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dnn: add crop flag to blobFromImage
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@ -695,12 +695,14 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
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* @param scalefactor multiplier for @p image values.
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* @param swapRB flag which indicates that swap first and last channels
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* in 3-channel image is necessary.
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* @details input image is resized so one side after resize is equal to corresponing
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* @param crop flag which indicates whether image will be cropped after resize or not
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* @details if @p crop is true, input image is resized so one side after resize is equal to corresponing
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* dimension in @p size and another one is equal or larger. Then, crop from the center is performed.
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* If @p crop is false, direct resize without cropping and preserving aspect ratio is performed.
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* @returns 4-dimansional Mat with NCHW dimensions order.
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*/
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CV_EXPORTS_W Mat blobFromImage(const Mat& image, double scalefactor=1.0, const Size& size = Size(),
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const Scalar& mean = Scalar(), bool swapRB=true);
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const Scalar& mean = Scalar(), bool swapRB=true, bool crop=true);
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/** @brief Creates 4-dimensional blob from series of images. Optionally resizes and
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* crops @p images from center, subtract @p mean values, scales values by @p scalefactor,
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* swap Blue and Red channels.
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@ -711,12 +713,14 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
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* @param scalefactor multiplier for @p images values.
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* @param swapRB flag which indicates that swap first and last channels
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* in 3-channel image is necessary.
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* @details input image is resized so one side after resize is equal to corresponing
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* @param crop flag which indicates whether image will be cropped after resize or not
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* @details if @p crop is true, input image is resized so one side after resize is equal to corresponing
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* dimension in @p size and another one is equal or larger. Then, crop from the center is performed.
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* If @p crop is false, direct resize without cropping and preserving aspect ratio is performed.
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* @returns 4-dimansional Mat with NCHW dimensions order.
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*/
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CV_EXPORTS_W Mat blobFromImages(const std::vector<Mat>& images, double scalefactor=1.0,
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Size size = Size(), const Scalar& mean = Scalar(), bool swapRB=true);
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Size size = Size(), const Scalar& mean = Scalar(), bool swapRB=true, bool crop=true);
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/** @brief Convert all weights of Caffe network to half precision floating point.
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* @param src Path to origin model from Caffe framework contains single
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@ -85,15 +85,15 @@ static String toString(const T &v)
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}
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Mat blobFromImage(const Mat& image, double scalefactor, const Size& size,
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const Scalar& mean, bool swapRB)
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const Scalar& mean, bool swapRB, bool crop)
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{
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CV_TRACE_FUNCTION();
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std::vector<Mat> images(1, image);
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return blobFromImages(images, scalefactor, size, mean, swapRB);
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return blobFromImages(images, scalefactor, size, mean, swapRB, crop);
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}
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Mat blobFromImages(const std::vector<Mat>& images_, double scalefactor, Size size,
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const Scalar& mean_, bool swapRB)
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const Scalar& mean_, bool swapRB, bool crop)
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{
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CV_TRACE_FUNCTION();
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std::vector<Mat> images = images_;
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@ -104,13 +104,18 @@ Mat blobFromImages(const std::vector<Mat>& images_, double scalefactor, Size siz
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size = imgSize;
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if (size != imgSize)
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{
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float resizeFactor = std::max(size.width / (float)imgSize.width,
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size.height / (float)imgSize.height);
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resize(images[i], images[i], Size(), resizeFactor, resizeFactor);
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Rect crop(Point(0.5 * (images[i].cols - size.width),
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0.5 * (images[i].rows - size.height)),
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size);
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images[i] = images[i](crop);
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if(crop)
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{
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float resizeFactor = std::max(size.width / (float)imgSize.width,
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size.height / (float)imgSize.height);
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resize(images[i], images[i], Size(), resizeFactor, resizeFactor);
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Rect crop(Point(0.5 * (images[i].cols - size.width),
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0.5 * (images[i].rows - size.height)),
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size);
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images[i] = images[i](crop);
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}
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else
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resize(images[i], images[i], size);
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}
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if(images[i].depth() == CV_8U)
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images[i].convertTo(images[i], CV_32F);
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