def get_nearest_feature( image, this_point, n=2000 ): """ Get the n-nearest features to a specified image coordinate. Features are determined using cvGoodFeaturesToTrack. """ _red = cv.cvScalar (0, 0, 255, 0); _green = cv.cvScalar (0, 255, 0, 0); _blue = cv.cvScalar (255,0,0,0); _white = cv.cvRealScalar (255) _black = cv.cvRealScalar (0) quality = 0.01 min_distance = 4 N_best = n win_size = 11 grey = cv.cvCreateImage (cv.cvGetSize (image), 8, 1) eig = cv.cvCreateImage (cv.cvGetSize (image), 32, 1) temp = cv.cvCreateImage (cv.cvGetSize (image), 32, 1) # create a grey version of the image cv.cvCvtColor ( image, grey, cv.CV_BGR2GRAY) points = cv.cvGoodFeaturesToTrack ( grey, eig, temp, N_best, quality, min_distance, None, 3, 0, 0.04) # refine the corner locations better_points = cv.cvFindCornerSubPix ( grey, points, cv.cvSize (win_size, win_size), cv.cvSize (-1, -1), cv.cvTermCriteria (cv.CV_TERMCRIT_ITER | cv.CV_TERMCRIT_EPS, 20, 0.03)) eigs = [] for i in range(len(points)): eigs.append(cv.cvGetMat(eig)[int(points[i].y)][int(points[i].x)]) mypoints = np.matrix(np.zeros((len(points)*2),dtype=float)).reshape(len(points),2) dists = [] for i,point in enumerate(points): mypoints[i,0]=point.x mypoints[i,1]=point.y dists.append( np.linalg.norm(mypoints[i,:]-this_point) ) dists = np.array(dists) sorteddists = dists.argsort() cv.cvDrawCircle ( image, points[ sorteddists[0] ], 5, _green, 2, 8, 0 ) return better_points[ sorteddists[0] ]
def getGoodFeatures(image): # XXX: BETA but working # TODO: Clean up!! Comments properly # This is mostly copy/paster from /usr/share/opencv/samples/python/lkdemo.py """Find features using OpenCV's cvFindGoodFeatures""" win_size = 10 MAX_COUNT = 500 # create the images we need grey = cv.cvCreateImage (cv.cvGetSize (image), 8, 1) eig = cv.cvCreateImage (cv.cvGetSize (grey), 32, 1) temp = cv.cvCreateImage (cv.cvGetSize (grey), 32, 1) # Make a b/w copy cv.cvCvtColor(image, grey, cv.CV_BGR2GRAY) # the default parameters quality = 0.01 min_distance = 10 # search the good points points = cv.cvGoodFeaturesToTrack ( grey, eig, temp, MAX_COUNT, quality, min_distance, None, 3, 1, 0.04) # refine the corner locations cv.cvFindCornerSubPix ( grey, points, cv.cvSize (win_size, win_size), cv.cvSize (-1, -1), cv.cvTermCriteria (cv.CV_TERMCRIT_ITER | cv.CV_TERMCRIT_EPS, 20, 0.03)) # release the temporary images for good meassure cv.cvReleaseImage (eig) cv.cvReleaseImage (temp) cv.cvReleaseImage (grey) return points
def get_nearest_feature(image, this_point, n=2000):
"""
Get the n-nearest features to a specified image coordinate.
Features are determined using cvGoodFeaturesToTrack.
"""
_red = cv.cvScalar(0, 0, 255, 0)
_green = cv.cvScalar(0, 255, 0, 0)
_blue = cv.cvScalar(255, 0, 0, 0)
_white = cv.cvRealScalar(255)
_black = cv.cvRealScalar(0)
quality = 0.01
min_distance = 4
N_best = n
win_size = 11
grey = cv.cvCreateImage(cv.cvGetSize(image), 8, 1)
eig = cv.cvCreateImage(cv.cvGetSize(image), 32, 1)
temp = cv.cvCreateImage(cv.cvGetSize(image), 32, 1)
# create a grey version of the image
cv.cvCvtColor(image, grey, cv.CV_BGR2GRAY)
points = cv.cvGoodFeaturesToTrack(grey, eig, temp, N_best, quality,
min_distance, None, 3, 0, 0.04)
# refine the corner locations
better_points = cv.cvFindCornerSubPix(
grey, points, cv.cvSize(win_size, win_size), cv.cvSize(-1, -1),
cv.cvTermCriteria(cv.CV_TERMCRIT_ITER | cv.CV_TERMCRIT_EPS, 20, 0.03))
eigs = []
for i in range(len(points)):
eigs.append(cv.cvGetMat(eig)[int(points[i].y)][int(points[i].x)])
mypoints = np.matrix(np.zeros((len(points) * 2),
dtype=float)).reshape(len(points), 2)
dists = []
for i, point in enumerate(points):
mypoints[i, 0] = point.x
mypoints[i, 1] = point.y
dists.append(np.linalg.norm(mypoints[i, :] - this_point))
