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
I have an image here with a table.. In the column on the right the background is filled with noise
How to detect the areas with noise? I only want to apply some kind of filter on the parts with noise because I need to do OCR on it and any kind of filter will reduce the overall recognition
And what kind of filter is the best to remove the background noise in the image?
As said I need to do OCR on the image
I tried some filters/operations in OpenCV and it seems to work pretty well.
Step 1: Dilate the image -
kernel = np.ones((5, 5), np.uint8)
cv2.dilate(img, kernel, iterations = 1)
As you see, the noise is gone but the characters are very light, so I eroded the image.
Step 2: Erode the image -
kernel = np.ones((5, 5), np.uint8)
cv2.erode(img, kernel, iterations = 1)
As you can see, the noise is gone however some characters on the other columns are broken. I would recommend running these operations on the noisy column only. You might want to use HoughLines to find the last column. Then you can extract that column only, run dilation + erosion and replace this with the corresponding column in the original image.
Additionally, dilation + erosion is actually an operation called closing. This you could call directly using -
cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)
As #Ermlg suggested, medianBlur with a kernel of 3 also works wonderfully.
cv2.medianBlur(img, 3)
Alternative Step
As you can see all these filters work but it is better if you implement these filters only in the part where the noise is. To do that, use the following:
edges = cv2.Canny(img, 50, 150, apertureSize = 3) // img is gray here
lines = cv2.HoughLinesP(edges, 1, np.pi / 180, 100, 1000, 50) // last two arguments are minimum line length and max gap between two lines respectively.
for line in lines:
for x1, y1, x2, y2 in line:
print x1, y1
// This gives the start coordinates for all the lines. You should take the x value which is between (0.75 * w, w) where w is the width of the entire image. This will give you essentially **(x1, y1) = (1896, 766)**
Then, you can extract this part only like :
extract = img[y1:h, x1:w] // w, h are width and height of the image
Then, implement the filter (median or closing) in this image. After removing the noise, you need to put this filtered image in place of the blurred part in the original image.
image[y1:h, x1:w] = median
This is straightforward in C++ :
extract.copyTo(img, new Rect(x1, y1, w - x1, h - y1))
Final Result with alternate method
Hope it helps!
My solution is based on thresholding to get the resulted image in 4 steps.
Read image by OpenCV 3.2.0.
Apply GaussianBlur() to smooth image especially the region in gray color.
Mask the image to change text to white and the rest to black.
Invert the masked image to black text in white.
The code is in Python 2.7. It can be changed to C++ easily.
import numpy as np
import cv2
import matplotlib.pyplot as plt
%matplotlib inline
# read Danish doc image
img = cv2.imread('./imagesStackoverflow/danish_invoice.png')
# apply GaussianBlur to smooth image
blur = cv2.GaussianBlur(img,(5,3), 1)
# threshhold gray region to white (255,255, 255) and sets the rest to black(0,0,0)
mask=cv2.inRange(blur,(0,0,0),(150,150,150))
# invert the image to have text black-in-white
res = 255 - mask
plt.figure(1)
plt.subplot(121), plt.imshow(img[:,:,::-1]), plt.title('original')
plt.subplot(122), plt.imshow(blur, cmap='gray'), plt.title('blurred')
plt.figure(2)
plt.subplot(121), plt.imshow(mask, cmap='gray'), plt.title('masked')
plt.subplot(122), plt.imshow(res, cmap='gray'), plt.title('result')
plt.show()
The following is the plotted images by the code for reference.
Here is the result image at 2197 x 3218 pixels.
As I know the median filter is the best solution to reduce noise. I would recommend to use median filter with 3x3 window. See function cv::medianBlur().
But be careful when use any noise filtration simultaneously with OCR. Its can lead to decreasing of recognition accuracy.
Also I would recommend to try using pair of functions (cv::erode() and cv::dilate()). But I'm not shure that it will best solution then cv::medianBlur() with window 3x3.
I would go with median blur (probably 5*5 kernel).
if you are planning to apply OCR the image. I would advise you to the following:
Filter the image using Median Filter.
Find contours in the filtered image, you will get only text contours (Call them F).
Find contours in the original image (Call them O).
isolate all contours in O that have intersection with any contour in F.
Faster solution:
Find contours in the original image.
Filter them based on size.
