I'm working on a project and I need to identify the number of grains in pictures with several of these grains touching, overlapping. Can someone help me with this ? Thanks a lot
Train a model of object detection: You can train some model like object detection (YOLOv5) or some other and calculate the count.
This is a very broad question so I will give you a broad answer, you could train a computer vision model, or you could use simple image processing using open cv:
Learn about color threshholding: everything that is not the color of the grain becomes a black pixel, everything else becomes a white pixel.
Learn about MorphologyEx, to disconnect connected grains.
Get the numbers of contours of white objects in the blacked out image.
All of those are simple google searches that will achieve what you desire. Using open CV.
Related
I made a tif image based on a 3d model of a woodsheet. (x, y, z) represents a point in a 3d space. I simply map (x, y) to a pixel position in the image and (z) to the greyscale value of that pixel. It worked as I have imagined. Then I ran into a low-resolution problem when I tried to print it. The tif image would get pixilated badly as soon as it zooms out. My research suggests that I need to increase the resolution of the image. So I tried a few super-resolution algos found from online sources, including this one https://learnopencv.com/super-resolution-in-opencv/
The final image did get a lot bigger in resolution (10+ times larger in either dimension) but the same problem persists - it gets pixilated as soon as it zooms out, just about the same as the original image.
Looks like quality of an image has something to do not only with resolution of it but also something else. When I say quality of image, I mean how clear the wood texture is in the image. And when I enlarge it, how sharp/clear the texture remains in the image. Can anyone shed some light on this? Thank you.
original tif
The algo generated tif is too large to be included here (32M)
Gigapixel enhanced tif
Update - Here is a recently achieved result: with a GAN-based solution
It has restored/invented some of the wood grain details. But the models need to be retrained.
In short, it is possible to do this via deep learning reconstruction like the Super Resolution package you referred to, but you should understand what something like this is trying to do and whether it is fit for purpose.
Generic algorithms like the Super Resolution is trained on variety of images to "guess" at details that is not present in the original image, typically using generative training methods like using the low vs high resolution version of the same image as training data.
Using a contrived example, let's say you are trying to up-res a picture of someone's face (CSI Zoom-and-Enhance style!). From the algorithm's perspective, if a black circle is always present inside a white blob of a certain shape (i.e. a pupil in an eye), then next time it the algorithm sees the same shape it will guess that there should be a black circle and fill in a black pupil. However, this does not mean that there is details in the original photo that suggests a black pupil.
In your case, you are trying to do a very specific type of up-resing, and algorithms trained on generic data will probably not be good for this type of work. It will be trying to "guess" what detail should be entered, but based on a very generic and diverse set of source data.
If this is a long-term project, you should look to train your algorithm on your specific use-case, which will definitely yield much better results. Otherwise, simple algorithms like smoothing will help make your image less "blocky", but it will not be able to "guess" details that aren't present.
I am trying to detect plants in the photos, i've already labeled photos with plants (with labelImg), but i don't understand how to train model with only background photos, so that when there is no plant here model can tell me so.
Do I need to set labeled box as the size of image?
p.s. new to ml so don't be rude, please)
I recently had a problem where all my training images were zoomed in on the object. This meant that the training images all had very little background information. Since object detection models use space outside bounding boxes as negative examples of these objects, this meant that the model had no background knowledge. So the model knew what objects were, but didn't know what they were not.
So I disagree with #Rika, since sometimes background images are useful. With my example, it worked to introduce background images.
As I already said, object detection models use non-labeled space in an image as negative examples of a certain object. So you have to save annotation files without bounding boxes for background images. In the software you use here (labelImg), you can use verify image to say that it saves the annotation file of the image without boxes. So it saves a file that says it should be included in training, but has no bounding box information. The model uses this as negative examples.
In your case, you don't need to do anything in that regard. Just grab the detection data that you created and train your network with it. When it comes to testing, you usually set a threshold for bounding boxes accuracy, because you may get lots of them so you only want the ones with the highest confidence.
