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; | |
} |
Hii can you please help me with the matlab implemention of this code . I m not able to find a function in matlab which works same as warp perspective.
@anujonthemove Could you upload an example input output image?
Thanks!
Hi Can you help me with the matlab implemention, too? Please help me
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?
I did this automatically by finding the vanishing point - see my paper
https://link.springer.com/article/10.1007/s00138-010-0289-5
(PDF link: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.227.8116&rep=rep1&type=pdf)
What are your units for focal length and camera distance?
Ry sinus sign seems to be wrong.
I would like to thank you for your post, but I have 2 question about it
what does the variable dist_=500 refer to ?
what are your units for the distance and focal length variables?
I would like to thank you for your post, but I have 2 question about it what does the variable dist_=500 refer to ? what are your units for the distance and focal length variables?
hi, did you find answers?
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, 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.
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
@rohithagaram: please check the step Mat transformationMat = K * (T * (R * A1)); which computes a 3-by-3 matrix which is the required transformation matrix. Hope this helps!