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PatternLocalisation.lua
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PatternLocalisation.lua
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--[[
PatternLocalisation.lua
Copyright (c) 2018, Xamla and/or its affiliates. All rights reserved.
This program is free software; you can redistribute it and/or
modify it under the terms of the GNU General Public License
as published by the Free Software Foundation; either version 2
of the License, or any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
--]]
local cv = require "cv"
cv.flann = require "cv.flann"
require "cv.highgui"
require "cv.videoio"
require "cv.imgproc"
require "cv.calib3d"
require "cv.imgcodecs"
require "cv.features2d"
local autocal = require 'auto_calibration.env'
local PatternLocalisation = torch.class("PatternLocalisation", autocal)
function PatternLocalisation:__init()
self.patDictData = {}
self.pattern = {}
self.pattern.width = 1
self.pattern.height = 1
self.pattern.pointDist = 0
self.colorTab = { -- list of colors for visualisation
{255,0,0}, {255,170,0}, {95,204,41}, {0,102,153}, {180,61,204}, {204,0,0}, {153,102,0}, {42,255,0}, {0,127,255}, {255,0,255},
{255,128,128}, {255,204,102}, {0,153,51}, {0,77,153}, {153,0,128}, {255,106,77}, {255,213,0}, {0,255,128}, {0,85,255}, {255,102,229},
{153,77,61}, {153,128,0}, {61,204,133}, {0,51,153}, {255,0,170}, {255,85,0}, {255,255,0}, {0,255,212}, {102,136,204}, {153,77,128},
{153,51,0}, {255,255,128}, {0,153,128}, {0,0,255}, {153,0,77}, {255,153,102}, {153,153,77}, {0,255,255}, {0,0,204}, {255,128,191},
{255,128,0}, {173,204,20}, {0,204,204}, {153,102,255}, {255,0,85}, {153,77,0}, {170,255,0}, {0,213,255}, {170,0,255}, {255,77,136},
{255,179,102}, {112,153,31}, {0,128,153}, {102,0,153}, {153,0,26}, {153,115,77}, {191,255,128}, {0,170,255}, {122,61,153}, {153,77,89}
}
self:generateDefaultCircleFinderParams()
self.camIntrinsics = nil
self.stereoCalibration = nil
self.debugParams = {
circleSearch = false,
clustering = false,
pose = false
}
-- parameters for DBScan
self.DBScanEps = 80 -- defines the size of the point neighbourhood
self.DBScanMinPts = 6 -- minimum number of points required to form a dense region
-- constants, used by DBScan
self.clusterStatus = {
kUnprocessed = 0,
kProcessed = 1,
kNoise = 2,
kAssigned = 4
}
-- rescaling factor for visualisation
self.imgShowRescale = 0.5
end
function PatternLocalisation:setDBScanParams(eps, minPts)
self.DBScanEps = eps
self.DBScanMinPts = minPts
end
function PatternLocalisation:setPatternData(width, height, pointDist)
self.pattern.width = width
self.pattern.height = height
self.pattern.pointDist = pointDist
end
function PatternLocalisation:setPatternIDdictionary(dict)
self.patDictData = dict
end
function PatternLocalisation:setCamIntrinsics(camCalib)
self.camIntrinsics = camCalib
end
function PatternLocalisation:setStereoCalibration(stereoCalib)
self.stereoCalibration = stereoCalib
end
function PatternLocalisation:generateDefaultCircleFinderParams()
self.circleFinderParams = cv.SimpleBlobDetector_Params {}
self.circleFinderParams.thresholdStep = 5
self.circleFinderParams.minThreshold = 10
self.circleFinderParams.maxThreshold = 230
self.circleFinderParams.minRepeatability = 3
self.circleFinderParams.minDistBetweenBlobs = 5
self.circleFinderParams.filterByColor = false
self.circleFinderParams.blobColor = 0
self.circleFinderParams.filterByArea = true -- area of the circle in pixels
self.circleFinderParams.minArea = 200
self.circleFinderParams.maxArea = 1000
self.circleFinderParams.filterByCircularity = true
self.circleFinderParams.minCircularity = 0.6
self.circleFinderParams.maxCircularity = 10
self.circleFinderParams.filterByInertia = false
self.circleFinderParams.minInertiaRatio = 0.6
self.circleFinderParams.maxInertiaRatio = 10
self.circleFinderParams.filterByConvexity = true
self.circleFinderParams.minConvexity = 0.8
self.circleFinderParams.maxConvexity = 10
end
function PatternLocalisation:plotCrosshair(img, posX, posY, crossSize)
local irSize = {x = img:size()[2], y = img:size()[1]} -- to get x, y img size
local lineLength = crossSize or 6
local centerX = posX
local centerY = posY
cv.line {
img,
{(centerX) - 2, (centerY)},
{(centerX) - 2 - lineLength, (centerY)},
{0, 0, 0}
}
cv.line {
img,