Then you get/show the ones with highest bbox accuracies and there your go, you get your detection result and you can do what ever you want like cropping them using the bounding box coordinates you get.
If there are no plants, your network will likely create bboxes with an accuracy below your threshold (very low confidence) and then, you just ignore them.
i need some advice in a computer vision projekt that i am working on. I am trying to extract a corner in the image below. The edge im searching for is marked yellow in the right image. The edge detection is always failing because the edge is too blurred in the middle.
I run this process with opencv and python.
I started to remove the white dots with a threshold method. After that a big median blur (31-53). After that a adaptive Threshod method to seperate the areas left and right from the corners. But the sepearation is always bad because the edge is barely visible.
Is there some other way to extract this edge or do i have to try with a better camera?
Thanks for your help.
First do you have other dataset? because it is hard to discuss it just from 1 input.
Couple things that you can do.
The best is you change the camera of imaging technique to have a better and clear edge.
When it is hard to do so. Try model-based fitting.If you image is repeatable in all class. I can observe some circles on the right and 2 sharp straight-line edges on the left. Your wanted red soft edge circle is in the middle of those 2 apparent features. That can be considered as a model. then you can always use some other technique for the pixel in-between those 2 region(because they are easy to detect) . Those technique includes but not limit to histogram equalization, high pass filter or even wavelet transform.
The Wost way is to use parameter fitting to do. What you want to segment is sth not a strong edge and sth not a smooth plane. So you can tweak the canny edge detect to find those edge which is not so strong. I do not support this method. If you really no choice and no other image, then you can try it.
Last way is to use deep learning based method to train and auto segment this part out. This method might work. but it needs you to have hundred if not thousands of dataset and labels.
Regards
Shenghai Yuan
Introduction
I am newbie in statistics and NN.
I will describe my prolem below.
Problem
I want to find when the object on the video (images as well), will have following parameters:
the smallest diameter,
when the shape is closest to circle,
when the object have highest brightness.
Example:
https://imgur.com/a/pPIH6GX
Possible approaches
I started with neural networks (tensorflow) and object detection module, which detect objects similiar to circles, then I calculate the area of bounidng box. Then I started exploring mask RCNN networks and I am considering to obtain area of the mask. But it didn't solve my problem.
Asking for advice
The question is how to add other parameters? Like brightness or shape? Could you please give me some suggestions? Research papers, articles or post your thoughts in this field. I am waiting for deep conversation 😊
Many thanks!
Update 1
To detect circles Hough Transform can be used.
To find the smallest diameter I should consider thinness ratio.
Any other thoughts?
You should consider learning basics of image processing and computer vision first.
Locating circles on the image is a basic problem, so it's well-studied and has plenty of robust solutions.
There is no even need to use NN in your particular case (find a circle-like shapes at synthetic image you provided).
Refer to Wikipedia enter link description here to start with basics.
I am currently working on a system for robust hand detection.
The first step is to take a photo of the hand (in HSV color space) with the hand placed in a small rectangle to determine the skin color. I then apply a thresholding filter to set all non-skin pixels to black and all skin pixels white.
So far it works quite well, but I wanted to ask if there is a better way to solve this? For example, I found a few papers mentioning concrete color spaces for caucasian people, but none with a comparison for asian/african/caucasian color-tones.
By the way, I'm working with OpenCV via Python bindings.
Have you taken a look at the camshift paper by Gary Bradski? You can download it from here
I used the the skin detection algorithm a year ago for detecting skin regions for hand tracking and it is robust. It depends on how you use it.
The first problem with using color for tracking is that it is not robust to lighting variations or like you mentioned, when people have different skin tones. However this can be solved easily as mentioned in the paper by:
Convert image to HSV color space.
Throw away the V channel and consider the H and S channel and hence
discount for lighting variations.
