homography

I am trying to find the transformation matrix H so that i can multiply the (x,y) pixel coordinates and get the (x,y) real world coordinates. Here is my code: import cv2 import numpy as np from numpy.linalg import inv if __name__ == '__main__' : D=[159.1,34.2] I=[497.3,37.5] G=[639.3,479.7] A=[0,478.2] # Read source image. im_src = cv2.imread('/home/vivek/june_14.png') # Four corners of the book in source image pts_src = np.array([D,I,G,A]) # Read destination image. im_dst = cv2.imread('/home/vivek/june_14.png') # Four corners of the book in destination image. print "img1 shape:",im_dst.shape

When finding a reference image in a scene using SURF, I would like to crop the found object in the scene, and "straighten" it back using warpPerspective and the reversed homography matrix. Meaning, let's say I have this SURF result: Now, I would like to crop the found object in the scene: and "straighten" only the cropped image with warpPerspective using the reversed homography matrix. The result I'm aiming at is that I'll get an image containing, roughly, only the object, and some distorted leftovers from the original scene (as the cropping is not a 100% the object alone). Cropping the found

I'm trying to build static augmented reality scene over a photo with 4 defined correspondences between coplanar points on a plane and image. Here is a step by step flow: User adds an image using device's camera. Let's assume it contains a rectangle captured with some perspective. User defines physical size of the rectangle, which lies in horizontal plane (YOZ in terms of SceneKit). Let's assume it's center is world's origin (0, 0, 0), so we can easily find (x,y,z) for each corner. User defines uv coordinates in image coordinate system for each corner of the rectangle. SceneKit scene is created

I have a panorama image, and a smaller image of buildings seen within that panorama image. What I want to do is recognise if the buildings in that smaller image are in that panorama image, and how the 2 images line up. For this first example, I'm using a cropped version of my panorama image, so the pixels are identical. import cv2 import matplotlib.pyplot as plt import matplotlib.image as mpimg import numpy as np import math # Load images cwImage = cv2.imread('cw1.jpg',0) panImage = cv2.imread('pan1.jpg',0) # Prepare for SURF image analysis surf = cv2.xfeatures2d.SURF_create(4000) # Find

I calculated the 3x3 homography matrix and I need to get rotation, translation, shear and scale to use them as parameters in the windows8 media element attributes ?! Tom Larkworthy see https://math.stackexchange.com/questions/78137/decomposition-of-a-nonsquare-affine-matrix def getComponents(normalised_homography): '''((translationx, translationy), rotation, (scalex, scaley), shear)''' a = normalised_homography[0,0] b = normalised_homography[0,1] c = normalised_homography[0,2] d = normalised_homography[1,0] e = normalised_homography[1,1] f = normalised_homography[1,2] p = math.sqrt(a*a + b*b)

I want to convert an image using control points according to this scheme extracted from here : A and B contains the coordinates of the source an target vertices. I am computing the transformation matrix as: A = [51 228; 51 127; 191 127; 191 228]; B = [152 57; 219 191; 62 240; 92 109]; X = imread('rectangle.png'); info = imfinfo('rectangle.png'); T = cp2tform(A,B,'projective'); Up to here it seems to properly work, because (using normalized coordinates) a source vertex produces its target vertex: H = T.tdata.T; > [51 228 1]*H ans = -248.2186 -93.0820 -1.6330 > [51 228 1]*H/ -1.6330 ans = 152

I have 2 consecutive images from a camera and I want to estimate the change in camera pose: I calculate the optical flow: Const MAXFEATURES As Integer = 100 imgA = New Image(Of [Structure].Bgr, Byte)("pic1.bmp") imgB = New Image(Of [Structure].Bgr, Byte)("pic2.bmp") grayA = imgA.Convert(Of Gray, Byte)() grayB = imgB.Convert(Of Gray, Byte)() imagesize = cvGetSize(grayA) pyrBufferA = New Emgu.CV.Image(Of Emgu.CV.Structure.Gray, Byte) _ (imagesize.Width + 8, imagesize.Height / 3) pyrBufferB = New Emgu.CV.Image(Of Emgu.CV.Structure.Gray, Byte) _ (imagesize.Width + 8, imagesize.Height / 3) features