Merge branch 'dev' of http://192.168.10.5:8099/sane/code_device into dev

hgdriver/ImageProcess/ImageApplyAutoCrop.cpp

@@ -4,7 +4,6 @@
 #include <opencv2/imgproc/hal/hal.hpp>
 #include "ImageApplyDispersion.h"
 
-#define COLOR_BACKGROUND_THRE 20
 #define FRONT_TOP 70
 #define FX_FY 0.5f
 
@@ -18,14 +17,13 @@ CImageApplyAutoCrop::CImageApplyAutoCrop()
 	, m_noise(8)
 	, m_indent(5)
 	, m_normalCrop(false)
-	, m_isDispersion(true)
 	, m_fx(1.0)
 	, m_fy(1.0)
 {
 }
 
 CImageApplyAutoCrop::CImageApplyAutoCrop(bool isCrop, bool isDesaskew, bool isFillBlank, const cv::Size& fixedSize, bool isConvex, bool isFillColor,
-	double threshold, int noise, int indent, bool normalCrop, bool dispersion, double fx, double fy)
+	double threshold, int noise, int indent, bool normalCrop, double fx, double fy)
 	: m_isCrop(isCrop)
 	, m_isDesaskew(isDesaskew)
 	, m_isFillBlank(isFillBlank)
@@ -36,7 +34,6 @@ CImageApplyAutoCrop::CImageApplyAutoCrop(bool isCrop, bool isDesaskew, bool isFi
 	, m_indent(indent)
 	, m_fixedSize(fixedSize)
 	, m_normalCrop(normalCrop)
-	, m_isDispersion(dispersion)
 	, m_fx(fx)
 	, m_fy(fy)
 {
@@ -50,7 +47,7 @@ void CImageApplyAutoCrop::apply(cv::Mat& pDib, int side)
 {
 	cv::Mat dst;
 	autoCrop_desaskew_fillBlank(pDib, dst, m_isCrop, m_isDesaskew, m_isFillBlank, m_fixedSize.width, m_fixedSize.height,
-		m_isConvexHull, m_isFillColor, m_threshold, m_noise, m_indent, m_normalCrop, m_isDispersion, m_fx, m_fy);
+		m_isConvexHull, m_isFillColor, m_threshold, m_noise, m_indent, m_normalCrop, m_fx, m_fy);
 	pDib = dst;
 }
 
@@ -99,14 +96,14 @@ void CImageApplyAutoCrop::myWarpAffine(cv::InputArray _src, cv::OutputArray _dst
 		M, interpolation, borderType, borderValue.val);
 }
 
-cv::Scalar CImageApplyAutoCrop::getBackGroudColor(const cv::Mat& image, int threshold)
+cv::Scalar CImageApplyAutoCrop::getBackGroudColor(const cv::Mat& image)
 {
 	if (image.channels() == 3)
 	{
 		uchar table[768] = { 0 };
 		int hist_bgr[3][256] = { 0 };
 		int width = image.cols, height = image.rows, bytesPerLine = image.step;
-		memset(table + threshold * 3, 255, 768 - threshold * 3);
+		memset(table, 255, 768);
 
 		unsigned char* ptr_data = image.data;
 		unsigned char b = 0;
@@ -136,7 +133,7 @@ cv::Scalar CImageApplyAutoCrop::getBackGroudColor(const cv::Mat& image, int thre
 		uchar table[256] = { 0 };
 		int hist_bgr[256] = { 0 };
 		int width = image.cols, height = image.rows, bytesPerLine = image.step;
-		memset(table + threshold, 255, 256 - threshold);
+		memset(table, 255, 256);
 		unsigned char* ptr_data = image.data;
 		unsigned char b = 0;
 		for (uint i = 0; i < height; i++, ptr_data += bytesPerLine)
@@ -153,7 +150,7 @@ cv::Scalar CImageApplyAutoCrop::getBackGroudColor(const cv::Mat& image, int thre
 }
 
 void CImageApplyAutoCrop::autoCrop_desaskew_fillBlank(cv::Mat& src, cv::Mat& dst, bool isAutoCrop, bool isDesaskew, bool isFillBlank, int dWidth, int dHeight,
-	bool isConvex, bool isColorBlank, double threshold, int noise, int indent, bool isNormalCrop, bool dispersion, double fx, double fy)
+	bool isConvex, bool isColorBlank, double threshold, int noise, int indent, bool isNormalCrop, double fx, double fy)
 {
 	if (src.empty()) return;
 