Threshold pixels with low saturation due to their instability.
Bin the selected skin region into a 2D histogram. (OpenCV"s calcHist
function) This histogram now acts as a model for skin.
Compute the "backprojection" (i.e. use the histogram to compute the "probability"
that each pixel in your image has the color of skin tone) using calcBackProject. Skin
regions will have high values.
You can then either use meanShift to look for the mode of the 2D
"probability" map generated by backproject or to detect blobs of
high "probability".
Throwing away the V channel in HSV and only considering H and S channels is really enough (surprisingly) to detect different skin tones and under different lighting variations. A plus side is that its computation is fast.
These steps and the corresponding code can be found in the original OpenCV book.
As a side note, I've also used Gaussian Mixture Models (GMM) before. If you are only considering color then I would say using histograms or GMM makes not much difference. In fact the histogram would perform better (if your GMM is not constructed to account for lighting variations etc.). GMM is good if your sample vectors are more sophisticated (i.e. you consider other features) but speed-wise histogram is much faster because computing the probability map using histogram is essentially a table lookup whereas GMM requires performing a matrix computation (for vector with dimension > 1 in the formula for multi-dimension gaussian distribution) which can be time consuming for real time applications.
So in conclusion, if you are only trying to detect skin regions using color, then go with the histogram method. You can adapt it to consider local gradient as well (i.e. histogram of gradients but possibly not going to the full extent of Dalal and Trigg's human detection algo.) so that it can differentiate between skin and regions with similar color (e.g. cardboard or wooden furniture) using the local texture information. But that would require more effort.
For sample source code on how to use histogram for skin detection, you can take a look at OpenCV"s page here. But do note that it is mentioned on that webpage that they only use the hue channel and that using both hue and saturation would give better result.
For a more sophisticated approach, you can take a look at the work on "Detecting naked people" by Margaret Fleck and David Forsyth. This was one of the earlier work on detecting skin regions that considers both color and texture. The details can be found here.
A great resource for source code related to computer vision and image processing, which happens to include code for visual tracking can be found here. And not, its not OpenCV.
Hope this helps.
Here is a paper on adaptive gaussian mixture model skin detection that you might find interesting.
Also, I remember reading a paper (unfortunately I can't seem to track it down) that used a very clever technique, but it required that you have the face in the field of view. The basic idea was detect the person's face, and use the skin patch detected from the face to identify the skin color automatically. Then, use a gaussian mixture model to isolate the skin pixels robustly.
Finally, Google Scholar may be a big help in searching for state of the art in skin detection. It's heavily researched in adademia right now as well as used in industry (e.g., Google Images and Facebook upload picture policies).
I have worked on something similar 2 years ago. You can try with Particle Filter (Condensation), using skin color pixels as input for initialization. It is quite robust and fast.
The way I applied it for my project is at this link. You have both a presentation (slides) and the survey.
If you initialize the color of the hand with the real color extracted from the hand you are going to track you shouldn't have any problems with black people.
For particle filter I think you can find some code implementation samples. Good luck.
It will be hard for you to find skin tone based on color only.
First of all, it depends strongly on the automatic white balance algorithm.
For example, in this image, any person can see that the color is skin tone. But for the computer it will be blue.
Second, correct color calibration in digital cameras is a hard thing, and it will be rarely accurate enough for your purposes.
You can see www.DPReview.com, to understand what I mean.
In conclusion, I truly believe that the color by itself can be an input, but it is not enough.
Well my experience with the skin modeling are bad, because:
1) lightning can vary - skin segmentation is not robust
2) it will mark your face also (as other skin-like objects)
I would use machine learning techniques like Haar training, which, in my opinion, if far more better approach than modeling and fixing some constraints (like skin detection + thresholding...)
As more robust then pixel colour you can use hand geometry model. First project model for particular gesture and the cross-correlate it with source image. Here is demo of this tchnique.