HOG descriptors are not the same thing as HOG detectors.

$ python >>> import cv2 >>> help(cv2.HOGDescriptor().detectMultiScale) Figure 1: The available parameters to the detectMultiScale function. Is there any relation between minimum and maximum size possible to detect with the parameters of the hog/svm detector of open cv ?I’m not sure what you mean by minimum/maximum size.

However, the Python binding of Is there any way to use Python + OpenCV to extract the HOG features directly from any image?If you want fast Python code for HOG features, I've ported the code to Cython: and edit the respective parameter values in xml file.Despite the fact that exist a method as said in previous answers:I would like to post a python implementation you can find on opencv's examples directory, hoping it can be useful to understand HOG funcionallity:I would disagree with the argument of peakxu. Or I can detect foreground objects with blobs analysis.You typically see SVMs, in particular Linear SVMs, because they are very fast. Use one or the other.Hey, Adrian here, author of the PyImageSearch blog. The downside is that the HOG + Linear SVM detector now has more data to process, thus making it substantially slower.I’ve trained my own HOG detector using the command “opencv_traincascade” with the “-featureType HOG” flag on and it succefully generated a .xml file as a HOG detector.How can I implement my own XML file on the functions “cv2.HOGDescriptor() hog.setSVMDetector()” ? I am currently looking into accessing HoG descriptors with OpenCV Python and will write back if I figure it out.In the computer vision literature, HOG features are widely used and quite successful, in particular as building block of the deformable parts model.

The final step collects the HOG descriptors from all blocks of a dense overlapping grid of blocks covering the detection window into a combined feature vector for use in the window classifier.

Are people not detected? Get your FREE 17 page Computer Vision, OpenCV, and Deep Learning Resource Guide PDF. I excluded reading from disk while timing.Shouldn't hog return a histogram? the HOG descriptor algorithm introduced by Navneet Dalal and Bill Triggs . any degrees of freedom in the "object" (i.e. This time we will use Histogram of Oriented Gradients (HOG) as feature vectors..

frontal people and sideways people...).Regarding the HOG detector in opencv: In theory you can upload another detector to be used with the features, but you cannot afaik get the features themselves. Example Code: Here is a snippet of code to initialize an cv2.HOGDescriptor with different parameters (The terms I used here are standard terms which are well defined in OpenCV documentation here): HOG特征：方向梯度直方图（Histogram of Oriented Gradients）import cv2 as cvimport numpy as npfrom matplotlib import pyplot as pltsrc = cv.imread("*.jpg")cv.imshow("input",src)hog = cv.HOGDescriptor()hog.setSVMDetector(cv.HOGDescriptor_getDefaultPeopleDetecto thus, if you have a trained detector (i.e.

I read it carefully and tried out your code with own pictures and videos.

I ran this for about 100 big images and it took 23 seconds approximately for both grayscale and RGB.

Center : Absolute value of y-gradient. Inside the course you'll learn how to perform:

persons) lead to bluring in the detector, and are not actually handled by it. In order to improve your detection rate, be sure to check the ‘C’ parameter of your SVM.

So just to clarify, nLevel is used to control the maximum number of layers of the image pyramid? Private self-hosted questions and answers for your enterpriseProgramming and related technical career opportunitiesit looks like your code outputs 13 numeric values per block, whereas the DPM paper outputs 32 numer entries per block. This will create a “soft classifier” and help with your detection rate.Another neat little trick you can do to create more training data is “mirror” your training images.

with MultiScale() ) regardless of the model I use, i get a pretty window right in the middle of the image :/ – Do you have any Idea of what could be the issue here?Congrats on training your own model David, that’s great.

常用的是winSize, blockSize, blockStride, cellSize, nbins这四个，分别是窗口大小（单位：像素）、block大小（单位：像素）、block步长（单位：像素）、cell大小（单位：像素）、bin的取值. site design / logo © 2020 Stack Exchange Inc; user contributions licensed under

I am currently looking into accessing HoG descriptors with OpenCV Python and will write back if I figure it out.In the computer vision literature, HOG features are widely used and quite successful, in particular as building block of the deformable parts model. Not only I found an improvement in detection accuracy, but it also runs faster. Line detection and timestamps, video, Python.

Left : Absolute value of x-gradient.

You may also check out all available functions/classes of the module cv2, or try the search function . However, I’m not sure why your detector would be falsely reporting a detection in the middle of the image each and every time. This section of the post details tricks you can use to speed up the detection process, especially related to the Raspberry Pi 2.could you help me please with the people detection in real time please, I’ve been trying but it doesn’t work, can you give me some ideas to the code, or send to me that part of the code, i really apreciate that.Hey Adrian – the link leads to the same post.

That may be an issue with your training data but unfortunately I’m not sure what the root cause is.Hey Alex — could you be a bit more specific when you say “it doesn’t work”?

A descriptor is the signature provided in an image patch by computing the HoG feature. If so, I think HOG + Linear SVM would likely be enough here.From this code, how do I adapt it for offline training? There are not Python bindings for it, so I unfortunately haven’t had a chance to play around with the GPU functions and do not have any insight there.if I want to use HOG + SVM as Traffic Sign detector, how should I do it?I there a way to use detectmultiscale to distinguish between several objectclasses.

The Overflow Blog The HOG detector in the end is "just" a rigid linear filter. Can't I change the window size?I ran both. The HOG window? cv2. I've read this post about how to use OpenCV's HOG-based pedestrian detector: I want to use HOG for detecting other types of objects in images (not just pedestrians).