biggreen = None
max_area = 0
for c in contours:
area = cv.contourArea(c)
if area == 0:
continue
if area > max_area:
max_area = area
biggreen = c
pos = num
num += 1
############## PROCESSING OBJECT ##############
if (num > 0 # Check if there is any green object
and int(cv.contourArea(biggreen)) > min_area # no noise
and streamfps.frames() >
50): # Start the predicting at a particular point
######## CALCULATE POSITION ########
# Calculate bounding box
#print(streamfps.frames())
(x, y, w, h) = cv.boundingRect(biggreen)
# Calculate x and y of the current observation
xo = x + int(w / 2)
yo = y + int(h / 2)
error = w # Error: somewhere in this region is the center
######## KALMAN FILTER Position #########
if yo < error:
mu, P, pred = kalman(mu, P, F, Q, B, a, None, H, R)
################ FIND GREEN PIXELS ################
# Convert BGR to HSV
hsv1 = cv.cvtColor(frame1, cv.COLOR_BGR2HSV)
hsv2 = cv.cvtColor(frame2, cv.COLOR_BGR2HSV)
# Threshold the HSV image to get only green colors
mask1 = cv.inRange(hsv1, lower, upper)
mask2 = cv.inRange(hsv2, lower, upper)
################ Find object ################
# get contours, find biggest contour (which is the ball), noise filter
contour1_ball = FindObject(mask1, lower, upper, min_area)
contour2_ball = FindObject(mask2, lower, upper, min_area)
if (contour1_ball is not None or contour2_ball is not None
) and streamfps.frames() > 0: # Check if there is any green object
(x1, y1, w1, h1) = BoundingBox(contour1_ball)
(x2, y2, w2, h2) = BoundingBox(contour2_ball)
list2dX_1.append(x1)
list2dY_1.append(y1)
list2dX_2.append(x2)
list2dY_2.append(y2)
if len(list3dYe) != 0:
average_x = sum(list3dXe) / len(list3dXe)
average_y = sum(list3dYe) / len(list3dYe)
xo, yo, zo = Coordinates3d(
x1,
y1,
x2,