@@ -186,7 +183,7 @@ void CImageApplyAutoCrop::autoCrop_desaskew_fillBlank(cv::Mat& src, cv::Mat& dst
 	hg::threshold_Mat(resizeMat, thre, threshold);
 
 	if (noise > 0)
-		cv::morphologyEx(thre, thre, cv::MORPH_OPEN, getStructuringElement(cv::MORPH_RECT, cv::Size(noise * FX_FY, 1)),
+		cv::morphologyEx(thre, thre, cv::MORPH_OPEN, getStructuringElement(cv::MORPH_RECT, cv::Size(cv::max(static_cast<int>(noise * FX_FY), 1), 1)),
 			cv::Point(-1, -1), 1, cv::BORDER_CONSTANT, cv::Scalar::all(0));
 
 	std::vector<cv::Vec4i> hierarchy;
@@ -213,18 +210,24 @@ void CImageApplyAutoCrop::autoCrop_desaskew_fillBlank(cv::Mat& src, cv::Mat& dst
 	}
 
 	cv::RotatedRect rect = hg::getBoundingRect(maxContour);
-
+	if (rect.size.width < 1 || rect.size.height < 1)
-	if (dispersion)
 	{
-		CImageApplyDispersion m_dispersion_apply;
+		dst = src;
-		cv::Mat mat_dispersion = src(cv::boundingRect(maxContour));
+		return;
-		m_dispersion_apply.apply(mat_dispersion, 0);
 	}
 
 	cv::Scalar blankColor;
 	if (isFillBlank)
 		if (isColorBlank)
-			blankColor = getBackGroudColor(resizeMat, COLOR_BACKGROUND_THRE);
+		{
+			cv::Rect boudingRect = cv::boundingRect(maxContour);
+			boudingRect.x *= FX_FY;
+			boudingRect.y *= FX_FY;
+			boudingRect.width *= FX_FY;
+			boudingRect.height *= FX_FY;
+
+			blankColor = getBackGroudColor(resizeMat(boudingRect));
+		}
 		else
 			blankColor = cv::Scalar::all(255);
 	else

hgdriver/ImageProcess/ImageApplyAutoCrop.h

@@ -2,8 +2,8 @@
  * ====================================================
 
  * 功能：自动裁剪、纠偏、除黑底
- * 作者：刘丁绿
+ * 作者：刘丁维
- * 生成时间＿020/4/21
+ * 生成时间：2020/4/21
  * 最近修改时间：2020/4/21  v1.0
 				 2020/7/22  v1.1     增加获取图像有效区域轮廓的接口maxContour（用于配合一体机的“跳过空白页”算法，PC端暂时无需使用＿
 				 2020/10/16 v1.2     修复自动裁剪尺寸精度丢失的BUG；提高除黑底缩进精度
@@ -29,7 +29,12 @@
 				 2022/10/31 v1.5	 增加横纵DPI缩放参数fx,fy。
 				 2022/11/19 v1.5.1	 修复文稿背景色识别算法的BUG。
 				 2022/11/19 v1.5.2	 修复灰度文稿背景色识别算法的BUG。
- * 版本号：v1.5.2
+				 2023/03/17 v1.5.3   修复背景除噪时，形态学处理kSize可能为0的BUG。
+				 2023/04/19 v1.5.4	 取消集成色散功能。
+				 2023/05/15 v1.5.5   优化文稿背景色识别算法。
+				 2023/05/23 v1.5.6	 修复纯黑图找不到外界轮廓会导致崩溃的BUG。
+				 2023/11/02 v1.6	 优化抗噪能力。采用自适应背景色二值化。
+ * 版本号：v1.5.6
 
