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I work with image processing in OpenCV in python.
My main problem is light deflection. Can these deflections be removed with some method?
I implemented a lot of code here, but cant find this particular lights deflections effect. 1)I implemented grayscale, sobel filter, median blur, histogram analysis for plates detections, but this deflections cause that my histogram is bad for edges from sobel filtering, removing these flashes cause that it should works good.
An input image:
Use a colorspace transformation. For instance, if you transform your image to the HSV space, you'll see the "light" components in the V("value") channel:
This is the HSV image:
This is the V channel:
This is the regions of the V channel above a certain level (i.e. a thresholded image):
Now, you can use this kind of stuff to get things done by removing the high values of this V channel, then merging the channels back again. Good luck!
NOTE: as you see, I'm not giving you the code. I think that this should be easy to program if you search the documentation on OpenCV's cvtColor, split/merge or threshold methods ;)
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I have a stock of photos, of a spinning disk of varying angles. I wish to find the edge of the top of the disk. The top is colored in a distinct black color in comparison to the rest of the photo.
A
B
I first tried using canny edge detection which does a decent job, but also identifies the bottom half of the disk, which I wish to avoid.
Next idea was to use the distinct black color- perhaps by dividing the photo into domains characterized by colors\intensities- and by choosing the largest domain\most black domain or some other parameter, perhaps to isolate that black circle, and only then to use canny edge detector.
Is there any existing function that can divide a greyscale image into domains? Transferring from matlab to python, so I'm new to it's syntax and functions.
Thanks
The Canny disaster goes on !
People playing with image processing keep willing to rely on edge detection when they have beautifully segmenting scenes. With a careful selection of a binarization threshold, you can extract the ellipse as a single piece.
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I have an analog speedometer image, with the needle pointing to the current speed. I am trying to find a way to get the speed that the needle is pointing out to. I tried using HoughCircles() from OpenCV, but it is throwing an error as the image contains only speedometer and which is a semi-circle. Any resources to help me move forward will be appreciated.
Assuming the needle will have a different colour to the rest of the speedometer OR its size is distinctly larger than the rest of the elements on the speedometer (which is often the case), I'll do something like below.
Convert the image to grayscale.
Apply colour thresholding (or size-based thresholding) to detect the pixel area representing the needle.
Use HoughLines() or HoughLinesP() functions in OpenCV to fit a line to the shape you detected in Step 2.
Now it's a matter of measuring the angle of the line you generated in Step 3 (example provided here: How can I determine the angle a line found by HoughLines function using OpenCV?)
You can then map the angle of the line to the speed through a simple equation (Will need to see an image of the speedometer to generate this).
let me know how it went.
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Is there any way to remove the lower pixel valued connected components from the below picture? Like I want the horizontal and vertical lines and small already printed texts removed except the handwritten text.
I want the handwritten text to be extracted like in this image.
Is there any specific library I can use for this purpose?
I was trying Python OpenCV's library to threshold the noise pixels out of the image but that is not giving perfect result.
The image I'm getting after using threshold is this.
If I increase the threshold then it is removing pixels from the handwritten text itself.
Combine the OpenCV methods erode and dilate. They are able to first (erode) get rid of noise and then amplify the main signale (dilate).
You can develop an algorithm based "connected component analysis" to remove undesired connected components. You just need to detect connected components and remove the small ones to extract the desired ones. A case study about it can be found in here and can be helpful for you to develop the solution for your case.
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I would like to transform my RGB image to grayscale image by not using converting function but with the red green blue values . For example, if my image is totally blue, it will be converted to white if I get blue components of it and it will be black if I get red components of my RGB image. It will be done in Python via OpenCV.
Thanks in advance.
The converting function that you are referring to does the same - it weights the R,G and B channel values of each pixel, and takes the sum. Since OpenCV uses the BGR colorspace on reading images, your conversion function will be something like this-
def rgbToGray(img):
grayImg = 0.0722*img(:,:,1) + 0.7152*img(:,:,2) + 0.2126*img(:,:,3)
return grayImg
The specific weights mentioned here are taken from the ITU-R BT.709 standard used for HDTV, developed by the ATSC (https://en.wikipedia.org/wiki/Grayscale)
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I have some images i'm using for face recognition.
Some of the images are very dark.
I don't want to use Histogram equalisation on all the images only on the dark ones.
How can i determine if an image is dark?
I'm using opencv in python.
I would like to understand the theory and the implementation.
Thanks
To determin if an image is dark, simply calculate the average intensity and judge it.
The problem for the recognition although is not that the image is dark, but that it has a low contrast. A bright image with the same contrast would yield the same bad results.
Histogram equalization is a method that is used to improve images for human vision. Humans have difficulties to distinguish between very similar intensity values. A problem that a computer does not have, unless your algorithm is somehow made to mimic human vision with all its flaws.
A low contrast image bears little information. There is no image enhancement algorithm in the world that will add any further information.
I won't get into too much detail about image characterization. You'll find plenty of resources online or in text books.
A simple measure would be to calculate the standard deviation of image regions you are interested in.
You can try using CLAHE instead of the using histogram equalization. I am not sure if it will work for all your images (if you post some images I can try), however, conceptually, it should work better than "global" histogram equalization.