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I am hoping that TensorFlow can turn this input, to this output.
Input: A floorplan PNG, and 1 - 5 images of a symbol
Output: The same floorplan, but with all matching symbols highlighted
I can do the hard work of figuring out HOW to do it, but I don't want to waste 2 weeks just to figure out it wouldn't be possible. I know I'd need to train it with multiple images, but I won't have more than 5 examples of a given symbol.
Does TensorFlow have these capabilities?
Thanks!
Yes, it is possible to use tensorflow to create a machine learning algorithm to do that for you, but I would bet that is not how you want to do this. First off, in order to do this in tensorflow, you would need to manually create a large number of training samples and spend a significant amount of time figuring out how to define the network and train it. Sure, you could do it, but I definitely wouldn't advise it.
If you have a specific set of symbols that you want to highlight, it would probably be better to use opencv to find and highlight the symbols. For example, in opencv, you could use Template Matching to find a specific symbol in the floor plan and then highlight them by modifying pixel color.
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I know nothing on the subject of deep learning.
I am looking for references to build a deep learning algorithm to detect ROI in given images. My goal is to compare deep learning algorithms with usual image processing algorithms I have already made.
The input images look like this :
The output of the algorithm should look like this :
Q1: Do you have any references that if I read them would let me build such a deep learning algorithm from start to finish ?
Q2: Otherwise, do such algorithms already exist and are freely available ? (Note: Such algorithms should produce precise ROI detection not broad rectangles encircling the bright regions).
You can try using Mask R-CNN. Refer to these links for your understanding:
https://github.com/matterport/Mask_RCNN
https://arxiv.org/abs/1703.06870
Basically, you need to make an annotation (polygonal) for your dataset with tools like VIA image annotation tool (https://www.robots.ox.ac.uk/~vgg/software/via/) or MakeSense (https://www.makesense.ai/). These are the open source tools that I can recommend. After training, the network can predict the bounding box as well as the boundary of the detected objects.
Your task is easy, don't worry about knowing anything about the topic, as your image shows what you are trying to achieve, I would suggest you try using semantic segmentation, you can search on youtube or read about Faster R-CNNthey are kinda related to what you want to do. Then you can compare the output results with the regular image processing.
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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 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 a collection of sample images to train a Haar-based classifier for face detection. I read that a ratio of 2 negative examples for each positive example is acceptable. I searched around the web and found many databases containing positive examples to train my classifier (that is, images that contain faces), however, I can't any database with negative examples. Right now, I have over 18000 positive examples. Where can I find 2000 or more negative examples?
use
http://tutorial-haartraining.googlecode.com/svn/trunk/data/negatives/
or any other image set that has no objects you need to recognize
NOTE: the number of samples you mention is too big, you don't need so much to obtain high accuracy
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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).