I want to be able to pass on multiple images to python and detect the contour of the object in the picture. The pictures all represent money bills, hence the ROI is always going to be rectangular. Whatever ive tried, im not able to exactly detect the money bill.
I tried canny edge detection but the transparent regions on the money bill (canadian money)example of a canadian bill make it hard to detect the whole bill. Does anyone have any suggestions on how to automatically crop out just the money bill? Thanks in advance!
It is an easy matter to binarize the image, as the background is perfectly white. Use a threshold level as close as possible to white, to reduce the effect of the shadow, bottom right.
Then after connected component labelling, the convex hull gives you a nice crop polygon.
If you prefer a quadrilateral, you can pick the extreme vertices in the four cardinal directions.
Related
I want to detect dust in the circle in the image, but there is a problem in that the edges are detected even in areas that are not actually defective. I am curious to see if it helps to handle the border part.
I would like to know how to clean up the pixels of the partial parts.
is there a way to detect the number of overlapping players in a blob?
I do contour detection and each detected contour is a player but how to know if there are players that a very close to each other that they are considered one.
for example to know that
this blob
has two players like in
this image.
If you didn't want to use machine learning you could try using the area of the contours by assigning a threshold. Generally speaking 1 person should only take up so much space, beyond that point you could assume that there MUST be someone else there by the area that is taken up.
Also depending on the teams clothing colors it would be pretty easy to extract contours by color. Red+blue for 1 team and white for the other in this example. Hope this helps!
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
I am doing a dice value recognition hobby project that I want to run on a Raspberry Pi. For now, I am just learning OpenCV as that seems like the hardest thing for me. I have gotten this far, where I have dilated, eroded and canny filtered out the dice. This has given me a hierarchy of contours. The image shows the bounding rectangles for the parent contours:
My question is: how would I proceed to count the pips? Is it better to do some template matching for face values, or should I mathematically test if a pip is in a valid position within the bounding box?
There could be multiple ways to do it:
Use hole filling and then morphological operator to filter circles.
Simpler approach would be using white pixel density (% of white pixels). Five dot would have higher white pixel density.
Use image moments (mathematical property which represents shape and structure of image) to train the neural network for different kinds of dice faces.
Reference:
Morphology
http://blogs.mathworks.com/pick/2008/05/23/detecting-circles-in-an-image/
As Sivam Kalra Said, there are many valid approaches.
I would go with template matching, as it should be robust and relatively easy to implement.
using your green regions in the canny image, copy each found die face from the original grayscale image into a smaller search image. The search image should be slightly larger than a die face, and larger than your 6 pattern images.
optionally normalize the search image
use cvMatchTemplate with each of the 6 possible dice patterns (I recommend the CV_TM_SQDIFF_NORMED algorithm, but test which works best)
find and store the global minimum in the result image for each of the 6 matches
rotate the search image in ~2° steps from 0° to 90°, and repeat the template match for each step
the dice pattern with the lowest minimum over all steps is the correct one.
contour hierechy could be a good and very easy option, but you need a perpendicular vision.
so you can do it with contours but fitting circles with som threshold
(sorry about my apalling english)
We have a web-cam in our office kitchenette focused at our coffee maker. The coffee pot is clearly visible. Both the location of the coffee pot and the camera are static. Is it possible to calculate the height of coffee in the pot using image recognition? I've seen image recognition used for quite complex stuff like face-recognition. As compared to those projects, this seems to be a trivial task of measuring the height.
(That's my best guess and I have no idea of the underlying complexities.)
How would I go about this? Would this be considered a very complex job to partake? FYI, I've never done any kind of imaging-related work.
Since the coffee pot position is stationary, get a sample frame and locate a single column of pixels where the minimum and maximum coffee quantities can easily be seen, in a spot where there are no reflections. Check the green vertical line segment in the following picture:
(source: nullnetwork.net)
The easiest way is to have two frames, one with the pot empty, one with the pot full (obviously under the same lighting conditions, which typically would be the case), convert to grayscale (colorsys.rgb_to_hsv each RGB pixel and keep only the v (3rd) component) and sum the luminosity of all pixels in the chosen line segment. Let's say the pot-empty case reaches a sum of 550 and the pot-full case a sum of 220 (coffee is dark). By comparing an input frame sum to these two sums, you can have a rough estimate of the percentage of coffee in the pot.
I wouldn't bet my life on the accuracy of this method, though, and the fluctuations even from second to second might be wild :)
N.B: in my example, the green column of pixels should extend to the bottom of the pot; I just provided an example of what I meant.
Steps that I'd try:
Convert the image in grayscale.
Binarize the image, and leave only the coffee. You can discover a good threshold manually through experimentation.
Blob extraction. Blob's area (number of pixels) is one way to calculate the height, ie area / width.
First do thresholding, then segmentation. Then you can more easily detect edges.
You're looking for edge detection. But you only need to do it between the brown/black of the coffee and the color of the background behind the pot.
make pictures of the pot with different levels of coffe in it.
downsample the image to maybe 4*10 pixels.
make the same in a loop for each new live picture.
calculate the difference of each pixels value compared to the reference images.
take the reference image with the least difference sum and you get the state of your coffe machine.
you might experiment if a grayscale version or only red or green might give better results.
if it gives problems with different light settings this aproach is useless. just buy a spotlight for the coffe machine, or lighten up, or darken each picture till the sum of all pixels reaches a reference value.