Add a new stereo matching validator for multiple camera calibration

aslam_offline_calibration/kalibr/python/kalibr_camera_validator_stereo_match

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+#!/usr/bin/env python
+# coding=utf-8
+from __future__ import division, print_function
+
+import argparse
+import thread
+import time
+
+import aslam_cv as acv
+import cv2
+import igraph
+import message_filters
+import numpy as np
+import rospy
+import sm
+from cv_bridge import CvBridge, CvBridgeError
+from sensor_msgs.msg import Image, CompressedImage
+
+import kalibr_common as kc
+
+# make numpy print prettier
+np.set_printoptions(suppress=True)
+
+
+class CameraChainValidator(object):
+ def __init__(self, chainConfig):
+
+ self.current_cam_msgs = None
+
+ self.chainConfig = chainConfig
+ self.numCameras = chainConfig.numCameras()
+ self.bridge = CvBridge()
+
+ # initialize the cameras in the chain
+ self.G = igraph.Graph(self.numCameras)
+ self.monovalidators = []
+ for cidx in range(0, self.numCameras):
+ camConfig = chainConfig.getCameraParameters(cidx)
+
+ # create a mono instance for each cam (detection and mono view)
+ monovalidator = MonoCameraValidator(camConfig)
+ self.monovalidators.append(monovalidator)
+
+ # add edges to overlap graph
+ overlaps = chainConfig.getCamOverlaps(cidx)
+ for overlap in overlaps:
+ # add edge if it isn't existing yet
+ try:
+ edge_idx = self.G.get_eid(cidx, overlap)
+ except:
+ self.G.add_edges([(cidx, overlap)])
+
+ # prepare the rectification maps
+ for edge in self.G.es:
+ cidx_src = edge.source
+ cidx_dest = edge.target
+
+ edge["rect_map"] = dict();
+ edge["R"] = dict();
+ edge["A"] = dict();
+
+ edge["rect_map"][cidx_src], \
+ edge["rect_map"][cidx_dest], \
+ edge["R"][cidx_src], \
+ edge["R"][cidx_dest], \
+ edge["A"][cidx_src], \
+ edge["A"][cidx_dest] = self.prepareStereoRectificationMaps(cidx_src, cidx_dest)
+
+ # register the callback for the synchronized images
+ sync_sub = message_filters.TimeSynchronizer([val.image_sub for val in self.monovalidators], 1000)
+ sync_sub.registerCallback(self.synchronizedCallback)
+
+ # initialize message throttler
+ self.timeLast = 0
+
+ thread.start_new_thread(self.processing_thread, ())
+
+ def processing_thread(self):
+ while True:
+ cam_msgs = self.current_cam_msgs
+ if cam_msgs is None:
+ continue
+
+ # process the images of all cameras
+ for cam_nr, msg in enumerate(cam_msgs):
+
+ # convert image to numpy
+ try:
+ if (type(msg) is CompressedImage):
+ cv_image = cv2.imdecode(np.fromstring(msg.data, np.uint8), cv2.IMREAD_COLOR)
+ else:
+ if (msg.encoding == "rgb8"):
+ cv_image = np.squeeze(np.array(self.bridge.imgmsg_to_cv2(msg, "mono8")))
+ else:
+ cv_image = self.bridge.imgmsg_to_cv2(msg)
+ np_image = np.array(cv_image)
+ if np_image.shape[1] > 1:
