tract_onnx::prelude::tract_ndarray::{Array3, ArrayD, ArrayViewD}; use tract_onnx::prelude::tract_ndarray::{Array3, ArrayD, ArrayViewD};
#[derive(Debug)]
pub enum ColorMode {
RGB,
RGBA,
L,
}
/// 定义支持的输入类型枚举 /// 定义支持的输入类型枚举
pub enum ImageInput { pub enum ImageInput {
Bytes(Vec<u8>), Bytes(Vec<u8>),
@@ -60,41 +66,7 @@ pub fn get_img_base64<P: AsRef<Path>>(image_path: P) -> Result<String> {
Ok(b64_string) Ok(b64_string)
} }
/// 处理 PNG 图像的 RGBA 透明背景，将透明部分设置为白色
///
/// 对应 Python 版 png_rgba_black_preprocess
pub fn png_rgba_black_preprocess(img: &DynamicImage) -> Result<DynamicImage> {
// 1. 获取原图尺寸
let (width, height) = (img.width(), img.height());
// 2. 创建一个等尺寸的纯白色 RGB 图像作为底色
// ImageBuffer::<Rgb<u8>, Vec<u8>>
let mut white_bg = ImageBuffer::from_fn(width, height, |_, _| {
Rgb([255, 255, 255])
});
// 3. 将原图复合到底色上
// 我们需要处理原图，将其转为 RGBA 确保有 alpha 通道可以参考
let rgba_img = img.to_rgba8();
// 遍历每一个像素进行复合（模拟 Python 的 paste 逻辑）
for (x, y, pixel) in rgba_img.enumerate_pixels() {
let alpha = pixel[3] as f32 / 255.0;
if alpha > 0.0 {
// 获取底色像素（白色）
let bg_pixel = white_bg.get_pixel_mut(x, y);
// 简单的 Alpha 复合公式：输出 = 源 * alpha + 背景 * (1 - alpha)
for i in 0..3 {
let fg = pixel[i] as f32;
let bg = bg_pixel[i] as f32;
bg_pixel[i] = (fg * alpha + bg * (1.0 - alpha)) as u8;
}
}
}
Ok(DynamicImage::ImageRgb8(white_bg))
}
/// 封装数组转图像的逻辑，对齐 Python 版 _numpy_to_pil_image /// 封装数组转图像的逻辑，对齐 Python 版 _numpy_to_pil_image
fn numpy_to_pil_image(array: ArrayViewD<u8>) -> Result<DynamicImage> { fn numpy_to_pil_image(array: ArrayViewD<u8>) -> Result<DynamicImage> {
let shape = array.shape(); let shape = array.shape();
@@ -143,11 +115,11 @@ fn numpy_to_pil_image(array: ArrayViewD<u8>) -> Result<DynamicImage> {
/// 对应 Python 的 png_rgba_black_preprocess /// 对应 Python 的 png_rgba_black_preprocess
/// 将带有透明通道的图片转换为白色背景的 RGB 图片 /// 将带有透明通道的图片转换为白色背景的 RGB 图片
#[allow(dead_code)]
pub fn png_rgba_white_preprocess(img: &DynamicImage) -> DynamicImage { pub fn png_rgba_white_preprocess(img: &DynamicImage) -> DynamicImage {
// 1. 检查是否包含透明通道，如果没有，直接克隆并返回 // 1. 检查是否包含透明通道，如果没有，直接克隆并返回
if !img.color().has_alpha() { if !img.color().has_alpha() {
return img.clone(); return DynamicImage::ImageRgb8(img.to_rgb8());
} }
let (width, height) = img.dimensions(); let (width, height) = img.dimensions();
@@ -160,83 +132,87 @@ pub fn png_rgba_white_preprocess(img: &DynamicImage) -> DynamicImage {
// 4. 遍历像素并手动进行 Alpha 混合 // 4. 遍历像素并手动进行 Alpha 混合
// 对应 Python 的 image.paste(img, ..., mask=img) // 对应 Python 的 image.paste(img, ..., mask=img)
