I'm very new on OpenCV and I'm still reading on it and experimenting, but how would I go about finding the 2 yellow objects in the following picture and extractive the horizontal distance in pixels from the center of the image ?
Note that the object are not necessarily yellow. I just used yellow ones right now for clarity (and maybe to make my life easier at first).
Any help appreciated ! Thanks.
This steps will helps you.
convert the image to HSV colour space and create a mask. check this tutorial
detect contour in a mask image.
compute center points by using
x, y, w, h = cv2.boundingRect(cnt)
(cx, cy) = (x+w/2, y+h//2)
compute the horizontal distance between center points.
horizontal_pixel_distance = cx1-cx2
Related
I am using this page to identify circles and their centers on my images. I know that top left hand corner is 0,0 point. But then i noticed that x and y coordinates returned for my circles' center are all positive. Why is that? shouldn't y coordinate be negative? I am talking about circles[i][1] values from the original code.
No. It is correct.
In computer vision, usually, the y-axis is inverted.
OpenCV follows this coordinate system:
Image from here.
I am trying to find circles in images and warp them back to a canonical view (i.e. as if looking into the center). However, circles in general project to ellipses under perspective transformations. So I am first detecting ellipses, roughly doing the following (in OpenCV):
1. Find contours in the image
2. Estimate area of contour
3. Fitting a bounded box to contour and estimating area by width/2 * height/2 * PI (area of ellipse)
4. checking if area of contour and estimated area of ellipse is < a threhsold
Assuming I have found an ellipse by this method, how can I rectify it back to a circle such that I "undo" the perspective transform (although not in plane rotation as this cannot be done I guess). For example, if it was a rectangle I would just compute the homography from the 4 corners of an uprigh rectangle to the detected projected one.
I have no idea how to do this with an ellipse, any help is much appreciated.
Thanks
A circle is indeed transformed into an ellipse by a perspective transformation, however its axes are not the same as the axes of the initial circle, as shown in this illustration:
(source: brian-curtis.com)
You can refer to this link for a detailled demonstration. As a consequence, the bounding rectangle of the ellipse is not the image of the initial square by the perspective tranformation.
EDIT:
This means that the center and the axes of the ellipse you observe are not the images, by the perspective mapping, of the center and axes of the original circle. I tried to make a clearer illustration:
On this image, I drew in green the axes and center of the original circle, after perspective transformation, and the axes and center of the ellipse in red. On this specific example, the vertical axis is not deformed by the perspective mapping, but it would be deformed in the general case. Hence, deforming a circle by a perspective transformation gives an ellipse, but the axes and center that you see are not the axes and center of the original circle.
As a consequence, you cannot simply use the top, bottom, left and right points on the ellipse (the red points, which can easily be detected from the ellipse) to map these onto the top, bottom, left and right points of the circle because they do not correspond under the perspective mapping (the green points do, but they cannot be detected easily from the ellipse).
In the end, I don't think that it is at all possible to estimate the perspective mapping from a single detected ellipse.
This looks like an indeterminate problem.
The projection of a rectangle supplies 8 equations in 8 unknowns (homography coefficients).
With an ellipse, you can only retrieve the center coordinates (2 DOF), the axis (2 DOF) and the axis orientation (1 DOF).
I also posted this topic in the Q&A forum at opencv.org but I don't know how many experts from here are reading this forum - so forgive me that I'm also trying it here.
I'm currently learning OpenCV and my current task is to measure the distance between two balls which are lying on a plate. My next step is to compare several cameras and resolutions to get a feeling how important resolution, noise, distortion etc. is and how heavy these parameters affect the accuracy. If the community is interested in the results I'm happy to share the results when they are ready! The camera is placed above the plate using a wide-angle lens. The width and height of the plate (1500 x 700 mm) and the radius of the balls (40 mm) are known.
My steps so far:
camera calibration
undistorting the image (the distortion is high due to the wide-angle lens)
findHomography: I use the corner points of the plate as input (4 points in pixels in the undistorted image) and the corner points in millimeters (starting with 0,0 in the lower left corner, up to 1500,700 in the upper right corner)
using HoughCircles to find the balls in the undistorted image
applying perspectiveTransform on the circle center points => circle center points now exist in millimeters
calculation the distance of the two center points: d = sqrt((x1-x2)^2+(y1-y2)^2)
The results: an error of around 4 mm at a distance of 300 mm, an error of around 25 mm at a distance of 1000 mm But if I measure are rectangle which is printed on the plate the error is smaller than 0.2 mm, so I guess the calibration and undistortion is working good.
