I am trying to calculate the area covered by a polygon on a map in square kilometers.
Based on the code from [1] and the corresponding paper [2] I have this code:
double area = 0;
auto coords = QList<QGeoCoordinate>{(QGeoCoordinate(50.542908183, 6.2521438908), QGeoCoordinate(50.250550175, 6.2521438908), QGeoCoordinate(50.250550175, 6.4901310043), QGeoCoordinate(50.542908183, 6.4901310043))};
for(int i=0; i<coords.size()-1; i++)
{
const auto &p1 = coords[i];
const auto &p2 = coords[i+1];
area += qDegreesToRadians(p2.longitude() - p1.longitude()) *
(2 + qSin(qDegreesToRadians(p2.latitude())) +
qSin(qDegreesToRadians(p1.latitude())));
}
area = area * 6378137.0 * 6378137.0 / 2.0;
qDebug() << "Area:" << (area/1000000);
qDebug() << coords;
But the calculated area is completely wrong. Also moving the polyon's vertices around results in strange results: Depending on the vertex the calculated area gets smaller althought the polgon's area is increased and vice verse. The calculated area also seems to depend on which vertex is used as start vertex.
Interestingly the signed are of a ring algorithm (getArea from [1]) returns correct results, meaning that the calculated area increases/decreases when the polygon's size is changed.
The code for calculating the area on a sphere was also used elsewhere so I am pretty sure that something is wrong with my implementation.
[1] https://github.com/openlayers/openlayers/blob/v2.13.1/lib/OpenLayers/Geometry/LinearRing.js#L251
[2] https://trs.jpl.nasa.gov/bitstream/handle/2014/40409/JPL%20Pub%2007-3%20%20w%20Errata.pdf?sequence=3&isAllowed=y
[3] Polygon area calculation using Latitude and Longitude generated from Cartesian space and a world file
I still could not find the error in my code but switching to the ringArea method from https://github.com/mapbox/geojson-area/blob/master/index.js works.
Related
I'm hoping to find a way using GEOSwift to take a series of user's LatLngs, construct a linestring and buffer it to always be 30 meters wide regardless of the user's location. I feel like there must be an easier way than the path I'm going down and any help would be appreciated.
Background:
From what I can tell the buffer functions width parameter is currently defined in decimal degrees as my coordinate system is EPSG 4326, which makes calculating the width in meters difficult. I can get a rough estimation of meters per decimal degree for both longitude or latitude with the Haversine formula.
The problem I have is the series of points can move both latitudinally and longitudinally. So the buffer width I need in these cases lies somewhere between ThirtyMetersInLatDegrees and ThirtyMetersInLngDegrees. And in this case the width to supply to the buffer function becomes a weird approximation/ average of the user's overall longitudinal and latitudinal movement throughout the linestring related to ThirtyMetersInLngDegrees and ThirtyMetersInLatDegrees.
i.e. assuming ThirtyMetersInLngDegrees is the max:
ThirtyMetersInLatDegrees <= bufferWidth <= ThirtyMetersInLngDegrees
How can I better accomplish this?
Here's how I'm calculating meters per decimal degree:
//Earth’s radius
let R=6378137.0
let deviceLatitude = 37.535997
let OneMeterInLatDegrees = 1/R * (180/Double.pi)
let OneMeterInLngDegrees = 1/(R*cos(Double.pi*deviceLatitude/180)) * (180/Double.pi)
let ThirtyMetersInLatDegrees = 30 * latDegreesPerMeter
let ThirtyMetersInLngDegrees = 30 * lngDegreesPerMeter
I have detected vehicles as a blob in OpenCV. Below is the blob.h file
class Blob {
public:
// member variables
std::vector<cv::Point> currentContour;
cv::Rect currentBoundingRect;
std::vector<cv::Point> centerPositions;
double dblCurrentDiagonalSize;
double dblCurrentAspectRatio;
bool blnCurrentMatchFoundOrNewBlob;
bool blnStillBeingTracked;
int intNumOfConsecutiveFramesWithoutAMatch;
cv::Point predictedNextPosition;
// function prototypes
Blob(std::vector<cv::Point> _contour);
void predictNextPosition(void);
};
What algorithm should I use to estimate the speed of the detected vehicle??
Thanks in Advance.
