I am trying to use CvAddWeighted for different size of images .I know I can crop the size of larger image to match with the smaller image but I dont want to.Is there any other technique ,with which I can Use cvAddWeighted with different size of images ?
It all depends on the relationship between your different images. Assuming the smaller ones are a subset of the biggest one, you can copy a small one into an empty image of maximum size and then use cvAddWhatever on all the resulting big images. Of course it implies that you know before hand the maximum size of your images (or that you can store them somehow)
You should set ROI on the images.
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I followed multiple example, to train a custom object detector in TensorflowJS . The main problem I am facing every where it is using pretrained model.
Pretrained models are fine for general use cases, but custom scenario it fails. For example, take this this is example form official Tensorflowjs examples, here it is using mobilenet, and mobilenet and mobilenet has image size restriction 224x224 which defeats all the purpose, because my images are big and also not of same ratio so resizing is not an option.
I have tried multiple example, all follows same path oneway or another.
What I want ?
Any example by which I can train a custom objector from scratch in Tensorflow.js.
Although the answer sounds simple but trust me I searching for this for multiple days. Any help will be greatly appreciated. Thanks
Currently it is not yet possible to use tensorflow object detection api in nodejs. But the image size should not be a restriction. Instead of resizing, you can crop your image and keep only the part that contain your object to be detected.
One approach will be like partition the image in 224x224 and run for all partitions but what if the object is between two partitions
The image does not need to be partitioned for it. When labelling the image, you will need to know the x, y coordinates (from the top left) and the w, h of the detected box. You only need to crop a part of the image that will contain the box. Cropping at the coordinates x - (224-w)/2, y- (224-h)/2 can be a good start. There are two issues with these coordinates:
the detected boxes will always be in the center, so the training will be biaised. To prevent it, a randomn factor can be used. x - (224-w)/r , y- (224-h)/r where r can be randomly taken from [1-10] for instance
if the detected boxes are bigger than 224 * 224 maybe you might first choose to resize the video keeping it ratio before cropping. In this case the boxe size (w, h) will need to be readjusted according to the scale used for the resizing
Lets say I have an image called Test.jpg.
I just figured out how to bring an image into the project by the following line:
FILE *infile = fopen("Stonehenge.jpg", "rb");
Now that I have the file, do I need to convert this file into a bmp image in order to apply a filter to it?
I have never worked with images before, let alone OpenCl so there is a lot that is going over my head.
I need further clarification on this part for my own understanding
Does this bmp image also need to be stored in an array in order to have a filter applied to it? I have seen a sliding window technique be used a couple of times in other examples. Is the bmp image pretty much split up into RGB values (0-255)? If someone can provide a link on this item that should help me understand this a lot better.
I know this may seem like a basic question to most but I do not have a mentor on this subject in my workplace.
Now that I have the file, do I need to convert this file into a bmp image in order to apply a filter to it?
Not exactly. bmp is a very specific image serialization format and actually a quite complicated one (implementing a BMP file parser that deals with all the corner cases correctly is actually rather difficult).
However what you have there so far is not even file content data. What you have there is a C stdio FILE handle and that's it. So far you did not even check if the file could be opened. That's not really useful.
JPEG is a lossy compressed image format. What you need to be able to "work" with it is a pixel value array. Either an array of component tuples, or a number of arrays, one for each component (depending on your application either format may perform better).
Now implementing image format decoders becomes tedious. It's not exactly difficult but also not something you can write down on a single evening. Of course the devil is in the details and writing an implementation that is high quality, covers all corner cases and is fast is a major effort. That's why for every image (and video and audio) format out there you usually can find only a small number of encoder and decoder implementations. The de-facto standard codec library for JPEG are libjpeg and libjpeg-turbo. If your aim is to read just JPEG files, then these libraries would be the go-to implementation. However you also may want to support PNG files, and then maybe EXR and so on and then things become tedious again. So there are meta-libraries which wrap all those format specific libraries and offer them through a universal API.
In the OpenGL wiki there's a dedicated page on the current state of image loader libraries: https://www.opengl.org/wiki/Image_Libraries
Does this bmp image also need to be stored in an array in order to have a filter applied to it?
That actually depends on the kind of filter you want to apply. A simple threshold filter for example does not take a pixel's surroundings into account. If you were to perform scanline signal processing (e.g. when processing old analogue television signals) you may require only a single row of pixels at a time.
The universal solution of course to keep the whole image in memory, but then some pictures are so HUGE that no average computer's RAM can hold them. There are image processing libraries like VIPS that implement processing graphs that can operate on small subregions of an image at a time and can be executed independently.
Is the bmp image pretty much split up into RGB values (0-255)? If someone can provide a link on this item that should help me understand this a lot better.