I thought about this and figured out three possible reasons:
findHomography was applied to points lying directly on the plate whereas the center points of the balls should be measured in the equatorial height => how can I change the result of findHomography to change this, i.e. to "move" the plane? The radius in mm is known.
the error increases with increasing distance of the ball to the optical center because the camera will not see the ball from the top, so the center point in the 2D projection of the image is not the same as in the 3D world - I will we projected further to the borders of the image. => are there any geometrical operations which I can apply on the found center to correct the value?
during undistortion there's probably a loss of information, because I produce a new undistorted image and go back to pixel accuracy although I have many floating point values in the distortion matrix. Shall I search for the balls in the distorted image and tranform only the center points with the distortion matrix? But I don't know what's the code for this task.
I hope someone can help me to improve this and I hope this topic is interesting for other OpenCV-starters.
Thanks and best regards!
Here are some thoughts to help you along... By no means "the answer", though.
First a simple one. If you have calibrated your image in mm at a particular plane that is distance D away, then points that are r closer will appear larger than they are. To get from measured coordinates to actual coordinates, you use
Actual = measured * (D-r)/D
So since the centers of the spheres are radius r above the plane, the above formula should answer part 1 of your question.
Regarding the second question: if you think about it, the center of the sphere that you see should be in the right place "in the plane of the center of the sphere", even though you look at it from an angle. Draw yourself a picture to convince yourself this is so.
Third question: if you find the coordinates of the spheres in the distorted image, you should be able to transform them to the corrected image using perspectiveTransform. This may improve accuracy a little bit - but I am surprised at the size of errors you see. How large is a single pixel at the largest distance (1000mm)?
EDIT
You asked about elliptical projections etc. Basically, if you think of the optical center of the camera as a light source, and look at the shadow of the ball onto the plane as your "2D image", you can draw a picture of the rays that just hit the sides of the ball, and determine the different angles:
It is easy to see that P (the mid point of A and B) is not the same as C (the projection of the center of the sphere). A bit more trig will show you that the error C - (A+B)/2 increases with x and decreases with D. If you know A and B you can calculate the correct position of C (given D) from:
C = D * tan( (atan(B/D) + atan(A/D)) / 2 )
The error becomes larger as D is smaller and/or x is larger. Note D is the perpendicular (shortest) distance from the lens to the object plane.
This only works if the camera is acting like a "true lens" - in other words, there is no pincushion distortion, and a rectangle in the image plane maps into a rectangle on the sensor. The above combined with your own idea to fit in the uncorrected ('pixel') space, then transform the centers found with perspectiveTransform, ought to get you all the way there.
See what you can do with that!
I have an image with a equilateral triangle and a rectangle:
And I want to detect 3 corner of the triangle only. I follow the OpenCV Harris corner detector tutorial I see that all the corner-point of the triangle have the threshold = 80 (when all the 4 corner-point of the rectangle threshold = 255). But I did not find the link between threshold and degree.
How can I find the corner that in the range of [55,65] degree, for example?
Here is the output Mat http://pastebin.com/raw.php?i=qNidEAG0
P/s: I very new to CV, hope you can give some more detail!
It seems that I found possible solution. I've implemented it on Mathematica and able to explain basic steps.
Use find corners operator and take strongest corners. Use Harris operator.
Find contours (cv::FindContours).
For each corner in each contour draw a circle and find point of intersection between circle and contour. There is no ready function for it in OpenCV and you should implement it yourself.
Now for each corner you have coordinates of three points: corner, and two points on sides of contour. It is enough to evaluate angles using dot product:
Result:
Given an object on a plain white background, does anybody know if OpenCV provides functionality to easily detect an object from a captured frame?
I'm trying to locate the corner/center points of an object (rectangle). The way I'm currently doing it, is by brute force (scanning the image for the object) and not accurate. I'm wondering if there is functionality under the hood that i'm not aware of.