UPDATE
Here is the code I have tried to estimate the speed, but it doesn't put the text plus it crashes.
for (auto blob : blobs) {
if (blob.blnStillBeingTracked == true && blob.centerPositions.size() >= 2) {
int prevFrameIndex = (int)blob.centerPositions.size() - 2;
int currFrameIndex = (int)blob.centerPositions.size() - 1;
if (blob.centerPositions[prevFrameIndex].y > (intHorizontalLinePosition-50) && blob.centerPositions[currFrameIndex].y <= intHorizontalLinePosition) {
int distance = blob.centerPositions[currFrameIndex].y - blob.centerPositions[0].y;
int tickCount = cv::getTickCount();
int time = (tickCount - blob.firstTickCount)/cv::getTickFrequency();
int speed = distance/time;
double dblFontScale = blobs[currFrameIndex].dblCurrentDiagonalSize / 10.0;
int intFontThickness = (int)std::round(dblFontScale * 1.0);
std::cout<<"Speed: "<<speed<<std::endl;
cv::putText(img, std::to_string(speed), blobs[currFrameIndex].centerPositions.back(), CV_FONT_HERSHEY_SIMPLEX, dblFontScale, SCALAR_GREEN, intFontThickness);
}
}
}
In order to predict the vehicle's speed in a 3-dimensional space from a 2D image in the general case, you need to know the orientation of the vehicle (direction of travel) and distance from the camera.
If you know for example that the vehicle is travelling perpendicular to the direction the camera points (moving directly across the frame, not toward or away from the camera at all), you can use either
a) A known distance from the camera to the road and basic trigonometry, or
b) Markers of known distance
to calculate the velocity of the vehicle using several frames.
If you know the vehicle is travelling directly toward or directly away from the camera, you can use the change in width/height of the image outline to get a sense of the vehicle's speed. If you can also identify when the vehicle passes a landmark at a known distance from the camera, you can calculate the actual width/height of the vehicle and therefore accurately calculate the speed using that known width/height and rate of change of the size of the 2D projection of the vehicle.
Update
Given the additional information, it seems you can determine what Y position in the camera's 2D image corresponds to a particular distance down the road. If you measure two such points, you can count how long it takes for the lower bounds of currentBoundingRect to pass from the first point to the second point, e.g. in the diagram below to move from y=800 to y=200.
If it takes 2 seconds to move from y=800 to y=200, it also takes 2 seconds to move 100m - 50m = 50m, or 50m/2 seconds = 25m/second.
I am currently using a Project Tango tablet for robotic obstacle avoidance. I want to create a matrix of z-values as they would appear on the Tango screen, so that I can use OpenCV to process the matrix. When I say z-values, I mean the distance each point is from the Tango. However, I don't know how to extract the z-values from the TangoXyzIjData and organize the values into a matrix. This is the code I have so far:
public void action(TangoPoseData poseData, TangoXyzIjData depthData) {
byte[] buffer = new byte[depthData.xyzCount * 3 * 4];
FileInputStream fileStream = new FileInputStream(
depthData.xyzParcelFileDescriptor.getFileDescriptor());
try {
fileStream.read(buffer, depthData.xyzParcelFileDescriptorOffset, buffer.length);
fileStream.close();
} catch (IOException e) {
e.printStackTrace();
}
Mat m = new Mat(depthData.ijRows, depthData.ijCols, CvType.CV_8UC1);
m.put(0, 0, buffer);
}
Does anyone know how to do this? I would really appreciate help.
The short answer is it can't be done, at least not simply. The XYZij struct in the Tango API does not work completely yet. There is no "ij" data. Your retrieval of buffer will work as you have it coded. The contents are a set of X, Y, Z values for measured depth points, roughly 10000+ each callback. Each X, Y, and Z value is of type float, so not CV_8UC1. The problem is that the points are not ordered in any way, so they do not correspond to an "image" or xy raster. They are a random list of depth points. There are ways to get them into some xy order, but it is not straightforward. I have done both of these:
render them to an image, with the depth encoded as color, and pull out the image as pixels
use the model/view/perspective from OpenGL and multiply out the locations of each point and then figure out their screen space location (like OpenGL would during rendering). Sort the points by their xy screen space. Instead of the calculated screen-space depth just keep the Z value from the original buffer.
or
wait until (if) the XYZij struct is fixed so that it returns ij values.