  * ====================================================
  */
@@ -39,29 +44,28 @@
 
 #include "ImageApply.h"
 
-class CImageApplyDispersion;
 class GIMGPROC_LIBRARY_API CImageApplyAutoCrop : public CImageApply
 {
 public:
 	CImageApplyAutoCrop();
 
-	/*
+	/// <summary>
-	 * isCrop      [in]:自动幅面裁剪使能，true自动裁剪，false为固定裁剿
+	/// 构造函数
-	 * isDesaskew  [in]:自动纠偏使能，true自动纠偏，false为不纠偏
+	/// </summary>
-	 * isFillBlank [in]:黑底填充使能，true为填充，false为不填充
+	/// <param name="isCrop">自动幅面裁剪使能，true自动裁剪，false为固定裁切</param>
-	 * fixedSize   [in]:固定幅面尺寸，当isCrop为false时生效，结果尺寸按fixedSize大小输出，单位像紿
+	/// <param name="isDesaskew">自动纠偏使能，true自动纠偏，false为不纠偏</param>
-	 * isConvex    [in]:黑底填充时的填充方式,true为凸多边形填充，false为凹多边形填充，默认true
+	/// <param name="isFillBlank">黑底填充使能，true为填充，false为不填充</param>
-	 * isFillColor [in]:黑底填充时采用自适应色彩填充，false为白色填充，true为自适应文稿底色填充，默认false
+	/// <param name="fixedSize">固定幅面尺寸，当isCrop为false时生效，结果尺寸按fixedSize大小输出，单位像素</param>
-	 * threshold   [in]:二值化阈值，取值范囿0, 255)，默访0
+	/// <param name="isConvex">黑底填充时的填充方式,true为凸多边形填充，false为凹多边形填充，默认true</param>
-	 * noise       [in]:除噪像素，能够消除noise宽度的背景竖条纹干扰，默访
+	/// <param name="isFillColor">黑底填充时采用自适应色彩填充，false为白色填充，true为自适应文稿底色填充，默认false</param>
-	 * indent      [in]:轮廓缩进，裁剪、纠偏或者黑底填充时，对探索到的纸张轮廓进行缩进indent像素，默访
+	/// <param name="threshold">二值化阈值，取值范囿[-1, 255]，默认40。当threshold<0时，采用根据背景自适应二值化。</param>
-	 * normalCrop  [in]:为true时，m_isCrop m_isDesaskew m_isFillBlank失效，固定裁切采用最传统的裁切方式，默认false
+	/// <param name="noise">除噪像素，能够消除noise宽度的背景竖条纹干扰，默认8。</param>
-	 * dispersion  [in]:为true时，除色散；false时不除色散。默认为true
+	/// <param name="indent">轮廓缩进，裁剪、纠偏或者黑底填充时，对探索到的纸张轮廓进行缩进indent像素，默认8</param>
-	 * fx		   [in]:横向缩放比例。默认1.0
+	/// <param name="normalCrop">为true时，m_isCrop m_isDesaskew m_isFillBlank失效，固定裁切采用最传统的裁切方式，默认false</param>
-	 * fy		   [in]:纵向缩放比例。默认1.0
+	/// <param name="fx">横向缩放比例。默认1.0</param>
-	*/
+	/// <param name="fy">纵向缩放比例。默认1.0</param>
 	CImageApplyAutoCrop(bool isCrop, bool isDesaskew, bool isFillBlank, const cv::Size& fixedSize, bool isConvex = true,
-		bool isFillColor = false, double threshold = 40, int noise = 8, int indent = 5, bool normalCrop = false, bool dispersion = true, double fx = 1.0, double fy = 1.0);
+		bool isFillColor = false, double threshold = 40, int noise = 8, int indent = 5, bool normalCrop = false, double fx = 1.0, double fy = 1.0);
 
 	virtual ~CImageApplyAutoCrop();
 
