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
Related
I struggle to find a solution to the following problem :
I used opencv to mark all connected white pixels with a unique label.
Now i got a group of those elements.
Those objects often are 90% rectangular but most of the time contain some extra lines and stuff.
Im searching for a algorithm which does thr following :
-get biggest Rectangle out of image(within the same label)
- fast performance
-maybe even filter, larget rectangle which contains at least xx% Pixels with the same label
Maybe someone can help me
Thanks a lot
Edit: Example Pictures(in this case fo licence plate location):
my desired output of the algorithm would be the rectangle of the plate(and of curse all other rectangles in the image, im gonna filter them later)
Important the rectangles may be rotated !
My suggestion
make sure to fill small holes either by blob analysis or mathematical morphology;
compute the distance map in the white areas;
binarize the distance map with a threshold equal to the half plate height.
The rectangles will appear as line segments long as the plate width minus the plate height. You may locate them by fitting rotated rectangular bounding boxes; they must have a large aspect ratio.
I've an image like this one ![enter image description here][1]. The non-black part is expanded at each iteration. So, after a certain point, I need to enlarge the final image so the non-black one can fit in. For now, what I'm doing is to find the contour of the non-black image,find the bounding box of the contours and check the width/height of the box. At a first time it works, but after some iterations my program finds a bounding box of size 1 (it seems that it doesn't find any contour). What the problem could be?
Ps: the program is a mosaic from a video file, I followed the opencv tutorial for find homography and other stuff.
EDIT
Sorry but I had to remove images
Just a suggestion:
It's easier to simply iterate through each element in the matrix and record the coordinates of the uppermost, bottommost, leftmost and rightmost non-zero elements. These will be the four corners of your up-right bounding rectangle. Of course it is not necessarily the rectangle of the minimum area enclosing the non-zero pixels (not a rotated rectangle), but further can be used as a ROI.
I am trying to detect a ball in an filtered image.
In this image I've already removed the stuff that can't be part of the object.
Of course I tried the HoughCircle function, but I did not get the expected output.
Either it didn't find the ball or there were too many circles detected.
The problem is that the ball isn't completly round.
Screenshots:
I had the idea that it could work, if I identify single objects, calculate their center and check whether the radius is about the same in different directions.
But it would be nice if it detect the ball also if he isn't completely visible.
And with that method I can't detect semi-circles or something like that.
EDIT: These images are from a video stream (real time).
What other method could I try?
Looks like you've used difference imaging or something similar to obtain the images you have..? Instead of looking for circles, look for a more generic loop. Suggestions:
Separate all connected components.
For every connected component -
Walk around the contour and collect all contour pixels in a list
Suggestion 1: Use least squares to fit an ellipse to the contour points
Suggestion 2: Study the curvature of every contour pixel and check if it fits a circle or ellipse. This check may be done by computing a histogram of edge orientations for the contour pixels, or by checking the gradients of orienations from contour pixel to contour pixel. In the second case, for a circle or ellipse, the gradients should be almost uniform (ask me if this isn't very clear).
Apply constraints on perimeter, area, lengths of major and minor axes, etc. of the ellipse or loop. Collect these properties as features.
You can either use hard-coded heuristics/thresholds to classify a set of features as ball/non-ball, or use a machine learning algorithm. I would first keep it simple and simply use thresholds obtained after studying some images.
Hope this helps.
I am using opencv to detect shapes, I have to detect a shape like this
this will be a hand drawn picture, and i am using kinect to detect the picture by checking on frames. Now I get 6 points for these kind of pictures, by removing noise, edge detection, finding contours and than approxDP, now I am not able to detect the shape is right faced, left face, down or up. As the points location change for each picture.
So I want to know given two points, how can I check if there is a line segment between the points or not. After getting there two points, I can calculate from remaining points position that where is the shape pointing.
If anyone has better solution, please share.
Assume you have two endpoints, and your line segment have pixel value greater than zero(in the above image you may need to use threshold binary inverted).
Create a mask image by drawing the line with thickness=1 pixel.
Copy the source image to new Mat using above Mask.
Now count non-zero pixel in the resultant Mat,
if it is zero there is no line between the points,
and if the count non-zero gives a value you can cross-check it by comparing with the length of line you drawn in mask.
Imagine a plain rectangular bitmap of, say, 1024x768 pixels filled with white. There are a few (non-overlapping) sprites drawn onto the bitmap: circles, squares and triangles.
Is there an algorithm (possibly even a C++ implementation) which, given the bitmap and the color which is the background color (white, in the above example), yields a list containing the smallest bounding rectangles for each of the sprites?
Here's some sample: On the left side you can see a sample bitmap which my code is given (together with the information that the 'background' is white). On the right side you can see the same image together with the bounding rectangles of the four shapes (in red); the algorithm I'm looking for computes the geometry of these rectangles.
Some painting programs have a similiar feature for selecting shapes: they can even compute seemingly arbitrary bounding polygons. Instead of dragging a selection rectangle manually, you can click the 'background' (what's background and what's not is determined by some threshold) and then the tool automatically computes the shape of the object drawn onto the background. I need something like this, except that I'm perfectly fine if I just have the rectangular bounding areas for objects.
I became aware of OpenCV; it appears to be relevant (it seems to be a library which includes every graphics algorithm I can think of - and then some) but in the fast amount of information I couldn't find the way to the algorithm I'm thinking of. I would be surprised if OpenCV couldn't do this, but I fear you've got to have a PhD to use it. :-)
Here is the great article on the subject:
http://softsurfer.com/Archive/algorithm_0107/algorithm_0107.htm
I think that PhD is not required here :)
These are my first thoughts, none complicated, except for the edge detection
For each square,
if it's not-white
mark as "found"
if you havn't found one next to it already
add it to points list
for each point in the points list
use basic edge detection to find outline
keep track of bounds while doing so
add bounds to shapes list
remove duplicates from shapes list. (this can happen for concave shapes)
I just realized this will consider white "holes" (like in your leftmost circle in your sample) to be it's own shape. If the first "loop" is a flood fill, it doesn't have this problem, but will be much slower/take much more memory.
The basic edge detection I was thinking of was simple:
given eight cardinal directions left, downleft, etc...
given two relative directions cw(direction-1) and ccw(direction+1)
starting with a point "begin"
set bounds to point
find direction d, where the begin+d is not white, and begin+cw(d) is white.
set current to begin+d
do
if current is outside of bounds, increase bounds
set d = cw(d)
while(cur+d is white or cur+ccw(d) is not white)
d = ccw(d)
cur = cur + d;
while(cur != begin
http://ideone.com/
There's a quite a few edge cases not considered here: what if begin is a single point, what if it runs to the edge of the picture, what if start point is only 1 px wide, but has blobs to two sides, probably others... But the basic algorithm isn't that complicated.