update 裁剪图不改变通道

AlgorithmModule/include/ImageAllResult.h

@@ -62,6 +62,8 @@ public:
     cv::Rect CropRoi = cv::Rect(0, 0, 0, 0);
     // 检测的原始图片
     cv::Mat detImg;
+    // 摆正后的彩色检测图(保留原图通道,供裁剪图 _RE_CutImg 输出;灰度源图时为空)
+    cv::Mat detColorImg;
     // AI 推理的mask图片
     cv::Mat AIMaskImg;
     // 结果图片

AlgorithmModule/src/ImgCheckAnalysisy.cpp

@@ -480,6 +480,63 @@ int ImgCheckAnalysisy::Adapt_Config(RotatedRect tplOuterRect, RotatedRect curOut
 	return 0;
 }
 
+// 应用内 OpenCV 线程池随首个调用线程(通常为先绑核的检测线程)初始化成单线程，parallel_for 实际串行，
+// 全分辨率 warp 要 150~200ms；这里把输出按行切条、用独立线程并行 warp，
+// 结果与整图 warpAffine 逐像素一致(已离线验证 maxdiff=0)，耗时降到约 1/3
+static void WarpAffineBandParallel(const cv::Mat &src, cv::Mat &dst, const cv::Mat &affine,
+								   const cv::Size &size, int nThreads = 8, int interp = cv::INTER_LINEAR,
+								   int borderType = cv::BORDER_REPLICATE)
+{
+	dst.create(size, src.type());
+	nThreads = std::max(1, std::min(nThreads, size.height));
+	int bandH = (size.height + nThreads - 1) / nThreads;
+	int nBands = (size.height + bandH - 1) / bandH;
+
+	auto warpBand = [&](int b)
+	{
+		// 新建线程默认继承调用线程的 CPU 亲和性(检测线程已绑单核)，这里放开到全核，让条带真正并行
+		cpu_set_t cpus;
+		CPU_ZERO(&cpus);
+		long ncpu = sysconf(_SC_NPROCESSORS_ONLN);
+		for (long i = 0; i < ncpu; i++)
+		{
+			CPU_SET(i, &cpus);
+		}
+		pthread_setaffinity_np(pthread_self(), sizeof(cpus), &cpus);
+
+		int y0 = b * bandH;
+		int h = std::min(bandH, size.height - y0);
+		// warpAffine 默认接收正向矩阵(src->dst)并内部求逆；条带只把输出平移项减去 y0
+		cv::Mat matBand = affine.clone();
+		matBand.at<double>(1, 2) -= y0;
+		cv::warpAffine(src, dst(cv::Rect(0, y0, size.width, h)), matBand,
+					   cv::Size(size.width, h), interp, borderType);
+	};
+
+	std::vector<std::thread> threads;
+	threads.reserve(nBands);
+	int b = 0;
+	try
+	{
+		for (; b < nBands; b++)
+		{
+			threads.emplace_back(warpBand, b);
+		}
+	}
+	catch (const std::system_error &)
+	{
+		// 线程资源不足:剩余条带留在本线程串行补完
+	}
+	for (auto &t : threads)
+	{
+		t.join();
+	}
+	for (; b < nBands; b++)
+	{
+		warpBand(b);
+	}
+}
+
 int ImgCheckAnalysisy::CheckRun()
 {
 
@@ -596,7 +653,18 @@ int ImgCheckAnalysisy::CheckRun()
 	{
 		cv::cvtColor(colorPatch, grayPatch, cv::COLOR_BGRA2GRAY);
 	}
-	cv::warpAffine(grayPatch, m_pImageAllResult->detImg, matPad, cv::Size(detW, detH), cv::INTER_LINEAR, cv::BORDER_REPLICATE);
+	WarpAffineBandParallel(grayPatch, m_pImageAllResult->detImg, matPad, cv::Size(detW, detH));
+
+	// 彩色原图时再输出一张同样摆正的彩色检测图：供裁剪图(_RE_CutImg)保存时保留原图通道
+	// (检测算法仍只用灰度 detImg)
+	if (colorPatch.channels() == 3 || colorPatch.channels() == 4)
+	{
+		WarpAffineBandParallel(colorPatch, m_pImageAllResult->detColorImg, matPad, cv::Size(detW, detH));
+	}
+	else
+	{
+		m_pImageAllResult->detColorImg.release();
+	}
 
