I have two cameras setted horizontally (close to each other). I have left camera cam1 and right camera cam2.
First I calibrate cameras (I want to calibrate 50 pairs of images):
I calibrate both cameras separetely using cv::calibrateCamera()
I calibrate stereo using cv::stereoCalibrate()
My questions:
In stereoCalibrate - I assumed that the order of cameras data is important. If data from left camera should be the imagePoints1 and from right camera it should be imagePoints2 or vice versa or it doesn't matters as long as order of cameras is the same in every point of program?
In stereoCalibrate - I get RMS error around 15,9319 and average reprojection error around 8,4536. I get that values if I use all images from cameras. In other case: first I save images, I select pairs where whole chessboard is visible (all of chessborad's squares is in camera view and every square is visible in its entirety) I get RMS around 0,7. If that means that only offline calibration is good and if I want to calibrate camera I should select good images manually? Or there is some way to do calibration online? By online I mean that I start capture view from camera and on every view I found chessboard corners and after stop capture view from camera I calibrate camera.
I need only four values of distortion but I get five of them (with k3). In old api version cvStereoCalibrate2 I got only four values but in cv::stereoCalibrate I don't know how to do this? Is it even possible or the only way is to get 5 values and use only four of them later?
My code:
Mat cameraMatrix[2], distCoeffs[2];
distCoeffs[0] = Mat(4, 1, CV_64F);
distCoeffs[1] = Mat(4, 1, CV_64F);
vector<Mat> rvec1, rvec2, tvec1, tvec2;
double rms1 = cv::calibrateCamera(objectPoints, imagePoints[0], imageSize, cameraMatrix[0], distCoeffs[0],rvec1, tvec1, CALIB_FIX_K3, TermCriteria(
TermCriteria::COUNT+TermCriteria::EPS, 30, DBL_EPSILON));
double rms2 = cv::calibrateCamera(objectPoints, imagePoints[1], imageSize, cameraMatrix[1], distCoeffs[1],rvec2, tvec2, CALIB_FIX_K3, TermCriteria(
TermCriteria::COUNT+TermCriteria::EPS, 30, DBL_EPSILON));
qDebug()<<"Rms1: "<<rms1;
qDebug()<<"Rms2: "<<rms2;
Mat R, T, E, F;
double rms = cv::stereoCalibrate(objectPoints, imagePoints[0], imagePoints[1],
cameraMatrix[0], distCoeffs[0],
cameraMatrix[1], distCoeffs[1],
imageSize, R, T, E, F,
TermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, 1e-5),
CV_CALIB_FIX_INTRINSIC+
CV_CALIB_SAME_FOCAL_LENGTH);
I had a similar problem. My problem was that I was reading the left images and the right images by assuming that both were sorted. Here a part of the code in Python
I fixed by using "sorted" in the second line.
images = glob.glob(path_left)
for fname in sorted(images):
img = cv2.imread(fname)
gray1 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Find the chess board corners
ret, corners1 = cv2.findChessboardCorners(gray1, (n, m), None)
# If found, add object points, image points (after refining them)
if ret == True:
i = i + 1
print("Cam1. Chess pattern was detected")
objpoints1.append(objp)
cv2.cornerSubPix(gray1, corners1, (5, 5), (-1, -1), criteria)
imgpoints1.append(corners1)
cv2.drawChessboardCorners(img, (n, m), corners1, ret)
cv2.imshow('img', img)
cv2.waitKey(100)
The only thing why is the order of cameras/image sets important is the rotation and translation you get from stereoCalibrate function. The image set you put into the function as first is taken as the base. So the rotation and translation you get is how is the second camera translated and rotated from the first camera. Of course you can just reverse the result, which is the same as switching image sets. This of course holds only if the images in both sets are corresponding to each other (their order).
This is a bit tricky, but there are few reasons why you are getting this big RMS error.
