I'm new to object detection and computer vision, but I'm working on a project where I'm taking pictures of disks and I'm hoping to receive a confidence level. For example, if the disk is kind of round but slightly jagged on the edges it can return "80% circle". Is this possible?
I would check out Hough Circles if I were you. I used this technique for several projects during my MS degree. It works really well and you can set the parameters to give you different margins about what does and doesnt count as a circle. It wont give you a specific confidence level, but there are ways for doing that if thats what youre trying to accomplish. That would be more of a classification problem and you could approach it different ways. Anyway, heres the resource on the Hough Circles...
https://www.pyimagesearch.com/2014/07/21/detecting-circles-images-using-opencv-hough-circles/
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I'm currently an intern at a quality inspector company. My job is to write a program that can detect faulty products (for example, missing screw). They take a picture of every single product. My idea is that I choose an image which could serve as a benchmark and I would compare the other images to that, with the SSIM score, and maybe display the faulty part with a rectangle. Is this a viable idea? (Its a strange internship, because it seems like I'm the only one who can code there...) that's why I'm asking here.
It sounds good idea if your goal is to classify different objects within images comparing benchmark image.
But in my experience, SSIM score was sensitive to angle, light or environment.
So in conclusion, if your goal is to classify different objects in images, your idea would work. But if your goal is to classify exactly same objects, it might not be able to classify.
I'm trying to learn computer vision and more specifically open-cv in python.
I want to make a program that would track my barbell in a video and show me its path. (I know apps like this exists but I want to make it myself). I tried using the Canny edge detection and the HoughCircles functions but I seem to get everything but a good result.
I have been using this code to find the edges of my image:
gray = cv.cvtColor(src=img, code=cv.COLOR_BGR2GRAY)
blur = cv.blur(gray, (2,2))
canny = cv.Canny(blur, 60, 60)
And then this code to find the circle:
circles = cv.HoughCircles(canny, cv.HOUGH_GRADIENT, dp=2, minDist=1000, circles=None,maxRadius=50)
This is the result:
Result
left = original image with detected circle // right = canny image
Is this the right way to go or should I use another method?
Train the YOLO model for the barbell to detect barbel object is better than anything you tried with OpenCV. You need at least 500 images. Those images can be found on the internet easily. This tutorial is kick start tutorial on YOLO. Let's give a try.
If you tweak the parameters of HoughCircles it may recognize the barbell [EDIT: but with more preprocessing, gamma correction, blurring etc., so better not], however OpenCV has many algorithms for such object tracking - only a region from the image has to be specified first (if that's OK).
In your case the object is always visible and is not changing much, so I guess many of the available algorithms would work fine.
OpenCV has a built-in function for selection:
initBB = cv2.selectROI("Frame", frame, fromCenter=False, showCrosshair=True)
See this tutorial for tracking: https://www.pyimagesearch.com/2018/07/30/opencv-object-tracking/
The summary from the author suggestion is:
CSRT Tracker: Discriminative Correlation Filter (with Channel and Spatial Reliability). Tends to be more accurate than KCF but slightly slower. (minimum OpenCV 3.4.2)
Use CSRT when you need higher object tracking accuracy and can tolerate slower FPS throughput
I guess accuracy is what you want, if it is for offline usage.
Can you share a sample video?
What's your problem exactly? Why do you track the barbell? Do you need semantic segmentation or normal detection? These are important questions. Canny is a very basic approach It' needs a very stable background to use it. That's why there is deep learning to handle that kind of problem If we need to talk about deep learning you can use MaskRCNN, yolvoV4, etc. there are many available solutions out there.
I have been going over this for days now and have hit a road block as I am too scared to try out my hypothesis.
I would like to find out the number of grayed rectangular boxes in this image. However, I am not sure how I can do that. I was thinking of two ways:
i. Getting area of the connected components, calculating their median and getting the number of components between a certain percentile of the area (may sound pretty strange).
ii. Making a machine learning model and find out the similar boxes in the image and count them.
However, I would like them to be more generalized so that I will need to be able to make the solution fit other images that I would need to be processed.
Here is my source Image:
Any sort of help/suggestions and even solutions would be greatly appreciated.
Thanks in advance!
Maybe you are losing a lot of image information with filtering...Do you have an unfiltered source image too? I suppose ML approach would work pretty nice then.
I noticed you could achieve better resolution if your camera is 90 rotated (If you could affect this)
My experiment involves subjecting a substance to pressure that makes the substance eventually crack. The crack grows with time and pressure applied. I have a set-up to take a picture of the substance at fixed intervals of time.
I need to measure how fast crack grows.How do I go about this? (I can code in Python).
Is there a way to measure live speed or speed of growth of crack from one frame to another?
Google drive link to series of pictures taken - https://drive.google.com/open?id=189cv8B4rm3lhSgT6OYfI_aN0Xmqi-tYi
Kindly advise.
