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@anujonthemove
Last active July 13, 2024 17:14
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Bird's eye view perspective transformation using OpenCV
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import cv2
import numpy as np
import math
PI = 3.1415926
frameWidth = 640
frameHeight = 480
def update_perspective(val):
alpha = (cv2.getTrackbarPos("Alpha", "Result") - 90) * PI / 180
beta = (cv2.getTrackbarPos("Beta", "Result") - 90) * PI / 180
gamma = (cv2.getTrackbarPos("Gamma", "Result") - 90) * PI / 180
focalLength = cv2.getTrackbarPos("f", "Result")
dist = cv2.getTrackbarPos("Distance", "Result")
image_size = (frameWidth, frameHeight)
w, h = image_size
A1 = np.array([[1, 0, -w / 2],
[0, 1, -h / 2],
[0, 0, 0],
[0, 0, 1]], dtype=np.float32)
RX = np.array([[1, 0, 0, 0],
[0, math.cos(alpha), -math.sin(alpha), 0],
[0, math.sin(alpha), math.cos(alpha), 0],
[0, 0, 0, 1]], dtype=np.float32)
RY = np.array([[math.cos(beta), 0, -math.sin(beta), 0],
[0, 1, 0, 0],
[math.sin(beta), 0, math.cos(beta), 0],
[0, 0, 0, 1]], dtype=np.float32)
RZ = np.array([[math.cos(gamma), -math.sin(gamma), 0, 0],
[math.sin(gamma), math.cos(gamma), 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]], dtype=np.float32)
R = np.dot(np.dot(RX, RY), RZ)
T = np.array([[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, dist],
[0, 0, 0, 1]], dtype=np.float32)
K = np.array([[focalLength, 0, w / 2, 0],
[0, focalLength, h / 2, 0],
[0, 0, 1, 0]], dtype=np.float32)
transformationMat = np.dot(np.dot(np.dot(K, T), R), A1)
destination = cv2.warpPerspective(source, transformationMat, image_size, flags=cv2.INTER_CUBIC + cv2.WARP_INVERSE_MAP)
cv2.imshow("Result", destination)
source = cv2.imread('frame.jpg') # Replace with your image file path
cv2.namedWindow("Result", cv2.WINDOW_NORMAL)
cv2.createTrackbar("Alpha", "Result", 90, 180, update_perspective)
cv2.createTrackbar("Beta", "Result", 90, 180, update_perspective)
cv2.createTrackbar("Gamma", "Result", 90, 180, update_perspective)
cv2.createTrackbar("f", "Result", 500, 2000, update_perspective)
cv2.createTrackbar("Distance", "Result", 500, 2000, update_perspective)
update_perspective(0)
cv2.waitKey(0)
cv2.destroyAllWindows()
import cv2
import numpy as np
import math
PI = 3.1415926
frameWidth = 640
frameHeight = 480
def update_perspective(val):
alpha = (cv2.getTrackbarPos("Alpha", "Result") - 90) * PI / 180
beta = (cv2.getTrackbarPos("Beta", "Result") - 90) * PI / 180
gamma = (cv2.getTrackbarPos("Gamma", "Result") - 90) * PI / 180
focalLength = cv2.getTrackbarPos("f", "Result")
dist = cv2.getTrackbarPos("Distance", "Result")
image_size = (frameWidth, frameHeight)
w, h = image_size
A1 = np.array([[1, 0, -w / 2],
[0, 1, -h / 2],
[0, 0, 0],
[0, 0, 1]], dtype=np.float32)
RX = np.array([[1, 0, 0, 0],
[0, math.cos(alpha), -math.sin(alpha), 0],
[0, math.sin(alpha), math.cos(alpha), 0],
[0, 0, 0, 1]], dtype=np.float32)
RY = np.array([[math.cos(beta), 0, -math.sin(beta), 0],
[0, 1, 0, 0],
[math.sin(beta), 0, math.cos(beta), 0],
[0, 0, 0, 1]], dtype=np.float32)
RZ = np.array([[math.cos(gamma), -math.sin(gamma), 0, 0],
[math.sin(gamma), math.cos(gamma), 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]], dtype=np.float32)
R = np.dot(np.dot(RX, RY), RZ)
T = np.array([[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, dist],
[0, 0, 0, 1]], dtype=np.float32)
K = np.array([[focalLength, 0, w / 2, 0],
[0, focalLength, h / 2, 0],
[0, 0, 1, 0]], dtype=np.float32)
transformationMat = np.dot(np.dot(np.dot(K, T), R), A1)
ret, frame = capture.read()
if not ret:
return
destination = cv2.warpPerspective(frame, transformationMat, image_size, flags=cv2.INTER_CUBIC + cv2.WARP_INVERSE_MAP)
cv2.imshow("Result", destination)
frameWidth = 640
frameHeight = 480
capture = cv2.VideoCapture(filename) # Replace with your video file path
cv2.namedWindow("Result", cv2.WINDOW_NORMAL)
cv2.createTrackbar("Alpha", "Result", 90, 180, update_perspective)