First, I'm not sure how you detect your chessboard corners, but if the whole chessboard is not visible and you provide valid chessboard model, findChessboardCorners should return false as it does not detect the chessboard. So you're able to automatically (=online) omit these "chessless" images. Of course you have to throw away also the image from second camera, even if that one is valid, to preserve correct order in both sets.
Second option is to back-project all corners for each image and calculate reprojection error for all images separately (not only for whole calibration). Then you can select, for example, best 3/4 images by this error and recalculate calibration without outliers.
Other reason could be the time sync between snapping images from 2 cameras. If the delay is big and you move with the chessboard continuously, you're actually trying to match projections of slightly translated chessboard.
If you want robust online version I'm afraid you will end up with the second option, as it helps you also get rid of blurred images, wrong detections due to light conditions and so. You just need to set the threshold (how many images you will cut of as outliers) carefully to not throw away valid data.
I'm not that sure in this field, but I would say you can calculate 5 of them and use only four coz it looks like you just cut off higher order of Taylor series. But I cannot guarantee it's true.
Related
I have a segmented image as shown here
i want to fit a curve along the top pixels of the segmented image(show as red curve) and i want to find the top point along the curve show in blue. I have already worked on basic idea like traversing through the top to bottom and collecting the top point along each column. i want to know is there any easy solution for this problem like directly taking out the boundary pixels and find the top point.I am using MATLAB for this problem
%download the image
img = logical(imread('http://i.stack.imgur.com/or2iX.png'));
%for some reason it appeared RGB with big solid borders.
%to monochrome
img = img(:,:,1);
%remove borders
img = img(~all(img,2), ~all(img,1));
%split into columns
cimg = num2cell(img,1);
%find first nonzero element per column
ridx = cellfun(#(x) find(x,1,'first'), cimg);
figure, imshow(img)
hold on
%image dim1 is Y, dim2 is X
plot(1:size(img,2),ridx-1,'r','linewidth',2)
%find top point
[yval, xval] = min(ridx);
If you want a smoother curve, try polyfit/polyval
#EDIT
If we want the line to have break at gaps between connected components, we should change the code to something like
bord_idx = sub2ind(size(img), ridx, 1:size(img,2));
regs=regionprops(bwlabel(img),'pixelidxlist');
regs_idx = struct2cell(regs);
split_step = cellfun(#(x) sum(ismember(bord_idx,x)), regs_idx);
split_step = split_step(split_step>0);
split_yvals = mat2cell(ridx',split_val);
split_xvals = mat2cell([1:size(img,2)]',split_val);
figure, imshow(img)
hold on
for k = 1:length(split_step),
plot(split_xvals{k}, split_yvals{k}, 'r', 'linewidth', 2),
end
However, the result is not ideal if one region is positioned over the other. If the "shadowed" points are needed, you should try bwtraceboundary or convexhull and find where the border turns down
As far as "simplest matlab solution" by which I think you mean built in matlab functions: imclose()->edge()->bwboundaries()->findpeaks()'on each boundary'->'filter results based on width and magnitude of peaks'. *you will need to tune all the parameters in these functions, I am just listing what would get you there if appropriately applied.
As far as processing speed is concerned, I think I would have done exactly what you did, basically collecting the top edge from a top down column search and then looking for the point of highest inflection. As soon as you start doing processing of any type, you start doing several operations per pixel which will quickly become more expensive than your initial search (just requires that your image and target are simple enough)
That being said, here are some ideas that may help:
1:If you run a sufficiently heavy closing (dilate->erode), that should fill in all that garbage at the bottom.
2: If you know that your point of interest is not at left or right of picture (boundaries), you could take the right and left edge points and calculate a slope to be applied as an offset to flatten the whole image.
3: If your image always has the large dark linear region below the peak as seen here, you could locate those edges with houghlines looking for verticals and then search only the columns between them.
4: If speed is a concern, you could do a more sophisticated search pattern than left to right, as your peak has a pretty good distribution around it which could help with faster localization of maxima.