{(centerX) + 2, (centerY)},
{(centerX) + 2 + lineLength, (centerY)},
{0, 0, 0}
}
cv.line {
img,
{(centerX), (centerY) - 2},
{(centerX), (centerY) - 2 - lineLength},
{0, 0, 0}
}
cv.line {
img,
{(centerX), (centerY) + 2},
{(centerX), (centerY) + 2 + lineLength},
{0, 0, 0}
}
for i = -1, 1, 2 do
cv.line {
img,
{(centerX) - 4, (centerY) + i},
{(centerX) - 2 - lineLength, (centerY) + i},
{250, 200, 200}
}
cv.line {
img,
{(centerX) + 4, (centerY) + i},
{(centerX) + 2 + lineLength, (centerY) + i},
{250, 200, 200}
}
cv.line {
img,
{(centerX) + i, (centerY) - 4},
{(centerX) + i, (centerY) - 2 - lineLength},
{250, 200, 200}
}
cv.line {
img,
{(centerX) + i, (centerY) + 4},
{(centerX) + i, (centerY) + 2 + lineLength},
{250, 200, 200}
}
end
end
-- Generate ground truth circle center points of the calibration pattern.
-- Z is set to 0 for all points.
function PatternLocalisation:generatePatternPoints(pointsX, pointsY, pointSize)
-- calculates the groundtruth x, y, z positions of the points of the asymmetric circle pattern
local corners = torch.FloatTensor(pointsX * pointsY, 1, 3):zero()
local i = 1
for y = 1, pointsY do
for x = 1, pointsX do
corners[i][1][1] = (2 * (x - 1) + (y - 1) % 2) * pointSize
corners[i][1][2] = (y - 1) * pointSize
corners[i][1][3] = 0
i = i + 1
end
end
return corners
end
function PatternLocalisation:rotVectorToMat3x3(vec)
-- transform a rotation vector as e.g. provided by solvePnP to a 3x3 rotation matrix using the Rodrigues' rotation formula
local theta = torch.norm(vec)
local r = vec / theta
r = torch.squeeze(r)
local mat = torch.Tensor({{0, -1 * r[3], r[2]}, {r[3], 0, -1 * r[1]}, {-1 * r[2], r[1], 0}})
r = r:resize(3, 1)
return torch.eye(3) * math.cos(theta) + (r * r:t()) * (1 - math.cos(theta)) + mat * math.sin(theta)
end
function PatternLocalisation:mirrorPatternPoints(points, patWidth, patHeight, imgDebug)
-- resort points
local pointsResorted = torch.FloatTensor(patWidth * patHeight, 1, 2)
for col = 0, patHeight - 1 do
for row = 0, patWidth - 1 do
pointsResorted[col * patWidth + row + 1][1][{}] =
points[(patWidth) * (patHeight - col) - patWidth + row + 1][1][{}]
end
end
if imgDebug ~= nil then
for i = 1, patWidth * patHeight do
self:plotCrosshair(imgDebug, pointsResorted[i][1][1], pointsResorted[i][1][2], 8)
print("point " .. pointsResorted[i][1][1] .. ", y=" .. pointsResorted[i][1][2])
cv.imshow {"debug", imgDebug}
cv.waitKey {1000}
end
end
return pointsResorted
end
local function findMarker(markerList, markerId)
for i,m in ipairs(markerList) do
if m.id == markerId then
return m
end
end
return nil
end
function PatternLocalisation:calcCamPose_original(inputImg, camIntrinsics, patternData, doDebug, imgShowInput)
doDebug = doDebug or false
local camPoseFinal
local blobDetector = cv.SimpleBlobDetector {self.circleFinderParams}
local found, points =
cv.findCirclesGrid {
image = inputImg,
patternSize = {height = patternData.height, width = patternData.width},
flags = cv.CALIB_CB_ASYMMETRIC_GRID + cv.CALIB_CB_CLUSTERING,
blobDetector = blobDetector
}
if found then
local points3d = self:generatePatternPoints(patternData.width, patternData.height, patternData.pointDist)
local poseFound, poseCamRotVector, poseCamTrans =
cv.solvePnP {
objectPoints = points3d,
imagePoints = points,
cameraMatrix = camIntrinsics,
distCoeffs = torch.DoubleTensor(1, 5):zero()
}
local poseCamRotMatrix = self:rotVectorToMat3x3(poseCamRotVector)
camPoseFinal = torch.DoubleTensor(4, 4):zero()
camPoseFinal[{{1, 3}, {1, 3}}] = poseCamRotMatrix
camPoseFinal[{{1, 3}, {4}}] = poseCamTrans
camPoseFinal[4][4] = 1
end
if doDebug then
local imgShow
if imgShowInput ~= nil then
imgShow = imgShowInput
else
imgShow = self:grayToRGB(inputImg)
end
if found then
cv.drawChessboardCorners {image = imgShow, patternSize = pattern, corners = points, patternWasFound = found}
end
imgShow = cv.resize {imgShow, {imgShow:size(2) * self.imgShowRescale, imgShow:size(1) * self.imgShowRescale}}
cv.imshow {"camPoseDebug", imgShow}
cv.waitKey {1000}
cv.destroyWindow {winname = "camPoseDebug"}
end
return camPoseFinal, points
end
function PatternLocalisation:calcCamPose(inputImg, camIntrinsics, patternData, doDebug, imgShowInput, patternID)
doDebug = doDebug or false
imgShowInput = imgShowInput or nil
patternID = patternID or nil
local camPoseFinal
print("Searching calibration target.")