for (x, y, pixel) in rgba_img.enumerate_pixels() { // 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
let alpha = pixel[3] as f32 / 255.0; for (x, y, bg_pixel) in background.enumerate_pixels_mut() {
// 安全性说明：x, y 源自 background 尺寸，与 rgba_img 一致，get_pixel 是安全的
let src_pixel = rgba_img.get_pixel(x, y);
let alpha_u8 = src_pixel[3];
if alpha >= 1.0 { match alpha_u8 {
// 完全不透明，直接覆盖 // 情况 A：完全不透明，直接覆盖背景色
background.put_pixel(x, y, Rgb([pixel[0], pixel[1], pixel[2]])); 255 => {
} else if alpha > 0.0 { bg_pixel.0 = [src_pixel[0], src_pixel[1], src_pixel[2]];
// 半透明，执行 Alpha 混合公式： (src * alpha) + (dst * (1 - alpha)) }
let bg_pixel = background.get_pixel(x, y); // 情况 B：完全透明，保持背景色（白色），无需操作
let r = (pixel[0] as f32 * alpha + bg_pixel[0] as f32 * (1.0 - alpha)) as u8; 0 => {
let g = (pixel[1] as f32 * alpha + bg_pixel[1] as f32 * (1.0 - alpha)) as u8; continue;
let b = (pixel[2] as f32 * alpha + bg_pixel[2] as f32 * (1.0 - alpha)) as u8; }
background.put_pixel(x, y, Rgb([r, g, b])); // 情况 C：半透明，进行 Alpha 混合计算
_ => {
let alpha = alpha_u8 as f32 / 255.0;
let inv_alpha = 1.0 - alpha;
bg_pixel[0] = (src_pixel[0] as f32 * alpha + 255.0 * inv_alpha).round() as u8;
bg_pixel[1] = (src_pixel[1] as f32 * alpha + 255.0 * inv_alpha).round() as u8;
bg_pixel[2] = (src_pixel[2] as f32 * alpha + 255.0 * inv_alpha).round() as u8;
}
} }
// alpha == 0 的情况不需要处理，因为背景已经是白色了
} }
DynamicImage::ImageRgb8(background) DynamicImage::ImageRgb8(background)
} }
pub fn image_to_numpy(image: &DynamicImage, target_mode: &str) -> Result<Array3<u8>> { pub fn image_to_numpy(image: &DynamicImage, mode: ColorMode) -> Result<Array3<u8>> {
// 1. 模式转换 (对应 image.convert(target_mode)) // 1. 模式转换 (对应 image.convert(target_mode)),此函数在时保留看后续优化是否需要替代image_to_ndarray
// Rust image 库通过 to_rgb8, to_luma8 等方法实现转换 // Rust image 库通过 to_rgb8, to_luma8 等方法实现转换
let (width, height) = image.dimensions(); let (width, height) = image.dimensions();
match target_mode { let (channels, raw) = match mode {
"RGB" => { ColorMode::RGB => (3, image.to_rgb8().into_raw()),
let rgb_img = image.to_rgb8(); ColorMode::L => (1, image.to_luma8().into_raw()),
let raw = rgb_img.into_raw(); ColorMode::RGBA => (4, image.to_rgba8().into_raw()),
// shape 为 [Height, Width, Channels] -> [H, W, 3] };
Array3::from_shape_vec((height as usize, width as usize, 3), raw)
.map_err(|e| anyhow!("Failed to build ndarray: {}", e)) Array3::from_shape_vec((height as usize, width as usize, channels), raw)
}, .map_err(|e| anyhow!("Failed to build ndarray: {}", e))