I have this 3D array in MATLAB (V: vertical, H: horizontal, t: time frame)
Figures below represent images obtained using imagesc function after slicing the array in terms of t axis
area in black represents damage area and other area is intact
each frame looks similar but has different amplitude
I am trying to visualize only defect area and get rid of intact area
I tried to use 'threshold' method to get rid of intact area as below
NewSet = zeros(450,450,200);
for kk = 1:200
frame = uwpi(:,:,kk);
STD = std(frame(:));
Mean = mean(frame(:));
for ii = 1:450
for jj =1:450
if frame(ii, jj) > 2*STD+Mean
NewSet(ii, jj, kk) = frame(ii, jj);
else
NewSet(ii, jj, kk) = NaN;
end
end
end
end
However, since each frame has different amplitude, result becomes
Is there any image processing method to get rid of intact area in this case?
Thanks in advance
You're thresholding based on mean and standard deviation, basically assuming your data is normally distributed and looking for outliers. But your model should try to distinguish values around zero (noise) vs higher values. Your data is not normally distributed, mean and standard deviation are not meaningful.
Look up Otsu thresholding (MATLAB IP toolbox has it). It's model does not perfectly match your data, but it might give reasonable results. Like most threshold estimation algorithms, it uses the image's histogram to determine the optimal threshold given some model.
Ideally you'd model the background peak in the histogram. You can find the mode, fit a Gaussian around it, then cut off at 2 sigma. Or you can use the "triangle method", which finds the point along the histogram that is furthest from the line between the upper end of the histogram and the top of the background peak. A little more complex to explain, but trivial to implement. We have this implemented in DIPimage (http://www.diplib.org), M-file code is visible so you can see how it works (look for the function threshold)
Additionally, I'd suggest to get rid of the loops over x and y. You can type frame(frame<threshold) = nan, and then copy the whole frame back into NewSet in one operation.
Do I clearly understand the question, ROI is the dark border and all it surrounds? If so I'd recommend process in 3D using some kind of region-growing technique like watershed or active snakes with markers by imregionalmin. The methods should provide segmentation result even if the border has small holes. Than just copy segmented object to a new 3D array via logic indexing.
I will have two images.
They will be either the same or almost the same.
But sometimes either of the images may have been moved by a few pixels on either axis.
What would be the best way to detect if there is such a move going on?
Or better still, what would be the best way to manipulate the images so that they fix for this unwanted movement?
If the images are really nearly identical, and are simply translated (i.e. not skewed, rotated, scaled, etc), you could try using cross-correlation.
When you cross-correlate an image with itself (this is the auto-correlation), the maximum value will be at the center of the resulting matrix. If you shift the image vertically or horizontally and then cross-correlate with the original image the position of the maximum value will shift accordingly. By measuring the shift in the position of the maximum value, relative to the expected position, you can determine how far an image has been translated vertically and horizontally.
Here's a toy example in python. Start by importing some stuff, generating a test image, and examining the auto-correlation:
import numpy as np
from scipy.signal import correlate2d
# generate a test image
num_rows, num_cols = 40, 60
image = np.random.random((num_rows, num_cols))
# get the auto-correlation
correlated = correlate2d(image, image, mode='full')
# get the coordinates of the maximum value
max_coords = np.unravel_index(correlated.argmax(), correlated.shape)
This produces coordinates max_coords = (39, 59). Now to test the approach, shift the image to the right one column, add some random values on the left, and find the max value in the cross-correlation again:
image_translated = np.concatenate(
(np.random.random((image.shape[0], 1)), image[:, :-1]),
axis=1)
correlated = correlate2d(image_translated, image, mode='full')
new_max_coords = np.unravel_index(correlated.argmax(), correlated.shape)
This gives new_max_coords = (39, 60), correctly indicating the image is offset horizontally by 1 (because np.array(new_max_coords) - np.array(max_coords) is [0, 1]). Using this information you can shift images to compensate for translation.
Note that, should you decide to go this way, you may have a lot of kinks to work out. Off-by-one errors abound when determining, given the dimensions of an image, where the max coordinate 'should' be following correlation (i.e. to avoid computing the auto-correlation and determining these coordinates empirically), especially if the images have an even number of rows/columns. In the example above, the center is just [num_rows-1, num_cols-1] but I'm not sure if that's a safe assumption more generally.
