I have a black area around my image and I want to create a mask using OpenCV C++ that selects just this black area so that I can paint it later. How can i do that without affecting the image itself?
I tried to convert the image to grayscale and then using threshold to convert it to binary, but it affects my image since the result contains black pixels from inside the image.
Another Question : if i want to crop the image instead of paint it, how can i do it??
Thanks in advance,
I would solve the problem like this:
Inverse-binarize the image with a threshold of 1 (i.e. all pixels with the value 0 are set to 1, all others to 0)
use cv::findContours to find white segments
remove segments that don't touch image borders
use cv::drawContours to draw the remaining segments to a mask.
There is probably a more efficient solution in terms of runtime efficiency, but you should be able to prototype my solution quite quickly.
Related
I'm working with OpenCV 3.4.8 with C++11 and I'm trying to blend images together.
In this example I have 2 images (thiers mask shown in the screen belowe). I have georeference, so I can easy calculate corners of this images in the final image.
The data outside the masks are black.
My code looks like something like that:
std::vector<cv::UMat> inputImages;
std::vector<cv::UMat> masks;
std::vector<cv::Point> corners;
std::vector<cv::Size> imgSizes;
/*
here is code where I load images, create thier masks
(like in the screen above) and calculate corners.
*/
cv::Ptr<cv::detail::SeamFinder> seamFinder = new cv::detail::DpSeamFinder();
seamFinder->find(inputImages, corners, masks);
cv::Ptr<cv::detail::Blender> blender = new cv::detail:: MultiBandBlender(false);
blender->prepare(corners, imgSizes);
for(size_t i = 0; i < inputImages.size(); i++)
{
blender->feed(inputImages[i], masks[i], corners[i]);
}
cv::UMat blendedImg, outMask;
blender->blend(blendedImg, outMask);
SeamFinder gives me result like in the screen above. Finded seam lines looks good and Im very satisied form them. But the other problem occurs in the next step. The MultiBandBlender is making strange white streaks when the seam line goes on the end of the data.
This is an example:
When I don't use blender, but just use masks to cut the oryginal images and just add (cv::add()) images together with additional alpha channel (made from masks) I get very good results without any holes and strange colors, but I need to have more smoothed transition :/
Can anyone help me? When I create MultiBand Blender with smaller num_bands the white streaks are smaller, and with the num_bands = 0 the results looks like with just adding images.
I looked at feed() and blend() methods in the MultiBandBlender and I think that it is connected with Gaussian or Laplacian pyramid and the final restoring images from Laplacian pyramid in the blend() method.
EDIT1:
When Gaussian and Laplacian pyramids are created the copyMakeBorder(), which prevents the MultiBandBlender from making this white streaks when images are fully filled with the data. So in my case I think that I need to create my blender almost the same like MultiBandBlender, but copyMakeBorder() method in the feed() method change to the something that will "extend" my image inside the mask, like #AlexanderKondratskiy suggested.
Now I don't know how to achive correct "extend" similar to BORDER_REFLECT or BORDER_REFLECT_101.
I suspect your input images contain white pixels outside those masks. The white banding occurs around the areas where the seam follows the mask exactly. For Laplacian for example, pixels outside the mask do influence the final result, as each layer of a pyramid is essentially some blurring kernel on the image.
If you have some kind of good data outside the mask, keep it. If you do not, I suggest "extending" your image beyond the mask to maintain a smooth transition.
Edit:
Here's two things you could try, unless someone with more experience with OpenCV comes along.
To prove/disprove my hypothesis, fill the black region with just the average or median color within the mask. This should make the transition to the outside region less sharp, and hopefully reduce the artefacts. If that does not happen, my answer is wrong.
