def __init__(
self,
args=utility.parse_args(),
det_model_dir=r"D:\python_work\Ar_project\PaddleOCR\inference\ch_det_mv3_db",
rec_model_dir=r"D:\python_work\Ar_project\PaddleOCR\inference\ch_rec_mv3_crnn",
rec_char_dict_path=r"D:\python_work\Ar_project\PaddleOCR\ppocr\utils\ppocr_keys_v1.txt"
):
print("init ocrsystem")
args.det_model_dir = det_model_dir
args.rec_model_dir = rec_model_dir
args.rec_char_dict_path = rec_char_dict_path
# print(args,111)
# image_file_list = get_image_file_list(args.image_dir)
self.text_sys = TextSystem(args)
print("ocrsystem ready!")
def main():
args = util.parse_args()
session = init_oculus()
port = args['port']
baud = args['baud']
dryrun = args['dryrun']
gp = inputs.devices.gamepads
if len(gp) == 0 or 'microsoft' not in str(gp[0]).lower():
print("Xbox controller not detected")
exit(session)
return
# Read from the gamepad in a different thread since the inputs library
# blocks program execution
try:
gamepad_thread = Thread(target=gamepad_loop)
gamepad_thread.daemon = True
gamepad_thread.start()
# If we are developing, we don't worry about the serial port
if dryrun:
stream_loop(session, -1, True)
num_tries = 0
while(True):
try:
with serial.Serial(port, baud, timeout=0) as ser:
print("Connected to embedded")
tx = Transmitter(ser)
stream_loop(session, tx)
except serial.serialutil.SerialException as e:
if(num_tries % 100 == 0):
if(str(e).find("FileNotFoundError")):
print("Port not found. Retrying...(attempt {0})".format(num_tries))
else:
print("Serial exception. Retrying...(attempt {0})".format(num_tries))
time.sleep(0.01)
num_tries = num_tries + 1
except (KeyboardInterrupt, SystemExit) as e:
print("Interrupted: {0}".format(e))
pass
exit(session)
if (iter_id + 1) % cfg.snapshot_iter == 0:
save_model("model_iter{}".format(iter_id))
print("Snapshot {} saved, average loss: {}, \
average time: {}".format(
iter_id + 1, snapshot_loss / float(cfg.snapshot_iter),
snapshot_time / float(cfg.snapshot_iter)))
if args.enable_ce and iter_id == cfg.max_iter - 1:
if devices_num == 1:
print("kpis\ttrain_cost_1card\t%f" %
(snapshot_loss / float(cfg.snapshot_iter)))
print("kpis\ttrain_duration_1card\t%f" %
(snapshot_time / float(cfg.snapshot_iter)))
else:
print("kpis\ttrain_cost_8card\t%f" %
(snapshot_loss / float(cfg.snapshot_iter)))
print("kpis\ttrain_duration_8card\t%f" %
(snapshot_time / float(cfg.snapshot_iter)))
snapshot_loss = 0
snapshot_time = 0
except fluid.core.EOFException:
py_reader.reset()
save_model('model_final')
if __name__ == '__main__':
args = parse_args()
print_arguments(args)
train()
kmer_sample_file=kmer_sample_file_ref,
kmer_pheno_file=kmer_pheno_file)
process_file(create_kmer_sample_map, unique_kmers_file, q=q, lock=lock, **kwargs)
sample_matrix = np.zeros((n_samples, n_samples))
num_kmers = 0
# write all chunks to output files sequentially
while not q.empty():
q_num_kmers, q_sample_matrix = q.get()
num_kmers += q_num_kmers
sample_matrix += q_sample_matrix
# create sample similarity file if the similarities tsv does not exist
if not file_exists(similar_sample_file) or not file_exists(dissimilar_sample_file):
similar_sample(sample_matrix, num_kmers, similarities_tsv,
hist_orig_file, hist_sim_scaled_file, hist_dissim_scaled_file,
similar_sample_file, dissimilar_sample_file)
if (not file_exists(similar_sample_file) or not file_exists(dissimilar_sample_file)) and file_exists(similarities_tsv):
similar_sample(None, None, similarities_tsv, hist_orig_file,
hist_sim_scaled_file, hist_dissim_scaled_file,
similar_sample_file, dissimilar_sample_file)
# create kmer int map
if not file_exists(uim_file):
int_maps.create_kmer_int_map(kmer_sample_file, uim_file)
if __name__ == '__main__':
parse_args()
main()
def visualize_read_write(model, criterion, optimizer, config_obj):
T = 10
config_obj.config_dict['num_batches'] = 20
config_obj.config_dict['batch_size'] = 1
seqs_loader = utility.load_dataset(config_obj, max=T, min=T)
for batch_num, X, Y, act in seqs_loader:
result = evaluate_single_batch(model, criterion, X, Y)
plot_visualization(X, result, model.N)
if __name__ == '__main__':
# pdb.set_trace()
args = utility.parse_args()
config_type = args['configtype']
config_file = args['configfile']
load_checkpoint = args['load_checkpoint']
plot_all_average_flag = args['plot_all_average']
visualize_read_write_flag = args['visualize_read_write']
if plot_all_average_flag:
plot_all_average_costs()
else:
config_obj = config.Configuration(config_type, config_file)
config = config_obj.config_dict
model, criterion, optimizer = models.build_model(config)
seqs_loader = utility.load_dataset(config_obj)
if visualize_read_write_flag:
model, list_seq_num, list_loss, list_cost = loadCheckpoint(
path=config['filename'])
auc_metric.accumulate(),
100 * args.batch_size / (time.time() - batch_begin)))
batch_begin = time.time()
total_loss = 0.0
batch_id += 1
logger.info("epoch %d is finished and takes %f s" %
(epoch, time.time() - begin))
# save model and optimizer
logger.info(
"going to save epoch {} model and optimizer.".format(epoch))
paddle.save(deepfm.state_dict(),
path=os.path.join(args.model_output_dir,
"epoch_" + str(epoch), ".pdparams"))
paddle.save(optimizer.state_dict(),
path=os.path.join(args.model_output_dir,
"epoch_" + str(epoch), ".pdopt"))
logger.info("save epoch {} finished.".format(epoch))
# eval model
deepfm.eval()
eval(epoch)
deepfm.train()
paddle.enable_static()
if __name__ == '__main__':
args = utils.parse_args()
utils.print_arguments(args)
train(args)
for image_file in image_file_list:
img, flag = check_and_read_gif(image_file)
if not flag:
img = cv2.imread(image_file)
if img is None:
logger.info("error in loading image:{}".format(image_file))
continue
valid_image_file_list.append(image_file)
img_list.append(img)
try:
rec_res, predict_time = text_recognizer(img_list)
except Exception as e:
print(e)
logger.info(
"ERROR!!!! \n"
"Please read the FAQ: https://github.com/PaddlePaddle/PaddleOCR#faq \n"
"If your model has tps module: "
"TPS does not support variable shape.\n"
"Please set --rec_image_shape='3,32,100' and --rec_char_type='en' "
)
exit()
for ino in range(len(img_list)):
print("Predicts of %s:%s" % (valid_image_file_list[ino], rec_res[ino]))
print("Total predict time for %d images:%.3f" %
(len(img_list), predict_time))
if __name__ == "__main__":
main(utility.parse_args())
#!/usr/bin/env python
from bhmm import BHMM
from utility import parse_args
if __name__ == "__main__":
args = parse_args()
bhmm = BHMM(args)
bhmm.run()