But for many cases -- especially those with images that are almost exactly the same and only translated -- this approach should work quite well.
I am trying to figure out an algorithm for detecting the free surface from a PIV image (see attached). The major problem is that in the flow under consideration gas bubbles are injected into the fluid, these rise up due to buoyancy and tend to sit on top of the surface. I don't want these to be mistaken for the free surface (actually want the '2nd' edge underneath them) - I'm struggling to figure out how to include that in the algorithm.
Ideally, I want an array of x and y values representing coordinates of the free surface (like a continuous, smooth curve).
My initial approach was to scan the picture left to right, one column at a time, find an edge, move to the next column etc... That works somewhat ok, but fails as soon as the bubbles appear and my 'edge' splits in two. So I am wondering if there is some more sophisticated way of going about it.
If anybody have any expertise in the area of image processing/edge detection, any advice would be greatly appreciated.
Typical PIV image
Desired outcome
I think you can actually solve the problem by using morphologic methods.
A = imread('./MATLAB/ZBhAM.jpg');
figure;
subplot 131;
imshow(A)
subplot 132;
B = double(A(:,:,1));
B = B/255;
B = im2bw(B, 0.1);
imshow(B);
subplot 133;
st = strel('diamond', 5);
B = imerode(B, st);
B = imdilate(B, st);
B = imshow(B);
This gives the following result:
As you can see this approach is not perfect mostly because I picked a random value for the threshold in im2bw, if you use an adaptive threshold for the different column of your images you should have something better.
Try to work on your lighting otherwise.
I'm building a photographic film scanner. The electronic hardware is done now I have to finish the mechanical advance mechanism then I'm almost done.
I'm using a line scan sensor so it's one pixel width by 2000 height. The data stream I will be sending to the PC over USB with a FTDI FIFO bridge will be just 1 byte values of the pixels. The scanner will pull through an entire strip of 36 frames so I will end up scanning the entire strip. For the beginning I'm willing to manually split them up in Photoshop but I would like to implement something in my program to do this for me. I'm using C++ in VS. So, basically I need to find a way for the PC to detect the near black strips in between the images on the film, isolate the images and save them as individual files.
Could someone give me some advice for this?
That sounds pretty simple compared to the things you've already implemented; you could
calculate an average pixel value per row, and call the resulting signal s(n) (n being the row number).
set a threshold for s(n), setting everything below that threshold to 0 and everything above to 1
Assuming you don't know the exact pixel height of the black bars and the negatives, search for periodicities in s(n). What I describe in the following is total overkill, but that's how I roll:
use FFTw to calculate a discrete fourier transform of s(n), call it S(f) (f being the frequency, i.e. 1/period).
find argmax(abs(S(f))); that f represents the distance between two black bars: number of rows / f is the bar distance.
S(f) is complex, and thus has an argument; arctan(imag(S(f_max))/real(S(f_max)))*number of rows will give you the position of the bars.
To calculate the width of the bars, you could do the same with the second highest peak of abs(S(f)), but it'll probably be easier to just count the average length of 0 around the calculated center positions of the black bars.
To get the exact width of the image strip, only take the pixels in which the image border may lie: r_left(x) would be the signal representing the few pixels in which the actual image might border to the filmstrip material, x being the coordinate along that row). Now, use a simplistic high pass filter (e.g. f(x):= r_left(x)-r_left(x-1)) to find the sharpest edge in that region (argmax(abs(f(x)))). Use the average of these edges as the border location.
By the way, if you want to write a source block that takes your scanned image as input and outputs a stream of pixel row vectors, using GNU Radio would offer you a nice method of having a flow graph of connected signal processing blocks that does exactly what you want, without you having to care about getting data from A to B.
I forgot to add: Use the resulting coordinates with something like openCV, or any other library capable of reading images and specifying sub-images by coordinates as well as saving to new images.