cv2.createTrackbar("Beta", "Result", 90, 180, update_perspective)
cv2.createTrackbar("Gamma", "Result", 90, 180, update_perspective)
cv2.createTrackbar("f", "Result", 500, 2000, update_perspective)
cv2.createTrackbar("Distance", "Result", 500, 2000, update_perspective)
while True:
update_perspective(0)
if cv2.waitKey(1) & 0xFF == 27: # Press 'Esc' to exit
break
capture.release()
cv2.destroyAllWindows()
// OpenCV imports
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
// C++ imports
#include <iostream>
// namespaces
using namespace std;
using namespace cv;
#define PI 3.1415926
int frameWidth = 640;
int frameHeight = 480;
/*
* This code illustrates bird's eye view perspective transformation using opencv
* Paper: Distance Determination for an Automobile Environment using Inverse Perspective Mapping in OpenCV
* Link to paper: https://www.researchgate.net/publication/224195999_Distance_determination_for_an_automobile_environment_using_Inverse_Perspective_Mapping_in_OpenCV
* Code taken from: http://www.aizac.info/birds-eye-view-homography-using-opencv/
*/
int main(int argc, char const *argv[]) {
if(argc < 2) {
cerr << "Usage: " << argv[0] << " /path/to/video/" << endl;
cout << "Exiting...." << endl;
return -1;
}
// get file name from the command line
string filename = argv[1];
// capture object
VideoCapture capture(filename);
// mat container to receive images
Mat source, destination;
// check if capture was successful
if( !capture.isOpened()) throw "Error reading video";
int alpha_ = 90, beta_ = 90, gamma_ = 90;
int f_ = 500, dist_ = 500;
namedWindow("Result", 1);
createTrackbar("Alpha", "Result", &alpha_, 180);
createTrackbar("Beta", "Result", &beta_, 180);
createTrackbar("Gamma", "Result", &gamma_, 180);
createTrackbar("f", "Result", &f_, 2000);
createTrackbar("Distance", "Result", &dist_, 2000);
while( true ) {
capture >> source;
resize(source, source,Size(frameWidth, frameHeight));
double focalLength, dist, alpha, beta, gamma;
alpha =((double)alpha_ -90) * PI/180;
beta =((double)beta_ -90) * PI/180;
gamma =((double)gamma_ -90) * PI/180;
focalLength = (double)f_;
dist = (double)dist_;
Size image_size = source.size();
double w = (double)image_size.width, h = (double)image_size.height;
// Projecion matrix 2D -> 3D
Mat A1 = (Mat_<float>(4, 3)<<
1, 0, -w/2,
0, 1, -h/2,
0, 0, 0,
0, 0, 1 );
// Rotation matrices Rx, Ry, Rz
Mat RX = (Mat_<float>(4, 4) <<
1, 0, 0, 0,
0, cos(alpha), -sin(alpha), 0,
0, sin(alpha), cos(alpha), 0,
0, 0, 0, 1 );
Mat RY = (Mat_<float>(4, 4) <<
cos(beta), 0, -sin(beta), 0,
0, 1, 0, 0,
sin(beta), 0, cos(beta), 0,
0, 0, 0, 1 );
Mat RZ = (Mat_<float>(4, 4) <<
cos(gamma), -sin(gamma), 0, 0,
sin(gamma), cos(gamma), 0, 0,
0, 0, 1, 0,
0, 0, 0, 1 );
// R - rotation matrix
Mat R = RX * RY * RZ;
// T - translation matrix
Mat T = (Mat_<float>(4, 4) <<
1, 0, 0, 0,
0, 1, 0, 0,
0, 0, 1, dist,
0, 0, 0, 1);
// K - intrinsic matrix
Mat K = (Mat_<float>(3, 4) <<
focalLength, 0, w/2, 0,
0, focalLength, h/2, 0,
0, 0, 1, 0
);
Mat transformationMat = K * (T * (R * A1));
warpPerspective(source, destination, transformationMat, image_size, INTER_CUBIC | WARP_INVERSE_MAP);
imshow("Result", destination);
waitKey(100);
}
return 0;
}
@ershat-dl
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Hi, What is the purpose of your Matrix A1? You mention it transforms 2D into 3D, right? How? and why you do the negative of half the width and height of the image? Isn't matrix A1 a linear transformation that implies a translation of the image?

hi, did you find answers?

Hi, No I didn't.

I think you should take a look at this, I am still working on it, but this is worked to me.

https://stackoverflow.com/questions/48576087/birds-eye-view-perspective-transformation-from-camera-calibration-opencv-python
https://docs.opencv.org/3.4.0/d9/dab/tutorial_homography.html#tutorial_homography_Demo3

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