In case you mean "pixel array" instead of BMP (remember, BMP is a specific data structure), then no. Pixel component values may be of any scalar type and value range. And there are in fact colour spaces in which there are value regions which are mathematically necessary but do not denote actually sensible colours.
When it comes down to pixel data, an image is just a n-dimensional array of scalar component tuples where each component's value lies in a given range of values. It doesn't get more specific for that. Only when you introduce colour spaces (RGB, CMYK, YUV, CIE-Lab, CIE-XYZ, etc.) you give those values specific colour-meaning. And the choice of data type is more or less arbitrary. You can either use 8 bits per component RGB (0..255), 10 bits (0..1024) or floating point (0.0 .. 1.0); the choice is yours.
If there is a given 2d array of an image, where threshold has been done and now is in binary information.
Is there any particular way to process this image to that I get multiple blob's coordinates on the image?
I can't use openCV because this process needs to run simultaneously on 10+ simulated robots on a custom simulator in C.
I need the blobs xy coordinates, but first I need to find those multiple blobs first.
Simplest criteria of pixel group size should be enough. But I don't have any clue how to start the coding.
PS: Single blob should be no problem. Problem is multiple blobs.
Just a head start ?
Have a look at QuickBlob which is a small, standalone C library that sounds perfectly suited for your needs.
QuickBlob comes with a small command-line tool (csv-blobs) that outputs the position and size of each blob found within the input image:
./csv-blobs white image.png
X,Y,size,color
28.37,10.90,41,white
51.64,10.36,42,white
...
Here's an example (output image is produced thanks to the show-blobs.py tiny Python utility that comes with QuickBlob):
You can go through the binary image labeling the connected parts with an algorithm like the following:
Create a 2D array of ints, labelArray, that will hold the labels of the connected regions and initiate it to all zeros.
Iterate over each binary pixel, p, row by row
A. If p is true and the corresponding value for this position in the labelArray is 0 (unlabeled), assign it to a new label and do a breadth-first search that will add all surrounding binary pixels that are also true to that same label.
The only issue now is if you have multiple blobs that are touching each other. Because you know the size of the blobs, you should be able to figure out how many blobs are in a given connected region. This is the tricky part. You can try doing a k-means clustering at this point. You can also try other methods like using binary dilation.
I know that I am very late to the party, but I am just adding this for the benefipeople who are researching this problem.
Here is a nice description that might fit your needs.
http://www.mcs.csueastbay.edu/~grewe/CS6825/Mat/BinaryImageProcessing/BlobDetection.htm
What is the best (minimize size) compression algorthm for images with only two colors? And the fastest?
It depends on what the image looks like. If it's mostly large blotches of color then you may do well with Run Length Encoding. If it's constantly changing colors, you might make a matrix of bits corresponding to the pixels and then compress that with LZW.
The method used by PNG would be pretty good, it combines prediction and compression.
DjVu is know to compress certain 2-color bitmaps, like text, pretty well.
Jbig2 would be one of the best compressions and it is supported by Adobereader
I have completed an image processing algorithm where I extract certain features from two similar images.
I'm using OpenCV2.1 and I wish to showcase a comparison between these two similar images. I wish to combine both the images into one, where the final image will have both the images next to one another. Like in the figure below.
Also, the black dots are the similarities my algorithm has found, now I want to mark them with digits. Where, point 1 on the right is the corresponding matching point on the left.**
What OpenCV functions are useful for this work?
If you really want them in the same window, and assuming they have same width and height (if they are similar they should have same width and height). You could try to create an image with a final width twice bigger than the width of your 2 similar images. And then use ROI to copy them.
You can write a new function to encapsulate these (usefull) functions in one function in order to have a nice code.
Mat img1,img2; //They are previously declared and of the same width & height
Mat imgResult(img1.rows,2*img1.cols,img1.type()); // Your final image
Mat roiImgResult_Left = imgResult(Rect(0,0,img1.cols,img1.rows)); //Img1 will be on the left part
Mat roiImgResult_Right = imgResult(Rect(img1.cols,0,img2.cols,img2.rows)); //Img2 will be on the right part, we shift the roi of img1.cols on the right
Mat roiImg1 = img1(Rect(0,0,img1.cols,img1.rows));
Mat roiImg2 = img2(Rect(0,0,img2.cols,img2.rows));
roiImg1.copyTo(roiImgResult_Left); //Img1 will be on the left of imgResult
roiImg2.copyTo(roiImgResult_Right); //Img2 will be on the right of imgResult
Julien,
The easiest way I can think right now would be to create two windows instead of one. You can do it using cvNamedWindow(), and then position them side by side with cvMoveWindow().
After that if you now the position of the similarities on the images, you can draw your text near them. Take a look at cvInitFont(), cvPutText().