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crop parameter usage in blobFromImage() calls
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‎doc/tutorials/dnn/dnn_googlenet/dnn_googlenet.markdown

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@@ -37,11 +37,7 @@ Explanation
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-# Read input image and convert to the blob, acceptable by GoogleNet
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@snippet dnn/caffe_googlenet.cpp Prepare blob
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Firstly, we resize the image and change its channel sequence order.
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Now image is actually a 3-dimensional array with 224x224x3 shape.
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Next, we convert the image to 4-dimensional blob (so-called batch) with 1x3x224x224 shape by using special cv::dnn::blobFromImages constructor.
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We convert the image to a 4-dimensional blob (so-called batch) with 1x3x224x224 shape after applying necessary pre-processing like resizing and mean subtraction using cv::dnn::blobFromImage constructor.
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-# Pass the blob to the network
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@snippet dnn/caffe_googlenet.cpp Set input blob

‎samples/dnn/squeezenet_halide.cpp

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@@ -79,8 +79,7 @@ int main(int argc, char **argv)
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exit(-1);
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}
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resize(img, img, Size(227, 227)); // SqueezeNet v1.1 predict class by 3x227x227 input image.
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Mat inputBlob = blobFromImage(img, 1.0, Size(), Scalar(), false); // Convert Mat to 4-dimensional batch.
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Mat inputBlob = blobFromImage(img, 1.0, Size(227, 227), Scalar(), false, false); // Convert Mat to 4-dimensional batch.
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//! [Prepare blob]
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//! [Set input blob]

‎samples/dnn/ssd_object_detection.cpp

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@@ -10,36 +10,6 @@ using namespace cv::dnn;
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#include <cstdlib>
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using namespace std;
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const size_t width = 300;
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const size_t height = 300;
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static Mat getMean(const size_t& imageHeight, const size_t& imageWidth)
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{
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Mat mean;
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const int meanValues[3] = {104, 117, 123};
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vector<Mat> meanChannels;
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for(int i = 0; i < 3; i++)
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{
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Mat channel((int)imageHeight, (int)imageWidth, CV_32F, Scalar(meanValues[i]));
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meanChannels.push_back(channel);
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}
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cv::merge(meanChannels, mean);
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return mean;
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}
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static Mat preprocess(const Mat& frame)
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{
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Mat preprocessed;
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frame.convertTo(preprocessed, CV_32F);
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resize(preprocessed, preprocessed, Size(width, height)); //SSD accepts 300x300 RGB-images
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Mat mean = getMean(width, height);
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cv::subtract(preprocessed, mean, preprocessed);
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return preprocessed;
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}
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const char* classNames[] = {"background",
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"aeroplane", "bicycle", "bird", "boat",
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"bottle", "bus", "car", "cat", "chair",
@@ -126,9 +96,7 @@ int main(int argc, char** argv)
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cvtColor(frame, frame, COLOR_BGRA2BGR);
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//! [Prepare blob]
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Mat preprocessedFrame = preprocess(frame);
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Mat inputBlob = blobFromImage(preprocessedFrame, 1.0f, Size(), Scalar(), false); //Convert Mat to batch of images
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Mat inputBlob = blobFromImage(frame, 1.0f, Size(300, 300), Scalar(104, 117, 123), false, false); //Convert Mat to batch of images
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//! [Prepare blob]
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//! [Set input blob]

‎samples/dnn/tf_inception.cpp

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@@ -78,12 +78,7 @@ int main(int argc, char **argv)
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exit(-1);
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}
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cv::Size inputImgSize = cv::Size(224, 224);
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if (inputImgSize != img.size())
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resize(img, img, inputImgSize); //Resize image to input size
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Mat inputBlob = blobFromImage(img); //Convert Mat to image batch
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Mat inputBlob = blobFromImage(img, 1.0f, Size(224, 224), Scalar(), true, false); //Convert Mat to batch of images
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//! [Prepare blob]
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inputBlob -= 117.0;
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//! [Set input blob]

‎samples/dnn/torch_enet.cpp

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@@ -76,7 +76,7 @@ int main(int argc, char **argv)
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exit(-1);
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}
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Mat inputBlob = blobFromImage(img, 1./255, Size(1024, 512), Scalar(), true, false); //Convert Mat to image batch
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Mat inputBlob = blobFromImage(img, 1./255, Size(1024, 512), Scalar(), true, false); //Convert Mat to batch of images
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//! [Prepare blob]
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//! [Set input blob]

‎samples/dnn/yolo_object_detection.cpp

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@@ -14,9 +14,6 @@ using namespace std;
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using namespace cv;
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using namespace cv::dnn;
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const size_t network_width = 416;
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const size_t network_height = 416;
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static const char* about =
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"This sample uses You only look once (YOLO)-Detector (https://arxiv.org/abs/1612.08242) to detect objects on camera/video/image.\n"
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"Models can be downloaded here: https://pjreddie.com/darknet/yolo/\n"
@@ -104,13 +101,8 @@ int main(int argc, char** argv)
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if (frame.channels() == 4)
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cvtColor(frame, frame, COLOR_BGRA2BGR);
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//! [Resizing without keeping aspect ratio]
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Mat resized;
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resize(frame, resized, Size(network_width, network_height));
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//! [Resizing without keeping aspect ratio]
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//! [Prepare blob]
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Mat inputBlob = blobFromImage(resized, 1 / 255.F); //Convert Mat to batch of images
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Mat inputBlob = blobFromImage(frame, 1 / 255.F, Size(416, 416), Scalar(), true, false); //Convert Mat to batch of images
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//! [Prepare blob]
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//! [Set input blob]

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