def main(coef, n_classes, frames, fps, width, height, channels):
seg1_target = target_generator(lambda x: segmentation1(x, 0.03 * coef))
seg2_target = target_generator(lambda x: segmentation2(x, 0.04 * coef))
@target_generator
def class_target(x):
return classification(x, 0.05 * coef, n_classes)
target_list = [seg1_target, seg2_target, class_target]
queue_list = [(queue.Queue(), queue.Queue()) for target in target_list]
thread_list = [
Thread(target=target, args=qio)
for target, qio in zip(target_list, queue_list)
]
for thread in thread_list:
thread.start()
time_recorder = TimeRecord()
size = (height, width, channels)
for frame in fake_video(frames=frames, fps=fps, size=size):
with time_recorder:
for qi, _ in queue_list:
qi.put(frame)
for _, qo in queue_list:
qo.get()
time_recorder.report()
def do_segmentation1(sleep_time):
output = segmentation1(G["frame"], sleep_time)
np.copyto(G["output_segmentation1"],
output) # TODO: adapt segmentation1 to use pre-allocated memory
def process(frame, base_sleep_time, n_classes):
rs1 = segmentation1(frame, base_sleep_time * 0.03)
rs2 = segmentation2(frame, base_sleep_time * 0.04)
rs3 = classification(frame, base_sleep_time * 0.05, n_classes)
return rs1, rs2, rs3
def seg1_target(x):
return segmentation1(x, 0.03 * g_dict["coef"])
def process(self, x):
return segmentation1(x, self.sleep_time)