Edit Details:
The size about the same as a small soda can. The camera is positioned above the object, to give it a 2D/Rectangle feel. The orientation/angle from from the camera is random, which is calculated from the corner points.
It's just a white background, with the object on it (black). The quality of the shot is about what you'd expect to see from a Logitech webcam.
Once I get the corner points, I calculate the center. The center point is then converted to centimeters.
It's refining just 'how' I get those 4 corners is what I'm trying to focus on. You can see my brute force method with this image: Image
There's already an example of how to do rectangle detection in OpenCV (look in samples/squares.c), and it's quite simple, actually.
Here's the rough algorithm they use:
0. rectangles <- {}
1. image <- load image
2. for every channel:
2.1 image_canny <- apply canny edge detector to this channel
2.2 for threshold in bunch_of_increasing_thresholds:
2.2.1 image_thresholds[threshold] <- apply threshold to this channel
2.3 for each contour found in {image_canny} U image_thresholds:
2.3.1 Approximate contour with polygons
2.3.2 if the approximation has four corners and the angles are close to 90 degrees.
2.3.2.1 rectangles <- rectangles U {contour}
Not an exact transliteration of what they are doing, but it should help you.
Hope this helps, uses the moment method to get the centroid of a black and white image.
cv::Point getCentroid(cv::Mat img)
{
cv::Point Coord;
cv::Moments mm = cv::moments(img,false);
double moment10 = mm.m10;
double moment01 = mm.m01;
double moment00 = mm.m00;
Coord.x = int(moment10 / moment00);
Coord.y = int(moment01 / moment00);
return Coord;
}
OpenCV has heaps of functions that can help you achieve this. Download Emgu.CV for a C#.NET wrapped to the library if you are programming in that language.
Some methods of getting what you want:
Find the corners as before - e.g. "CornerHarris" OpenCV function
Threshold the image and calculate the centre of gravity - see http://www.roborealm.com/help/Center%20of%20Gravity.php ... this is the method i would use. You can even perform the thresholding in the COG routine. i.e. cog_x += *imagePtr < 128 ? 255 : 0;
Find the moments of the image to give rotation, center of gravity etc - e.g. "Moments" OpenCV function. (I haven't used this)
(edit) The AForge.NET library has corner detection functions as well as an example project (MotionDetector) and libraries to connect to webcams. I think this would be the easiest way to go, assuming you are using Windows and .NET.
Since no one has posted a complete OpenCV solution, here's a simple approach:
Obtain binary image. We load the image, convert to grayscale, and then obtain a binary image using Otsu's threshold
Find outer contour. We find contours using findContours and then extract the bounding box coordinates using boundingRect
Find center coordinate. Since we have the contour, we can find the center coordinate using moments to extract the centroid of the contour
Here's an example with the bounding box and center point highlighted in green
Input image -> Output
Center: (100, 100)
Center: (200, 200)
Center: (300, 300)
So to recap:
Given an object on a plain white background, does anybody know if OpenCV provides functionality to easily detect an object from a captured frame?
First obtain a binary image (Canny edge detection, simple thresholding, Otsu's threshold, or Adaptive threshold) and then find contours using findContours. To obtain the bounding rectangle coordinates, you can use boundingRect which will give you the coordinates in the form of x,y,w,h. To draw the rectangle, you can draw it with rectangle. This will give you the 4 corner points of the contour. If you wanted to obtain the center point, use
moments to extract the centroid of the contour
Code
import cv2
import numpy as np
# Load image, convert to grayscale, and Otsu's threshold
image = cv2.imread('1.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
# Find contours and extract the bounding rectangle coordintes
# then find moments to obtain the centroid
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
# Obtain bounding box coordinates and draw rectangle
x,y,w,h = cv2.boundingRect(c)
cv2.rectangle(image, (x, y), (x + w, y + h), (36,255,12), 2)
# Find center coordinate and draw center point
M = cv2.moments(c)
cx = int(M['m10']/M['m00'])
cy = int(M['m01']/M['m00'])
cv2.circle(image, (cx, cy), 2, (36,255,12), -1)
print('Center: ({}, {})'.format(cx,cy))
cv2.imshow('image', image)
cv2.waitKey()
It is usually called blob analysis in other machine vision libraries. I haven't used opencv yet.