dists = np.array(dists)
sorteddists = dists.argsort()
cv.cvDrawCircle(image, points[sorteddists[0]], 5, _green, 2, 8, 0)
return better_points[sorteddists[0]]
def __FindCorner(self, filename): #find the corners of images, and save all corner points in self.vKeyPoints
self.img = highgui.cvLoadImage (filename)
greyimg = cv.cvCreateImage(cv.cvSize(self.img.width, self.img.height), 8,1)
hsvimg = cv.cvCreateImage(cv.cvGetSize(self.img), 8, 3)
cv.cvCvtColor(self.img, hsvimg, cv.CV_RGB2HSV)
cv.cvCvtColor (hsvimg, greyimg, cv.CV_BGR2GRAY)
eigImage = cv.cvCreateImage(cv.cvGetSize(greyimg), cv.IPL_DEPTH_32F, 1)
tempImage = cv.cvCreateImage(cv.cvGetSize(greyimg), cv.IPL_DEPTH_32F, 1)
self.points = cv.cvGoodFeaturesToTrack(greyimg, eigImage,tempImage, 2000, 0.01, 5, None, 3,0,0.01 )
self.points2 = cv.cvFindCornerSubPix(greyimg, self.points,cv.cvSize(20, 20),
cv.cvSize(-1, -1), cv.cvTermCriteria(cv.CV_TERMCRIT_ITER |cv.CV_TERMCRIT_EPS, 20, 0.03))
cv.cvReleaseImage(eigImage)
cv.cvReleaseImage(tempImage)
cv.cvSetZero (image)
if need_to_init:
# we want to search all the good points
# create the wanted images
# search the good points
points [1] = cv.cvGoodFeaturesToTrack (
grey, eig, temp,
MAX_COUNT,
quality, min_distance, None, 3, 0, 0.04)
# refine the corner locations
cv.cvFindCornerSubPix (
grey,
points [1],
cv.cvSize (win_size, win_size), cv.cvSize (-1, -1),
cv.cvTermCriteria (cv.CV_TERMCRIT_ITER | cv.CV_TERMCRIT_EPS,
20, 0.03))
elif len (points [0]) > 0:
# we have points, so display them
# calculate the optical flow
[points [1], status], something = cv.cvCalcOpticalFlowPyrLK (
prev_grey, grey, prev_pyramid, pyramid,
points [0], len (points [0]),
(win_size, win_size), 3,
len (points [0]),
None,
cv.cvTermCriteria (cv.CV_TERMCRIT_ITER|cv.CV_TERMCRIT_EPS,
20, 0.03),
def harrisResponse(frame):
"""pyvision/point/DetectorHarris.py
Runs at 10.5 fps...
"""
#gray = cv.cvCreateImage( cv.cvGetSize(image), 8, 1 )
#corners = cv.cvCreateImage( cv.cvGetSize(image), 32, 1 )
#cv.cvCvtColor( image, gray, cv.CV_BGR2GRAY )
#cv.cvCornerHarris(gray,corners,15)
# This could be done in a persistant way
# create the images we need
image = cv.cvCreateImage(cv.cvGetSize(frame), 8, 3)
grey = cv.cvCreateImage(cv.cvGetSize(frame), 8, 1)
prev_grey = cv.cvCreateImage(cv.cvGetSize(frame), 8, 1)
pyramid = cv.cvCreateImage(cv.cvGetSize(frame), 8, 1)
prev_pyramid = cv.cvCreateImage(cv.cvGetSize(frame), 8, 1)
eig = cv.cvCreateImage(cv.cvGetSize(frame), cv.IPL_DEPTH_32F, 1)
temp = cv.cvCreateImage(cv.cvGetSize(frame), cv.IPL_DEPTH_32F, 1)
points = [[], []]
# copy the frame, so we can draw on it
cv.cvCopy(frame, image)
# create a grey version of the image
cv.cvCvtColor(image, grey, cv.CV_BGR2GRAY)
# search the good points
points[1] = cv.cvGoodFeaturesToTrack(grey, eig, temp, MAX_COUNT, quality,
min_distance, None, 3, 0, 0.04)
# refine the corner locations
cv.cvFindCornerSubPix(
grey, points[1], cv.cvSize(win_size, win_size), cv.cvSize(-1, -1),
cv.cvTermCriteria(cv.CV_TERMCRIT_ITER | cv.CV_TERMCRIT_EPS, 20, 0.03))
if len(points[0]) > 0:
# we have points, so display them
# calculate the optical flow
[points[1], status], something = cv.cvCalcOpticalFlowPyrLK(
prev_grey, grey, prev_pyramid, pyramid, points[0], len(points[0]),
(win_size, win_size), 3, len(points[0]), None,
cv.cvTermCriteria(cv.CV_TERMCRIT_ITER | cv.CV_TERMCRIT_EPS, 20,
0.03), flags)
# initializations
point_counter = -1
new_points = []
for the_point in points[1]:
# go trough all the points
# increment the counter
point_counter += 1
if add_remove_pt:
# we have a point to add, so see if it is close to
# another one. If yes, don't use it
dx = pt.x - the_point.x
dy = pt.y - the_point.y
if dx * dx + dy * dy <= 25:
