108 lines
4.4 KiB
Python
108 lines
4.4 KiB
Python
import numpy as np
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import cv2
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from tqdm import tqdm
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# Set the path to the images captured by the left and right cameras
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pathL = "/home/ros/Bureau/ca_ur5/damier/Left/"
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pathR = "/home/ros/Bureau/ca_ur5/damier/Right/"
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print("Extracting image coordinates of respective 3D pattern ....\n")
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# Termination criteria for refining the detected corners
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criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
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objp = np.zeros((8*5,3), np.float32)
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objp[:,:2] = np.mgrid[0:8,0:5].T.reshape(-1,2)
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img_ptsL = []
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img_ptsR = []
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obj_pts = []
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for i in tqdm(range(1,5)):
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imgL = cv2.imread(pathL+"L%d.jpg"%i)
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imgR = cv2.imread(pathR+"R%d.jpg"%i)
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imgL_gray = cv2.imread(pathL+"L%d.jpg"%i,0)
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imgR_gray = cv2.imread(pathR+"R%d.jpg"%i,0)
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outputL = imgL.copy()
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outputR = imgR.copy()
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retR, cornersR = cv2.findChessboardCorners(outputR,(8,5),None)
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retL, cornersL = cv2.findChessboardCorners(outputL,(8,5),None)
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if retR and retL:
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obj_pts.append(objp)
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cv2.cornerSubPix(imgR_gray,cornersR,(11,11),(-1,-1),criteria)
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cv2.cornerSubPix(imgL_gray,cornersL,(11,11),(-1,-1),criteria)
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cv2.drawChessboardCorners(outputR,(8,5),cornersR,retR)
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cv2.drawChessboardCorners(outputL,(8,5),cornersL,retL)
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cv2.imshow('cornersR',outputR)
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cv2.imshow('cornersL',outputL)
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cv2.waitKey(0)
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img_ptsL.append(cornersL)
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img_ptsR.append(cornersR)
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print("Calculating left camera parameters ... ")
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# Calibrating left camera
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retL, mtxL, distL, rvecsL, tvecsL = cv2.calibrateCamera(obj_pts,img_ptsL,imgL_gray.shape[::-1],None,None)
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hL,wL= imgL_gray.shape[:2]
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new_mtxL, roiL= cv2.getOptimalNewCameraMatrix(mtxL,distL,(wL,hL),1,(wL,hL))
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print("Calculating right camera parameters ... ")
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# Calibrating right camera
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retR, mtxR, distR, rvecsR, tvecsR = cv2.calibrateCamera(obj_pts,img_ptsR,imgR_gray.shape[::-1],None,None)
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hR,wR= imgR_gray.shape[:2]
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new_mtxR, roiR= cv2.getOptimalNewCameraMatrix(mtxR,distR,(wR,hR),1,(wR,hR))
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print("Stereo calibration .....")
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flags = 0
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flags |= cv2.CALIB_FIX_INTRINSIC
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# Here we fix the intrinsic camara matrixes so that only Rot, Trns, Emat and Fmat are calculated.
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# Hence intrinsic parameters are the same
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criteria_stereo= (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
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# This step is performed to transformation between the two cameras and calculate Essential and Fundamenatl matrix
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retS, new_mtxL, distL, new_mtxR, distR, Rot, Trns, Emat, Fmat = cv2.stereoCalibrate(obj_pts,
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img_ptsL,
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img_ptsR,
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new_mtxL,
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distL,
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new_mtxR,
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distR,
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imgL_gray.shape[::-1],
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criteria_stereo,
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flags)
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# Once we know the transformation between the two cameras we can perform stereo rectification
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# StereoRectify function
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rectify_scale= 1 # if 0 image croped, if 1 image not croped
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rect_l, rect_r, proj_mat_l, proj_mat_r, Q, roiL, roiR= cv2.stereoRectify(new_mtxL, distL, new_mtxR, distR,
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imgL_gray.shape[::-1], Rot, Trns,
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rectify_scale,(0,0))
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# Use the rotation matrixes for stereo rectification and camera intrinsics for undistorting the image
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# Compute the rectification map (mapping between the original image pixels and
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# their transformed values after applying rectification and undistortion) for left and right camera frames
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Left_Stereo_Map= cv2.initUndistortRectifyMap(new_mtxL, distL, rect_l, proj_mat_l,
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imgL_gray.shape[::-1], cv2.CV_16SC2)
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Right_Stereo_Map= cv2.initUndistortRectifyMap(new_mtxR, distR, rect_r, proj_mat_r,
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imgR_gray.shape[::-1], cv2.CV_16SC2)
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print("Saving parameters ......")
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cv_file = cv2.FileStorage("data/params_py.xml", cv2.FILE_STORAGE_WRITE)
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cv_file.write("Left_Stereo_Map_x",Left_Stereo_Map[0])
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cv_file.write("Left_Stereo_Map_y",Left_Stereo_Map[1])
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cv_file.write("Right_Stereo_Map_x",Right_Stereo_Map[0])
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cv_file.write("Right_Stereo_Map_y",Right_Stereo_Map[1])
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cv_file.release()
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