 	// 4、检测图坐标系下 m_outer_roi 即整张检测图；原图坐标系下 CutRoi 仍取旋转矩形外接区域(兼容旧语义/JSON)
 	m_outer_roi = cv::Rect(0, 0, detW, detH);
@@ -2567,7 +2635,10 @@ int ImgCheckAnalysisy::ImgPreDet()
 		creatsavedir();
 	}
 
-	m_CheckResult_shareP->cutSrcimg = m_pImageAllResult->detImg;
+	// 裁剪图(_RE_CutImg)保留原图通道：源图为彩色时用彩色摆正图,否则回退灰度检测图
+	m_CheckResult_shareP->cutSrcimg = m_pImageAllResult->detColorImg.empty()
+										  ? m_pImageAllResult->detImg
+										  : m_pImageAllResult->detColorImg;
 	ResizeImg();
 
 	m_pImageAllResult->fscale_detToresult_x = m_pImageAllResult->resultImg.cols * 1.0f / m_pImageAllResult->detImg.cols;

example/deal.cpp

@@ -1016,16 +1016,13 @@ int deal::SendImgToCheck(std::shared_ptr<JC_IMAGE_INFO_> pDetImageInfo, IN_IMG_S
     }
     if (status == IN_IMG_Status_End)
     {
-        // 一张图上下拆分成两个产品（_0 上半、_1 下半），给两个产品分别发送结束标志
+        // 不再上下拆图：一张图对应一个产品，只给该产品发送一次结束标志
-        for (int pi = 0; pi < 2; pi++)
+        std::shared_ptr<shareImage> tem = std::make_shared<shareImage>();
+        tem->strImgProductID = pDetImageInfo->strProductID;
+        tem->Status = -1;
+        if (m_pALLImgCheckAnalysisy)
         {
-            std::shared_ptr<shareImage> tem = std::make_shared<shareImage>();
+            m_pALLImgCheckAnalysisy->SetDataRun_SharePtr(tem);
-            tem->strImgProductID = pDetImageInfo->strProductID + "_" + std::to_string(pi);
-            tem->Status = -1;
-            if (m_pALLImgCheckAnalysisy)
-            {
-                m_pALLImgCheckAnalysisy->SetDataRun_SharePtr(tem);
-            }
         }
         return 0;
     }
@@ -1079,38 +1076,7 @@ int deal::SendImgToCheck(std::shared_ptr<JC_IMAGE_INFO_> pDetImageInfo, IN_IMG_S
 
     int re = 0;
 
-    // 一张图拍多个产品：上下拆分成多张，子图产品ID加索引后缀，分别送检
+    // 不再上下拆图：一张图对应一个产品，整图直接送检（产品ID不加索引后缀）
-    std::vector<cv::Mat> splitList;
-    if (!pDetImageInfo->img.empty() && pDetImageInfo->img.rows >= 2)
-    {
-        int half = pDetImageInfo->img.rows / 2;
-        splitList.push_back(pDetImageInfo->img(cv::Rect(0, 0, pDetImageInfo->img.cols, half)).clone());
-        splitList.push_back(pDetImageInfo->img(cv::Rect(0, half, pDetImageInfo->img.cols, pDetImageInfo->img.rows - half)).clone());
-    }
-
-    if (splitList.size() > 1)
-    {
-        for (size_t i = 0; i < splitList.size(); i++)
-        {
-            std::shared_ptr<shareImage> sub = std::make_shared<shareImage>();
-            *sub = *tem;
-            sub->img = splitList[i];
-            sub->strImgProductID = tem->strImgProductID + "_" + std::to_string(i);
-            // 同一产品（上半/下半）可能包含多个通道的图，子图作为中间图，待所有通道送完后再统一发送结束标志
-            sub->Status = IN_IMG_Status_Other;
-            if (m_pALLImgCheckAnalysisy)
-            {
-                re = m_pALLImgCheckAnalysisy->SetDataRun_SharePtr(sub);
-                if (re != 0)
-                {
-                    return re;
-                }
-            }
-            m_nPushImgCount++;
-        }
-        return 0;
-    }
-
     if (m_pALLImgCheckAnalysisy)
     {
         re = m_pALLImgCheckAnalysisy->SetDataRun_SharePtr(tem);