Blur (3x3 box)
Threshold at 127
Result:
If you are very worried of removing pixels that could hurt your OCR detection. Without adding artefacts ea be as pure to the original as possible. Then you should create a blob filter. And delete any blobs that are smaller then n pixels or so.
Not going to write code, but i know this works great as i use this myself, though i dont use openCV (i wrote my own multithreaded blobfilter out of speed reasons). And sorry but i cannot share my code here. Just describing how to do it.
If processing time is not an issue, a very effective method in this case would be to compute all black connected components, and remove those smaller than a few pixels. It would remove all the noisy dots (apart those touching a valid component), but preserve all characters and the document structure (lines and so on).
The function to use would be connectedComponentWithStats (before you probably need to produce the negative image, the threshold function with THRESH_BINARY_INV would work in this case), drawing white rectangles where small connected components where found.
In fact, this method could be used to find characters, defined as connected components of a given minimum and maximum size, and with aspect ratio in a given range.
I had already faced the same issue and got the best solution.
Convert source image to grayscale image and apply fastNlMeanDenoising function and then apply threshold.
Like this -
fastNlMeansDenoising(gray,dst,3.0,21,7);
threshold(dst,finaldst,150,255,THRESH_BINARY);
ALSO use can adjust threshold accorsing to your background noise image.
eg- threshold(dst,finaldst,200,255,THRESH_BINARY);
NOTE - If your column lines got removed...You can take a mask of column lines from source image and can apply to the denoised resulted image using BITWISE operations like AND,OR,XOR.
Try thresholding the image like this. Make sure your src is in grayscale. This method will only retain the pixels which are between 150 and 255 intensity.
threshold(src, output, 150, 255, CV_THRESH_BINARY | CV_THRESH_OTSU);
You might want to invert the image as you are trying to negate the gray pixels. After the operation, invert it again to get your desired result.
In my previous question here's the link. According to the answer I have obtained the desired image which is white flood filled.
Now after applying the morphological operation of erosion on the white flood filled image, I get the new masked image.
Your answer helped a lot. Now what I am trying to do is that I am multiplying the new masked image with the original grayscaled image in order to get the veins pattern. But it gives me the same image as result which I get after performing erosion on the white flood filled image. After completing this step I have to apply the Laplacian function to get the veins pattern. I am attaching the original image and the result image that I want. I hope you will look into the matter.
Original Image.
Result Image.
If I am right in understanding you, you only want to extract the veins from the grayscale hand image, right? To do something like this, you would obviously multiply both of them as,
finalimg = grayimg * veinmask;
If you have done the above I think it would be more helpful to post a portion of your code so experts here might be able to point out whats wrong, also the output image that you're getting, and the one you want would also help.
I hope I understand you correctly. You have a gray scale image showing a hand (first image in your question)
You create a mask image that looks like the second image you posted.
Multiplication of both results in the mask image?
If that is the case check your values. If you work within a byte image your mask image must contain values 0 and 1, not 0 and 255 as the multiplication results for non-zero mask pixels otherwise exceed 255!
I have the following image example image
I need to recognize the Character within it. To do this i want to produce a mask to remove everything but the red box.
What is the best method i could use to do this?
Currently I use a hsv filter to produce a mask, and use the mask to crop the image to just the red square.
cvtColor(src, HSV, COLOR_BGR2HSV);
inRange(HSV, Scalar(H_MIN, S_MIN, V_MIN), Scalar(H_MAX, S_MAX, V_MAX), HSVmask);
src.copyTo(src2, HSVmask);
This produces a poor mask as it has the T itself excluded depending on how i view the image, i need a more robust approach.
-I was wondering if there is a way to make a box around the significant white region and use the box as my mask instead, however i'm not sure on how to implement that on opencv.
With the mask i apply a canny edge detection, then i find the contours and fill the contours.
My results
Contour Output and Mask
The result i get isn't robust enough, i ideally need to be able to detect a large sign on a grass field from an aerial video feed, so it needs to work with the red square with different sizes.
Is there any way in which i can improve this process in general?
The character recognition method hasn't been decided yet.
If you just want to recognize the character
Convert image to gray scale.
Since the character is white and rest of image is colored, the character in the gray image will still be white. So take a threshold and suppress all non white pixels.
Run your classifier to recognize the character.
If you want to extract the red box
Locate the red pixels, then crop image.
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.