def callback(self,data):
global initial_flag
global contour
#global contour1
global Buffer
global Buffer_images
global old_gray
global totalF
global Score
global p0
global pts
global jac
global trackers
global threshR
global pts1
global temp
#cv_image = self.bridge.imgmsg_to_cv2(data, "bgr8")
old_frame1 = self.bridge.imgmsg_to_cv2(data, "bgr8")
#plt.imshow(old_frame1)
#plt.show()
if not initial_flag:
######################################################### REFERENCE FOR TABBING
old_frame = cv.resize(old_frame1, None, fx=ratio, fy=ratio)
#plt.imshow(old_frame)
#plt.show()
#old_frame_c = old_frame.copy()
means = []
pts1 = contour #Drawing.Draw(old_frame)
print("PTS 1 : ")
#print(pts1)
print("PTS CONTOUR1 : ")
#print(contour1)
#print("=========================================")
#print(pts1)
#print("=========================================")
old_gray, old_gray1, temp, gray_orig = Preprocessing.Track_init(old_frame, contour1)
# old_gray = frame rescaled, processed, with edge mask
# old_gray1 = frame rescaled, processed, with edge mask and with mask over ROI
# temp = frame rescaled, processed, cropped over boundingbox of ROI. Template for model
# gray_orig = frame rescaled, processed
#plt.imshow(old_gray1)
#plt.show()
Buffer, Buffer_images = BufferStartUp.start(Buffer, Buffer_images, temp)
# Buffer = buffer data
# Buffer_images = list of model images
p0, pts = Tracking.init(old_gray, pts1) # p0 are detected features, pts are vertices of the polygons
# p0 = kaze features found
# pts = input argument of vertices/points of the contour is reshaped and parsed for numpy
#print(p0)
#print(pts)
############# Initialize various variables to be used
#############################################################################################################
totalF = len(p0) # total number of features found/detected at (re-)initialization
Score = 1 # jaccard score of most recent (re-)initialization
threshR = 10 # tracker ransacReprojThreshold
jac = [] # jaccard score history
trackers = [] # list of active trackers
img_array = [] # not used
times = [] # execution times
initial_flag = True
else:
########################################## 2 ############################################################
############# Just a small test to see if the input is an image, and if it is non NULL
try:
frame1 = cv.resize(old_frame1, None, fx=ratio, fy=ratio)
# resize by predefined ratio
#frame_orig = frame1.copy()
# copy
except:
print("")
start = time.time()
frame_gray, frame_o, frame_proc, temp = Preprocessing.Track(frame1)
# frame_gray = frame rescaled, processed, with edge mask
# frame_o = original input frame is being sent back
# frame_proc = frame rescaled, processed
# temp = frame rescaled, processed, cropped over boundingbox of ROI. Template for model. Returned regardless of edge.
######### This function called "Tracking.do" does the tracking and reinitialization. There is a LOT of code inside.
# What Trackers.do returns
# img = most recent acquired frame with tracked contour drawn on top
# p0 = kaze features found
# pts = input argument of vertices/points of the contour is reshaped and parsed for numpy
# Buffer = buffer data
# Buffer_images = list of model images
# TotalF = total number of features found/detected at (re-)initialization
# Score = jaccard score of most recent (re-)initialization
# jac = jaccard score history
# trackers = trackers that are active and have been tracked from last frame to current frame
img, p0, pts, Buffer, Buffer_images, totalF, Score, jac, trackers = \
Tracking.do(old_gray, frame_gray, frame_proc, temp, Buffer, Buffer_images, frame_o, totalF, Score, p0, pts,
jac, trackers, threshR)
# Arguments used for Tracking.do
# old_gray = image/frame from previous loop cycle. It is frame_gray from old loop
# frame_gray = current mose recent frame rescaled, processed, with edge mask
# frame_proc = current most recent frame rescaled, processed
# temp = current most recent frame rescaled, processed, cropped over boundingbox of ROI. Template for model. Returned regardless of edge.
