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@ -16,6 +16,12 @@
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#include <cstdlib>
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#include <fstream>
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// ===================== Pin 整体偏移估计参数 =====================
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static const double PIN_SHIFT_MIN_SUPPORT_RATIO = 0.80; // 判定整体偏移所需的最小支持度(支持pin数 / 模板pin总数)
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static const float PIN_SHIFT_BIN_SIZE_PX = 4.0f; // 位移投票的网格尺寸(px), 同时作为峰值邻域半径
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static const float PIN_SHIFT_MAX_GAP_RATIO = 0.5f; // 整体位移量级上限 = 相邻pin最小间距 * 该比例
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// ======================================================================================
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// 分类映射配置文件（每行：label索引:缺陷名称；冒号后名称为该缺陷类型名称，行的顺序即缺陷类型索引）
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static const std::string CLS_LABEL_CONFIG_FILE = "/home/aidlux/BOE/UseModel_ZB/KK_cls.txt";
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@ -359,7 +365,7 @@ int ImgCheckAnalysisy::GetStatus()
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std::string ImgCheckAnalysisy::GetVersion()
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{
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return std::string("BOE_1.3.0");
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return std::string("BOE_1.3.1");
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}
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std::string ImgCheckAnalysisy::GetErrorInfo()
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@ -820,9 +826,7 @@ int ImgCheckAnalysisy::CheckRun()
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m_outer_roi = curOuterBound;
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}
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#else
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// 检测PIN偏移/缺失对位置基准最敏感:PIN 位置的真实基准是产品内腔(内定位),
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// 因此优先用内定位计算仿射(当前内边缘 -> 模板内边缘);模板/当前内定位无效时回退外定位。
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// 注意: 这里不再对原图做 warpAffine, 只计算矩阵, 并把模板 Pin 轮廓反变换到当前图坐标系。
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// 不对原图做 warpAffine, 只计算矩阵, 并把模板 Pin 轮廓反变换到当前图坐标系。
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bool bUseInnerAlign = (m_tplInnerRect.size.width > 0 && m_tplInnerRect.size.height > 0 &&
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m_inner_rroi.size.width > 0 && m_inner_rroi.size.height > 0);
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if (bUseInnerAlign)
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@ -2253,14 +2257,13 @@ int ImgCheckAnalysisy::Pin_Qx_Det(const cv::Mat &img)
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Mat pin_mask;
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int re = m_Pin_QX_Det.GetPinMask(img, &m_Edge_DetConfig, pin_mask);
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// 统计tplPinMask的blob边缘点，并绘制到tplImg上
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// 获取当前pinmask
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vector<vector<Point>> pin_contours;
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vector<Vec4i> hierarchy;
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cv::findContours(pin_mask, pin_contours, hierarchy, cv::RETR_EXTERNAL, cv::CHAIN_APPROX_NONE);
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//缩放比例
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const float pin_scale_x = (pin_mask.cols > 0) ? static_cast<float>(img.cols) / pin_mask.cols : 0.0f;
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const float pin_scale_y = (pin_mask.rows > 0) ? static_cast<float>(img.rows) / pin_mask.rows : 0.0f;
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// 将轮廓点从 mask 坐标系 映射回 tplImg 坐标系
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m_curPinContours.clear();
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m_curPinContours.reserve(pin_contours.size());
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for (size_t i = 0; i < pin_contours.size(); i++)
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@ -2312,6 +2315,129 @@ int ImgCheckAnalysisy::Pin_Qx_Det(const cv::Mat &img)
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}
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std::vector<bool> curUsed(nCur, false);
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// 整体偏移估计与消除(阈值见文件顶部 PIN_SHIFT_* 常量):
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// 若足够多的模板 pin 都具有方向一致、量级接近的位移, 则把模板 pin 轮廓整体平移到与检测 pin 一致的位置,
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// 后续匹配/偏移判据均基于平移后的模板 pin
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{
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// 相邻 pin 最小间距: 用于限制整体偏移的量级(整体偏移不应接近一个 pin 间距, 否则无法与"错配一列"区分)
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float minGap = 0.0f;
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for (int i = 0; i < nTpl; i++)
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{
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for (int j = i + 1; j < nTpl; j++)
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{
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float d = static_cast<float>(cv::norm(tplCenters[i] - tplCenters[j]));
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if (minGap <= 0.0f || d < minGap)
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{
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minGap = d;
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}
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}
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}
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if (nCur > 0)
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{
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// 1) 每个模板 pin 取最近检测 pin 的位移作为候选位移(最近检测 pin 必定存在, nCur > 0)
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std::vector<cv::Point2f> disp(nTpl);
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for (int i = 0; i < nTpl; i++)
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{
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int nearestIdx = 0;
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double bestD = 1e12;
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for (int j = 0; j < nCur; j++)
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{
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double d = cv::norm(curCenters[j] - tplCenters[i]);
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if (d < bestD)
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{
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bestD = d;
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nearestIdx = j;
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}