local found = false
local points
if inputImg:dim() == 3 then
if inputImg:size(3) > 1 then
-- extract green channel (e.g. of color cams with RGB Bayer Matrix)
local greenImgLeft = inputImg[{{},{},2}]:clone()
inputImg = greenImgLeft
end
end
local foundMarkers = self:processImg(inputImg, false) -- if too many circle points are found, point clusters are detected in each of which we search for the pattern
if next(foundMarkers) ~= nil then
if patternID ~= nil then
local m = findMarker(foundMarkers, patternID)
if m then
points = m.points
found = true
else
print(string.format("[Warning] Pattern with ID %d not found", patternID))
end
else
points = foundMarkers[1].points
found = true
end
else
print('[Warning] Trying fallback with standard findCirclesGrid() function...')
found, points = cv.findCirclesGrid { image = inputImg,
patternSize = { height = self.pattern.height, width = self.pattern.width },
flags = cv.CALIB_CB_ASYMMETRIC_GRID
}
if found and patternID ~= nil then
local id = self:getPatternId(inputImg, points, self.pattern)
if id ~= patternID then
print(string.format("[Warning] Pattern with ID %d not found", patternID))
found = false
end
else
if not found then
print("[Warning] No pattern found")
end
end
end
if found then
local points3d = self:generatePatternPoints(patternData.width, patternData.height, patternData.pointDist)
local poseFound, poseCamRotVector, poseCamTrans =
cv.solvePnP {
objectPoints = points3d,
imagePoints = points,
cameraMatrix = camIntrinsics,
distCoeffs = torch.DoubleTensor(1, 5):zero()
}
local poseCamRotMatrix = self:rotVectorToMat3x3(poseCamRotVector)
camPoseFinal = torch.DoubleTensor(4, 4):zero()
camPoseFinal[{{1, 3}, {1, 3}}] = poseCamRotMatrix
camPoseFinal[{{1, 3}, {4}}] = poseCamTrans
camPoseFinal[4][4] = 1
end
if doDebug then
local imgShow
if imgShowInput ~= nil then
imgShow = imgShowInput
else
imgShow = self:grayToRGB(inputImg)
end
if found then
cv.drawChessboardCorners {image = imgShow, patternSize = { height = patternData.height, width = patternData.width }, corners = points, patternWasFound = found}
end
imgShow = cv.resize {imgShow, {imgShow:size(2) * self.imgShowRescale, imgShow:size(1) * self.imgShowRescale}}
cv.imshow {"camPoseDebug", imgShow}
cv.waitKey {500}
end
return camPoseFinal, points
end
function PatternLocalisation:calcCamPoseViaPlaneFit(imgLeft, imgRight, whichCam, doDebug, imgShowInput, patternID)
local whichCam = whichCam or "left"
local doDebug = doDebug or false
local imgShowInput = imgShowInput or nil
local patternID = patternID or nil
local camPoseFinal
stereoCalib = self.stereoCalibration
local leftCamMat = stereoCalib.camLeftMatrix
local rightCamMat = stereoCalib.camRightMatrix
local leftDistCoeffs = stereoCalib.camLeftDistCoeffs
local rightDistCoeffs = stereoCalib.camRightDistCoeffs
local rightLeftCamTrafo = stereoCalib.trafoLeftToRightCam
-- Stereo Rectify:
local R = rightLeftCamTrafo[{{1, 3}, {1, 3}}]:double()
local T = rightLeftCamTrafo[{{1, 3}, {4}}]:double()
local leftR = torch.DoubleTensor(3, 3)
local rightR = torch.DoubleTensor(3, 3)
local leftP = torch.DoubleTensor(3, 4)
local rightP = torch.DoubleTensor(3, 4)
local Q = torch.DoubleTensor(4, 4)
cv.stereoRectify {
cameraMatrix1 = leftCamMat,
distCoeffs1 = leftDistCoeffs,
cameraMatrix2 = rightCamMat,
distCoeffs2 = rightDistCoeffs,
imageSize = {imgLeft:size(2), imgLeft:size(1)},
R = R,
T = T,
R1 = leftR,
R2 = rightR,
P1 = leftP,
P2 = rightP,
Q = Q,