+ np_image = cv2.cvtColor(np_image, cv2.COLOR_RGB2GRAY)
+ except CvBridgeError, e:
+ print(e)
+
+ # get the corresponding monovalidator instance
+ validator = self.monovalidators[cam_nr]
+
+ # undistort the image
+ if type(validator.camera.geometry) == acv.DistortedOmniCameraGeometry:
+ validator.undist_image = validator.undistorter.undistortImageToPinhole(np_image)
+ else:
+ validator.undist_image = validator.undistorter.undistortImage(np_image)
+
+ # generate all rectification views
+ for edge in self.G.es:
+ cidx_src = edge.source
+ cidx_dest = edge.target
+ self.generatePairView(cidx_src, cidx_dest)
+
+ cv2.waitKey(1)
+
+ def synchronizedCallback(self, *cam_msgs):
+ # throttle image processing
+ rate = 100 # Hz
+ timeNow = time.time()
+ if (timeNow - self.timeLast < 1.0 / rate) and self.timeLast != 0:
+ return
+ self.timeLast = timeNow
+ self.current_cam_msgs = cam_msgs
+
+ # returns transformation T_to_from
+ def getTransformationCamFromTo(self, cidx_from, cidx_to):
+ # build pose chain (target->cam0->baselines->camN)
+ lowid = min((cidx_from, cidx_to))
+ highid = max((cidx_from, cidx_to))
+
+ T_high_low = sm.Transformation()
+ for cidx in range(lowid, highid):
+ baseline_HL = self.chainConfig.getExtrinsicsLastCamToHere(cidx + 1)
+ T_high_low = baseline_HL * T_high_low
+
+ if cidx_from < cidx_to:
+ T_BA = T_high_low
+ else:
+ T_BA = T_high_low.inverse()
+
+ return T_BA
+
+ def rectifyAndStereoMatching(self, imageA, mapA, imageB, mapB):
+ # rectify images
+ rect_image_A = cv2.remap(imageA,
+ mapA[0],
+ mapA[1],
+ cv2.INTER_LINEAR)
+
+ rect_image_B = cv2.remap(imageB,
+ mapB[0],
+ mapB[1],
+ cv2.INTER_LINEAR)
+
+ # stereo match
+ scale_downsample = parsed.scale
+ rect_image_A = cv2.resize(rect_image_A, dsize=(
+ rect_image_A.shape[1] // scale_downsample,
+ rect_image_A.shape[0] // scale_downsample,
+ ))
+ rect_image_B = cv2.resize(rect_image_B, dsize=(
+ rect_image_B.shape[1] // scale_downsample,
+ rect_image_B.shape[0] // scale_downsample,
+ ))
+ # flip
+ rect_image_A = cv2.rotate(rect_image_A, cv2.ROTATE_180)
+ rect_image_B = cv2.rotate(rect_image_B, cv2.ROTATE_180)
+
+ if parsed.matcher == 'bm':
+ # BM Matching
+ stereo = cv2.StereoBM_create(numDisparities=32, blockSize=15)
+ elif parsed.matcher == 'sgbm':
+ # SGBM Matching
+ window_size = 5
+ stereo = cv2.StereoSGBM_create(
+ minDisparity=0,
+ numDisparities=48, # max_disp has to be dividable by 16 f. E. HH 192, 256
+ blockSize=5,
+ P1=8 * 3 * window_size ** 2,
+ # wsize default 3; 5; 7 for SGBM reduced size image; 15 for SGBM full size image (1300px and above); 5 Works nicely
+ P2=32 * 3 * window_size ** 2,
+ disp12MaxDiff=1,
+ uniquenessRatio=15,
+ speckleWindowSize=0,
+ speckleRange=2,
+ preFilterCap=63,
+ mode=cv2.STEREO_SGBM_MODE_SGBM_3WAY
+ )
+ else:
