def gen_map():
instance.gmutex.acquire()
api.update_map_loc('alice', '40.442885', '-79.94263')
api.update_map_loc('bob', '40.444885', '-79.94463')
instance.gmutex.release()
map_loc_str = instance.map_loc
marker_list = []
strs = map_loc_str.split(":")
if len(strs) == 1:
return
for i in range(1, len(strs)):
loc = strs[i]
data = loc.split("#")
marker_list.append([float(data[1]), float(data[2]), data[3]])
gps_data = [marker_list[0][0], marker_list[0][1]]
des = generate_map_and_desc(gps_data, marker_list)
gui.update_image(instance.name + '_map.png')
img_predicted = predictor.predict(img_in)[0]
else:
img_predicted = capture.img0
# interpolate (temporal blur) on output image
img_out = msa.utils.np_lerp(img_out, img_predicted,
1 - params.Prediction.post_time_lerp)
# update frame states
if not params.Main.liveshow:
frame_stats.verbose = params.Main.verbose
frame_stats.update()
# update gui
i = 0
if not params.Main.liveshow:
gui.update_image(++i, capture.img0)
gui.update_image(++i, img_in)
gui.update_stats(frame_stats.str + " | " + capture.frame_stats.str)
gui.update_image(++i, img_out)
gui.process_events()
time.sleep(params.Main.sleep_s)
# cleanup
capture.close()
gui.close()
capture = None
predictor = None
# run prediction
if params.Prediction.enabled and predictor:
img_predicted = predictor.predict(img_in)[0]
else:
img_predicted = capture.img0
# interpolate (temporal blur) on output image
img_out = msa.utils.np_lerp(img_out, img_predicted,
1 - params.Prediction.post_time_lerp)
# update frame states
frame_stats.verbose = params.Main.verbose
frame_stats.update()
# update gui
gui.update_image(0, capture.img0)
gui.update_image(1, img_in)
gui.update_image(2, img_out)
gui.update_stats(frame_stats.str + " | " + capture.frame_stats.str)
gui.process_events()
time.sleep(params.Main.sleep_s)
# cleanup
capture.close()
gui.close()
capture = None
predictor = None
print('Finished')