I too wish to use Tango for object avoidance for robotics. I've had some success by simplifying the use case to be only interested in the distance of any object located at the center view of the Tango device.
In Java:
private Double centerCoordinateMax = 0.020;
private TangoXyzIjData xyzIjData;
final FloatBuffer xyz = xyzIjData.xyz;
double cumulativeZ = 0.0;
int numberOfPoints = 0;
for (int i = 0; i < xyzIjData.xyzCount; i += 3) {
float x = xyz.get(i);
float y = xyz.get(i + 1);
if (Math.abs(x) < centerCoordinateMax &&
Math.abs(y) < centerCoordinateMax) {
float z = xyz.get(i + 2);
cumulativeZ += z;
numberOfPoints++;
}
}
Double distanceInMeters;
if (numberOfPoints > 0) {
distanceInMeters = cumulativeZ / numberOfPoints;
} else {
distanceInMeters = null;
}
Said simply this code is taking the average distance of a small square located at the origin of x and y axes.
centerCoordinateMax = 0.020 was determined to work based on observation and testing. The square typically contains 50 points in ideal conditions and fewer when held close to the floor.
I've tested this using version 2 of my tango-caminada application and the depth measuring seems quite accurate. Standing 1/2 meter from a doorway I slid towards the open door and the distance changed form 0.5 meters to 2.5 meters which is the wall at the end of the hallway.
Simulating a robot being navigated I moved the device towards a trash can in the path until 0.5 meters separation and then rotated left until the distance was more than 0.5 meters and proceeded forward. An oversimplified simulation, but the basis for object avoidance using Tango depth perception.
You can do this by using camera intrinsics to convert XY coordinates to normalized values -- see this post - Google Tango: Aligning Depth and Color Frames - it's talking about texture coordinates but it's exactly the same problem
Once normalized, move to screen space x[1280,720] and then the Z coordinate can be used to generate a pixel value for openCV to chew on. You'll need to decide how to color pixels that don't correspond to depth points on your own, and advisedly, before you use the depth information to further colorize pixels.
The main thing is to remember that the raw coordinates returned are already using the basis vectors you want, i.e. you do not want the pose attitude or location
So, here is my situation. I have created a object detection program which is based on color object detection. My program detects the color red and it works perfectly. But here is the problems i am facing:-
Whenever there are more than one red object in the surrounding, my program detects them and it cannot really track one object at that time(i.e it tracks other red objects of various sizes in the background. It shows me the error that "too much noise in the background". As you can see in the "threshold image" attached, it detects the round object (which is my tracking object) and my cap which is red in color. I want my program to detect only my tracking object("which is a round shaped coke cap"). How can i achieve that? Please help me out. I have my engineering design contest in few days and i have to demo my program infront of my lecturers. My program should only be able to detect and track the object which i want. Thanks
My code for the objectdetection program is a little long. So, i am hereby explaining the code as follows- I captured a frame from the webcam frame-converted it to HSV- used HSV Inrange filter to filter out the other colors but red- applied morphological operations on the filtered image. This all goes in my main function
I am using a frame resolution of 1280*720 for my webcam frame. It kind of slows down my program but it was a trade off which i had to do for performing gesture controlled operations. Anyways here is my drawobjectfunction and trackfilteredobjectfunction.
