I have scanned images of A5 pages. In the scan they only take up half the page and I want to remove the parts with nothing in it.
I have used canny edge detection to get the edged image and now was wondering how I could crop it so the resulting image is the area within the 4 corner pixels.
image for reference
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Given a 3D cuboid and an equirectangular image, I want to map the image onto the inside of a cuboid (only the side faces, not top or bottom). To do this I want to create textures for each face of the cuboid from the equirectangular image.
The image is from a room and I have labelled the top and bottom corners in the panorama which correspond to corners of my cuboid. The image is not from the inside of the room and I wish to create a texture for each face.
I tried solution given here: Convert 2:1 equirectangular panorama to cube map
But that is for a cube, where each texture is the same size. I have looked at other resources but I feel I need another explanation such that I can fully understand how to do this.
Thanks!
I have an image with a gentle gradient background and sharp foreground features that I want to detect. I wish to find green arcs in the image. However, the green arcs are only green relative to the background (they are semitransparent). The green arcs are also only one or two pixels wide.
As a result, for each pixel of the image, I want to subtract the average color of the surrounding pixels. The surrounding 5x5 or 10x10 pixels would be sufficient. This should leave the foreground features relatively untouched but remove the soft background, and I can then select the green channel for further processing.
Is there any way to do this efficiently? It doesn't have to be particularly accurate, but I want to run it at 30Hz on a 1080p image.
You can achieve this by doing a simple box blur on the image and then subtracting that from the original image:
blur(img,blurred,Size(15,15));
diff=img-blurred;
I tried this on a 1920x1080 image and the blur took 25ms and the subtract took 15ms on a decent spec iMac.
If your background is not changing fast, you could calculate the blur over the space of a few frames in a second thread and keep re-using it till you recalculate it again a few frames later then you would only have 15ms subtraction to do for each of your 30fps rather than 45ms of processing.
Depending on how smooth the background gradient is, edge detection followed by dilation might work well for you.
Edge detection will output the 3 lines that you've shown in the sample and discard the background gradient (again, depending on smoothness). Dilating the image then will thicken the edges so that your line contours are more significant.
You can superimpose this edge map on your original image as:
Mat src,edges;
//perform edge detection on src and output in edges.
Mat result;
cvtColor(edges,edges,cv2.COLOR_GRAY2BGR);//change mask to a 3 channel image
subtract(edges,src,result);
subtract(edges,result);
The result should contain only the 3 lines with 3 colours. From here on you can apply color filtering to select the green one.
I'm required to create a map of galaxies based on the following image,
http://www.nasa.gov/images/content/690958main_p1237a1.jpg
Basically I need to smooth the image first using a mean filter then apply thresholding to the image.
However, I'm also asked to detect only large galaxies in the image. So what should I adjust the smoothing mask or thresholding in order to achieve that goal?
Both: by smoothing the picture first, the pixels around smaller galaxies will "blend" with the black space and, thus, shift to a lower intensity value. This lower intensity can then be thresholded, leaving only the white centres of bigger galaxies.
Currently i am trying to read a square card by using an OCR engine. But before processing image, i want during capturing card image, user should only capture card not other surrounding noise. So for that i looked for overlay & able to create a overlay on camera screen but it is not that useful. So right now i am looking forward some help, how to draw a contour / a outline around a square card when user see it in camera eye as this example.
for ex.
Any body has done this before ?
At first use cvCanny to detect all contours on your image.
Then you can use Standard Hough Line Transform for detection of all lines on the image.
Then you can calculate their intersections and find 4 points: the leftmost and the rightmost of the top and the bottom of the image.
You can ignore small lines which are on the the left and right borders of the image by changing the property of threshold.
I am trying to crop a picture on right on along the contour. The object is detected using surf features and than i want to crop the image of extactly as detected.
When using crop some outside boundaries of other object is includes. I want to crop along the green line below. OpenCV has RotatedRect but i am unsure if its good for cropping.
Is there way to perfectly crop along the green line
I assume you get you get your example from http://docs.opencv.org/doc/tutorials/features2d/feature_homography/feature_homography.html, so what you can do is to find the minimum axis aligned bounding box around the green bounding box, crop it from the image, use the inverted homography (H.inv()) matrix to transform that sub image into a new image (call cv::warpPerspective), and then crop your green bounding box (it should be axis aligned in your new image).
You can get the equations of the lines from the end points for each. Use these equations to check whether any given pixel lies within the green box or not i.e. does it lie between the left and right lines and between the top and bottom lines. Run this over the entire image and reset anything that doesn't lie within the box to black.
Not sure about in-built functionality to do this, but this simple methodology is guaranteed to work. For higher accuracy, you may want to consider sub-pixel checks.