"L" | "GRAY" => {
let gray_img = image.to_luma8();
let raw = gray_img.into_raw();
// shape 为 [H, W, 1]
Array3::from_shape_vec((height as usize, width as usize, 1), raw)
.map_err(|e| anyhow!("Failed to build ndarray: {}", e))
},
"RGBA" => {
let rgba_img = image.to_rgba8();
let raw = rgba_img.into_raw();
// shape 为 [H, W, 4]
Array3::from_shape_vec((height as usize, width as usize, 4), raw)
.map_err(|e| anyhow!("Failed to build ndarray: {}", e))
},
_ => Err(anyhow!("Unsupported target_mode: {}", target_mode)),
}
} }
pub fn numpy_to_image(array: ArrayViewD<u8>, mode: &str) -> Result<DynamicImage> { pub fn numpy_to_image(array: ArrayViewD<u8>, mode: ColorMode) -> Result<DynamicImage> {
let shape = array.shape(); let shape = array.shape();
// 1. 基础维度检查 (必须是 H, W, C 三维数组)
if shape.len() != 3 {
bail!("Expected a 3D array (H, W, C), but got {}D", shape.len());
}
let height = shape[0] as u32;
let width = shape[1] as u32;
let channels = shape[2];
// 2. 检查通道数是否与模式匹配
let expected_channels = match mode {
ColorMode::L => 1,
ColorMode::RGB => 3,
ColorMode::RGBA => 4,
};
if channels != expected_channels {
bail!(
"Mode {:?} expects {} channels, but array has {}",
mode,
expected_channels,
channels
);
}
// 确保数据连续性 (C-order) // 确保数据连续性 (C-order)
let standard = array.as_standard_layout(); let standard = array.as_standard_layout();
let (raw_data, _) = standard.to_owned().into_raw_vec_and_offset(); let (raw_data, _) = standard.to_owned().into_raw_vec_and_offset();
let height = shape[0] as u32;
let width = shape[1] as u32;
match mode { match mode {
"L" => { ColorMode::L => ImageBuffer::<Luma<u8>, _>::from_raw(width, height, raw_data)
ImageBuffer::<Luma<u8>, _>::from_raw(width, height, raw_data) .map(DynamicImage::ImageLuma8),
.map(DynamicImage::ImageLuma8) ColorMode::RGB => ImageBuffer::<Rgb<u8>, _>::from_raw(width, height, raw_data)
.ok_or_else(|| anyhow!("Failed to create Luma image")) .map(DynamicImage::ImageRgb8),
}, ColorMode::RGBA => ImageBuffer::<Rgba<u8>, _>::from_raw(width, height, raw_data)
"RGB" => { .map(DynamicImage::ImageRgba8),
ImageBuffer::<Rgb<u8>, _>::from_raw(width, height, raw_data)
.map(DynamicImage::ImageRgb8)
.ok_or_else(|| anyhow!("Failed to create RGB image"))
},
"RGBA" => {
ImageBuffer::<Rgba<u8>, _>::from_raw(width, height, raw_data)
.map(DynamicImage::ImageRgba8)
.ok_or_else(|| anyhow!("Failed to create RGBA image"))
},
_ => Err(anyhow!("Unsupported mode: {}", mode)),
} }
.ok_or_else(|| anyhow!("Failed to construct ImageBuffer. Buffer size might be incorrect."))
