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 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 get_thresholded(self,gray_source,threshold): #Allocate a new image threshed=cv.cvCreateImage(cv.cvGetSize(gray_source),gray_source.depth,1) #Subtract 255 from all values in the image? cv.cvSubRS(gray_source,cv.cvRealScalar(255),threshed,None) #Apply a binary threshold to the image cv.cvThreshold(gray_source,threshed,threshold,255,cv.CV_THRESH_BINARY) #Release the source image cv.cvReleaseImage(gray_source) return threshed
#! /usr/bin/env python
print "OpenCV Python version of contours"
# import the necessary things for OpenCV
from opencv import cv
from opencv import highgui
# some default constants
_SIZE = 500
_DEFAULT_LEVEL = 3
# definition of some colors
_red = cv.cvScalar (0, 0, 255, 0);
_green = cv.cvScalar (0, 255, 0, 0);
_white = cv.cvRealScalar (255)
_black = cv.cvRealScalar (0)
# the callback on the trackbar, to set the level of contours we want
# to display
def on_trackbar (position):
# create the image for putting in it the founded contours
contours_image = cv.cvCreateImage (cv.cvSize (_SIZE, _SIZE), 8, 3)
# compute the real level of display, given the current position
levels = position - 3
# initialisation
_contours = contours
#! /usr/bin/env python
print "OpenCV Python version of contours"
# import the necessary things for OpenCV
from opencv import cv
from opencv import highgui
# some default constants
_SIZE = 500
_DEFAULT_LEVEL = 3
# definition of some colors
_red = cv.cvScalar(0, 0, 255, 0)
_green = cv.cvScalar(0, 255, 0, 0)
_white = cv.cvRealScalar(255)
_black = cv.cvRealScalar(0)
# the callback on the trackbar, to set the level of contours we want
# to display
def on_trackbar(position):
# create the image for putting in it the founded contours
contours_image = cv.cvCreateImage(cv.cvSize(_SIZE, _SIZE), 8, 3)
# compute the real level of display, given the current position
levels = position - 3
# initialisation
_contours = contours
def read(self): frame=self.input.read() if self.enabled: cv.cvSubRS(frame, cv.cvRealScalar(255), frame) return frame