flags = 0
}
-- Undistortion + rectification:
local mapAImgLeft, mapBImgLeft =
cv.initUndistortRectifyMap {
cameraMatrix = leftCamMat,
distCoeffs = leftDistCoeffs,
R = leftR,
newCameraMatrix = leftP,
size = {imgLeft:size(2), imgLeft:size(1)},
m1type = cv.CV_32FC1
}
local imgLeftRectUndist =
cv.remap {src = imgLeft, map1 = mapAImgLeft, map2 = mapBImgLeft, interpolation = cv.INTER_NEAREST}
local mapAImgRight, mapBImgRight =
cv.initUndistortRectifyMap {
cameraMatrix = rightCamMat,
distCoeffs = rightDistCoeffs,
R = rightR,
newCameraMatrix = rightP,
size = {imgRight:size(2), imgRight:size(1)},
m1type = cv.CV_32FC1
}
local imgRightRectUndist =
cv.remap {src = imgRight, map1 = mapAImgRight, map2 = mapBImgRight, interpolation = cv.INTER_NEAREST}
-- Detect all circle points of the pattern in the left/right image
-- (see "AutoCalibration.lua", function "extractPoints")
--------------------------------------------------------------------
print("Searching calibration target.")
local ok1, ok2 = false, false
local circlesGridPointsLeft
local circlesGridPointsRight
-- Searching calibration target in left camera image:
if imgLeftRectUndist:dim()> 2 and imgLeftRectUndist:size(3) > 1 then
-- extract green channel (e.g. of color cams with RGB Bayer Matrix)
local greenImgLeft = imgLeftRectUndist[{{},{},2}]:clone()
imgLeftRectUndist = greenImgLeft
end
local foundMarkersLeft = self:processImg(imgLeftRectUndist, false) -- if too many circle points are found, point clusters are detected in each of which is searched for the pattern
if next(foundMarkersLeft) ~= nil then
if patternID ~= nil then
local m = findMarker(foundMarkersLeft, patternID)
if m then
circlesGridPointsLeft = m.points
ok1 = true
else
print(string.format("[Warning] Pattern with ID %d not found in left image", patternID))
end
else
circlesGridPointsLeft = foundMarkersLeft[1].points
ok1 = true
end
else
print('[Warning] Trying fallback with standard findCirclesGrid() function...')
local ok, points = cv.findCirclesGrid { image = imgLeftRectUndist,
patternSize = { height = self.pattern.height, width = self.pattern.width },
flags = cv.CALIB_CB_ASYMMETRIC_GRID
}
if ok and patternID ~= nil then
local id = self:getPatternId(imgLeftRectUndist, points, self.pattern)
if id == patternID then
circlesGridPointsLeft = points
ok1 = true
else
print(string.format("[Warning] Pattern with ID %d not found in left image", patternID))
end
else
if ok then
circlesGridPointsLeft = points
ok1 = true
else
print("[Warning] No pattern found in left image")
end
end
end
-- Searching calibration target in right camera image:
if imgRightRectUndist:dim()> 2 and imgRightRectUndist:size(3) > 1 then
-- extract green channel (e.g. of color cams with RGB Bayer Matrix)
local greenImgRight = imgRightRectUndist[{{},{},2}]:clone()
imgRightRectUndist = greenImgRight
end
local foundMarkersRight = self:processImg(imgRightRectUndist, false) -- if too many circle points are found, point clusters are detected in each of which is searched for the pattern
if next(foundMarkersRight) ~= nil then
if patternID ~= nil then
local m = findMarker(foundMarkersRight, patternID)
if m then
circlesGridPointsRight = m.points
ok2 = true
else
print(string.format("[Warning] Pattern with ID %d not found in right image", patternID))
end
else
circlesGridPointsRight = foundMarkersRight[1].points
ok2 = true
end
else
print('[Warning] Trying fallback with standard findCirclesGrid() function...')
