I am currently trying very hard to figure out a way to make these four trapezoid images into one nice image. The final image should look something like this(used photoshop to make it):
That above image will be complied with four of these images:
The problem is that when I try to rotate and combine these images, the black surroundings come into the final image as well like this:
How am I supposed to rid of the blacked out area or make it transparent? I've tried using a mask but that only make the black area white instead. I have also tried using the alpha channel, but that didn't work(although maybe I was doing wrong). Any ideas on what I can do in OpenCV?
I did actually figure it out. I did it with these steps:
Create two SAME SIZED black backgrounds with numpy zeros
Put one image in each background where you want them(for me, it was left and top)
Then all you need to do is cv.add(first, second)
The reason it works is because black pixels are 0,0,0 so adding to a pixel that is, say, 25,62,34, the pixel doesn't change and thus rids of the black corner.
Related
i am quite new to Python and i try to write some code for image analysing.
Here is my initial image:
Initial image
After splitting the image in to the rgb channels, converting in to gradient, using a threshold and merging them back together i get the following image:
Gradient/Threshold
Now i have to draw contours around the black areas and get the size of the surrounded areas. I just dont know how to do it, since my trials with find/draw.contours in opencv are not succesfull at all.
Maybe someone also knows an easier way to get that from the initial image.
Hope someone can help me here!
I am coding in Python 3.
Try adaptive thresholding on the grayscale image of the input image.
Also play with the last two parameters of the adaptive thresholding. You will find good results as I have shown in the image. (Tip: Create trackbar and play with value, this will be quick and easy method to get best values of these params.)
Have you any ideas how can I with python and OpenCV get subimages on original image by that mask? I need separated subimages of every white area.
Because it's not rects it's hard to get them separated.
I think you are looking for connectedComponentsWithStats(), which will give you connected components (i.e., one label per white area). The result will be a labeled image with a separate label for each component.
From this, it is easy to extract the part of the image with a specific label.
When we use some image processing library to rotate an image, the rotated image will always contains some black area. For example, I use the following python code to rotate an image:
from scipy import misc
img = misc.imread('test.jpg')
img = misc.imrotate(img,15)
misc.imsave('rotated.jpg')
The image is as follows:
My question is: how can I rotate an image without producing black area. I believe there exists some interpolation method to compensate for the missing area, which makes the image more natural.
It will be appreciated if anyone can provide a python code to achieve my task.
If you want to 'clone' or 'heal' the missing areas based on some part of the background, that's a complex problem, usually done with user intervention (in tools like Photoshop or GIMP).
Alternatives would be to fill the background with a calculated average colour - or just leave the original image. Neither will look 'natural' though.
The only approach that will work for all images will be to crop the rotated image to the largest rectangle within the rotated area. That will achieve your objective of having no black areas and looking natural, but at the cost of reducing the image size.
isnt there a simple paint fill function in your "some image library" ?, simple do that at all 4 corner pixels and then make it white or so.
I have a set of grayscale images, like this:
This is an example image as I cannot post the original image. Each image has an area with a texture, a pure white watermark (pos), and lots of unwanted black space.
Ideally this image should be cropped to:
The watermark can be slightly different in each image, but is always very thin pure white text.
The pictures can look very different, here is another example
this one only needs cropping on the left
another one:
this one needs to be cropped on top and bottom:
and another one
this one needs to be cropped at the top and right. Note that I left the watermark in this picture. Ideally the watermark would be removed as well, but I guess it is easier without.
here is a picture of the watermark how it looks in reality.
The images vary in size, but are usually large (over 2000x2000).
I am looking for a solution in python (cv2 maybe).
my first idea was to use something like this:
Python & OpenCV: Second largest object
but this solution code fails for me
I work in C# and C++ and don't work in python but can suggest you the logic.
You need to run two scan of the image, one row wise and other columns wise.
Since you said the unwanted part of image is always black, just read the pixel values in both scan. If the color of all the pixels in a certain row is black then you can elemminate or delete that row. Similar steps can be followed for column wise scanning.
Now we cannot just eleminate the rows and columns so easily, so just note down the redundant rows and columns and then you can crop your image using following code:( I will code in C# with emgucv library but it is easy to understand for python)
Mat original_image = new Mat();
Rect ROI = new Rect(x,y,width,height);
Mat image_needed_to_crop = new Mat(original_image,ROI);
This code just extracts only the region of interest from the original image.
I'm trying to count the number of distinct colors in an image using img.getcolors(). However, this does not distinguish between transparent and black pixels - they both report as one pixel colored [0,0,0].
How can I distinguish between transparent and black pixels? Many of the images I need to process are largely black on a transparent background.
For test purposes, I'm using a PNG I created which is half transparent, half black. len(img.getcolors()) is 1.
Embarrassing answer:
I was using convert('RGB') before calling getcolors(). Without the conversion, a 4-value tuple comes back with an alpha channel.