} }
pub fn image_to_ndarray(img: &DynamicImage) -> Array3<u8> { pub fn image_to_ndarray(img: &DynamicImage) -> Array3<u8> {
let (width, height) = img.dimensions(); let (width, height) = img.dimensions();
@@ -251,6 +227,7 @@ pub fn image_to_ndarray(img: &DynamicImage) -> Array3<u8> {
Array3::from_shape_vec((height as usize, width as usize, 3), raw_data) Array3::from_shape_vec((height as usize, width as usize, 3), raw_data)
.expect("Failed to construct ndarray from image") // 建议显式报错，而不是返回全黑图 .expect("Failed to construct ndarray from image") // 建议显式报错，而不是返回全黑图
} }
#[allow(dead_code)] #[allow(dead_code)]
fn save_rust_result(result: &ImageBuffer<Luma<f32>, Vec<f32>>, filename: &str) { fn save_rust_result(result: &ImageBuffer<Luma<f32>, Vec<f32>>, filename: &str) {
let (width, height) = result.dimensions(); let (width, height) = result.dimensions();

148
src/slide_model.rs
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@@ -1,15 +1,16 @@
use crate::cv2::{min_max_loc, rgb_to_gray, ndarray_to_luma8, abs_diff};
use crate::image_io::image_to_ndarray;
use anyhow::{Context, Result, anyhow}; use anyhow::{Context, Result, anyhow};
use image::{DynamicImage, GenericImageView}; use image::{DynamicImage, GenericImageView};
use tract_onnx::prelude::tract_ndarray::{Array2, Array3, ArrayView2, ArrayView3, Axis, s};
use imageproc::template_matching::{match_template, MatchTemplateMethod};
use image::{ImageBuffer, Luma}; use image::{ImageBuffer, Luma};
use crate::image_io::image_to_ndarray;
use crate::cv2::rgb_to_gray;
use imageproc::edges::canny;
use imageproc::distance_transform::Norm; use imageproc::distance_transform::Norm;
use imageproc::edges::canny;
use imageproc::morphology::{close, open}; use imageproc::morphology::{close, open};
use imageproc::region_labelling::{connected_components, Connectivity}; use imageproc::region_labelling::{Connectivity, connected_components};
use imageproc::template_matching::{MatchTemplateMethod, match_template};
use std::cmp::{max, min}; use std::cmp::{max, min};
use imageproc::contrast::{threshold, ThresholdType};
use tract_onnx::prelude::tract_ndarray::{Array2, Array3, ArrayView2, ArrayView3, Axis, s};
pub struct SlideResult { pub struct SlideResult {
pub target: [i32; 2], pub target: [i32; 2],
@@ -28,26 +29,26 @@ impl Slide {
/// 对应 Python: slide_match /// 对应 Python: slide_match
pub fn slide_match( pub fn slide_match(
&self, &self,
target_pil: &DynamicImage, target_image: &DynamicImage,
background_pil: &DynamicImage, background_image: &DynamicImage,
simple_target: bool, simple_target: bool,
) -> Result<SlideResult> { ) -> Result<SlideResult> {
let target_array = image_to_ndarray(target_pil); let target_array = image_to_ndarray(target_image);
let background_array = image_to_ndarray(background_pil); let background_array = image_to_ndarray(background_image);
self.perform_slide_match(target_array.view(), background_array.view(),simple_target) self.perform_slide_match(target_array.view(), background_array.view(), simple_target)