local ok, points = cv.findCirclesGrid { image = imgRightRectUndist,
patternSize = { height = self.pattern.height, width = self.pattern.width },
flags = cv.CALIB_CB_ASYMMETRIC_GRID
}
if ok and patternID ~= nil then
local id = self:getPatternId(imgRightRectUndist, points, self.pattern)
if id == patternID then
circlesGridPointsRight = points
ok2 = true
else
print(string.format("[Warning] Pattern with ID %d not foundin right image", patternID))
end
else
if ok then
circlesGridPointsRight = points
ok2 = true
else
print("[Warning] No pattern found in right image")
end
end
end
print(string.format("ok1: %s", ok1))
print(string.format("ok2: %s\n", ok2))
if doDebug then
local imgShowLeft = self:grayToRGB(imgLeftRectUndist)
if ok1 then
cv.drawChessboardCorners {image = imgShowLeft, patternSize = { height = self.pattern.height, width = self.pattern.width }, corners = circlesGridPointsLeft, patternWasFound = ok1}
end
imgShowLeft = cv.resize {imgShowLeft, {imgShowLeft:size(2) * self.imgShowRescale, imgShowLeft:size(1) * self.imgShowRescale}}
cv.imshow {"camPoseLeftDebug", imgShowLeft}
cv.waitKey {500}
local imgShowRight = self:grayToRGB(imgRightRectUndist)
if ok2 then
cv.drawChessboardCorners {image = imgShowRight, patternSize = { height = self.pattern.height, width = self.pattern.width }, corners = circlesGridPointsRight, patternWasFound = ok2}
end
imgShowRight = cv.resize {imgShowRight, {imgShowRight:size(2) * self.imgShowRescale, imgShowRight:size(1) * self.imgShowRescale}}
cv.imshow {"camPoseRightDebug", imgShowRight}
cv.waitKey {500}
end
local ok = ok1 and ok2
if ok then
-- Save 2d grid points.
local nPoints = self.pattern.width * self.pattern.height
local leftProjPoints = torch.DoubleTensor(2, nPoints) -- 2D coordinates (x,y) x #Points
local rightProjPoints = torch.DoubleTensor(2, nPoints)
for i = 1, nPoints do
leftProjPoints[1][i] = circlesGridPointsLeft[i][1][1]
leftProjPoints[2][i] = circlesGridPointsLeft[i][1][2]
rightProjPoints[1][i] = circlesGridPointsRight[i][1][1]
rightProjPoints[2][i] = circlesGridPointsRight[i][1][2]
end
local resulting4DPoints = torch.DoubleTensor(4, nPoints)
-- TriangulatePoints:
---------------------
cv.triangulatePoints {leftP, rightP, leftProjPoints, rightProjPoints, resulting4DPoints}
local resulting3DPoints = torch.DoubleTensor(nPoints, 3)
for i = 1, nPoints do
resulting3DPoints[i][1] = resulting4DPoints[1][i] / resulting4DPoints[4][i]
resulting3DPoints[i][2] = resulting4DPoints[2][i] / resulting4DPoints[4][i]
resulting3DPoints[i][3] = resulting4DPoints[3][i] / resulting4DPoints[4][i]
end
local pointsInCamCoords = torch.DoubleTensor(nPoints, 3)
if whichCam == "left" then
for i = 1, nPoints do
pointsInCamCoords[i] = torch.inverse(leftR) * resulting3DPoints[i]
end
else
for i = 1, nPoints do
pointsInCamCoords[i] = torch.inverse(rightR) * resulting3DPoints[i]
end
end
-- Generate plane through detected 3d points to get the transformation
----------------------------------------------------------------------
-- of the pattern into the coordinatesystem of the camera:
----------------------------------------------------------
-- Plane fit with pseudo inverse:
local A = torch.DoubleTensor(nPoints, 3) -- 2-dim. tensor of size 168 x 3
local b = torch.DoubleTensor(nPoints) -- 1-dim. tensor of size 168
for i = 1, nPoints do
A[i][1] = pointsInCamCoords[i][1]
A[i][2] = pointsInCamCoords[i][2]
A[i][3] = 1.0
b[i] = pointsInCamCoords[i][3]
end
-- Calculate x = A^-1 * b = (A^T A)^-1 * A^T * b (with pseudo inverse of A)
local At = A:transpose(1, 2)
local x = torch.mv(torch.mm(torch.inverse(torch.mm(At, A)), At), b)
-- Determine z-axis as normal on plane:
local n = torch.DoubleTensor(3)
n[1] = x[1]
n[2] = x[2]
n[3] = -1.0
local z_unit_vec = torch.div(n, torch.norm(n))
-- Determine x-axis along left boundary points of the pattern
local x_direction = pointsInCamCoords[self.pattern.width] - pointsInCamCoords[1]
local x_unit_vec = torch.div(x_direction, torch.norm(x_direction))
-- Determine y-axis along top boundary points of the pattern:
local y_direction = pointsInCamCoords[nPoints - self.pattern.width + 1] - pointsInCamCoords[1]
local y_unit_vec = torch.div(y_direction, torch.norm(y_direction))
-- Check, whether the normal vector z_unit_vec points into the correct direction.
