def _calibrate_camera(self) : source = CameraInputProvider.get_frame(self) success, corners = cv.cvFindChessboardCorners(source, cv.cvSize(self.grid[0],self.grid[1])) n_points = self.grid[0]*self.grid[1] grid_x = self.capture_dims[0]/self.grid[0] grid_y = self.capture_dims[1]/self.grid[1] dest = [] for i in range(0,self.grid[0]) : for j in range(0,self.grid[1]) : dest.append((j*grid_x,i*grid_y)) self.dest = dest s = cv.cvCreateMat(n_points,2,cv.CV_32F) d = cv.cvCreateMat(n_points,2,cv.CV_32F) p = cv.cvCreateMat(3,3,cv.CV_32F) for i in range(n_points): s[i,0] = corners[i].x s[i,1] = corners[i].y d[i,0] = dest[i][0] d[i,1] = dest[i][1] results = cv.cvFindHomography(s,d,p) self.matrix = p
def find_homography(points1, points2):
cv_homography = cv.cvCreateMat(3, 3, cv.CV_32FC1)
cv_points1 = numpymat2cvmat(points1)
cv_points2 = numpymat2cvmat(points2)
cv.cvFindHomography(cv_points1, cv_points2, cv_homography)
return cvmat2numpymat(cv_homography)
def find_homography(points1,points2): cv_homography = cv.cvCreateMat(3,3,cv.CV_32FC1) cv_points1 = numpymat2cvmat(points1) cv_points2 = numpymat2cvmat(points2) cv.cvFindHomography(cv_points1,cv_points2,cv_homography) return cvmat2numpymat(cv_homography)
def read(self):
source = self.input.read()
if not self.calibrated:
success, corners = cv.cvFindChessboardCorners(source, cv.cvSize(self.grid[0],self.grid[1]))
if success == 1 :
cv.cvDrawChessboardCorners(source,cv.cvSize(self.grid[0],self.grid[1]),corners,len(corners))
#self.debug_print('CVPerspective: success, corners = (%d,%s(%d))'%(success,corners,len(corners)))
n_points = self.grid[0]*self.grid[1]
grid_x = self.dims[0]/(self.grid[0]+1)
grid_y = self.dims[1]/(self.grid[1]+1)
self.dest = []
for i in range(1,self.grid[0]+1) :
for j in range(1,self.grid[1]+1) :
self.dest.append((j*grid_x,i*grid_y))
# loop through corners (clockwise wrapped), figure out which is closest to origin
four_corners = [corners[0],corners[self.grid[0]-1],corners[-1],corners[self.grid[0]*(self.grid[1]-1)]]
distances = []
for i,corner in enumerate(four_corners) :
distances.append((sqrt(corner.x**2+corner.y**2),i))
min_corner = min(distances)
#self.debug_print(distances)
#self.debug_print(min_corner)
if min_corner[1] != 0 :
rotator = array([corners])
rotator = rotator.reshape((self.grid))
rotator = rot90(rotator,min_corner[1])
corners = rotator.flatten().tolist()
s = cv.cvCreateMat(n_points,2,cv.CV_32F)
d = cv.cvCreateMat(n_points,2,cv.CV_32F)
p = cv.cvCreateMat(3,3,cv.CV_32F)
for i in range(n_points):
s[i,0] = corners[i].x
s[i,1] = corners[i].y
d[i,0] = self.dest[i][0]
d[i,1] = self.dest[i][1]
results = cv.cvFindHomography(s,d,p)
self.matrix = p
self.settings.perspective.calibrated = True
#self.debug_print('projection matrix:%s'%p)
if self.calibrated :
try : # workaround for now
dst = cv.cvCreateImage(cv.cvSize(self.dims[0],self.dims[1]),source.depth,source.nChannels)
cv.cvWarpPerspective( source, dst, self.matrix)
except AttributeError :
pass
else:
return dst
return source