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}
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disp[i] = curCenters[nearestIdx] - tplCenters[i];
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}
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// 2) 网格投票找主位移(峰值并列时优先取量级更小的)
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const float binSize = PIN_SHIFT_BIN_SIZE_PX;
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std::map<std::pair<int, int>, int> hist;
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int bestCount = 0;
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float bestX = 0.0f;
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float bestY = 0.0f;
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for (int i = 0; i < nTpl; i++)
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{
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std::pair<int, int> key(static_cast<int>(std::lround(disp[i].x / binSize)),
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static_cast<int>(std::lround(disp[i].y / binSize)));
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int cnt = ++hist[key];
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float cx = key.first * binSize;
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float cy = key.second * binSize;
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if (cnt > bestCount ||
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(cnt == bestCount && cx * cx + cy * cy < bestX * bestX + bestY * bestY))
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{
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bestCount = cnt;
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bestX = cx;
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bestY = cy;
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}
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}
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// 3) 峰值邻域(±1 档)内的候选位移取均值, 同时统计支持度
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double sumX = 0.0;
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double sumY = 0.0;
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int sumN = 0;
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for (int i = 0; i < nTpl; i++)
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{
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if (std::fabs(disp[i].x - bestX) <= binSize &&
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std::fabs(disp[i].y - bestY) <= binSize)
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{
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sumX += disp[i].x;
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sumY += disp[i].y;
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sumN++;
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}
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}
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cv::Point2f globalShift(0.0f, 0.0f);
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if (sumN > 0)
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{
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globalShift.x = static_cast<float>(sumX / sumN);
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globalShift.y = static_cast<float>(sumY / sumN);
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}
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else
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{
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globalShift.x = bestX;
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globalShift.y = bestY;
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}
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// 4) 支持度达标 且 位移量级小于 pin 间距的限定比例 -> 判定为整体偏移
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const double supportRatio = static_cast<double>(sumN) / nTpl;
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const float shiftLen = static_cast<float>(cv::norm(globalShift));
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const bool bGlobalShift = (supportRatio >= PIN_SHIFT_MIN_SUPPORT_RATIO) &&
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(minGap <= 0.0f || shiftLen < minGap * PIN_SHIFT_MAX_GAP_RATIO);
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if (bGlobalShift)
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{
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// 模板 pin 轮廓整体平移, 使其与检测 pin 一致
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const int shiftX = cvRound(globalShift.x);
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const int shiftY = cvRound(globalShift.y);
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for (int i = 0; i < nTpl; i++)
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{
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for (auto &pt : m_tplPinContoursCur[i])
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{
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pt.x += shiftX;
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pt.y += shiftY;
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}
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// 与前面的统计口径保持一致: 中心/外包框由平移后的轮廓重新计算
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tplCenters[i] = getCenter(m_tplPinContoursCur[i]);
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tplRects[i] = cv::boundingRect(m_tplPinContoursCur[i]);
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}
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m_pdetlog->AddCheckstr(PrintLevel_0, "Pin Check",
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"global offset removed: template pins shifted dx %.2f dy %.2f mm (%.1f %.1f px), support %d/%d",
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globalShift.x * m_fImgage_Scale_X, globalShift.y * m_fImgage_Scale_Y,
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globalShift.x, globalShift.y, sumN, nTpl);
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}
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else if (bestCount > 0)
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{
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m_pdetlog->AddCheckstr(PrintLevel_1, DET_LOG_LEVEL_3, "Pin Check",
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"global offset not applied: dx %.1f dy %.1f px, support %d/%d (ratio %.2f, pin gap %.1f px)",
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globalShift.x, globalShift.y, sumN, nTpl, supportRatio, minGap);
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}
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}
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}
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// 3、偏移阈值直接使用基础参数 pin_x_offset / pin_y_offset (mm)，与换算为 mm 的偏移量直接比较
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float offsetThX = 0.0f; // mm
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float offsetThY = 0.0f; // mm
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liusiyang