Wouldn’t it be fine and dandy to add augmented reality to a Lego scene. You say No, but stick with me on this.
Silanthi Film Video Songs Free Download. Before we get going though, we need to calibrate our webcam and prove some basic 3D effects. Camera Calibration Why calibrate our webcam? Well, we need to sort any picture distortion – such as straight lines appearing curved, or some areas of image appearing closer than expected – before we start to render our 3D objects. The provides all the detail. Here’s my checklist of what I’ll be using: • Windows 7 PC • Python Tools for Visual Studio • Logitech HD720p webcam • Printout of a ‘chessboard’ grid • An unnerving aptitude for tedium Okay, here’s my grid printout through the eye of my webcam: I used Microsoft Word tables to construct the grid, but sensible people would probably print the.
I am trying to calibrate camera using OpenCV tools according to the following this guide. Taskbar Repair Tool Plus Keygen Crack there. The problem is that function findChessboardCorners cannot find any.
The idea is that we will project 3D images onto this grid, which will be placed within a Lego scene. And when I say Lego scene, I mean a couple of Lego policemen with a Lego motorbike and Lego ambulance. Probably a Lego nurse administering CPR. That sort of thing.
So let the calibration begin! First up, I want to obtain 10 sample images of my grid at different angles and locations (pretty much mimicking the OpenCV sample images at sources/samples/cpp/left01.jpg – left14.jpg). Thanks a lot, Rdmiligan.
Your second image worked. I got the ret = true and the corners. However, I used the openCV code: # If found, add object points, image points (after refining them) if ret == True: objpoints.append(objp) corners2 = cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria) imgpoints.append(corners2) # Draw and display the corners img = cv2.drawChessboardCorners(img, (7,6), corners2, ret) cv2.imshow(‘img’,img) cv2.waitKey(500) I have two new problems: 1) the (-215) size.width>0 && size.height>0 in function cv::imshow error and 2) the grid image window freezes and the program crashed. Thanks again.
Basics This is going to be a small section. During the last session on camera calibration, you have found the camera matrix, distortion coefficients etc. Given a pattern image, we can utilize the above information to calculate its pose, or how the object is situated in space, like how it is rotated, how it is displaced etc. For a planar object, we can assume Z=0, such that, the problem now becomes how camera is placed in space to see our pattern image. So, if we know how the object lies in the space, we can draw some 2D diagrams in it to simulate the 3D effect. Let’s see how to do it.
Our problem is, we want to draw our 3D coordinate axis (X, Y, Z axes) on our chessboard’s first corner. X axis in blue color, Y axis in green color and Z axis in red color.
So in-effect, Z axis should feel like it is perpendicular to our chessboard plane. First, let’s load the camera matrix and distortion coefficients from the previous calibration result. Def draw ( img, corners, imgpts ): corner = tuple ( corners [ 0 ].
Ravel ()) img = cv2. Line ( img, corner, tuple ( imgpts [ 0 ]. Ravel ()), ( 255, 0, 0 ), 5 ) img = cv2. Line ( img, corner, tuple ( imgpts [ 1 ]. Ravel ()), ( 0, 255, 0 ), 5 ) img = cv2. Line ( img, corner, tuple ( imgpts [ 2 ]. Ravel ()), ( 0, 0, 255 ), 5 ) return img Then as in previous case, we create termination criteria, object points (3D points of corners in chessboard) and axis points.
Axis points are points in 3D space for drawing the axis. We draw axis of length 3 (units will be in terms of chess square size since we calibrated based on that size). So our X axis is drawn from (0,0,0) to (3,0,0), so for Y axis. For Z axis, it is drawn from (0,0,0) to (0,0,-3). Negative denotes it is drawn towards the camera. Criteria = ( cv2.
TERM_CRITERIA_EPS + cv2. TERM_CRITERIA_MAX_ITER, 30, 0.001 ) objp = np. Zeros (( 6 * 7, 3 ), np. Float32 ) objp [:,: 2 ] = np. Mgrid [ 0: 7, 0: 6 ].
Reshape ( - 1, 2 ) axis = np. Float32 ([[ 3, 0, 0 ], [ 0, 3, 0 ], [ 0, 0, - 3 ]]). Reshape ( - 1, 3 ) Now, as usual, we load each image. Search for 7x6 grid. If found, we refine it with subcorner pixels. Then to calculate the rotation and translation, we use the function, cv2.solvePnPRansac().
Once we those transformation matrices, we use them to project our axis points to the image plane. In simple words, we find the points on image plane corresponding to each of (3,0,0),(0,3,0),(0,0,3) in 3D space. Once we get them, we draw lines from the first corner to each of these points using our draw() function. For fname in glob. Glob ( 'left*.jpg' ): img = cv2. Imread ( fname ) gray = cv2. CvtColor ( img, cv2.
COLOR_BGR2GRAY ) ret, corners = cv2. FindChessboardCorners ( gray, ( 7, 6 ), None ) if ret == True: corners2 = cv2. CornerSubPix ( gray, corners,( 11, 11 ),( - 1, - 1 ), criteria ) # Find the rotation and translation vectors.
Rvecs, tvecs, inliers = cv2. SolvePnPRansac ( objp, corners2, mtx, dist ) # project 3D points to image plane imgpts, jac = cv2. ProjectPoints ( axis, rvecs, tvecs, mtx, dist ) img = draw ( img, corners2, imgpts ) cv2. Imshow ( 'img', img ) k = cv2. WaitKey ( 0 ) & 0xff if k == 's': cv2. Imwrite ( fname [: 6 ] + '.png', img ) cv2.
DestroyAllWindows () See some results below. Notice that each axis is 3 squares long.. Def draw ( img, corners, imgpts ): imgpts = np.
Int32 ( imgpts ). Reshape ( - 1, 2 ) # draw ground floor in green img = cv2. DrawContours ( img, [ imgpts [: 4 ]], - 1,( 0, 255, 0 ), - 3 ) # draw pillars in blue color for i, j in zip ( range ( 4 ), range ( 4, 8 )): img = cv2. Line ( img, tuple ( imgpts [ i ]), tuple ( imgpts [ j ]),( 255 ), 3 ) # draw top layer in red color img = cv2. DrawContours ( img, [ imgpts [ 4:]], - 1,( 0, 0, 255 ), 3 ) return img Modified axis points. They are the 8 corners of a cube in 3D space.