In terms of what is probably a good generalization of "BORDER_REFLECT" when the edge is arbitrary, you could try something like this:
Find the centroid c of the mask polygon
For each pixel p outside the mask, think of the line between it and c
Calculate point p' along this line that is the same distance inside the mask area, as p is from the mask edge. (i.e. you're reflecting along the mask edge)
Linearly interpolate the color of from the neighbors of p' (as it's position may not fall exactly in the middle of a pixel). That's the color of pixel p
As we all know,we can use the function cv::getOptimalNewCameraMatrix() with alpha = 1 to get a new CameraMatrix. Then we use the function cv::undistort() with the new CameraMatrix can get the image after dedistortion. However, I find the image after distortion is as large as the original image and some part of the image after distortion covered by black.
So my question is ：Does this mean that the original image pixel is lost? and is there any way to avoid pixel loss or get the image whose size larger than origin image with opencv?
cv::Mat NewKMatrixLeft = cv::getOptimalNewCameraMatrix(KMatrixLeft,DistMatrixLeft ,cv::Size(image.cols,image.rows),1);
cv::undistort(image, show_image, KMatrixLeft, DistMatrixLeft,NewKMatrixLeft);
The size of image and show_image are both 640*480,however from my point of view,the size of image after distortion should be larger than 640*480 because some part of it is meaningless.
Thanks!
In order to correct distortion, you basically have to reverse the process that caused the initial distortion. This implies that pixels are stretched and squashed along various directions to correct the distortion. In some cases, this would move the pixels away from the image edge. In OpenCV, this is handled by inserting black pixels. There is nothing wrong with this approach. You can then choose how to crop it to remove the black pixels at the edges.
I am using Python 2.7 and I used following Python and Matlab function for removing noises and fill holes in this image
.
1. Code to remove noise and fill holes using Python and Opencv
img = cv2.imread("binar.png",0)
kernel = np.ones((5,5),np.uint8)
open = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)
close = cv2.morphologyEx(open, cv2.MORPH_CLOSE, kernel)
Code used in python and scipy using ndimage.binary_closing:
im = cv2.imread("binar.png", cv2.IMREAD_GRAYSCALE)
open_img = ndimage.binary_opening(im)
close_img = ndimage.binary_closing(open_img)
clg = close_img.astype(np.int)
Code used in Matlab: I used imfill and bwareaopen.
The results I got is shown below:
First image from using nd.image.binary_closing. My problem is it doesn't fill all white blobs fully. We can see inbetween minor black portion are still present.
Second image from using cv2.morphologyEx. Same problem in this also, as it also has some minor white portion in between white blobs. Here I faced one more problem. It converts some white pixels into black which should not be otherwise. I mentioned those areas with red color in image 2. Red highlighted portions is connected with larger one blobs but even then they get converted into black pixels.
Third image I got from MATLAB processing in which imfill work perfectly without converting essential white pixels into black.
So, my question is, Is there any method for Python 2.7 with which I can remove noises below certain area and fill the white blobs accurately as in Matlab? One more thing is, I want to find out the centroids and areas of those final processed blobs in last for further used. I can find out these using cv2.connectedComponentsWithStats but I want to find area and centroids after removing noises and filling blobs.
Thanks.
(I think this is not duplicate because I want to do it in Python not in Matlab. )
From Matlab's imfill() documentation:
BW2= imfill(BW,locations) performs a flood-fill operation on background pixels of the input binary image BW, starting from the points specified in locations. (...)
BW2= imfill(BW,'holes') fills holes in the input binary image BW. In this syntax, a hole is a set of background pixels that cannot be reached by filling in the background from the edge of the image.
I2= imfill(I) fills holes in the grayscale image I. In this syntax, a hole is defined as an area of dark pixels surrounded by lighter pixels.
