I am using the CAMshift algorithm for my final year project but am stuck on a point. I am not able to terminate the algorithm automatically. Even after I remove the object from in front of the camera, the algorithm keeps tracking. I have heard about the termination criteria but don't know whether it is applicable here or not. Any help is appreciated. Thanks in advance.
Here's my code on github: code
Thanks
Ok I figured it out myself. The ROI in the actual image can be cropped so as to get a new image. Comparing the actual object size and the ROI size, if the ratio is beyond a specific value, we can determine that the object is lost.
In my case, the ratio is taken as 0.3
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I want to build a script that can count and box fishes in a given image with the assumption that the image will always contain fishes (any number) and no other animals.
My current approach is just to count the number of contours detected in an image. I use canny edge detection with dilation and erosion. I also discard contours that are too small, treating them as noise.
This approach seems to be working fine if the fishes are nicely separated. But once they are intersecting, or covering each other, things start to get messy.
Currently, I am just using images from google.
What other approach can I use for this problem? Still a newbie in image processing.
Thank you so much!
What other approach can I use for this problem?
You can try to detect just a particular feature of the fish, such as an eye, mouth, or fin. You can try to detect whole fish, and also count particular features as above; if the numbers don't match you can deduce that you've found some partial fish.
I have used Tensorflow object detection for quite awhile now. I am more of a user, I dont really know how it works. I am wondering is it possible to train it to recognize an object is something and not something? For example, I want to detect cracks on the tiles. Can i use object detection to do so where i show an image of a tile and it can tell me if there is a crack (and also show the location), or it will tell me if there is no crack on the tile?
I have tried to train using pictures with and without defect, using 2 classes (1 for defect and 1 for no defect). But the results keep showing both (if the picture have defect) in 1 picture. Is there a way to show only the one with defect?
Basically i would like to do defect checking. This is a simplistic case of 1 defect. but the actual case will have a few defects.
Thank you.
In case you're only expecting input images of tiles, either with defects or not, you don't need a class for no defect.
The API adds a background class for everything which is not the other classes.
So you simply need to state one class - defect, and tiles which are not detected as such are not defected.
So in your training set - simply give bounding boxes of defects, and no bounding box in case of no defect, and then your model should learn to detect the defects as mentioned above.
I am an undergraduate student working with detecting defects on a surface of an object, in a given digital image using image processing technique. I am planning on using OpenCV library to get image processing functions. Currently I am trying to decide on which defect detection algorithm to use, in order to detect defects. This is one of my very first projects related to this field, so it will be appreciated if I can get some help related to this issue. The reference image with a defect (missing teeth in the gear), which I am currently working with is uploaded as a link below ("defective gear image").
defective gear image
Get the convex hull of a gear (which is a polygon) and shrink is slightly so that it crosses the teeth. Make sure that the centroid of the gear is the fixed point.
Then sample the pixels along the hull, preferably using equidistant points (divide the perimeter by a multiple of the number of teeth). The unwrapped profile will be a dashed line, with missing dashes corresponding to missing teeth, and the problem is reduced to 1D.
You can also try a polar unwarping, making the outline straight, but you will need an accurate location of the center.
First, this Calculating camera ray direction to 3d world pixel helped me a bit in understanding what the virtual camera setup is like. I don't understand how the vectors work in this setup, and I thought normalized device coordinates had to be used which led me to this page http://www.scratchapixel.com/lessons/3d-basic-lessons/lesson-6-rays-cameras-and-images/building-primary-rays-and-rendering-an-image/. What I am trying to do is build a ray tracer, and as the question states, find out the pixels position in order to shoot out a ray. What I really, really really would like, is an actually example showing a virtual camera setup, screen resolution and how to calculate a pixels position, then transform to world space coordinates. Experts!, Thank you for your help! :D
Multiply a matrix by the coordinates. What matrix? There are lots of choices. For example XNA uses a projection matrix, view matrix and world matrix. Applying all of them transforms pixel coordinates into world coordinates or vice versa. Breaking it down this way helps to understand the different transformations going on so you can more easily construct the matrices.
Isn't this webpage providing you already with 4 pages of explanation on how these rays are built? It seems like you haven't made the effort to read the content of the link you are referring to. I would suggest you read it first, try to understand it, maybe look at the source code they provide and come back with a real question regarding what you potentially don't understand.
It's all there, and I am not going to re-write what these people seem to have put a lot of energy already to explain! (nor should anybody else really ...).
I'm looking for ways to determine the quality of a photography (jpg). The first thing that came into my mind was to compare the file-size to the amount of pixel stored within. Are there any other ways, for example to check the amount of noise in a jpg? Does anyone have a good reading link on this topic or any experience? By the way, the project I'm working on is written in C# (.net 3.5) and I use the Aurigma Graphics Mill for image processing.
Thanks in advance!
I'm not entirely clear what you mean by "quality", if you mean the quality setting in the JPG compression algorithm then you may be able to extract it from the EXIF tags of the image (relies on the capture device putting them in and no-one else overwriting them) for your library see here:
http://www.aurigma.com/Support/DocViewer/30/JPEGFileFormat.htm.aspx
If you mean any other sort of "quality" then you need to come up with a better definition of quality. For example, over-exposure may be a problem in which case hunting for saturated pixels would help determine that specific sort of quality. Or more generally you could look at statistics (mean, standard deviation) of the image histogram in the 3 colour channels. The image may be out of focus, in which case you could look for a cutoff in the spatial frequencies of the image Fourier transform. If you're worried about speckle noise then you could try applying a median filter to the image and comparing back to the original image (more speckle noise would give a larger change) - I'm guessing a bit here.
If by "quality" you mean aesthetic properties of composition etc then - good luck!
The 'quality' of an image is not measurable, because it doesn't correspond to any particular value.
If u take it as number of pixels in the image of specific size its not accurate. You might talk about a photograph taken in bad light conditions as being of 'bad quality', even though it has exactly the same number of pixels as another image taken in good light conditions. This term is often used to talk about the overall effect of an image, rather than its technical specifications.
I wanted to do something similar, but wanted the "Soylent Green" option and used people to rank images by performing comparisons. See the question responses here.
I think you're asking about how to determine the quality of the compression process itself. This can be done by converting the JPEG to a BMP and comparing that BMP to the original bitmap from with the JPEG was created. You can iterate through the bitmaps pixel-by-pixel and calculate a pixel-to-pixel "distance" by summing the differences between the R, G and B values of each pair of pixels (i.e. the pixel in the original and the pixel in the JPEG) and dividing by the total number of pixels. This will give you a measure of the average difference between the original and the JPEG.
Reading the number of pixels in the image can tell you the "megapixel" size(#pixels/1000000), which can be a crude form of programatic quality check, but that wont tell you if the photo is properly focused, assuming it is supposed to be focused (think fast-motion objects, like trains), nor weather or not there is something in the pic worth looking at, that will require a human, or pigeon if you prefer.