local cross_product = torch.DoubleTensor(3)
cross_product = torch.cross(x_unit_vec, y_unit_vec)
cross_product = torch.div(cross_product, torch.norm(cross_product))
if z_unit_vec * cross_product < 0.0 then
z_unit_vec = -1.0 * z_unit_vec
end
-- x_unit_vec * z_unit_vec has to be zero.
-- Map x_unit_vec onto plane.
local new_x_unit_vec = x_unit_vec - x_unit_vec * z_unit_vec * z_unit_vec
if new_x_unit_vec * x_unit_vec < 0.0 then
new_x_unit_vec = -1.0 * new_x_unit_vec
end
-- Determine new y_unit_vec as cross product of x_unit_vec and z_unit_vec:
local new_y_unit_vec = torch.cross(new_x_unit_vec, z_unit_vec)
if new_y_unit_vec * y_unit_vec < 0.0 then
new_y_unit_vec = -1.0 * new_y_unit_vec
end
local newer_x_unit_vec = torch.cross(new_y_unit_vec, z_unit_vec)
newer_x_unit_vec = torch.div(newer_x_unit_vec, torch.norm(newer_x_unit_vec))
new_y_unit_vec = torch.div(new_y_unit_vec, torch.norm(new_y_unit_vec))
z_unit_vec = torch.div(z_unit_vec, torch.norm(z_unit_vec))
-- Transform pattern coordinate system into camera coordinate system:
-- M_B->A = (x_unit_vec, y_unit_vec, z_unit_vec, support vector)
camPoseFinal = torch.DoubleTensor(4, 4):zero()
camPoseFinal[{{1, 3}, {1}}] = newer_x_unit_vec
camPoseFinal[{{1, 3}, {2}}] = new_y_unit_vec
camPoseFinal[{{1, 3}, {3}}] = z_unit_vec
camPoseFinal[{{1, 3}, {4}}] = pointsInCamCoords[1] -- = support vector
camPoseFinal[4][4] = 1
else
print("PATTERN NOT FOUND!!!")
camPoseFinal = torch.eye(4)
end
return ok, camPoseFinal, circlesGridPointsLeft, circlesGridPointsRight
end
function PatternLocalisation:calcCamPoseMirrored(pointsResorted)
local points3d = self:generatePatternPoints(self.pattern.width, self.pattern.height, self.pattern.pointDist)
local poseFound, poseCamRotVector, poseCamTrans =
cv.solvePnP {
objectPoints = points3d,
imagePoints = pointsResorted,
cameraMatrix = self.camIntrinsics,
distCoeffs = torch.DoubleTensor(1, 5):zero()
}
print(poseFound)
print(poseCamRotVector)
print("done")
local poseCamRotMatrix = self:rotVectorToMat3x3(poseCamRotVector)
local camPoseFinal = torch.DoubleTensor(4, 4):zero()
camPoseFinal[{{1, 3}, {1, 3}}] = poseCamRotMatrix
camPoseFinal[{{1, 3}, {4}}] = poseCamTrans
camPoseFinal[4][4] = 1
return camPoseFinal
end
-- Calculates a rectangle of a point set (torch.Tensor of dim n,2) , usefull for ROI or cropping
function PatternLocalisation:calcBoundingRect(pointTensor, border, imgWidth, imgHeight)
local points = torch.squeeze(pointTensor)
local rectPoints = cv.boundingRect {points}
local rect = torch.IntTensor(2, 2):zero() -- top left and bottom right corner of the ROI rect
if rectPoints.x - border >= 1 then
rect[1][1] = rectPoints.x - border
else
rect[1][1] = 1
end
if rectPoints.y - border >= 1 then
rect[1][2] = rectPoints.y - border
else
rect[1][2] = 1
end
if rectPoints.x + rectPoints.width + border < imgWidth then
rect[2][1] = rectPoints.x + rectPoints.width + border
else
rect[2][1] = imgWidth
end
if rectPoints.y + rectPoints.height + border < imgHeight then
rect[2][2] = rectPoints.y + rectPoints.height + border
else
rect[2][2] = imgHeight
end
return rect
end
-- Calculates the cam pose for a pattern. The pattern has to be the only pattern within the region of interesst defined by rectROI
function PatternLocalisation:calcCamPoseROI(inputImg, rectROI, doDebug)
doDebug = doDebug or false
if inputImg:dim() ~= 2 then
print("Only 2dim grayscale images supported (for speed reason). Provided image has size ")
print(inputImg:size())
return nil
end
