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)
Related
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/
for my school project, I need to find images in a large dataset. I'm working with python and opencv. Until now, I've managed to find an exact match of an image in the dataset but it takes a lot of time even though I had 20 images for the test code. So, I've searched few pages of google and I've tried the code on these pages
image hashing
building an image hashing search engine
feature matching
Also, I've been thinking to search through the hashed dataset, save their paths, then find the best feature matching image among them. But most of the time, my narrowed down working area is so much different than what is my query image.
The image hashing is really great. It looks like what I need but there is a problem: I need to find an exact match, not similar photos. So, I'm asking you guys, if you have any suggestion or a piece of code might help or improve the reference code that I've linked, can you share it with me? I'd be really happy to try or research what you guys send or suggest.
opencv is probably the wrong tool for this. The algorithms there are geared towards finding similar matches, not exact ones. The general idea is to use machine learning to teach the code to recognize what a car looks like so it can detect cars in videos, even when the color or form changes (driving in the shadow, different make, etc).
I've found two approaches work well when trying to build an image database.
Use a normal hash algorithm like SHA-256 plus maybe some metadata (file or image size) to find matches
Resize the image down to 4x4 or even 2x2. Use the pixel RGB values as "hash".
The first approach is to reduce the image to a number. You can then put the number in a look up table. When searching for the image, apply the same hashing algorithm to the image you're looking for. Use the new number to look in the table. If it's there, you have a match.
Note: In all cases, hashing can produce the same number for different pictures. So you have to compare all the pixels of two pictures to make sure it's really an exact match. That's why it sometimes helps to add information like the picture size (in pixels, not file size in bytes).
The second approach allows to find pictures which very similar to the eye but in fact slightly different. Imagine cropping off a single pixel column on the left or tilting the image by 0.01°. To you, the image will be the same but for a computer, they will by totally different. The second approach tries to average small changes out. The cost here is that you will get more collisions, especially for B&W pictures.
Finding exact image matches using hash functions can be done with the undouble library (Disclaimer: I am also the author). It works using a multi-step process of pre-processing the images (grayscaling, normalizing, and scaling), computing the image hash, and the grouping of images based on a threshold value.
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.
So I have quite an interesting image segmentation problem. Here, I have scraped instagram photos which are stacked vertically.
see image here(too long to post): https://imgur.com/a/gPr2J
What I am trying to do is quite simple. I just want to extract each post image from the screenshot, and save it to some directory. I am trying to find ways to make this work, like cropping by pixel color at a certain height but none of it is working perfectly.
Any method that would quickly segment this image. Python BTW.
I think you should start with segmenting each post out. Use the gaps between each post (which are always uniform) to segment each post out.
Then approach capturing the image inside the post - breaking this down into 2 different problems will make your algorithm simpler in my opinion.
I have a few ideas, not entirely sure how will they work for you, but thought they might give you some leads to try out:
1) All these instagram images seems to have a "heart" shaped icon just below the image you want to extract. Maybe figuring out detecting the heart shape might be good idea? Once you have found the "heart" you can look for the image just above it. Since it is a UI, my hope is that all the images that you want to extract will be a fixed number of pixels above the "heart". Moreover, they should also have the same height and width, I think.
2) Another possible idea is to find the edges in the image. Again, the images you want to extract seem to have a strong edge with respect to their background (but so does text and other UI elements). However, these edges should ideally have the largest area (which is also mostly fixed) enclosed between them. So, after finding the edges, you can use the find contours in function in opencv and then filter out the contours which have an area greater than a threshold. Have you tried something like this?