Is there a way to cut the part of crosshairs that is away from the axis lines? See the image below for my example.
This is what i have achieved so far:
Related
I am refining an algorithm for collision avoidance I wrote for an ASV using a monocular camera.
In order to have a simple yet effective system, the algorithm relies on edge detection using a Canny filter, after proper erosion/dilation to remove noise and shading spots.
The final image is a black and white frame where, starting from the bottom, the first contour found and the upper part gets coloured in white while the rest is black: so black can be interpreted as "free space" whereas white as "obstacle".
The rover should use this reactive collision avoidance system to steer accordingly, avoiding close obstacles.
Now I am having some problems to decide how to actually find the way to go.
This is my approach so far.
The frame is split in columns whose width is exactly 1° relative bearing, then columns are grouped in three super-columns (corresponding to the directions turn left, go on, turn right) and the robot steers on the direction where the most black space is found.
Do you have any other idea on how to find where to steer/proceed given the output of this algorithm? Thank you
Image processing example
initial frame
filtering
occupancy frame
column divisions
command -> steer left
I am trying to develop box sorting application in qt and using opencv. I want to measure width and length of box.
As shown in image above i want to detect only outermost lines (ie. box edges), which will give me width and length of box, regardless of whatever printed inside the box.
What i tried:
First i tried using Findcontours() and selected contour with max area, but the contour of outer edge is not enclosed(broken somewhere in canny output) many times and hence not get detected as a contour.
Hough line transform gives me too many lines, i dont know how to get only four lines am interested in out of that.
I tried my algorithm as,
Convert image to gray scale.
Take one column of image, compare every pixel with next successive pixel of that column, if difference in there value is greater than some threshold(say 100) that pixel belongs to edge, so store it in array. Do this for all columns and it will give upper line of box parallel to x axis.
Follow the same procedure, but from last column and last row (ie. from bottom to top), it will give lower line parallel to x axis.
Likewise find lines parallel to y axis as well. Now i have four arrays of points, one for each side.
Now this gives me good results if box is placed in such a way that its sides are exactly parallel to X and Y axis. If box is placed even slightly oriented in some direction, it gives me diagonal lines which is obvious as shown in below image.
As shown in image below i removed first 10 and last 10 points from all four arrays of points (which are responsible for drawing diagonal lines) and drew the lines, which is not going to work when box is tilted more and also measurements will go wrong.
Now my question is,
Is there any simpler way in opencv to get only outer edges(rectangle) of box and get there dimensions, ignoring anything printed on the box and oriented in whatever direction?
I am not necessarily asking to correct/improve my algorithm, but any suggestions on that also welcome. Sorry for such a big post.
I would suggest the following steps:
1: Make a mask image by using cv::inRange() (documentation) to select the background color. Then use cv::not() to invert this mask. This will give you only the box.
2: If you're not concerned about shadow, depth effects making your measurment inaccurate you can proceed right away with trying to use cv::findContours() again. You select the biggest contour and store it's cv::rotatedRect.
3: This cv::rotatedRect will give you a rotatedRect.size that defines the width en the height of your box in pixels
Since the box is placed in a contrasting background, you should be able to use Otsu thresholding.
threshold the image (use Otsu method)
filter out any stray pixels that are outside the box region (let's hope you don't get many such pixels and can easily remove them with a median or a morphological filter)
find contours
combine all contour points and get their convex hull (idea here is to find the convex region that bounds all these contours in the box region regardless of their connectivity)
apply a polygon approximation (approxPolyDP) to this convex hull and check if you get a quadrangle
if there are no perspective distortions, you should get a rectangle, otherwise you will have to correct it
if you get a rectangle, you have its dimensions. You can also find the minimum area rectangle (minAreaRect) of the convexhull, which should directly give you a RotatedRect
I have some objects (spheres or cubes or something else) and I want to cut them when the protude out of my bounding-box and put the cut off part back in the szene on the opposit side of the bounding-box.
Cutting the objects works fine and storeing the cut of parts works fine too.
But I do not know how i can move them, apart from checking every possible position (cut at the x-axis, cut at the x- and the y-axis, ....) with mass-if-statements...
Is there a better way?
Another tricky question. What you can see here is my physical pyramid built with 3 leds which form a triangle in 1 plane and another led in the mid center, about 18mm above the other 3. The 4th one makes the triangle to a pyramid. (You may can see it better if you look on the right triangle. This one is rotated about the horizontal achsis, and you can see a diode on a stick very well).
The second picture shows my running program. The left box shows the raw picture of the leds (photo with ir-filter). The picture in the center shows that my program found the points and is also able to tell which point is which, based on some conditions (like C is always where the both lines with maximal distance betweens diodes intersect; and the both longest lengths are always a and b). But dont care about this, i know the points are 100% correctly found.
