Given an image with say just two coloured points in it.. Is it possible to crop the image from the coordinates of the first colured point to the coordinates of the second coloured point .
A sample image where i have to crop between two green points
This is possible, if the colored points have a distinct range of color when compared with the rest of the image.
Algorithm:
1. Convert the image to HSV color space
2. Scan the image while looking for pixels in the range of hue and saturation of the color/s of the points.
3. Record minimum and maximum X,Y coordinates of the points that match.
4. calculate the bounding box of the region using the coordinates.
5. Crop the image using the bounding box.
You can try to follow these steps and edit the question with code if/when you come up with errors. Uploading a sample image somewhere and linking to it will help us provide better answers.
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I'm trying to blindly detect signals in a spectra.
one way that came to my mind is to detect rectangles in the waterfall (a 2D matrix that can be interpret as an image) .
Is there any fast way (in the order of 0.1 second) to find center and width of all of the horizontal rectangles in an image? (heights of rectangles are not considered for me).
an example image will be uploaded (Note I know that all rectangles are horizontal.
I would appreciate it if you give me any other suggestion for this purpose.
e.g. I want the algorithm to give me 9 center and 9 coordinates for the above image.
Since the rectangle are aligned, you can do that quite easily and efficiently (this is not the case with unaligned rectangles since they are not clearly separated). The idea is first to compute the average color of each line and for each column. You should get something like that:
Then, you can subtract the background color (blue), compute the luminance and then compute a threshold. You can remove some artefact using a median/blur before.
Then, you can just scan the resulting 1D array filled with binary values so to locate where each rectangle start/stop. The center of each rectangle is ((x_start+x_end)/2, (y_start+y_end)/2).
I have two images, one that is a monochrome one which is a mask and another one with full color. What I need to do is find the CGRect of the mask (white pixels) in the other full color one.
What I did is to first find the contour of the mask using the Vision framework. Now, this returns a CGPath which is normalised. How can I translate this path into coordinates to the other image? Both have been scaled the same way to make them the same size so the translation should be "easy" but I can't figure it out.
I am learning JavaCV and want to extract part of images dynamically based on color.
As identification I am outlining the region which I need to extract with a color. Is there anyway I can do extract ROI based on color outline. Any help appreciated.
Here is the Sample Image
it is quite simple. Since your figure has 4 corners hence you ought to follow the following steps.
1.identify the orientation of the image and store the points in a MatofPoint2f in a specific order.
(clock wise or anti clockwise- For this you can use Math.atan2(p1(y)-centerpoint(y),p1(x)-centerpoint(x)) and then sort the points according to the result of the equation. find the center point by finding the avg all the xcoords and y coords or any method you prefer).
2.Create a MatofPoint2f containing the corner coords of the result image size you want the cropped image in.
3.use Imgproc.getPerspectiveTransform() to perform the cropping.
4.Finally use Imgproc.warpPerspective() to obtain the output that is desired.
And for creating the border of the ROI the best way to go is to threshold the image by using some specific range so as to extract only those parts of the spectrum which is required.
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.
I want to overlay an image in a given image. I have created a mask with an area, where I can put this picture:
Image Hosted by ImageShack.us http://img560.imageshack.us/img560/1381/roih.jpg
The problem is, that the white area contains a black area, where I can't put objects.
How can I calculate efficiently where the subimage must to put on? I know about some functions like PointPolygonTest. But it takes very long.
EDIT:
The overlay image must put somewhere on the white place.
For example at the place from the blue rectangle.
Image Hosted by ImageShack.us http://img513.imageshack.us/img513/5756/roi2d.jpg
If I understood correctly, you would like to put an image in a region (as big as the image) that is completely white in the mask.
In this case, in order to get valid regions, I would apply an erosion to the mask using a kernel of the same size as the image to be inserted. After erosion, all valid regions will be white.
The image you show however has no 200*200 regions that is entirely white, so I must have misunderstood...
But if you what to calculate the region with the least black in the mask, you could apply a blur instead of an erosion and look for the maximal intensity pixel in the blurred mask.
In both case you want to insert the sub-image so that its centre is on the position maximal intensity pixel of the eroded/blurred mask.
Edit:
If you are interested in finding the region that would be the most distant from any black pixel to put the sub-image, you can define its centre as the maximal value of the distance transform of the mask.
Good luck,