+ raise NotImplementedError('Stereo matching method {} not supported'.format(parsed.method))
+
+ # stereo compute
+ disparity = stereo.compute(rect_image_A, rect_image_B)
+ # normalize
+ disparity = np.int_(256 * np.float_(disparity - disparity.min()) / (disparity.max() - disparity.min())) \
+ .astype(np.uint8)
+ # print(disparity.max(), disparity.min())
+ depth_map = cv2.applyColorMap((255 - disparity).astype(np.uint8), cv2.COLORMAP_JET)
+ # remove useless boundary
+ depth_map[np.where((disparity == 0) | (rect_image_A == 0) | (rect_image_B == 0))] = 0
+ # combine the images
+ np_rect_image = np.hstack((rect_image_A, rect_image_B))
+
+ return np_rect_image, depth_map
+
+ def generatePairView(self, camAnr, camBnr):
+ # prepare the window
+ windowName = "Rectified view (cam{0} and cam{1})".format(camAnr, camBnr)
+ windowNameDisparity = "Disparity from (cam{0} and cam{1})".format(camAnr, camBnr)
+ # cv2.namedWindow(windowName, 1)
+
+ # get the mono validators for each cam
+ camA = self.monovalidators[camAnr]
+ camB = self.monovalidators[camBnr]
+
+ # rectify the undistorted images
+ edge_idx = self.G.get_eid(camAnr, camBnr)
+ edge = self.G.es[edge_idx]
+
+ np_image_rect, depth_map = self.rectifyAndStereoMatching(camA.undist_image,
+ edge["rect_map"][camAnr],
+ camB.undist_image,
+ edge["rect_map"][camBnr])
+
+ # draw some epilines
+ np_image_rect = cv2.cvtColor(np_image_rect, cv2.COLOR_GRAY2BGR)
+ n = 10
+ for i in range(0, n):
+ y = int(np_image_rect.shape[0] * i / n)
+ cv2.line(np_image_rect, (0, y), (2 * np_image_rect.shape[1], y), (0, 255, 0))
+
+ # cv2.imshow(windowName, cv2.resize(np_image_rect, (np_image_rect.shape[1] // 2, np_image_rect.shape[0] // 2)))
+
+ # depth_map[np.where(rect_image_B == 0)] = 0
+ cv2.imshow(windowName, np_image_rect)
+ cv2.imshow(windowNameDisparity, depth_map)
+
+ def prepareStereoRectificationMaps(self, camAnr, camBnr):
+ # get the camera parameters for the undistorted cameras
+ camIdealA = self.monovalidators[camAnr].undist_camera.projection().getParameters().flatten()
+ camIdealB = self.monovalidators[camBnr].undist_camera.projection().getParameters().flatten()
+ camIdealA = np.array([[camIdealA[0], 0, camIdealA[2]], [0, camIdealA[1], camIdealA[3]], [0, 0, 1]])
+ camIdealB = np.array([[camIdealB[0], 0, camIdealB[2]], [0, camIdealB[1], camIdealB[3]], [0, 0, 1]])
+ imageSize = (self.monovalidators[camAnr].undist_camera.projection().ru(),
+ self.monovalidators[camAnr].undist_camera.projection().rv())
+
+ # get the baseline between the cams
+ baseline_BA = self.getTransformationCamFromTo(camAnr, camBnr)
+
+ ##
+ # A.Fusiello, E. Trucco, A. Verri: A compact algorithm for recification of stereo pairs, 1999
+ ##
+ Poa = np.matrix(camIdealA) * np.hstack(
+ (np.matrix(np.eye(3)), np.matrix(np.zeros((3, 1))))) # use camA coords as world frame...