Then on the right picture are some calculated values, like the height between C and c and so on. I would be able to calculate more, but i didnt bother to care for now, cause I am stuck.
I want to calculate the pyramids rotation and translation in the 3 dimensional space.
The yellow points are the leds after rotation arround an axis throught the center of the triangle in camera z- direction. So now i do not have to worry about this, when calculating the other 2. The Rotation arround the horizontal axis, and the rotation arround the vertical axis. I could easily calculate this with the lengths of the distance from the center of the triangle to the 4th diode (as you can see the 4th diode moves on the image plane with rotation), or the lengths of the both axes.
But my problem is the unknown depth.
It affects all lengths (a,b,c, and also the lengths from the center to the 4th diode if we call this d and e). I know the measurments of the real pyramid, with a tolerance of +-5% or so, but they get also affected by the zoom. So how do i deal with this?
I thought of an equation with a ratio between something with the lengths of the horizontal axis, the length of the vertical axis, the angles alpha, beta and gamma, and the lengths d and e.
Alpha, beta and gamma only get affected by rotation arround the axes (which i want to know. i want to know the rotation and the zoom), where a rotation arround one axis has the opposite effect than a rotation arround the other. So if you rotate arround both axes in the same angle, the ratio between the length of the axes is the same as before.
The zoom (real: how close it is to the camera; what i want to know in 1st place: multiplication factor 2x, 3x,0.5, 0,4322344,.....) does not affect the angles, but all the lengths: a,b,c,d,e,hc (vertical length of axis), hx (i have not calculated it yet, but it would be easy. the name hx can vary, i just thought of something random right now; it is the length of the horizontal axis) in the same way (i guess).
You see i have thought of many, but i think i am too dumb.
So, is there any math genius out there wo can give me the right equations, for either the rotation OR/AND the zoomfactor?
(i also thought about using Posit/Downhill- Simplex, and so on, but this would be the nicest, since i already know so much, like all Points, and so on and so on)
Please, please, i need your help really bad! I am writing this in C++ and with help of OpenCV if you need to know, but i think its more a mathematical problem.
Thanks in advance!
Ah, and Alpha seems to be always the same as Beta!
Edit: Had to delete the second picture
Have a look to Boost Geometry or here also
Have a look at SolvePnP() in OpenCV. Even if you don't use it directly, the documentation has citations for the methods used.
Good Morning everybody,
Today I wanna concern about the topic "Image Manipulation in C++".
So far I am able to filter all the noisy stuff out of the picture and change the color to black and white.
But now I have two questions.
First Question:
Below you see a screenshot of the image. What is the best way to find out how to rotate the text. In the end it would be nice if the text is horizontal. Does anybody have a good link or an example.
Second Question:
How to go on? Do you think I should send the image to an "Optical Character Recognizer" (a) or should I filter out each letter (b)?
If the answer is (a) what is the smallest ocr lib? All libs I found so far seem to be overpowered and difficult to implement in an existing project. (like gocr or tesseract)
If the answer is (b) what is the best way to save each letter as an own image? Shoul i search for an white pixel an than go from pixel to pixel an save the coordinates in an 2D Array? What is with the letter "i" ;)
Thanks to everybody who will help me to find my way!Sorry for the strange english above. I'm still a language noob :-)
The usual name for the problem in your first question is "Skew Correction"
You may Google for it (lot of references). A nice paper here, showing for example how to get this:
An easy way to start (but not as good as the previously mentioned), is to perform a Principal Component Analysis:
For your first question:
First, Remove any "specs" of noisy white pixels that aren't part of the letter sequence. A gentle low-pass filter (pixel color = average of surrounding pixels) followed by a clamping of the pixel values to pure black or pure white. This should get rid of the little "dot" underneath the "a" character in your image and any other specs.
Now search for the following pixels:
xMin = white pixel with the lowest x value (white pixel closest to the left edge)
xMax = white pixel with the largest x value (white pixel closest to the right edge)
yMin = white pixel with the lowest y value (white pixel closest to the top edge)
yMax = white pixel with the largest y value (white pixel closest to the bottom edge)
with these four pixel values, form a bounding box: Rect(xMin, yMin, xMax, yMax);
compute the area of the bounding box and find the center.
using the center of the bounding box, rotate the box by N degrees. (You can pick N: 1 degree would be an ok value).
Repeat the process of finding xMin,xMax,yMin,yMax and recompute the area
Continue rotating by N degrees until you've rotated K degrees. Also rotate by -N degrees until you've rotated by -K degrees. (Where K is the max rotation... say 30 degrees). At each step recompute the area of the bounding box.
The rotation that produces the bounding box with the smallest area is likely the rotation that aligns the letters parallel to the bottom edge (horizontal alignment).
You could measure the height to each white pixel from the bottom and find how much the text is leaning. It's a very simple approach but it worked fine for me when I tried it.