.map_err(|e| anyhow!("滑块匹配失败: {}", e)) .map_err(|e| anyhow!("滑块匹配失败: {}", e))
} }
/// 对应 Python: slide_comparison /// 对应 Python: slide_comparison
/// 用于比较带坑位的图片与原始背景图，定位差异点 /// 用于比较带坑位的图片与原始背景图，定位差异点
pub fn slide_comparison( pub fn slide_comparison(
&self, &self,
target_pil: &DynamicImage, target_image: &DynamicImage,
background_pil: &DynamicImage, background_image: &DynamicImage,
) -> Result<SlideResult> { ) -> Result<SlideResult> {
// 1. 转换为 ndarray (HWC RGB) // 1. 转换为 ndarray (HWC RGB)
let target_array = image_to_ndarray(target_pil); let target_array = image_to_ndarray(target_image);
let background_array = image_to_ndarray(background_pil); let background_array = image_to_ndarray(background_image);
// 2. 执行比较逻辑 (对应 _perform_slide_comparison) // 2. 执行比较逻辑 (对应 _perform_slide_comparison)
self.perform_slide_comparison(target_array.view(), background_array.view()) self.perform_slide_comparison(target_array.view(), background_array.view())
@@ -63,25 +64,32 @@ impl Slide {
// 1. 计算图像差异并灰度化 (对应 cv2.absdiff + cv2.cvtColor) // 1. 计算图像差异并灰度化 (对应 cv2.absdiff + cv2.cvtColor)
// 使用 OpenCV 标准权重公式：0.299R + 0.587G + 0.114B // 使用 OpenCV 标准权重公式：0.299R + 0.587G + 0.114B
let mut diff_buffer = ImageBuffer::new(w as u32, h as u32); // let mut diff_buffer = ImageBuffer::new(w as u32, h as u32);
for y in 0..h { // for y in 0..h {
for x in 0..w { // for x in 0..w {
let r_diff = (target[[y, x, 0]] as i16 - background[[y, x, 0]] as i16).abs() as f32; // let r_diff = (target[[y, x, 0]] as i16 - background[[y, x, 0]] as i16).abs() as f32;
let g_diff = (target[[y, x, 1]] as i16 - background[[y, x, 1]] as i16).abs() as f32; // let g_diff = (target[[y, x, 1]] as i16 - background[[y, x, 1]] as i16).abs() as f32;
let b_diff = (target[[y, x, 2]] as i16 - background[[y, x, 2]] as i16).abs() as f32; // let b_diff = (target[[y, x, 2]] as i16 - background[[y, x, 2]] as i16).abs() as f32;
//
// let gray_diff = (0.299 * r_diff + 0.587 * g_diff + 0.114 * b_diff) as u8;
// diff_buffer.put_pixel(x as u32, y as u32, Luma([gray_diff]));
// }
// }
// 1. 计算差异数组 (复用 cv2::absdiff)
let diff_array = abs_diff(&target, &background);
let gray_diff = (0.299 * r_diff + 0.587 * g_diff + 0.114 * b_diff) as u8; // 2. 转换为灰度数组 (复用你的 cv2::rgb_to_gray)
diff_buffer.put_pixel(x as u32, y as u32, Luma([gray_diff])); let gray_array = rgb_to_gray(diff_array.view());
} // 3. 转为 ImageBuffer 以使用 imageproc 的高级功能
} let gray_buffer = ndarray_to_luma8(gray_array.view());
// 2. 二值化 (对应 cv2.threshold(..., 30, 255, cv2.THRESH_BINARY)) // 2. 二值化 (对应 cv2.threshold(..., 30, 255, cv2.THRESH_BINARY))
let mut binary = ImageBuffer::new(w as u32, h as u32); // let mut binary = ImageBuffer::new(w as u32, h as u32);
for (x, y, pixel) in diff_buffer.enumerate_pixels() { // for (x, y, pixel) in diff_buffer.enumerate_pixels() {
let val = if pixel.0[0] > 30 { 255u8 } else { 0u8 }; // let val = if pixel.0[0] > 30 { 255u8 } else { 0u8 };