# Buffer = buffer data
# Buffer_images = list of model images
# frame_o = current most recent original input frame is being sent back
# totalF = total number of features detected at (re-)intialization. Used as reference to see how much of the initial trackers have been lost/found
# Score = jaccard score of most recent (re-)initialization
# p0 = kaze features found
# pts = input argument of vertices/points of the contour is reshaped and parsed for numpy
# jac = jaccard score history
# trackers = trackers that have been tracked to the last frame, from the one before it. Active trackers that we want to try to find in this loop
# threshR = tracker ransacReprojThreshold
########################################## 3 ############################################################
#############################################################################################################
#############################################################################################################
########### Just computes current fps, and prints it on the image.
t = 1/(time.time() - start)
cv.putText(img, 'FPS: ' + str(t), (0, 30), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
old_gray = frame_gray.copy()
msg = Int16MultiArray()
msg.data = np.reshape(pts, len(pts) * len(pts[0][0])) # , order='F')
'''
# preparation of point message to be published
a1l = len(pts)
a1l0 = len(pts[0][0])
a1 = np.reshape(pts, a1l * a1l0) # , order='F')
msg.data = a1
# This is almost always zero there is no empty padding at the start of your data
msg.layout.data_offset = 0
# create two dimensions in the dim array
msg.layout.dim = [MultiArrayDimension(), MultiArrayDimension()]
# dim[0] is the vertical dimension of your matrix
msg.layout.dim[0].label = "axes"
msg.layout.dim[0].size = a1l0
msg.layout.dim[0].stride = a1l * a1l0
# dim[1] is the horizontal dimension of your matrix
msg.layout.dim[1].label = "vertices"
msg.layout.dim[1].size = a1l
msg.layout.dim[1].stride = a1l
'''
#except CvBridgeError as e:
# print(e)
try:
self.contour_pub.publish(msg)
self.image_pub.publish(self.bridge.cv2_to_imgmsg(img, "bgr8"))
except CvBridgeError as e:
print(e)
# frame_o = original input frame is being sent back
# frame_proc = frame rescaled, processed
# temp = frame rescaled, processed, cropped over boundingbox of ROI. Template for model. Returned regardless of edge.
# What Trackers.do returns
# img = most recent acquired frame with tracked contour drawn on top
# p0 = kaze features found
# pts = input argument of vertices/points of the contour is reshaped and parsed for numpy
# Buffer = buffer data
# Buffer_images = list of model images
# TotalF = total number of features found/detected at (re-)initialization
# Score = jaccard score of most recent (re-)initialization
# jac = jaccard score history
# trackers = trackers that are active and have been tracked from last frame to current frame
img, p0, pts, Buffer, Buffer_images, totalF, Score, jac, trackers = \
Tracking.do(old_gray, frame_gray, frame_proc, temp, Buffer, Buffer_images, frame_o, totalF, Score, p0, pts, jac,
trackers, threshR)
# Arguments used for Tracking.do
# old_gray = image/frame from previous loop cycle. It is frame_gray from old loop
# frame_gray = current mose recent frame rescaled, processed, with edge mask
# frame_proc = current most recent frame rescaled, processed
# temp = current most recent frame rescaled, processed, cropped over boundingbox of ROI. Template for model. Returned regardless of edge.
# Buffer = buffer data
# Buffer_images = list of model images
# frame_o = current most recent original input frame is being sent back
# totalF = total number of features detected at (re-)intialization. Used as reference to see how much of the initial trackers have been lost/found
# Score = jaccard score of most recent (re-)initialization
# p0 = kaze features found
# pts = input argument of vertices/points of the contour is reshaped and parsed for numpy
# jac = jaccard score history
# trackers = trackers that have been tracked to the last frame, from the one before it. Active trackers that we want to try to find in this loop
# threshR = tracker ransacReprojThreshold
