So, I have the following Image
I want to detect the white lump in that image ignoring the thin white line.
I need help with the approach that I can use to detect it with OpenCV python.
Please help, I don't want the code but need a direction on how to go about it.
Thank you
I suggest the following solution:
Make it binary.
Perform opening morphological transformation.
Related
how can I extract white paper(that contains Dots 'braille language') from image in image processing ?
I tried a lot of things but I want to extract it completely so I can do threshold on the result.
One way you might implement braille detection is using a Hough transform from OpenCV's library.
There is a function cv.HoughCircles() that allows you to specify a radius for circles you are looking for, assuming each sheet of braille paper will be the same distance from your camera you have a known tolerance for that.
I would just be worried about the clarity of white braille bumps on white background paper, which perhaps could be fixed using a perpendicular light source to create some shadows for each bump.
I need your advice, guys! So I am trying to create a mask for a football (i.e. soccer) pitch, and if I only use filtering by the most common hue/saturation/value values, I get the following image
As you can see, the desired part is inside the grey boundary I drawn and there is a lot of noise here - trees, lines, external objects, and of course I would like to get rid of it. The desired outcome is something similar to this:
I thought about an algorithm that would transform the first image into another by analyzing each pixel's surrounding and color it white if more than threshold% of pixels into a (x, x) square is white, otherwise black.
Do you know if there is an implementation on openCV or similar libraries for this or I should build it from scratch?
Also, maybe you can propose other way to deal with the noise and external objects? I already tried the morphological transform and blurring techniques, but either I don't do it right or it doesn't work well for my problem.
Thank you in advance for your advice!
I actually found an easy implementation of the algo I proposed - I simply use cv2.blur on the image and then filter with cv2.inRange, so it does exactly what I wanted it to do.
How to remove the shadows of the seeds? Also I would like to know if there is a way to change the color of all the seeds to red colour?
It seems rather easy to detect the seeds since your background is homogeneous. You can start by some simple image processing (contrast enhancement, thresholding, contour detection) to detect the seeds and then you can plot red blobs (with the same area as the detected regions) on the original image. As for the shadows, you can check this question (How to remove the shadow in image by using openCV?).
I think you can solve with this paper and it will make you interesting.
The algorithm described there works quite well and this will be a good example for you in using opencv.
And you can find the source code here
Regards.
I am working on an application where I need feature like Cam Scanner where document is to be detected in an image. For that I am using Canny Edge detection followed by Hough Transform.
The results look promising but the text in the document is creating issues as explained via images below:
Original Image
After canny edge detection
After hough transform
My issue lies in the third image, the text in original mage near the bottom has forced hough transform to detect the horizontal line(2nd cluster from bottom).
I know I can take the largest quadrilateral and that would work fine in most cases, but still I want to know any other ways where in this processing I can ignore the effect of text on the edges.
Any help would be appreciated.
I solved the issue of text with the help of median filter of size 15(square) in an image of 500x700.
Median filter doesn't affect the boundaries of the paper, but can help eliminate the text completely.
Using that I was able to get much more effective boundaries.
Another approach you could try is to use thresholding to find the paper boundaries. This would create a binary image. You can then examine the blobs of white pixels and see if any are large enough to be the paper and have the right dimensions. If it fits the criteria, you can find the min/max points of this blob to represent the paper.
There are several ways to do the thresholding, including iterative, otsu, and adaptive.
Also, for best results you may have to dilate the binary image to close the black lines in the table as shown in your example.
I am new to openCV and python both. I am trying to count people in an image. The image is supposed to be captured with an overhead camera or the way a CCTV camera is placed.
I have converted the colored image into binary image and then inverted the binary image. Then I used bitwise OR on original and inverted binary image so that the background is white and the people are colored.
How to count these people? Is it necessary to use a classifier or can i just count the contours ,if yes then how to count them?
Plus there are some issues with the technique I'm using.
Faces of people are light in color so sometimes only hair are getting extracted.
The dark objects other than people also get extracted.
If the floor is dark it won't give the binary image that is needed.
So is there any other method to achieve what I'm trying to do here?
Not sure but it may worth to check there.
It explain how to perform face recognition using openCV and python in pictures and extand it to webcam here, it's not quite what your looking for but may give you some clue/