binary.put_pixel(x, y, Luma([val])); // binary.put_pixel(x, y, Luma([val]));
} // }
let binary = threshold(&gray_buffer, 30, ThresholdType::Binary);
// 3. 形态学操作去噪 (对应 cv2.morphologyEx) // 3. 形态学操作去噪 (对应 cv2.morphologyEx)
// 闭运算 (Close): 先膨胀后腐蚀，用于填补缺口内的细小黑色空洞 // 闭运算 (Close): 先膨胀后腐蚀，用于填补缺口内的细小黑色空洞
// 开运算 (Open): 先腐蚀后膨胀，用于消除背景中的白色噪点点 // 开运算 (Open): 先腐蚀后膨胀，用于消除背景中的白色噪点点
@@ -102,7 +110,9 @@ impl Slide {
for pixel in labelled.pixels() { for pixel in labelled.pixels() {
let label = pixel.0[0]; let label = pixel.0[0];
if label == 0 { continue; } // 跳过背景 if label == 0 {
continue;
} // 跳过背景
let count = areas.entry(label).or_insert(0); let count = areas.entry(label).or_insert(0);
*count += 1; *count += 1;
if *count > max_area { if *count > max_area {
@@ -112,7 +122,12 @@ impl Slide {
} }
if max_label == 0 { if max_label == 0 {
return Ok(SlideResult { target: [0, 0], target_x: 0, target_y: 0, confidence: 0.0 }); return Ok(SlideResult {
target: [0, 0],
target_x: 0,
target_y: 0,
confidence: 0.0,
});
} }
// 5. 计算最大区域的边界框 (对应 cv2.boundingRect) // 5. 计算最大区域的边界框 (对应 cv2.boundingRect)
@@ -174,32 +189,27 @@ impl Slide {
background: ArrayView2<u8>, background: ArrayView2<u8>,
) -> Result<SlideResult> { ) -> Result<SlideResult> {
// 1. 将 ndarray 转换为 imageproc 需要的 ImageBuffer (无拷贝或轻量转换) // 1. 将 ndarray 转换为 imageproc 需要的 ImageBuffer (无拷贝或轻量转换)
let (th, tw) = target.dim();
let (bh, bw) = background.dim(); // let (bh, bw) = background.dim();
// 转换逻辑 (假设你已经有方法转回 ImageBuffer) // 转换逻辑 (假设你已经有方法转回 ImageBuffer)
let t_buf = self.ndarray_to_luma8(target); let t_buf = ndarray_to_luma8(target);
let b_buf = self.ndarray_to_luma8(background); let b_buf = ndarray_to_luma8(background);
t_buf.save("debug_rust_target.png").unwrap(); // t_buf.save("debug_rust_target.png").unwrap();
// 2. 调用 imageproc 的 NCC 算法 (等价于 cv2.TM_CCOEFF_NORMED) // 2. 调用 imageproc 的 NCC 算法 (等价于 cv2.TM_CCOEFF_NORMED)
let result = match_template(&b_buf, &t_buf, MatchTemplateMethod::CrossCorrelationNormalized); // 模板匹配 (完全对齐 cv2.matchTemplate(..., cv2.TM_CCOEFF_NORMED))
let result = match_template(
&b_buf,
&t_buf,
MatchTemplateMethod::CrossCorrelationNormalized,
);
// save_rust_result(&result, "debug_rust_target2.png"); // save_rust_result(&result, "debug_rust_target2.png");
// 3. 寻找最大值 (等价于 cv2.minMaxLoc) // 3. 寻找最大值 (等价于 cv2.minMaxLoc)
let mut max_val: f32 = -1.0; let (max_val, max_loc) = min_max_loc(&result);
let mut max_loc = (0, 0);
for (x, y, score) in result.enumerate_pixels() {
let s = score.0[0];
// 这里的 x, y 是左上角坐标
if s > max_val {
max_val = s;
max_loc = (x, y);
}
}
// 4. 计算中心点 (与 Python 逻辑完全一致) // 4. 计算中心点 (与 Python 逻辑完全一致)
let (th, tw) = target.dim();
let center_x = max_loc.0 as i32 + (tw as i32 / 2); let center_x = max_loc.0 as i32 + (tw as i32 / 2);
let center_y = max_loc.1 as i32 + (th as i32 / 2); let center_y = max_loc.1 as i32 + (th as i32 / 2);
