def base_cfg(name: str):
return GeneratorCfg(
num_image=5,
save_dir=CURRENT_DIR / "effect_layout_image" / name,
render_cfg=RenderCfg(
bg_dir=BG_DIR,
corpus=EnumCorpus(
EnumCorpusCfg(
items=["Hello World!"],
text_color_cfg=FixedTextColorCfg(),
**font_cfg,
), ),
),
)
def base_cfg(
name: str, corpus, corpus_effects=None, layout_effects=None, layout=None, gray=True
):
return GeneratorCfg(
num_image=50,
save_dir=OUT_DIR / name,
render_cfg=RenderCfg(
bg_dir=BG_DIR,
perspective_transform=perspective_transform,
gray=gray,
layout_effects=layout_effects,
layout=layout,
corpus=corpus,
corpus_effects=corpus_effects,
),
)
text_color_cfg=WhiteTextColorCfg(),
**font_cfg
),
),
corpus_effects=effects,
),
)
'''
up_normal_data1 = GeneratorCfg(
num_image=200000,
save_dir=OUT_DIR / "up_normal_data1",
render_cfg=RenderCfg(
bg_dir=BG_DIR,
perspective_transform=perspective_transform,
corpus=WordCorpus(
WordCorpusCfg(text_paths=[TEXT_DIR / "corpus-title_upper.txt"],
num_word=(2, 5),
filter_by_chars=True,
chars_file=CHAR_DIR / "dict.txt",
char_spacing=-0.1,
**font_cfg), ),
corpus_effects=effects,
),
)
up_normal_data2 = GeneratorCfg(
num_image=200000,
save_dir=OUT_DIR / "up_normal_data2",
render_cfg=RenderCfg(
bg_dir=BG_DIR,
perspective_transform=perspective_transform,
corpus=WordCorpus(
font_list_file=FONT_LIST_DIR / "font_list.txt",
font_size=(30, 31),
)
perspective_transform = NormPerspectiveTransformCfg(20, 20, 1.5)
chn_data = GeneratorCfg(
num_image=50,
save_dir=OUT_DIR / "char_corpus",
render_cfg=RenderCfg(
bg_dir=BG_DIR,
perspective_transform=perspective_transform,
corpus=CharCorpus(
CharCorpusCfg(text_paths=[
TEXT_DIR / "chn_text.txt", TEXT_DIR / "eng_text.txt"
],
filter_by_chars=True,
chars_file=CHAR_DIR / "chn.txt",
length=(5, 10),
char_spacing=(-0.3, 1.3),
**font_cfg), ),
corpus_effects=Effects(
[Line(0.5), OneOf([DropoutRand(), DropoutVertical()])]),
),
)
enum_data = GeneratorCfg(
num_image=50,
save_dir=OUT_DIR / "enum_corpus",
render_cfg=RenderCfg(
bg_dir=BG_DIR,
font_size=(45, 50),
)
perspective_transform = NormPerspectiveTransformCfg(25, 25, 0.15)
ocr_data = GeneratorCfg(
num_image=20000,
save_dir=OUT_DIR,
render_cfg=RenderCfg(
bg_dir=BG_DIR,
perspective_transform=perspective_transform,
corpus=CharCorpus(
CharCorpusCfg(
text_paths=[TEXT_DIR / "eng_caps.txt"
], # TEXT_DIR / "chn_text.txt",
filter_by_chars=True,
chars_file=CHAR_DIR / "eng_AZ09.txt",
length=(5, 25),
char_spacing=(0.30, 1.0),
**font_cfg,
), ),
corpus_effects=Effects([Padding(0.3),
Line(0.3),
DropoutRand(0.0001)]),
),
)
rand_data = GeneratorCfg(
num_image=30000,
save_dir=OUT_DIR,
render_cfg=RenderCfg(
corpus=EnumCorpus(
EnumCorpusCfg(
text_paths=[TEXT_DIR / "enum_text.txt"],
filter_by_chars=True,
chars_file=CHAR_DIR / "chn.txt",
**font_cfg
),
),
),
)
'''
rand_data = GeneratorCfg(
num_image=100000,
save_dir=OUT_DIR / "rand_corpus",
render_cfg=RenderCfg(
bg_dir=BG_DIR,
perspective_transform=perspective_transform,
corpus=RandCorpus(RandCorpusCfg(chars_file=CHAR_DIR / "eng.txt", length=(1, 16) ,**font_cfg),),
),
)
'''
eng_word_data = GeneratorCfg(
num_image=1,
save_dir=OUT_DIR / "word_corpus",
render_cfg=RenderCfg(
bg_dir=BG_DIR,
perspective_transform=perspective_transform,
corpus=WordCorpus(
WordCorpusCfg(
text_paths=[TEXT_DIR / "eng_text.txt"],