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DNN opencv with SSD resnet return wrong face dimension

asked 2018-05-28 19:45:21 -0500

lezan gravatar image

updated 2018-05-28 19:47:25 -0500

Hello, I playing with face and DNN but I cannot figure out of to solve an issue.

I am processing image 256x256. Using deploy.prototxt and res10_300x300_ssd_iter_140000.caffemodel (same one on dnn directory).

Some code.

cv::Mat faceROI;
cv::Mat image;

image = cv::imread(imagePath[imageId], CV_LOAD_IMAGE_COLOR);
cv::Mat imageDNNBlob = cv::dnn::blobFromImage(image, 1.0, cv::Size(300, 300), 
    Scalar(104.0, 177.0, 123.0), false, false);
netOpenCVDNN.setInput(imageDNNBlob, "data");
cv::Mat detection = netOpenCVDNN.forward("detection_out");
cv::Mat faces(detection.size[2], detection.size[3], CV_32F, detection.ptr<float>());
for (int i = 0; i < faces.rows; i++)
    float confidence =<float>(i, 2);
    if (confidence > 0.99)
        int xLeftBottom = static_cast<int>(<float>(i, 3) * image.cols);
        int yLeftBottom = static_cast<int>(<float>(i, 4) * image.rows);
        int xRightTop = static_cast<int>(<float>(i, 5) * image.cols);
        int yRightTop = static_cast<int>(<float>(i, 6) * image.rows);

        cv::Rect faceRect((int)xLeftBottom, (int)yLeftBottom, 
                    (int)(xRightTop - xLeftBottom), (int)(yRightTop - yLeftBottom));
    faceROI = cv::Mat(image, faceRect);

Nothing too exotic, I just write down what I found in resnet_ssd_face.cpp. When I try to extract ROI from image with faceROI = cv::Mat(image, faceRect) I get an error on wrong dimensions with faceRect, in fact (with a particular image) I get 257 as dimension (height).<float>(i, 6)return a float >1.

What I miss? Can some help to figure out?

I have also some questions about this example:

  1. netOpenCVDNN.forward return a Mat, where size[2] is the number of object found, size[3] numbers of property of each object? Am I right? Where can I find more info about what forward return? (Already checked here and here. I think it is related to the layer "detection_out" of prototxt, but I can not get it).
  2. Mat facesis a matrix with all faces found, right? Where each rows is a face detected and each rows (face) have some property (cols), right? So<float>(i, 2) is the confidence of i-th face and from 3 to 4 are dimensions of face. What position 0 and 1 contains?
  3. Why cv::Mat imageDNNBlob have a numbers of rows and cols like -1?
  4. Last one: I am using image of 256x256 dimension. Input layer of dnn use 300x300 as dimension. What is the right solution? Resize image? Change input layer? Is cv::Size(300, 300) right in blobFromImage?

Thanks in advance.

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it will get better,once you name things correctly, see here (though it's not your fault, it was already wrong in the (outdated) sample)

also, there's probably a reason for this

berak gravatar imageberak ( 2018-05-28 20:02:50 -0500 )edit

1 answer

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answered 2018-05-29 01:14:17 -0500

berak gravatar image

updated 2018-05-29 01:15:06 -0500

  1. unfortunately, there is no easy answer, it depends on the architecture, and what a network was trained upon. classification networks have a single layer of class predictions here, ssd style detection networks have N rows with 7 numbers, yolo3 ones have "regions" here.

  2. for those ssd detections, position 0 is the detection id (a sequential number), position 1 the classID (unused here, because we don't have cats & dogs, only faces here)

  3. those "blobs" are 4d tensors, and 4 dimensions don't fit into rows & cols, so those are -1, and you have to look at the size array to retrieve that information, size[0]==nImages, size[1]==numChannels, size[2]==H, size[3]==W

  4. yes, it was originally trained on 300x300 images. if you use a smaller one, it will get upscaled automatically. note, that it might get faster (but somewhat less accurate), if you use a smaller size, like (128,96) (used in the js demo)

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  1. Is it not enought to check the last layer of network?
  2. Perfect.
  3. Make sense. Thought could be a problem, it was not. Ok.
  4. So I will leave prototxt as is, on blobFromImage I can use cv::Size(256, 256) and without resize image to 300x300, right?
lezan gravatar imagelezan ( 2018-05-29 03:31:59 -0500 )edit

1: mostly. but again, some networks (like yolov3) require you to check more than the last output layer

4: yes.

berak gravatar imageberak ( 2018-05-29 03:35:25 -0500 )edit

Now is working like a charm. Thanks as always berak, your help is precious.

lezan gravatar imagelezan ( 2018-05-29 03:39:02 -0500 )edit

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Asked: 2018-05-28 19:45:21 -0500

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Last updated: May 29 '18