def initialise(self, im_gray0, tl, br):
#Initialise detector, descriptor, matcher
self.detector = cv2.FeatureDetector_create(self.DETECTOR)
self.descriptor = cv2.DescriptorExtractor_create(self.DESCRIPTOR)
self.matcher = cv2.DescriptorMatcher_create(self.MATCHER)
#Get initial keypoints in whole image
keypoints_cv = self.detector.detect(im_gray0)
#Remember keypoints that are in the rectangle as selected keypoints
ind = util.in_rect(keypoints_cv, tl, br)
selected_keypoints_cv = list(itertools.compress(keypoints_cv, ind))
selected_keypoints_cv, self.selected_features = self.descriptor.compute(im_gray0, selected_keypoints_cv)
selected_keypoints = util.keypoints_cv_to_np(selected_keypoints_cv)
num_selected_keypoints = len(selected_keypoints_cv)
if num_selected_keypoints == 0:
raise Exception('No keypoints found in selection')
#Remember keypoints that are not in the rectangle as background keypoints
background_keypoints_cv = list(itertools.compress(keypoints_cv, ~ind))
background_keypoints_cv, background_features = self.descriptor.compute(im_gray0, background_keypoints_cv)
background_keypoints = util.keypoints_cv_to_np(background_keypoints_cv)
#Assign each keypoint a class starting from 1, background is 0
self.selected_classes = array(range(num_selected_keypoints)) + 1
background_classes = zeros(len(background_keypoints_cv))
#Stack background features and selected features into database
self.features_database = vstack((background_features, self.selected_features))
#Same for classes
self.database_classes = hstack((background_classes, self.selected_classes))
#Get all distances between selected keypoints in squareform
pdist = scipy.spatial.distance.pdist(selected_keypoints)
self.squareform = scipy.spatial.distance.squareform(pdist)
#Get all angles between selected keypoints
angles = np.empty((num_selected_keypoints, num_selected_keypoints))
for k1,i1 in zip(selected_keypoints, range(num_selected_keypoints)):
for k2,i2 in zip(selected_keypoints, range(num_selected_keypoints)):
#Compute vector from k1 to k2
v = k2-k1
#Compute angle of this vector with respect to x axis
angle = math.atan2(v[1],v[0])
#Store angle
angles[i1,i2] = angle
self.angles = angles
#Find the center of selected keypoints
center = np.mean(selected_keypoints, axis=0)
#Remember the rectangle coordinates relative to the center
self.center_to_tl = np.array(tl) - center
self.center_to_tr = np.array([br[0],tl[1]]) - center
self.center_to_br = np.array(br) - center
self.center_to_bl = np.array([tl[0],br[1]]) - center
#Calculate springs of each keypoint
self.springs = selected_keypoints - center
#Set start image for tracking
self.im_prev = im_gray0
#Make keypoints 'active' keypoints
self.active_keypoints = np.copy(selected_keypoints)
#Attach class information to active keypoints
self.active_keypoints = hstack((selected_keypoints,self.selected_classes[:,None]))
#Remember number of initial keypoints
self.num_initial_keypoints = len(selected_keypoints_cv)
def initialise(self, im_gray0, tl, br):
# Initialise detector, descriptor, matcher
self.detector = cv2.FeatureDetector_create(self.DETECTOR)
self.descriptor = cv2.DescriptorExtractor_create(self.DESCRIPTOR)
self.matcher = cv2.DescriptorMatcher_create(self.MATCHER)
# Get initial keypoints in whole image
keypoints_cv = self.detector.detect(im_gray0)
# Remember keypoints that are in the rectangle as selected keypoints
ind = util.in_rect(keypoints_cv, tl, br)
selected_keypoints_cv = list(itertools.compress(keypoints_cv, ind))
selected_keypoints_cv, self.selected_features = self.descriptor.compute(
im_gray0, selected_keypoints_cv)
selected_keypoints = util.keypoints_cv_to_np(selected_keypoints_cv)
num_selected_keypoints = len(selected_keypoints_cv)
if num_selected_keypoints == 0:
raise Exception('No keypoints found in selection')
# Remember keypoints that are not in the rectangle as background keypoints
background_keypoints_cv = list(itertools.compress(keypoints_cv, ~ind))
background_keypoints_cv, background_features = self.descriptor.compute(
im_gray0, background_keypoints_cv)
_ = util.keypoints_cv_to_np(background_keypoints_cv)
# Assign each keypoint a class starting from 1, background is 0
self.selected_classes = array(range(num_selected_keypoints)) + 1
background_classes = zeros(len(background_keypoints_cv))
# Stack background features and selected features into database
self.features_database = vstack(
(background_features, self.selected_features))
# Same for classes
self.database_classes = hstack(
(background_classes, self.selected_classes))
# Get all distances between selected keypoints in squareform
pdist = scipy.spatial.distance.pdist(selected_keypoints)