The duplicate that I flagged shows ways to accomplish the third variant usually. However for many images, the second variant will still work fine and is extremely easy to accomplish. From the first variant you see that it mentions a flood-fill operation, which can be implemented in OpenCV with cv2.floodFill(). The second variant gives a really easy method---just flood fill from the edges, and the pixels left over are the black holes which can't be reached from outside. Then if you invert this image, you'll get white pixels for the holes, which you can add to your mask to fill in the holes.
import cv2
import numpy as np
# read image, ensure binary
img = cv2.imread('image.png', 0)
img[img!=0] = 255
# flood fill background to find inner holes
holes = img.copy()
cv2.floodFill(holes, None, (0, 0), 255)
# invert holes mask, bitwise or with img fill in holes
holes = cv2.bitwise_not(holes)
filled_holes = cv2.bitwise_or(img, holes)
cv2.imshow('', filled_holes)
cv2.waitKey()
Note that in this case, I just set the starting pixel for the background at (0,0). However it's possible that there could be, e.g., a white line going down the center which would cut off this operation to stop filling (i.e. stop finding the background) for the other half of the image. The more robust method would be to go through all of the edge pixels on the image, and flood fill every time you come across a black pixel. You can accomplish this more easily with the mask parameter in cv2.floodFill(), which allows you to continue to update the mask each time.
To find the centroids of each blob, you could use contour detection and cv2.moments() to find the centroids of each contour, or you could also do cv2.connectedComponentsWithStats() like you mentioned.
I am doing a project where i need to find a red laser dot. After changing to HSV color space model and thresholding individual H,S,V components and merging it , i found a laser dot with several noise as well , now i need to subtract all other image components except for the laser dot and the noise with their respective color so that i can process those frame for further processing like template matching to get only the laser dot reducing the noises. Hope you understand the question and Thank You, any similar help is appreciated.
What you're looking to do is apply a mask to an image. A mask is an image where any positive non-zero value acts as an indicator. What you want to do is use the mask to indicate which pixels in the original image you want.
The easiest way to apply a mask is to use the cv2.bitwise_and() function, with your thresholded image as the mask:
masked_img = cv2.bitwise_and(img, img, mask=thresholded_img)
As an example, if this is my image and this is my mask, then this would be the masked image.
I am using a LPDIRECT3DTEXTURE9 to hold my image.
This is the function used to display my picture.
int drawcharacter(SPRITE& person, LPDIRECT3DTEXTURE9& image)
{
position.x = (float)person.x;
position.y = (float)person.y;
sprite_handler->Draw(
image,
&srcRect,
NULL,
&position,
D3DCOLOR_XRGB(255,255,255));
return 0;
}
According to the book I have the RGB colour shown as the last parameter will not be displayed on screen, this is how you create transparency.
This works for the most part but leaves a pink line around my image and the edge of the picture. After trial and error I have found that if I go back into photoshop I can eliminate the pink box by drawing over it with the pink colour. This can be see with the ships on the left.
I am starting to think that photoshop is blending the edges of the image so that background is not all the same shade of pink though I have no proof.
Can anyone help fix this by programming or is the error in the image?
If anyone is good at photoshop can they tell me how to fix the image, I use png mostly but am willing to change if necessary.
edit: texture creation code as requested
character_image = LoadTexture("character.bmp", D3DCOLOR_XRGB(255,0,255));
if (character_image == NULL)
return 0;
You are loading a BMP image, which does not support transparency natively - the last parameter D3DCOLOR_XRGB(255,0,255) is being used to add transparency to an image which doesn't have any. The problem is that the color must match exactly, if it is off even by only one it will not be converted to transparent and you will see the near-magenta showing through.
Save your images as 24-bit PNG with transparency, and if you load them correctly there will be no problems. Also don't add the magenta background before you save them.
As you already use PNG, you can just store the alpha value there directly from Photoshop. PNG supports transparency out of the box, and it can give better appearance than what you get with transparent colour.
It's described in http://www.toymaker.info/Games/html/textures.html (for example).
Photoshop is anti-aliasing the edge of the image. If it determines that 30% of a pixel is inside the image and 70% is outside, it sets the alpha value for that pixel to 70%. This gives a much smoother result than using a pixel-based transparency mask. You seem to be throwing these alpha values away, is that right? The pink presumably comes from the way that Photoshop displays partially transparent pixels.