int H_MIN = 0;
int H_MAX = 256;
int S_MIN = 0;
int S_MAX = 256;
int V_MIN = 0;
int V_MAX = 256;
//default capture width and height
const int FRAME_WIDTH = 1280;
const int FRAME_HEIGHT = 720;
//max number of objects to be detected in frame
const int MAX_NUM_OBJECTS=50;
//minimum and maximum object area
const int MIN_OBJECT_AREA = 20*20;
const int MAX_OBJECT_AREA = FRAME_HEIGHT*FRAME_WIDTH/1.5;
void drawObject(int x, int y,Mat &frame){
circle(frame,Point(x,y),20,Scalar(0,255,0),2);
if(y-25>0)
line(frame,Point(x,y),Point(x,y-25),Scalar(0,255,0),2);
else line(frame,Point(x,y),Point(x,0),Scalar(0,255,0),2);
if(y+25<FRAME_HEIGHT)
line(frame,Point(x,y),Point(x,y+25),Scalar(0,255,0),2);
else line(frame,Point(x,y),Point(x,FRAME_HEIGHT),Scalar(0,255,0),2);
if(x-25>0)
line(frame,Point(x,y),Point(x-25,y),Scalar(0,255,0),2);
else line(frame,Point(x,y),Point(0,y),Scalar(0,255,0),2);
if(x+25<FRAME_WIDTH)
line(frame,Point(x,y),Point(x+25,y),Scalar(0,255,0),2);
else line(frame,Point(x,y),Point(FRAME_WIDTH,y),Scalar(0,255,0),2);
putText(frame,intToString(x)+","+intToString(y),Point(x,y+30),1,1,Scalar(0,255,0),2);
}
void trackFilteredObject(int &x, int &y, Mat threshold, Mat &cameraFeed){
Mat temp;
threshold.copyTo(temp);
//these two vectors needed for output of findContours
vector< vector<Point> > contours;
vector<Vec4i> hierarchy;
//find contours of filtered image using openCV findContours function
findContours(temp,contours,hierarchy,CV_RETR_CCOMP,CV_CHAIN_APPROX_SIMPLE );
//use moments method to find our filtered object
double refArea = 0;
bool objectFound = false;
if (hierarchy.size() > 0) {
int numObjects = hierarchy.size();
//if number of objects greater than MAX_NUM_OBJECTS we have a noisy filter
if(numObjects<MAX_NUM_OBJECTS){
for (int index = 0; index >= 0; index = hierarchy[index][0]) {
Moments moment = moments((cv::Mat)contours[index]);
double area = moment.m00;
//if the area is less than 20 px by 20px then it is probably just noise
//if the area is the same as the 3/2 of the image size, probably just a bad filter
//we only want the object with the largest area so we safe a reference area each
//iteration and compare it to the area in the next iteration.
if(area>MIN_OBJECT_AREA && area<MAX_OBJECT_AREA && area>refArea){
x = moment.m10/area;
y = moment.m01/area;
objectFound = true;
refArea = area;
}else objectFound = false;
}
//let user know you found an object
if(objectFound ==true){
putText(cameraFeed,"Tracking Object",Point(0,50),2,1,Scalar(0,255,0),2);
//draw object location on screen
drawObject(x,y,cameraFeed);}
}else putText(cameraFeed,"TOO MUCH NOISE! ADJUST FILTER",Point(0,50),1,2,Scalar(0,0,255),2);
}
}
Here is the link of the image; as you can see it also detects the red hat in the background along with the red cap of the coke bottle.
My observations:- Here is what i think, to achieve my desired goal of not detecting objects of unknown sizes of red color. I think i have to edit the value of maximum object area which i declared in the above program as (const int MAX_OBJECT_AREA = FRAME_HEIGHT*FRAME_WIDTH/1.5;). I think i have to change this value, that might eliminate the detection of bigger continous red pictures. But also, there is another problem some objects are not completely red in color and they have patches of red and other colors. So, if the detected area is within the range specfied in my program then my program detects those red patches too. What i mean to say is i was wearing a tshirt which has mixed colors and when i tested my program by wearing that tshirt, my program was able to detect the red color out of the other colors. Now, how do i solve this issue?
I think you can try out the following procedure:
obtain a circular kernel having roughly the same area as your object of interest. You can do it like: Mat kernel = getStructuringElement(MORPH_ELLIPSE, Size(d, d));
where d is the diameter of the disk.
perform normalized-cross-correlation or convolution of the filtered regions image with this kernel (I think normalized-cross-correlation would be better. And add an empty boarder around the kernel).
the peak of the resulting image should give you the location of the circular region in your filtered image (if you are using normalized-cross-correlation, you'll have to add the shift).
To speed things up, you can perform this at a reduced resolution.
You can filter out non-circular shapes by detecting circles in your thresholded image. OpenCV provides a built-on method to detect circles using Hough transform, more info here. You can take advantage of this function to retain only circles that have a radius in a given range.
Another possibility is to implement connected component labeling (CCL) into your demo program.
I believe that it was removed at some point in verions 2.x of OpenCV, but a basic implementation of the two-pass version is straightforward from the Wikipedia page.