local imgROI = torch.ByteTensor(inputImg:size(1), inputImg:size(2)):fill(255)
imgROI[{{rectROI[1][2], rectROI[2][2]}, {rectROI[1][1], rectROI[2][1]}}] =
inputImg[{{rectROI[1][2], rectROI[2][2]}, {rectROI[1][1], rectROI[2][1]}}]
if doDebug == true then
local imgShow
if self.imgShowRescale ~= nil then
imgShow = cv.resize {imgROI, {imgROI:size(2) * self.imgShowRescale, imgROI:size(1) * self.imgShowRescale}}
else
imgShow = imgROI
end
cv.imshow {"imgPatROI", imgShow}
cv.waitKey {1000}
cv.destroyWindow {winname = "imgPatROI"}
end
return self:processImg(imgROI)
end
function PatternLocalisation:findCircles(inputImg, doDebug)
-- Setup SimpleBlobDetector parameters.
local blobDetector = cv.SimpleBlobDetector {self.circleFinderParams}
local resultsTmp = blobDetector:detect {image = inputImg}
local results = {}
for i = 1, resultsTmp.size do
table.insert(
results,
{
radius = resultsTmp.data[i].size / 2.0,
angle = resultsTmp.data[i].angle,
pos = torch.DoubleTensor({resultsTmp.data[i].pt.x, resultsTmp.data[i].pt.y, 1})
}
)
end
local circleScale = 16
local shiftBits = 4
if doDebug == true then
local rescaleFactor = 1
if self.imgShowRescale ~= nil then
rescaleFactor = self.imgShowRescale
end
local imgScale = cv.resize {inputImg, {inputImg:size(2) * rescaleFactor, inputImg:size(1) * rescaleFactor}}
imgScale = self:grayToRGB(imgScale)
for key, val in ipairs(results) do
cv.circle {
img = imgScale,
center = {x = val.pos[1] * rescaleFactor * circleScale, y = val.pos[2] * rescaleFactor * circleScale},
radius = val.radius * rescaleFactor * circleScale,
color = {0, 255, 255},
thickness = 2,
lineType = cv.LINE_AA,
shift = shiftBits
}
end
cv.imshow {"circleFinder", imgScale}
cv.waitKey {500}
end
return results
end
-- cluster: list, containing index numbers of list circleCenters
function PatternLocalisation:expandCluster(
kdtree,
circleCenters,
eps,
minNPoints,
currPointIndex,
neighbours,
pointStatus,
cluster)
table.insert(cluster, currPointIndex)
pointStatus[currPointIndex] = bit.bor(pointStatus[currPointIndex], self.clusterStatus.kAssigned)
-- convert torchTensor to list so we can append points
local neighbourList = {}
for i = 1, neighbours:size(2) do
table.insert(neighbourList, neighbours[1][i])
end
local loopEnd = #neighbourList
local i = 1
while i <= loopEnd do
local index = neighbourList[i]
if bit.band(pointStatus[index], self.clusterStatus.kProcessed) == 0 then
pointStatus[index] = bit.bor(pointStatus[index], self.clusterStatus.kProcessed)
local queryPoint = torch.FloatTensor(1, 2)
queryPoint[1][1] = circleCenters[index].pos[1]
queryPoint[1][2] = circleCenters[index].pos[2]
local indices = torch.IntTensor(1, minNPoints):fill(0)
local dists = torch.FloatTensor(1, minNPoints):fill(0)
local tmp -- not used
tmp, indices, dists =
kdtree:radiusSearch {
query = queryPoint,
radius = eps * eps,
maxResults = minNPoints,
indices = indices,
dists = dists
}
if indices[1][minNPoints] > 0 then
for i = 1, indices:size(2) do
table.insert(neighbourList, indices[1][i])
loopEnd = #neighbourList
end
end
end
if bit.band(pointStatus[index], self.clusterStatus.kAssigned) == 0 then
table.insert(cluster, index)
pointStatus[index] = bit.bor(pointStatus[index], self.clusterStatus.kAssigned)
end
i = i + 1
end
end
function PatternLocalisation:DBScan(circleCenters, eps, minNPoints)
local clusterList = {}
if #circleCenters < 1 then
return clusterList -- return an empty cluster list because clustering less then one point is not possible
end