self.squareform = scipy.spatial.distance.squareform(pdist)
# Get all angles between selected keypoints
angles = np.empty((num_selected_keypoints, num_selected_keypoints))
for k1, i1 in zip(selected_keypoints, range(num_selected_keypoints)):
for k2, i2 in zip(selected_keypoints,
range(num_selected_keypoints)):
# Compute vector from k1 to k2
v = k2 - k1
# Compute angle of this vector with respect to x axis
angle = math.atan2(v[1], v[0])
# Store angle
angles[i1, i2] = angle
self.angles = angles
# Find the center of selected keypoints
center = np.mean(selected_keypoints, axis=0)
# Remember the rectangle coordinates relative to the center
self.center_to_tl = np.array(tl) - center
self.center_to_tr = np.array([br[0], tl[1]]) - center
self.center_to_br = np.array(br) - center
self.center_to_bl = np.array([tl[0], br[1]]) - center
# Calculate springs of each keypoint
self.springs = selected_keypoints - center
# Set start image for tracking
self.im_prev = im_gray0
# Make keypoints 'active' keypoints
self.active_keypoints = np.copy(selected_keypoints)
# Attach class information to active keypoints
self.active_keypoints = hstack(
(selected_keypoints, self.selected_classes[:, None]))
# Remember number of initial keypoints
self.num_initial_keypoints = len(selected_keypoints_cv)
def initialise(self, im_gray0, tl, tr, br, bl):
# Initialise detector, descriptor, matcher
self.detector = cv.BRISK_create()
self.descriptor = cv.xfeatures2d.LATCH_create(bytes=64)
self.matcher = cv.BFMatcher(
cv.NORM_HAMMING,
crossCheck=False) # set crossCheck to False to use knnMatch
# Get initial keypoints in whole image
keypoints_cv = self.detector.detect(im_gray0)
# Remember keypoints that are in the rectangle as selected keypoints
ind = util.in_square(keypoints_cv, tl, tr, br, bl)
self.selected_keypoints_cv = list(itertools.compress(
keypoints_cv, ind))
self.selected_keypoints_cv, self.selected_features = self.descriptor.compute(
im_gray0, self.selected_keypoints_cv)
selected_keypoints = util.keypoints_cv_to_np(
self.selected_keypoints_cv)
num_selected_keypoints = len(self.selected_keypoints_cv)
if num_selected_keypoints == 0:
raise Exception('No keypoints found in selection')
# Remember keypoints that are not in the rectangle as background keypoints
background_keypoints_cv = list(itertools.compress(keypoints_cv, ~ind))
background_keypoints_cv, background_features = self.descriptor.compute(
im_gray0, background_keypoints_cv)
_ = util.keypoints_cv_to_np(background_keypoints_cv)
# Assign each keypoint a class starting from 1, background is 0
self.selected_classes = array(list(range(num_selected_keypoints))) + 1
background_classes = zeros(len(background_keypoints_cv))
# Stack background features and selected features into database
self.features_database = vstack(
(background_features, self.selected_features))
# Same for classes
self.database_classes = hstack(
(background_classes, self.selected_classes))
# Get all distances between selected keypoints in squareform
pdist = scipy.spatial.distance.pdist(selected_keypoints)
self.squareform = scipy.spatial.distance.squareform(pdist)
# Get all angles between selected keypoints
angles = np.empty((num_selected_keypoints, num_selected_keypoints))
for k1, i1 in zip(selected_keypoints,
list(range(num_selected_keypoints))):
for k2, i2 in zip(selected_keypoints,
list(range(num_selected_keypoints))):
# Compute vector from k1 to k2
v = k2 - k1
# Compute angle of this vector with respect to x axis
angle = math.atan2(v[1], v[0])
# Store angle
angles[i1, i2] = angle
self.angles = angles
# Find the center of selected keypoints
center = np.mean(selected_keypoints, axis=0)
# Remember the rectangle coordinates relative to the center
self.center_to_tl = np.array(tl) - center
self.center_to_tr = np.array(tr) - center
self.center_to_br = np.array(br) - center
self.center_to_bl = np.array(bl) - center
# Calculate springs of each keypoint
self.springs = selected_keypoints - center
# Set start image for tracking
self.im_prev = im_gray0
# Make keypoints 'active' keypoints
self.active_keypoints = np.copy(selected_keypoints)
# Attach class information to active keypoints
self.active_keypoints = hstack(
(selected_keypoints, self.selected_classes[:, None]))
# Remember number of initial keypoints
self.initial_keypoints = np.copy(self.active_keypoints)
self.num_initial_keypoints = len(self.selected_keypoints_cv)
self.matched_ratio = 0.0
self.tl = util.array_to_float_tuple(tl)
self.tr = util.array_to_float_tuple(tr)
self.br = util.array_to_float_tuple(br)
self.bl = util.array_to_float_tuple(bl)
self.is_init = True