I Tried floodFill from OpenCV as per suggestions to this question. But the returned mask is as shown:
h, w = resized.shape[:2]
mask = np.zeros((h+2, w+2), np.uint8)
seed = (int(w/2),int(h/2))
# Floodfill from point (0, 0)
num,im,mask,rect = cv2.floodFill(resized, mask, (0,0), (255,0,0), (10,)*3, (10,)*3, floodflags)
I thought if I can get the co-ordinates of the rectangle bounding box that encloses the crack, I can track its co-ordinates across frames and measure the size of the crack and eventually the speed.
I tried thresholding as below:
th, im_th = cv2.threshold(im, 100, 255, cv2.THRESH_BINARY);
This gives:
I'm unsure if this will let me filter out the background and draw a bounding box over the crack alone. Please advise.
Thanks in advance.
Depending on how slowly the crack forms, you probably don't need a video; you'll likely wind up sampling every X frames anyway, and throwing all of the extra frames away. What you want is enough frames to get "incremental" changes in the crack without getting too many frames that it becomes too computationally expensive.
If you can carefully control the lighting conditions in your setup, then you're in luck! This becomes a very simple problem. You can take a histogram of the pixels (openCV has handles for this, but so does PIL and numpy); you should get two families of color; one that is the color of the outside of the substance, and another that is biased by the shadow in the crack.
You can also try dramatically increasing the contrast in each image/frame in order to get a binary mask of the crack, or running an edge detector over the image. These techniques will lead to frames that are substantially easier to process than the raw footage. You can even feed these into a skeletonization process in order to generate a vector-based representation of the line, in XY image coordinates.
If you can't control the lighting, or the sample is a similar color to the crack, you'll probably need to use object detection techniques, but it's unlikely there's an existing "crack detector," so you may either need to build your own, or look for what other detectors serve as a good proxy for the color and shape of the forming crack.
I'd highly recommend trying the first option if at all possible; pixel and histogram math is far easier than other techniques.
I appreciate you are only just getting started but you have some issues with your video. Firstly the lighting it is not best and it is not consistent because people are moving around in front of it and casting shadows - it also doesn't illuminate the the background behind the crack best - it would be better if it was at the height of the crack and shining more into it so that it better illuminates the background behind the crack. Secondly, you could do without the camera moving part way through the experiment!
Finally, if you want to measure things you need to calibrate, which at the very least means putting a ruler in the image - or scale lines on your background at fixed intervals. If you are doing all that you may as well make life easy for yourself and put markers of a specific colour/pattern, both different, on the top and bottom of the frame plates that are applying the load.
Finally then, you want to do something like a floodfill, or a fill just within the confines of your material (probably by masking) to fill the crack with a different colour. It is then pretty simple to measure the length of the crack and the left-most extent of the crack.
With a proper segmentation approach you are going to have a detailed geometry of the object extracted from a single frame. For example:
If you process multiple frames you will be able to see geometry evolution in time. Having that it should be easy to compare polygons to find form changes, cracks, etc:
I used to work with 4K video to get all required details and good accuracy. You might not need all that data, but video is still way more flexible.
Here is a complete example: https://youtu.be/g2KyfrBtTA4
Provide some examples if you want to get more detailed recommendations.
Update
Real examples are always helpful. So you can segment a crack:
or a substance:
or both:
Basically, you need to enhance overall quality of the input (focus, background under the substance, etc).
As Mark Setchell showed, you might get unwanted background as part of the result shape (the right side of the crack), so it is better to make sure that will not happen or just try to analyze only the substance.
Anyway, your task doesn't seem to be complex. It might be trivial if you can improve image quality and do some simplifications to the environment (some specific background, etc).
I am generating images (thumbnails) from a video every 3 seconds. Now I need to discard/remove all the similar images. Is there a way I could this?
I generate thumbnails using FFMPEG. I read about various image-diff solutions like given in this SO post, but I do not want to do this manually. How and what parameters should be considered that could tell if a particular image is similar to other images present.
You can calculate the Structural Similarity Index between images and based on the score keep or discard an image. There are other measures you can use, but basically a method that returns a score. Try PIL or OpenCV
https://pillow.readthedocs.io/en/3.1.x/reference/ImageChops.html?highlight=difference
https://www.pyimagesearch.com/2017/06/19/image-difference-with-opencv-and-python/
I dont have enough reputation to comment my idea on your problem, so i will just go ahead and post it as an answer in hope of helping you.
I am quite confused about the term "similar" but since you are reffering on video frames i am going to assume that you want to avoid having "similar" frames that have been captured because of poor camera movement. If that's the case you might want to consider using salient point descriptors.
To be more specific you can detect salient points (using for instance Harris) and then use a point descriptor algorithm (such as SURF) and discard the frames that have been found to have "too many" similar points with a pre-selected frame.
Keep in mind that in order for the above process to be successful, the frames must be as sharp as possible, i guess you don't want to extract as a thubnail a blurred frame anyway. So applying a blurred images detection might be useful in your case.