def main():
threshold_matrix = matrix.load_matrix("thresholds")
cost_matrix = matrix.load_matrix("dynamics")
loops = find_transitions(threshold_matrix, cost_matrix)
dg.plot_loops(loops)
if c.buildTable:
table = build_transition_table(loops)
matrix_util.save_matrix(table, "loops")
def main():
# Config option which allows us to skip the future cost processing. Useful for comparing
# results
if c.skipFutureCosts:
distance_matrix = matrix_util.load_matrix("dynamics")
else:
distance_matrix = matrix_util.load_matrix("futurecosts")
prob_matrix = create_probability_matrix(distance_matrix)
matrix_util.save_matrix(prob_matrix, "probabilities")
if c.displayVisualisations:
matrix_util.display_matrix(prob_matrix, "Determined Probabilities")
def main():
# Load the difference matrix from the previous step, transform it, then save it
diff_matrix = matrix_util.load_matrix("differences")
dynamic_matrix = preserve_dynamics(diff_matrix)
matrix_util.save_matrix(dynamic_matrix, "dynamics")
if c.displayVisualisations:
matrix_util.display_matrix(dynamic_matrix, "Dynamic Preserved Matrix")
def main():
# Using the dynamics matrix, we then produce and save the future cost matrix
dynamic_matrix = matrix_util.load_matrix("dynamics")
future_cost_matrix = generate_future_cost_matrix(dynamic_matrix)
matrix_util.save_matrix(future_cost_matrix, "futurecosts")
if c.displayVisualisations:
matrix_util.display_matrix(future_cost_matrix,
"Generated Future Costs")
def main():
# Load the video and the transitions
(cap, position_frame) = kf.source_video(c.absInputPath)
frame_probabilities = matrix.load_matrix("thresholds")
choice_labels = frame_chooser.generate_choices(frame_probabilities)
# Open a window we can output in to
window_name = d.random_play
cv2.namedWindow(window_name, cv2.WINDOW_OPENGL)
cv2.startWindowThread()
# If we've sub-sampled the matrix, this needs to be reflected when we play
frameskip = c.skipFrames
preserve_jump = frameskip
next_frame = 0
while True:
flag, frame = cap.read()
if flag:
cv2.imshow(window_name, frame)
position_frame = cap.get(cv2.cv.CV_CAP_PROP_POS_FRAMES)
else:
cap.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, position_frame - 1)
print d.frame_not_ready
cv2.waitKey(1000)
if preserve_jump == 0:
next_frame = find_random_frame(cap, frame_probabilities,
choice_labels)
preserve_jump = frameskip
else:
next_frame += 1
preserve_jump -= 1
cap.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, next_frame)
cv2.waitKey(50)
# Terminate the program if ESC is pressed
if cv2.waitKey(10) == 27:
cv2.destroyAllWindows()
break
# Terminate the program if window is closed
if cv2.getWindowProperty(window_name, 0) == -1:
cv2.destroyAllWindows()
break
def main():
future_matrix = matrix_util.load_matrix("probabilities")
threshold_title = "Not thresholded..."
# Determine Local Maxima
if c.useLocalMaxima:
future_matrix = determine_local_maxima(future_matrix)
threshold_title = "Local Maxima"
# Now Threshold
if c.useThreshold:
future_matrix = matrix_util.threshold_matrix(copy.copy(future_matrix),
c.thresholdValue)
if c.useLocalMaxima:
threshold_title = "Local Maxima & Thresholded at " + str(
c.thresholdValue)
else:
threshold_title = "Thresholded at " + str(c.thresholdValue)
matrix_util.save_matrix(future_matrix, "thresholds")
if c.displayVisualisations:
matrix_util.display_matrix(future_matrix, threshold_title)