@@ -105,23 +109,20 @@ public:
 
 	void setFixedSize(cv::Size size) { m_fixedSize = size; }
 
-	void setDispersion(bool enable) { m_isDispersion = enable; }
-
 	static void autoCrop_desaskew_fillBlank(cv::Mat& src, cv::Mat& dst, bool isAutoCrop, bool isDesaskew, bool isFillBlank, int dWidth, int dHeight,
-		bool isConvex = true, bool isColorBlank = false, double threshold = 40, int noise = 8, int indent = 5, bool isNormalCrop = false, bool dispersion = true,
+		bool isConvex = true, bool isColorBlank = false, double threshold = 40, int noise = 8, int indent = 5, bool isNormalCrop = false,
 		double fx = 1.0, double fy = 1.0);
 
 private:
 	static void myWarpAffine(cv::InputArray _src, cv::OutputArray _dst, cv::InputArray _M0, cv::Size dsize, int flags, int borderType, const cv::Scalar& borderValue);
 
-	static cv::Scalar getBackGroudColor(const cv::Mat& image, int threshold);
+	static cv::Scalar getBackGroudColor(const cv::Mat& image);
 private:
 	bool m_isCrop;
 	bool m_isDesaskew;
 	bool m_isFillBlank;
 	bool m_isConvexHull;
 	bool m_isFillColor;
-	bool m_isDispersion;
 
 	double m_threshold;
 	int m_noise;

hgdriver/ImageProcess/ImageApplyColorRecognition.cpp

@@ -1,28 +1,5 @@
 #include "ImageApplyColorRecognition.h"
 
-/// <summary>
-/// 检测图像是否是彩色。当前逻辑仅针对红色像素进行判断，即存在红色像素则为彩色，否则为非彩色
-/// </summary>
-/// <param name="image">待测图像</param>
-/// <returns>true为彩色，false为非彩色</returns>
-bool isColor(const cv::Mat& image, double threshold = 60)
-{
-	if (image.channels() != 3) return false;
-
-	cv::Mat pDib_resize;
-	cv::resize(image, pDib_resize, cv::Size(200, 200), 0, 0, cv::INTER_AREA);
-
-	cv::Mat hsv;
-	cv::cvtColor(pDib_resize, hsv, cv::COLOR_BGR2HSV_FULL);
-	std::vector<cv::Mat> hsv_channels;
-	cv::split(hsv, hsv_channels);
-
-	double minVal, maxVal;
-	cv::minMaxLoc(hsv_channels[1], &minVal, &maxVal);
-
-	return maxVal > threshold;
-}
-
 bool isGray(const cv::Mat& image)
 {
 	//if (image.channels() == 3) return true;
@@ -62,6 +39,7 @@ CImageApplyColorRecognition::~CImageApplyColorRecognition(void)
 {
 }
 
+#define HSV_S_THRE 30
 void CImageApplyColorRecognition::apply(cv::Mat& pDib, int side)
 {
 	if (pDib.channels() != 3)
@@ -69,7 +47,7 @@ void CImageApplyColorRecognition::apply(cv::Mat& pDib, int side)
 		m_result = Gray;
 		return;
 	}
-	m_result = isColor(pDib) ? Color : Gray;
+	m_result = isColor(pDib, HSV_S_THRE) ? Color : Gray;
 	if (m_result == Gray && pDib.channels() == 3)
 		cv::cvtColor(pDib, pDib, cv::COLOR_BGR2GRAY);
 
