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resources_learning_fromraw.py
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import numpy as np
from sys import argv
from define import define
from resources_generate import Report,load_raws,save_resource_numpy
from resources_learning import learning
resourcenames = {
'rival': 'rival',
'play_side': 'play_side',
'dead': 'dead'
}
def learning_rival(raws):
resourcename = resourcenames['rival']
report = Report(resourcename)
trimarea = define.areas_np['rival']
learning_targets = {}
evaluate_targets = {}
for filename, raw in raws.items():
if not 'screen' in raw.label.keys() or raw.label['screen'] != 'result':
continue
if not 'rival' in raw.label.keys():
continue
if raw.label['rival']:
trimmed = raw.np_value[trimarea]
learning_targets[filename] = trimmed
evaluate_targets[filename] = raw
report.append_log(f'source count: {len(learning_targets)}')
result = learning(learning_targets, report)
if result is None:
report.report()
return
for filename, raw in evaluate_targets.items():
is_rival = raw.label['rival']
trimmed = raw.np_value[trimarea]
recoged = np.all((result==0)|(trimmed==result))
if (recoged and is_rival) or (not recoged and not is_rival):
report.through()
else:
report.saveimage_errorvalue(trimmed, filename)
report.error(f'Mismatch {is_rival} {filename}')
save_resource_numpy(resourcename, result)
report.report()
def learning_playside(raws):
resourcename = resourcenames['play_side']
report = Report(resourcename)
trimareas = define.areas_np['play_side']
learning_targets = {}
evaluate_targets = {}
for filename, raw in raws.items():
if not 'screen' in raw.label.keys() or raw.label['screen'] != 'result':
continue
if not 'is_savable' in raw.label.keys() or raw.label['is_savable']:
continue
if not 'play_side' in raw.label.keys() or not raw.label['play_side'] in define.value_list['play_sides']:
continue
play_side = raw.label['play_side']
trimmed = raw.np_value[trimareas[play_side]]
learning_targets[filename] = trimmed
evaluate_targets[filename] = raw
report.append_log(f'source count: {len(learning_targets)}')
result = learning(learning_targets, report)
if result is None:
report.report()
return
for filename, raw in evaluate_targets.items():
recoged = None
for key, area in trimareas.items():
if np.all((result==0)|(raw.np_value[area]==result)):
recoged = key
play_side = raw.label['play_side']
if recoged == play_side:
report.through()
else:
report.saveimage_errorvalue(trimmed, filename)
report.error(f'Mismatch {recoged} {filename}')
save_resource_numpy(resourcename, result)
report.report()
def learning_dead(raws):
resourcename = resourcenames['dead']
report = Report(resourcename)
trimareas = define.areas_np['dead']
learning_targets = {}
evaluate_targets = {}
for filename, raw in raws.items():
if not 'screen' in raw.label.keys() or raw.label['screen'] != 'result':
continue
if not 'dead' in raw.label.keys():
continue
if not 'play_side' in raw.label.keys() or not raw.label['play_side'] in define.value_list['play_sides']:
continue
if raw.label['dead']:
play_side = raw.label['play_side']
trimmed = raw.np_value[trimareas[play_side]]
learning_targets[filename] = trimmed
evaluate_targets[filename] = raw
report.append_log(f'source count: {len(learning_targets)}')
result = learning(learning_targets, report)
if result is None:
report.report()
return
for filename, raw in evaluate_targets.items():
is_dead = raw.label['dead']
play_side = raw.label['play_side']
trimmed = raw.np_value[trimareas[play_side]]
recoged = np.all((result==0)|(trimmed==result))
if (recoged and is_dead) or (not recoged and not is_dead):
report.through()
else:
report.saveimage_errorvalue(trimmed, filename)
report.error(f'Mismatch {is_dead} {filename}')
save_resource_numpy(resourcename, result)
report.report()
if __name__ == '__main__':
if len(argv) == 1:
print('please argment.')
exit()
raws = load_raws()
if '-all' in argv or '-rival' in argv:
learning_rival(raws)
if '-all' in argv or '-playside' in argv:
learning_playside(raws)
if '-all' in argv or '-dead' in argv:
learning_dead(raws)