# too close
add_remove_pt = 0
continue
if not status[point_counter]:
# we will disable this point
continue
# this point is a correct point
new_points.append(the_point)
# draw the current point
cv.cvCircle(image, cv.cvPointFrom32f(the_point), 3,
cv.cvScalar(0, 255, 0, 0), -1, 8, 0)
# set back the points we keep
points[1] = new_points
# swapping
prev_grey, grey = grey, prev_grey
prev_pyramid, pyramid = pyramid, prev_pyramid
points[0], points[1] = points[1], points[0]
return image
eig = cv.cvCreateImage(cv.cvGetSize(grey), 32, 1)
temp = cv.cvCreateImage(cv.cvGetSize(grey), 32, 1)
# the default parameters
quality = 0.01
min_distance = 10
# search the good points
points[1] = cv.cvGoodFeaturesToTrack(grey, eig, temp, MAX_COUNT,
quality, min_distance, None,
3, 0, 0.04)
# refine the corner locations
cv.cvFindCornerSubPix(
grey, points[1], cv.cvSize(win_size, win_size),
cv.cvSize(-1, -1),
cv.cvTermCriteria(cv.CV_TERMCRIT_ITER | cv.CV_TERMCRIT_EPS, 20,
0.03))
# release the temporary images
cv.cvReleaseImage(eig)
cv.cvReleaseImage(temp)
elif len(points[0]) > 0:
# we have points, so display them
# calculate the optical flow
points[1], status = cv.cvCalcOpticalFlowPyrLK(
prev_grey, grey, prev_pyramid, pyramid, points[0],
len(points[0]), cv.cvSize(win_size, win_size), 3,
len(points[0]), None,
def harrisResponse(frame):
"""pyvision/point/DetectorHarris.py
Runs at 10.5 fps...
"""
#gray = cv.cvCreateImage( cv.cvGetSize(image), 8, 1 )
#corners = cv.cvCreateImage( cv.cvGetSize(image), 32, 1 )
#cv.cvCvtColor( image, gray, cv.CV_BGR2GRAY )
#cv.cvCornerHarris(gray,corners,15)
# This could be done in a persistant way
# create the images we need
image = cv.cvCreateImage (cv.cvGetSize (frame), 8, 3)
grey = cv.cvCreateImage (cv.cvGetSize (frame), 8, 1)
prev_grey = cv.cvCreateImage (cv.cvGetSize (frame), 8, 1)
pyramid = cv.cvCreateImage (cv.cvGetSize (frame), 8, 1)
prev_pyramid = cv.cvCreateImage (cv.cvGetSize (frame), 8, 1)
eig = cv.cvCreateImage (cv.cvGetSize (frame), cv.IPL_DEPTH_32F, 1)
temp = cv.cvCreateImage (cv.cvGetSize (frame), cv.IPL_DEPTH_32F, 1)
points = [[], []]
# copy the frame, so we can draw on it
cv.cvCopy (frame, image)
# create a grey version of the image
cv.cvCvtColor (image, grey, cv.CV_BGR2GRAY)
# search the good points
points [1] = cv.cvGoodFeaturesToTrack (
grey, eig, temp,
MAX_COUNT,
quality, min_distance, None, 3, 0, 0.04)
# refine the corner locations
cv.cvFindCornerSubPix (
grey,
points [1],
cv.cvSize (win_size, win_size), cv.cvSize (-1, -1),
cv.cvTermCriteria (cv.CV_TERMCRIT_ITER | cv.CV_TERMCRIT_EPS,
20, 0.03))
if len (points [0]) > 0:
# we have points, so display them
# calculate the optical flow
[points [1], status], something = cv.cvCalcOpticalFlowPyrLK (
prev_grey, grey, prev_pyramid, pyramid,
points [0], len (points [0]),
(win_size, win_size), 3,
len (points [0]),
None,
cv.cvTermCriteria (cv.CV_TERMCRIT_ITER|cv.CV_TERMCRIT_EPS,
20, 0.03),
flags)
# initializations
point_counter = -1
new_points = []
for the_point in points [1]:
# go trough all the points
# increment the counter
point_counter += 1
if add_remove_pt:
# we have a point to add, so see if it is close to
# another one. If yes, don't use it
dx = pt.x - the_point.x
dy = pt.y - the_point.y
if dx * dx + dy * dy <= 25:
# too close
add_remove_pt = 0
continue
if not status [point_counter]:
# we will disable this point
continue
# this point is a correct point
new_points.append (the_point)
# draw the current point
cv.cvCircle (image,
cv.cvPointFrom32f(the_point),
3, cv.cvScalar (0, 255, 0, 0),
-1, 8, 0)
# set back the points we keep
points [1] = new_points
# swapping
prev_grey, grey = grey, prev_grey
prev_pyramid, pyramid = pyramid, prev_pyramid
points [0], points [1] = points [1], points [0]
return image