local circleCentersTensor = torch.FloatTensor(#circleCenters, 2)
for i, val in ipairs(circleCenters) do
circleCentersTensor[i][1] = val.pos[1]
circleCentersTensor[i][2] = val.pos[2]
end
local params = cv.flann.KDTreeIndexParams {trees = 10}
local kdtree = cv.flann.Index {circleCentersTensor, params, cv.FLANN_DIST_EUCLIDEAN}
local pointStatus = torch.ByteTensor(#circleCenters):fill(self.clusterStatus.kUnprocessed)
for i = 1, #circleCenters do
if bit.band(pointStatus[i], self.clusterStatus.kProcessed) == 0 then
pointStatus[i] = bit.bor(pointStatus[i], self.clusterStatus.kProcessed)
local queryPoint = torch.FloatTensor(1, 2)
queryPoint[1][1] = circleCenters[i].pos[1]
queryPoint[1][2] = circleCenters[i].pos[2]
-- indices and dists are 1xn int and float tensors, respectivly
local indices = torch.IntTensor(1, minNPoints):fill(0)
local dists = torch.FloatTensor(1, minNPoints):fill(0)
local tmp -- not used
tmp, indices, dists =
kdtree:radiusSearch {
query = queryPoint,
radius = eps * eps,
maxResults = minNPoints,
indices = indices,
dists = dists
}
if indices[1][minNPoints] <= 0 or indices[1][minNPoints] ~= indices[1][minNPoints] then -- 0 or nan
pointStatus[i] = bit.bor(pointStatus[i], self.clusterStatus.kNoise)
else
local nextCluster = {}
table.insert(clusterList, nextCluster)
self:expandCluster(kdtree, circleCenters, eps, minNPoints, i, indices, pointStatus, nextCluster)
end
end
end
return clusterList
end
-- Debugging function
function PatternLocalisation:espChecking(circleCentersInput, queryX, queryY, debugImg)
local circleCenters = torch.FloatTensor(#circleCentersInput, 2)
for i, val in ipairs(circleCentersInput) do
circleCenters[i][1] = val.pos[1]
circleCenters[i][2] = val.pos[2]
end
local params = cv.flann.KDTreeIndexParams {trees = 10}
local kdtree = cv.flann.Index {circleCenters, params, cv.FLANN_DIST_EUCLIDEAN}
local queryPoint = torch.FloatTensor(1, 2)
queryPoint[1][1] = queryX
queryPoint[1][2] = queryY
local nNeighbours = 6
local indices, dists = kdtree:knnSearch {queryPoint, nNeighbours}
local circleScale = 16
local shiftBits = 4
local rescaleFactor = 1.0
cv.circle {
img = debugImg,
center = {x = queryPoint[1][1] * rescaleFactor * circleScale, y = queryPoint[1][2] * rescaleFactor * circleScale},
radius = 5 * circleScale,
color = {50, 50, 255},
thickness = 3,
lineType = cv.LINE_AA,
shift = shiftBits
}
for i = 1, nNeighbours do
local center = {}
center.x = circleCentersInput[indices[1][i]].pos[1] * rescaleFactor * circleScale
center.y = circleCentersInput[indices[1][i]].pos[2] * rescaleFactor * circleScale
local radius = circleCentersInput[indices[1][i]].radius * rescaleFactor * circleScale
cv.circle {
img = debugImg,
center = center,
radius = radius,
color = {255, 0, 255},
thickness = 1,
lineType = cv.LINE_AA,
shift = shiftBits
}
end
cv.imshow {"testESP", debugImg}
cv.waitKey {1000}
end
-- Set all pixels to color color, except inside the polygon defined by points
-- points is torch.IntTensor(nPolygonPoints, 2)
function PatternLocalisation:maskImage(inputImg, points, color)
color = color or 255
local mask = torch.ByteTensor(inputImg:size(1), inputImg:size(2), 1):fill(1)
cv.fillConvexPoly {mask, points, {0}}
local applyMask = torch.eq(mask, 1)
-- If image is RGB image, expand the mask to contain all three channels
if inputImg:dim() == 3 and inputImg:size(3) == 3 then
applyMask = applyMask:expand((#applyMask)[1], (#applyMask)[2], 3)
end
-- apply the mask to a copy of the provided image
local imgMasked = inputImg:clone()