277 lines
11 KiB
Rust
277 lines
11 KiB
Rust
use crate::cv2::{min_max_loc, rgb_to_gray, ndarray_to_luma8, abs_diff};
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use crate::image_io::image_to_ndarray;
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use anyhow::{Context, Result, anyhow};
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use image::{DynamicImage, GenericImageView};
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use image::{ImageBuffer, Luma};
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use imageproc::distance_transform::Norm;
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use imageproc::edges::canny;
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use imageproc::morphology::{close, open};
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use imageproc::region_labelling::{Connectivity, connected_components};
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use imageproc::template_matching::{MatchTemplateMethod, match_template};
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use std::cmp::{max, min};
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use imageproc::contrast::{threshold, ThresholdType};
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use tract_onnx::prelude::tract_ndarray::{Array2, Array3, ArrayView2, ArrayView3, Axis, s};
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pub struct SlideResult {
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pub target: [i32; 2],
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pub target_x: i32,
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pub target_y: i32,
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pub confidence: f64,
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}
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pub struct Slide;
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impl Slide {
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pub fn new() -> Self {
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Self
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}
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/// 对应 Python: slide_match
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pub fn slide_match(
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&self,
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target_image: &DynamicImage,
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background_image: &DynamicImage,
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simple_target: bool,
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) -> Result<SlideResult> {
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let target_array = image_to_ndarray(target_image);
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let background_array = image_to_ndarray(background_image);
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self.perform_slide_match(target_array.view(), background_array.view(), simple_target)
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.map_err(|e| anyhow!("滑块匹配失败: {}", e))
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}
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/// 对应 Python: slide_comparison
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/// 用于比较带坑位的图片与原始背景图，定位差异点
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pub fn slide_comparison(
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&self,
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target_image: &DynamicImage,
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background_image: &DynamicImage,
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) -> Result<SlideResult> {
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// 1. 转换为 ndarray (HWC RGB)
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let target_array = image_to_ndarray(target_image);
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let background_array = image_to_ndarray(background_image);
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// 2. 执行比较逻辑 (对应 _perform_slide_comparison)
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self.perform_slide_comparison(target_array.view(), background_array.view())
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.map_err(|e| anyhow!("滑块比较执行失败: {}", e))
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}
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/// 对应 Python: _perform_slide_comparison
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pub fn perform_slide_comparison(
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&self,
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target: ArrayView3<u8>,
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background: ArrayView3<u8>,
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) -> Result<SlideResult> {
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let (h, w, _) = target.dim();
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// 1. 计算图像差异并灰度化 (对应 cv2.absdiff + cv2.cvtColor)
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// 使用 OpenCV 标准权重公式：0.299R + 0.587G + 0.114B
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// let mut diff_buffer = ImageBuffer::new(w as u32, h as u32);
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// for y in 0..h {
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// for x in 0..w {
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// let r_diff = (target[[y, x, 0]] as i16 - background[[y, x, 0]] as i16).abs() as f32;
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// let g_diff = (target[[y, x, 1]] as i16 - background[[y, x, 1]] as i16).abs() as f32;
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// let b_diff = (target[[y, x, 2]] as i16 - background[[y, x, 2]] as i16).abs() as f32;
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//
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// let gray_diff = (0.299 * r_diff + 0.587 * g_diff + 0.114 * b_diff) as u8;
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// diff_buffer.put_pixel(x as u32, y as u32, Luma([gray_diff]));
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// }
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// }
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// 1. 计算差异数组 (复用 cv2::absdiff)
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let diff_array = abs_diff(&target, &background);
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// 2. 转换为灰度数组 (复用你的 cv2::rgb_to_gray)
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let gray_array = rgb_to_gray(diff_array.view());
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// 3. 转为 ImageBuffer 以使用 imageproc 的高级功能
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let gray_buffer = ndarray_to_luma8(gray_array.view());
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// 2. 二值化 (对应 cv2.threshold(..., 30, 255, cv2.THRESH_BINARY))
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// let mut binary = ImageBuffer::new(w as u32, h as u32);
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// for (x, y, pixel) in diff_buffer.enumerate_pixels() {
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// let val = if pixel.0[0] > 30 { 255u8 } else { 0u8 };
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// binary.put_pixel(x, y, Luma([val]));
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// }
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let binary = threshold(&gray_buffer, 30, ThresholdType::Binary);
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// 3. 形态学操作去噪 (对应 cv2.morphologyEx)
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// 闭运算 (Close): 先膨胀后腐蚀，用于填补缺口内的细小黑色空洞
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// 开运算 (Open): 先腐蚀后膨胀，用于消除背景中的白色噪点点
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let norm = Norm::LInf; // 对应 3x3 的矩形内核
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let radius = 1u8; // 1 表示 3x3 的范围，2 表示 5x5 的范围
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let closed = close(&binary, norm, radius);
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let cleaned = open(&closed, norm, radius);
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// 4. 寻找最大连通区域 (对应 findContours + max area)
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// connected_components 会给每个独立的白色区域打上不同的标签 (ID)
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let background_label = Luma([0u8]);
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let labelled = connected_components(&cleaned, Connectivity::Eight, background_label);
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// 统计每个标签出现的频率（即面积）
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let mut max_label = 0;
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let mut max_area = 0;
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let mut areas = std::collections::HashMap::new();
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for pixel in labelled.pixels() {
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let label = pixel.0[0];
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if label == 0 {
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continue;
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} // 跳过背景
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let count = areas.entry(label).or_insert(0);
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*count += 1;
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if *count > max_area {
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max_area = *count;
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max_label = label;
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}
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}
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if max_label == 0 {
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return Ok(SlideResult {
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target: [0, 0],
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target_x: 0,
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target_y: 0,
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confidence: 0.0,
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});
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}
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// 5. 计算最大区域的边界框 (对应 cv2.boundingRect)
