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')