def get_features(self, img):
surf = cv.SURF(self.hessian_th, self.octaves, self.layers, True)
keypoints = cv.vector_KeyPoint()
features = surf(img, cv.Mat(), keypoints)
return keypoints, np.array(features)
def get_features(self, img):
surf = cv.SURF(self.hessian_th, self.octaves, self.layers, True)
keypoints = cv.vector_KeyPoint()
features = surf(img, cv.Mat(), keypoints)
return keypoints, np.array(features)
cv.cvtColor(object_color, object, cv.CV_BGR2GRAY)
# corners
src_corners = [
cv.Point(0, 0),
cv.Point(object.cols, 0),
cv.Point(object.cols, object.rows),
cv.Point(0, object.rows)
]
dst_corners = [cv.Point()] * 4
# find keypoints on both images
surf = cv.SURF(500, 4, 2, True)
mask = cv.Mat()
tt = float(cv.getTickCount())
objectKeypoints = cv.vector_KeyPoint()
objectDescriptors = surf(object, mask, objectKeypoints)
print("Object Descriptors: %d\n" % len(objectKeypoints))
imageKeypoints = cv.vector_KeyPoint()
imageDescriptors = surf(image, mask, imageKeypoints)
print("Image Descriptors: %d\n" % len(imageKeypoints))
tt = float(cv.getTickCount()) - tt
print("Extraction time = %gms\n" % (tt / (cv.getTickFrequency() * 1000.)))
# create a correspond Mat
correspond = cv.Mat(image.rows + object.rows, image.cols, cv.CV_8UC1,
cv.Scalar(0))
# copy the images to correspond -- numpy way
correspond[:object.rows, :object.cols] = object[:]
correspond[object.rows:, :image.cols] = image[:]
print("Can not load %s and/or %s\n" \
"Usage: find_obj [<object_filename> <scene_filename>]\n" \
% (object_filename, scene_filename))
exit(-1)
object = cv.Mat(object_color.size(), cv.CV_8UC1)
cv.cvtColor( object_color, object, cv.CV_BGR2GRAY )
# corners
src_corners = [cv.Point(0,0), cv.Point(object.cols, 0), cv.Point(object.cols, object.rows), cv.Point(0, object.rows)]
dst_corners = [cv.Point()]*4
# find keypoints on both images
surf = cv.SURF(500, 4, 2, True)
mask = cv.Mat()
tt = float(cv.getTickCount())
objectKeypoints = cv.vector_KeyPoint()
objectDescriptors = surf(object, mask, objectKeypoints)
print("Object Descriptors: %d\n" % len(objectKeypoints))
imageKeypoints = cv.vector_KeyPoint()
imageDescriptors = surf(image, mask, imageKeypoints)
print("Image Descriptors: %d\n" % len(imageKeypoints))
tt = float(cv.getTickCount()) - tt
print("Extraction time = %gms\n" % (tt/(cv.getTickFrequency()*1000.)))
# create a correspond Mat
correspond = cv.Mat(image.rows+object.rows, image.cols, cv.CV_8UC1, cv.Scalar(0))
# copy the images to correspond -- numpy way
correspond[:object.rows, :object.cols] = object[:]
correspond[object.rows:, :image.cols] = image[:]