@@ -135,3 +113,26 @@ std::vector<CImageApplyColorRecognition::ColorType> CImageApplyColorRecognition:
 {
 	return m_results;
 }
+
+/// <summary>
+/// 检测图像是否是彩色。当前逻辑仅针对红色像素进行判断，即存在红色像素则为彩色，否则为非彩色
+/// </summary>
+/// <param name="image">待测图像</param>
+/// <returns>true为彩色，false为非彩色</returns>
+bool CImageApplyColorRecognition::isColor(const cv::Mat& image, double threshold)
+{
+	if (image.channels() != 3) return false;
+
+	cv::Mat pDib_resize;
+	cv::resize(image, pDib_resize, cv::Size(200, 200), 0, 0, cv::INTER_AREA);
+	//cv::imwrite("pDib_resize.bmp", pDib_resize);
+	cv::Mat hsv;
+	cv::cvtColor(pDib_resize, hsv, cv::COLOR_BGR2HSV_FULL);
+	std::vector<cv::Mat> hsv_channels;
+	cv::split(hsv, hsv_channels);
+
+	double minVal, maxVal;
+	cv::minMaxLoc(hsv_channels[1], &minVal, &maxVal);
+
+	return maxVal > threshold;
+}

hgdriver/ImageProcess/ImageApplyColorRecognition.h

@@ -10,7 +10,8 @@
  *               v1.3    2021/04/19  修改识别策略，能够识别占比1‰的彩色图像。只区分彩色和灰度图。
  *               v1.4    2021/06/18  调整二级色彩区间，从原来的[90, 200]调整为[50, 200]。
  *               v1.5    2023/11/22  调整彩色判定策略。
- * 版本号：v1.5
+ *               v1.5.1  2023//11/28 调整饱和度判定阈值
+ * 版本号：v1.5.1
  * ====================================================
  */
 
@@ -60,6 +61,9 @@ public:
     /// <returns>色彩类型数组</returns>
     std::vector<ColorType> getResults();
 
+private:
+    bool isColor(const cv::Mat& image, double threshold = 30);
+
 private:
     ColorType m_result;
     std::vector<ColorType> m_results;

hgdriver/ImageProcess/ImageApplyOutHole.cpp

@@ -212,7 +212,7 @@ void CImageApplyOutHole::apply(std::vector<cv::Mat>& mats, bool isTwoSide)
 	for (size_t i = 0; i < hole_contours.size(); i++)
 	{
 		std::vector<std::vector<cv::Point>> contourss_temp;
-		dilateContour(hole_contours[i], 5);
+		dilateContour(hole_contours[i], m_borderSize / 4);
 		contourss_temp.push_back(hole_contours[i]);
 		cv::Mat front_temp = mats[0](roi_front);
 		hg::fillPolys(front_temp, contourss_temp, color);

hgdriver/ImageProcess/ImageApplyOutHole.h

@@ -27,7 +27,8 @@
  *				 2022/11/17 v1.9.2	修复寻找孔洞轮廓BUG。
  *				 2023/05/16 v1.9.3	修复提取纸张最大轮廓的逻辑BUG。
  *				 2023/11/18 v1.10	替换形态学膨胀孔洞轮廓，改为特征矩阵膨胀轮廓说
- * 版本号：v1.10
+ *				 2023/11/28 v1.10.1	形态学kSize根据borderSize调整
+ * 版本号：v1.10.1
 
  * ====================================================
  */

hgdriver/hgdev/hg_scanner_300.cpp

@@ -737,7 +737,10 @@ int hg_scanner_300::set_kernelsnap_ver()
 		if (atoi(fw.substr(4, 6).c_str()) >= 221106 && atoi(fw.substr(4, 6).c_str()) < 230210)
 			firmware_sup_dpi_300 = true;
 		else if (atoi(fw.substr(4, 6).c_str()) >= 230210)
+		{
+			firmware_sup_dpi_300 = true;
 			firmware_sup_dpi_600 = true;
+		}	
 		else
 		{
 			firmware_sup_dpi_300 = false;