async def coro_show_image_mat(image_mat, text=None, title=None,
cell_size: tuple = None, block=False, image_name=None,
cbs=None, task_id=None):
"""
:param image_name: used as callback arguments
:param cbs: a tuple in order of (on_done, on_succeeded, on_failed, on_progress).
only `on_done` is dispatched.
:return: no return value
"""
# NOTE: matplot backend is single-threaded, and can only be accessed from its host thread.
import matplotlib.pyplot as plt
import asyncio
from async_ import amend_blank_cbs
on_done, on_succeeded, on_failed, on_progress = amend_blank_cbs(cbs)
ret = {'image_name': image_name}
try:
fig = show_image_mat(image_mat, text=text, title=title, cell_size=cell_size, block=block)
except Exception as e:
ret.update({'error': e})
on_done(ret)
return
if not block:
try:
async def coro_pause(delay_s):
plt.pause(delay_s)
await asyncio.sleep(0.1) # give control to other tasks in the same loop
while True: # IMPROVE: figure on_close() => manager.destroy(), but cannot hook to it.
fig.show()
await coro_pause(1)
except Exception as e: # for TKinter backend, a _tkinter.TclError
DEBUG(f'[coro_show_image_mats] show loop ended: {e}')
plt.close(fig)
on_done(ret)
async def coro_consume_files(abspath_or_list, cbs):
# nonlocal this_task
# assert this_task is not None, '`this_task` should have been assigned before entering related coro.'
import modules.data.decode_tf as decode_tf
import tensorflow as tf
DEBUG(f'[coro_consume_inputs]: {locals()}')
on_done, on_succeeded, on_failed, on_progress = amend_blank_cbs(
cbs)
filepaths = abspath_or_list if isinstance(
abspath_or_list, list) else [abspath_or_list]
result = {
} # data: tf.data.Dataset::{image_t}, error: optional(str)
# from helpers.tf_helper import image_example
# IMPROVE: try to use TFRecordDataset.from_tensors([tf_example])
data = DataManager._process_files(filepaths, **params_decode)
result.update({'data': data})
# # if show inputs
# try:
# asynctask = async_show_image_mats(image_mats)
# result.update({'asynctask_id': asynctask.id})
# except Exception as e:
# result.update({'error': e.__repr__()})
on_done(result)
# TODO: how to link to the next task (e.g. model.predict) so user can monitor process.
return result # == this_task.set_result(result)