// println!("Rust Target Width (tw): {}", tw); // println!("Rust Target Width (tw): {}", tw);
@@ -213,16 +223,6 @@ impl Slide {
}) })
} }
fn ndarray_to_luma8(&self, array: ArrayView2<u8>) -> ImageBuffer<Luma<u8>, Vec<u8>> {
let (height, width) = array.dim();
let mut buffer = ImageBuffer::new(width as u32, height as u32);
for y in 0..height {
for x in 0..width {
buffer.put_pixel(x as u32, y as u32, Luma([array[[y, x]]]));
}
}
buffer
}
/// 对应 Python: _edge_based_match /// 对应 Python: _edge_based_match
/// 基于边缘检测的滑块匹配 (对齐 Python _edge_based_match) /// 基于边缘检测的滑块匹配 (对齐 Python _edge_based_match)
pub fn edge_based_match( pub fn edge_based_match(
@@ -232,8 +232,8 @@ impl Slide {
) -> Result<SlideResult> { ) -> Result<SlideResult> {
// 1. 将 ndarray 转换为 ImageBuffer // 1. 将 ndarray 转换为 ImageBuffer
// 注意：Canny 和 match_template 需要 ImageBuffer 格式 // 注意：Canny 和 match_template 需要 ImageBuffer 格式
let t_buf = self.ndarray_to_luma8(target); let t_buf = ndarray_to_luma8(target);
let b_buf = self.ndarray_to_luma8(background); let b_buf = ndarray_to_luma8(background);
// 2. 边缘检测 (完全对齐 cv2.Canny(50, 150)) // 2. 边缘检测 (完全对齐 cv2.Canny(50, 150))
// 这步会生成黑底白线的二值化边缘图 // 这步会生成黑底白线的二值化边缘图
@@ -245,29 +245,14 @@ impl Slide {
// 3. 模板匹配 (完全对齐 cv2.matchTemplate(..., cv2.TM_CCOEFF_NORMED)) // 3. 模板匹配 (完全对齐 cv2.matchTemplate(..., cv2.TM_CCOEFF_NORMED))
// 在边缘图上计算归一化互相关系数 // 在边缘图上计算归一化互相关系数
let result_map = match_template( let result = match_template(
&background_edges, &background_edges,
&target_edges, &target_edges,
MatchTemplateMethod::CrossCorrelationNormalized MatchTemplateMethod::CrossCorrelationNormalized,
); );
// 4. 找到最佳匹配位置 (对齐 cv2.minMaxLoc) // 4. 找到最佳匹配位置 (对齐 cv2.minMaxLoc)
let mut max_val: f32 = -1.0; let (max_val, max_loc) = min_max_loc(&result);
let mut max_loc = (0, 0);
// 遍历匹配得分图
for (x, y, score) in result_map.enumerate_pixels() {
let s = score.0[0];
// 可以在此处加入你之前验证过的起始位过滤
// if x < 15 { continue; }
if s > max_val {
max_val = s;
max_loc = (x, y);
}
}
// 5. 计算中心位置 (对齐 Python 逻辑) // 5. 计算中心位置 (对齐 Python 逻辑)
// target_w, target_h 来自输入数组的维度 // target_w, target_h 来自输入数组的维度
let (th, tw) = target.dim(); let (th, tw) = target.dim();
@@ -287,4 +272,5 @@ impl Slide {
}) })
} }
} }

4
tests/ocr_test.rs
View File

@@ -57,7 +57,7 @@ fn test_full_classification() {
let ocr = DdddOcrBuilder::new().build().expect("模型加载失败"); let ocr = DdddOcrBuilder::new().build().expect("模型加载失败");
// 2. 加载测试图片 // 2. 加载测试图片
let img = image::open("samples/code3.png").expect("测试图片不存在"); let img = image::open("samples/code2.png").expect("测试图片不存在");
// 3. 执行识别 // 3. 执行识别
let result = ocr.classification(&img).expect("识别过程出错"); let result = ocr.classification(&img).expect("识别过程出错");
@@ -148,6 +148,6 @@ fn test_real_slide_comparison() {
// 验证基本逻辑：坐标不应为 0 (除非匹配失败) // 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_x, 171);
assert_eq!(result.target_y, 91); assert_eq!(result.target_y, 90);
assert!(result.confidence > 0.0); assert!(result.confidence > 0.0);
} }