procton's profile - activity

Thank you !

I am testing the python version of LBPHFaceRecognizer and results seems not bad, but I noted something strange. Making the prediction, let's say:

label= face_recognizer.predict(gray)
print('prediction', label)

I see that label is a tuple with 2 values, the first is the prediction, the second I guess is the probability. Now when probability is low the prediction is fine, while when it is high, I have false positive.

Maybe this value is not the probability, so the question is, what is its real meaning ? Thanks

Use of LBPHFaceRecognizer (python) I am testing the python version of LBPHFaceRecognizer and results seems not bad, but

Manage mp4 video Hi Community, I am afraid to know the answer, anyway this is may question: is it possible to load a mp4

Manage mp4 video Hi Community, I am afraid to know the answer, anyway this is may question: is it possible to load a mp4

You shoul do binary thresholding and then find contours. The biggest should approximate the elephant.

Well, I think that if someone posts a question on a technical forum as it is, he deserves something different than 'try

@Telegramm I did some experiments with Laplacian, but I am not sure of what could be good parameters in my scenario. Mor

Good catch @Balaji !!!, I also noticed the shift that you pointed out. My goal is to detect small blobs. Till now I was

How detect very small blobs I need to find differences on the following two images. The first one has very small blobs (

I updated my post with color images

How detect very small blobs I need to find differences on the following two images. The first one has very small blobs (

How detect very small blobs I need to find differences on the following two images. The first one has very small blobs (

How detect very small blobs I need to find differences on the following two images. The first one has very small blobs (

please, take a look here, you will find a formal definition with examples. https://docs.opencv.org/2.4/modules/imgproc/d

post a download link from gdrive or similar

Regarding image crop or anything else?

Take a look at https://www.cs.auckland.ac.nz/~m.rezaei/Tutorials/Creating_a_Cascade_of_Haar-Like_Classifiers_Step_by_Ste

Take a look at https://www.cs.auckland.ac.nz/~m.rezaei/Tutorials/Creating_a_Cascade_of_Haar-Like_Classifiers_Step_by_Ste

Try a look at https://docs.opencv.org/2.4/doc/tutorials/features2d/feature_homography/feature_homography.html#feature-ho

Hi All, the following is a quite difficult task so any kind of help, even simple, will be much appreciated.

After color segmentation I generated the following image

I need to get lines that approximates white areas (roads), as shown in the following hand-made image

Using probabilistic HoughLines with following parameters (image and lines are Mat instances):

Imgproc.HoughLinesP(image, lines, 2, Math.PI /180, 200, 100, 5);

I get the following result:

I tried several combinations of parameters and shown result seems to be the best that I can achieve.

My questions:

1. Overall, what do you think of this approach ?

2. Any idea on how to select 'strongest' lines after HoughLines transform ? Is there any way to cluster every bundle of lines into a single significative line ?

Thanks

You can do the following: find contours on this image, which is already binarized with black background so it's ready

Yes, but I always use Java :) even if it is the worst choice with Opencv

Well, after hard work I have a solution. I look forward your inputs, ideas and improvements. First, I did some noise re

Well, after hard work I have a solution. I look forward your inputs, ideas and improvments. First, I did some noise rem

I tried dbscan using Apache Maths. I am able to cluster centers of Houghlines, but I am not able to return back to lines

thanks moHe. Do you have concrete example ?

there is an additional complexity thay I omitted. I have a set of images (the one I posted is just an example), each cou

There should be 14 centers, since there are 14 segments. The main problem is that I don't know how to deal with object l

This may be useful: http://blog.codeonion.com/2015/11/25/creating-a-new-opencv-project-in-android-studio/

You may try the following tutorial http://blog.codeonion.com/2015/11/25/creating-a-new-opencv-project-in-android-studio/

Lines detection Hi All, the following is a quite difficult task so any kind of help, even simple, will be much appreciat

OK, this answer solves almost all questions. So which should be the correct approach ? Maybe random forest using ground

Road detection and machine learning My goal is to detect roads from high resolution aerial images. I would like to adopt

have you tried simple edge detection ? I think that some blurring (GaussianBlur), threshold and findContours should be e

I have a high resolution (about 30 cm) RGB pan sharpened image (3 channels, 16x3 bit). My need is to make some edge detection aimed to segment human artifacts.

I load the image with Mat rgbPanSharpen = Imgcodecs.imread(rgbPanSharpenImage, Imgcodecs.IMREAD_UNCHANGED); but after that I have some doubts on how proceed.

I think I could work on single channels (using Core.split) having single 16 bit images, may be also converting to 8 bit, but I am afraid this approach is lossy.

Which should be the correct approach to make edge detection on such kind of images ? (I don't ask for code, just the correct logical approch to follow)

This a sample image