CCL will assign a unique ID for each object after thresholding. You then have to implement matching between the objects at frame (T-1) and objects in frame (T) (for example based on some nearest distance criterion) and possibly trajectory filtering or smoothing, but this would definitely give you some extra-points.
I'm currently working on a XNA game prototype. I'm trying to achieve a isometric view of the game world (or is it othographic?? I'm not sure which is the right term for this projection - see pictures).
The world should a tile-based world made of cubic tiles (e.g. similar to Minecraft's world), and I'm trying to render it in 2D by using sprites.
So I have a sprite sheet with the top face of the cube, the front face and the side (visible side) face. I draw the tiles using 3 separate calls to drawSprite, one for the top, one for the side, one for the front, using a source rectangle to pick the face I want to draw and a destination rectangle to set the position on the screen according to a formula to convert from 3D world coordinates to isometric (orthographic?).
(sample sprite:
)
This works good as long as I draw the faces, but if I try to draw fine edges of each block (as per a tile grid) I can see that I get a random rendering pattern in which some lines are overwritten by the face itself and some are not.
Please note that for my world representation, X is left to right, Y is inside screen to outside screen, and Z is up to down.
In this example I'm working only with top face-edges. Here is what I get (picture):
I don't understand why some of the lines are shown and some are not.
The rendering code I use is (note in this example I'm only drawing the topmost layers in each dimension):
/// <summary>
/// Draws the world
/// </summary>
/// <param name="spriteBatch"></param>
public void draw(SpriteBatch spriteBatch)
{
Texture2D tex = null;
// DRAW TILES
for (int z = numBlocks - 1; z >= 0; z--)
{
for (int y = 0; y < numBlocks; y++)
{
for (int x = numBlocks - 1; x >=0 ; x--)
{
myTextures.TryGetValue(myBlockManager.getBlockAt(x, y, z), out tex);
if (tex != null)
{
// TOP FACE
if (z == 0)
{
drawTop(spriteBatch, x, y, z, tex);
drawTop(spriteBatch, x, y, z, outlineTexture);
}
// FRONT FACE
if(y == numBlocks -1)
drawFront(spriteBatch, x, y, z, tex);
// SIDE FACE
if(x == 0)
drawSide(spriteBatch, x, y, z, tex);
}
}
}
}
}
private void drawTop(SpriteBatch spriteBatch, int x, int y, int z, Texture2D tex)
{
int pX = OffsetX + (int)(x * TEXTURE_TOP_X_OFFRIGHT + y * TEXTURE_SIDE_X);
int pY = OffsetY + (int)(y * TEXTURE_TOP_Y + z * TEXTURE_FRONT_Y);
topDestRect.X = pX;
topDestRect.Y = pY;
spriteBatch.Draw(tex, topDestRect, TEXTURE_TOP_RECT, Color.White);
}
I tried using a different approach, creating a second 3-tiers nested for loop after the first one, so I keep the top face drawing in the first loop and the edge highlight in the second loop (I know, this is inefficient, I should also probably avoid having a method call for each tile to draw it, but I'm just trying to get it working for now).
The results are somehow better but still not working as expected, top rows are missing, see picture:
Any idea of why I'm having this problem? In the first approach it might be a sort of z-fighting, but I'm drawing sprites in a precise order so shouldn't they overwrite what's already there?
Thanks everyone
Whoa, sorry guys I'm an idiot :) I started the batch with SpriteBatch.begin(SpriteSortMode.BackToFront) but I didn't use any z-value in the draw.
I should have used SpriteSortMode.Deferred! It's now working fine. Thanks everyone!
Try tweaking the sizes of your source and destination rectangles by 1 or 2 pixels. I have a sneaking suspicion this has something to do with the way these rectangles are handled as sort of 'outlines' of the area to be rendered and a sort of off-by-one problem. This is not expert advice, just a fellow coder's intuition.
Looks like a sub pixel precision or scaling issue. Also try to ensure your texture/tile width/height is a power of 2 (32, 64, 128, etc.) as that could make the effect less bad as well. It's really hard to tell just from those pictures.
I don't know how/if you scale everything, but you should try to avoid rounding wherever possible (especially inside your drawTop() method). Every time you round some position/coordinate chances are good you might increase the error/random offsets. Try to use double (or better: float) coordinates instead of integer.