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let mut min_x = w as u32;
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let mut max_x = 0;
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let mut min_y = h as u32;
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let mut max_y = 0;
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for (x, y, pixel) in labelled.enumerate_pixels() {
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if pixel.0[0] == max_label {
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min_x = min(min_x, x);
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max_x = max(max_x, x);
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min_y = min(min_y, y);
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max_y = max(max_y, y);
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}
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}
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// 6. 计算中心点
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let rect_w = max_x - min_x;
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let rect_h = max_y - min_y;
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let center_x = (min_x + rect_w / 2) as i32;
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let center_y = (min_y + rect_h / 2) as i32;
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Ok(SlideResult {
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target: [center_x, center_y],
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target_x: center_x,
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target_y: center_y,
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confidence: 1.0, // Comparison 模式下通常认为找到即为 1.0
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})
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}
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/// 对应 Python: _perform_slide_match
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// 在 SlideEngine 中修改此入口进行测试
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fn perform_slide_match(
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&self,
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target: ArrayView3<u8>,
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background: ArrayView3<u8>,
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simple_target: bool, // 增加这个参数
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) -> Result<SlideResult> {
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// 1. 统一灰度化
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let target_gray = rgb_to_gray(target);
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let background_gray = rgb_to_gray(background);
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if simple_target {
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// 2a. 简单模式：直接在灰度图上匹配
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self.simple_template_match(target_gray.view(), background_gray.view())
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} else {
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// 2b. 复杂模式：先提取边缘，再匹配
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self.edge_based_match(target_gray.view(), background_gray.view())
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}
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}
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/// 对应 Python: _simple_template_match
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/// 使用 SAD (Sum of Absolute Differences) 算法
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/// 核心模板匹配：SAD + 有效像素过滤
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fn simple_template_match(
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&self,
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target: ArrayView2<u8>,
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background: ArrayView2<u8>,
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) -> Result<SlideResult> {
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// 1. 将 ndarray 转换为 imageproc 需要的 ImageBuffer (无拷贝或轻量转换)
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// let (bh, bw) = background.dim();
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// 转换逻辑 (假设你已经有方法转回 ImageBuffer)
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let t_buf = ndarray_to_luma8(target);
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let b_buf = ndarray_to_luma8(background);
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// t_buf.save("debug_rust_target.png").unwrap();
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// 2. 调用 imageproc 的 NCC 算法 (等价于 cv2.TM_CCOEFF_NORMED)
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// 模板匹配 (完全对齐 cv2.matchTemplate(..., cv2.TM_CCOEFF_NORMED))
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let result = match_template(
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&b_buf,
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&t_buf,
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MatchTemplateMethod::CrossCorrelationNormalized,
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);
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// save_rust_result(&result, "debug_rust_target2.png");
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// 3. 寻找最大值 (等价于 cv2.minMaxLoc)
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let (max_val, max_loc) = min_max_loc(&result);
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// 4. 计算中心点 (与 Python 逻辑完全一致)
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let (th, tw) = target.dim();
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let center_x = max_loc.0 as i32 + (tw as i32 / 2);
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let center_y = max_loc.1 as i32 + (th as i32 / 2);
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// println!("Rust Target Width (tw): {}", tw);
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// println!("Rust Best Max Loc X: {}", max_loc.0);
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// println!("Rust Final Center X: {}", center_x);
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Ok(SlideResult {
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target: [center_x, center_y],
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target_x: center_x,
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target_y: center_y,
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confidence: max_val as f64,
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})
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}
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/// 对应 Python: _edge_based_match
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/// 基于边缘检测的滑块匹配 (对齐 Python _edge_based_match)
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pub fn edge_based_match(
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&self,
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target: ArrayView2<u8>,
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background: ArrayView2<u8>,
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) -> Result<SlideResult> {
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// 1. 将 ndarray 转换为 ImageBuffer
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// 注意：Canny 和 match_template 需要 ImageBuffer 格式
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let t_buf = ndarray_to_luma8(target);
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let b_buf = ndarray_to_luma8(background);
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// 2. 边缘检测 (完全对齐 cv2.Canny(50, 150))
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// 这步会生成黑底白线的二值化边缘图
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let target_edges = canny(&t_buf, 50.0, 150.0);
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let background_edges = canny(&b_buf, 50.0, 150.0);
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// target_edges.save("debug_target_edges.png").ok();
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// background_edges.save("debug_bg_edges.png").ok();
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// 3. 模板匹配 (完全对齐 cv2.matchTemplate(..., cv2.TM_CCOEFF_NORMED))
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// 在边缘图上计算归一化互相关系数
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let result = match_template(
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&background_edges,
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&target_edges,
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MatchTemplateMethod::CrossCorrelationNormalized,
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);
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// 4. 找到最佳匹配位置 (对齐 cv2.minMaxLoc)
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let (max_val, max_loc) = min_max_loc(&result);
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// 5. 计算中心位置 (对齐 Python 逻辑)
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// target_w, target_h 来自输入数组的维度
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let (th, tw) = target.dim();
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let center_x = max_loc.0 as i32 + (tw as i32 / 2);
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let center_y = max_loc.1 as i32 + (th as i32 / 2);
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// 打印调试信息，方便与 Python 对比
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// println!("Edge Match: max_val: {}, max_loc: {:?}", max_val, max_loc);
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println!("-Rust Target Width (tw): {}", tw);
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println!("-Rust Best Max Loc X: {}", max_loc.0);
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println!("-Rust Final Center X: {}", center_x);
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Ok(SlideResult {
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target: [center_x, center_y],
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target_x: center_x,
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target_y: center_y,
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confidence: max_val as f64,
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})
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}
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}
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