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Does anyone know of or have a good tool for labeling image data to be used in training a DNN?
Specifically labeling 2 points in an image, like upperLeftCorner and lowerRightCorner, which then calculates a bouding box around the specified object. That's just an example but I would like to be able to follow the MSCoco data format.
Thanks!
You might try LabelMe, http://labelme.csail.mit.edu/Release3.0/
It's usually for outlines for segmentation, but I'm pretty sure it works fine for bounding boxes too.
I had a similar issue finding a tool that did bouding boxes for labeling image data, so I started this new project called LabelD (https://github.com/sweppner/labeld) that uses NodeJS and focuses on bouding boxes for annotation. It's still very much in alpha, but it's pretty easy to use and functional for labeling images!
Let me know if you have any questions!
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I'm new to YOLO and trying to make car counting application using YOLO. The cars is from video file. Is there any reference? Thank you
There are multiple places from which I suggest you try to learn:
1) The CNN course from coursera https://www.coursera.org/learn/convolutional-neural-networks
This course has a good explanation on yolo(There assignment is on car detection as well which can easily be extended to car counting) and the rest of the course is quite nice as well
2)https://towardsdatascience.com/yolo-you-only-look-once-real-time-object-detection-explained-492dc9230006
The article focus on a few implementation details and talks about the papers yolo and yolov2 , and helped me clear a out a few issues i had when i was trying to implement yolo
3)The original paper (although this may be too advanced ): https://arxiv.org/pdf/1506.02640v5.pdf
4)A keras implmentation : https://github.com/experiencor/keras-yolo2
A simple git clone if you wish to simply have the code , although i do not recommend this as it has very little actual learning and is simply a download and use option
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I am a sort of newbie to NLP world.
But anyway, I have just started my NLP project.
My task is about inferring hidden sentence in a paragraph.
Let me show you an example question.
a multiple choice question about inferring a clause in the blank
I want my machine learning model to extract some meaningful phrase from the given text(in above image, a paragraph)
I know that my question sounds quite ambiguous for you all. I just want to know even a small clue.
Thank you for your response in advance.
Skip-thought vectors are a system for predicting sentences from a context, by essentially constructing sentence-wide vectors. Might be useful, especially so in combination with context2vec if you want to build a custom model.
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I am working on a project wherein I am supposed to detect human beings from a live video stream which I get from a UAV's camera module. I do not need to create any rectangles or boxes around detected subjects, but just need to reply with a yes or no. I am fairly new to Open-CV and have no prior experience.
What I have tried:
I started by training my SVM on HOG features. My team gathered a few images from a UAV we had, with people in it. I then trained the SVM from the crops of those people. We got unsatisfactory results when we used the trained detector on the a video from sky with people. Moreover processing each frame turned out to be very slow , therefore the system became unusable.(it did work on still images to some extent).
My question:
I wanted to know if there is some other technique, library etc I could try for achieving good results. Please point me to the next step.
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I need to find some library(can be commercial) to detect the rectangle shapes from pictures like this one:
What libraries do you think they can do the job?
Also, I know there are many algorithms in image processing, which one you think can do this?
Thanks!
A quick attempt with Mathematica 8 produced this solution. It would be easy to play around some details.
Create a binary mask of the black ink, and then remove the small components (the digits):
binary = Binarize[img, .5];
bclean = ColorNegate[DeleteSmallComponents[ColorNegate[binary]]];
Now compute the connected components and remove the background component:
comp = DeleteBorderComponents[MorphologicalComponents[bclean]];
I assessed the result visually, using the command Colorize[comp].
From there on, the command ComponentMeasurements would get you to further analysis of the blobs you are interested in (cf. http://reference.wolfram.com/mathematica/ref/ComponentMeasurements.html).
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I have found OpenCV code that uses CvGaussBGStatModelParams structure, cvCreateGaussianBGModel, and other related functions. However, I haven't been able to find any explanations for how they work and how they are to be used and what they mean.
Any help would be appreciated.
These functions are undocumented (at least as far as the manual goes). However, if you look around in the source, you will find them in src/cvaux/cvbgfg_gaussmix.cpp. In there:
This is based on the "An Improved
Adaptive Background Mixture Model for
Real-time Tracking with Shadow
Detection" by P. KaewTraKulPong and R.
Bowden
http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf
The windowing method is used, but not
the shadow detection. I make some of
my own modifications which make more
sense. There are some errors in some
of their equations.
That link is probably a good start for you.