+ Pob = np.matrix(camIdealB) * np.hstack((np.matrix(baseline_BA.C()), np.matrix(baseline_BA.t()).T))
+
+ # optical centers (in camA's coord sys)
+ c1 = -np.linalg.inv(Poa[:, 0:3]) * Poa[:, 3]
+ c2 = -np.linalg.inv(Pob[:, 0:3]) * Pob[:, 3]
+
+ # get "mean" rotation between cams
+ old_z_mean = (baseline_BA.C()[2, :].flatten() + sm.Transformation().T()[2, 0:3]) / 2.0
+ v1 = c1 - c2 # newx-axis = direction of baseline
+ v2 = np.cross(np.matrix(old_z_mean).flatten(), v1.flatten()).T # new y axis orthogonal to new x and mean old z
+ v3 = np.cross(v1.flatten(), v2.flatten()).T # orthogonal to baseline and new y
+
+ # normalize
+ v1 = v1 / np.linalg.norm(v1)
+ v2 = v2 / np.linalg.norm(v2)
+ v3 = v3 / np.linalg.norm(v3)
+
+ # create rotation matrix
+ R = np.hstack((np.hstack((v1, v2)), v3)).T
+
+ # new intrinsic parameters
+ A = (camIdealA + camIdealB) / 2.0
+
+ # new projection matrices
+ Pna = A * np.hstack((R, -R * c1))
+ Pnb = A * np.hstack((R, -R * c2))
+
+ # rectyfing transforms
+ Ta = Pna[0:3, 0:3] * np.linalg.inv(Poa[0:3, 0:3])
+ Tb = Pnb[0:3, 0:3] * np.linalg.inv(Pob[0:3, 0:3])
+
+ Ra = R # camA=world, then to rectified coords
+ Rb = R * baseline_BA.inverse().C() # to world then to rectified coords
+
+ # create the rectification maps
+ rect_map_x_a, rect_map_y_a = cv2.initUndistortRectifyMap(camIdealA,
+ np.zeros((4, 1)),
+ Ra,
+ A,
+ imageSize,
+ cv2.CV_16SC2)
+
+ rect_map_x_b, rect_map_y_b = cv2.initUndistortRectifyMap(camIdealB,
+ np.zeros((4, 1)),
+ Rb,
+ A,
+ imageSize,
+ cv2.CV_16SC2)
+
+ return (rect_map_x_a, rect_map_y_a), (rect_map_x_b, rect_map_y_b), Ra, Rb, A, A
+
+
+class MonoCameraValidator(object):
+ def __init__(self, camConfig):
+
+ print("initializing camera geometry")
+ self.camera = kc.ConfigReader.AslamCamera.fromParameters(camConfig)
+
+ # print details
+ print("Camera {0}:".format(camConfig.getRosTopic()))
+ camConfig.printDetails();
+
+ self.topic = camConfig.getRosTopic()
+ self.windowName = "Camera: {0}".format(self.topic)
+ # cv2.namedWindow(self.windowName, 0)
+ # register the cam topic to the message synchronizer
+ if parsed.compressed_image:
+ self.image_sub = message_filters.Subscriber(self.topic, CompressedImage)
+ else:
+ self.image_sub = message_filters.Subscriber(self.topic, Image)
+
+ # create image undistorter
+ alpha = 1.0
+ scale = 1.0
+ self.undistorter = self.camera.undistorterType(self.camera.geometry, cv2.INTER_LINEAR, alpha, scale)
+
+ if type(self.camera.geometry) == acv.DistortedOmniCameraGeometry:
+ # convert omni image to pinhole image aswell
+ self.undist_camera = self.undistorter.getIdealPinholeGeometry()
+ else:
+ self.undist_camera = self.undistorter.getIdealGeometry()
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser(description='Validate the intrinsics of a camera.')
+ parser.add_argument('--cam', dest='chainYaml', help='Camera configuration as yaml file', required=True)
+ parser.add_argument('--verbose', action='store_true', dest='verbose', help='Verbose output')
+ parser.add_argument('--compressed_image', action='store_true', help='Using CompressedImage msg', required=False)
+ parser.add_argument('--scale', type=int, default=2,
+ help='Downsample scale, using larger downsample scale to speed up stereo matching',
+ required=False)
+ parser.add_argument('--matcher', default='sgbm', help='Stereo matching algorithm [sgbm|bm]', required=False)
+ parsed = parser.parse_args()
+
+ if parsed.verbose:
+ sm.setLoggingLevel(sm.LoggingLevel.Debug)
+ else:
+ sm.setLoggingLevel(sm.LoggingLevel.Info)
+
+ camchain = kc.ConfigReader.CameraChainParameters(parsed.chainYaml)
+
+ # create the validator
+ chain_validator = CameraChainValidator(camchain)
+
+ # ros message loops
+ rospy.init_node('kalibr_validator', anonymous=True)
+ try:
+ rospy.spin()
+ except KeyboardInterrupt:
+ print("Shutting down")
+
+ cv2.destroyAllWindows()