args = parser.parse_args()
path = args.imgpath
suffix = args.suffix
model_type = str(args.model).lower()
assert model_type in ["sr", "esr", "dsr", "ddsr", "rnsr"], 'Model type must be either "sr", "esr", "dsr", ' \
'"ddsr" or "rnsr"'
mode = str(args.mode).lower()
assert mode in ['fast', 'patch'], 'Mode of operation must be either "fast" or "patch"'
scale_factor = int(args.scale)
save = strToBool(args.save)
patch_size = int(args.patch_size)
assert patch_size > 0, "Patch size must be a positive integer"
if model_type == "sr":
model = models.ImageSuperResolutionModel()
elif model_type == "esr":
model = models.ExpantionSuperResolution()
elif model_type == "dsr":
model = models.DenoisingAutoEncoderSR()
elif model_type == "ddsr":
model = models.DeepDenoiseSR()
elif model_type == "rnsr":
model = models.ResNetSR()
model.upscale(path, scale_factor=scale_factor, save_intermediate=save, evaluate=False, mode=mode,
patch_size=patch_size, suffix=suffix)
assert model_type in ["sr", "esr", "dsr", "ddsr", "rnsr"], 'Model type must be either "sr", "esr", "dsr", ' \
'"ddsr" or "rnsr"'
mode = str(args.mode).lower()
assert mode in ['fast',
'patch'], 'Mode of operation must be either "fast" or "patch"'
scale_factor = int(args.scale)
save = strToBool(args.save)
patch_size = int(args.patch_size)
assert patch_size > 0, "Patch size must be a positive integer"
if model_type == "sr":
model = models.ImageSuperResolutionModel(scale_factor)
elif model_type == "esr":
model = models.ExpantionSuperResolution(scale_factor)
elif model_type == "dsr":
model = models.DenoisingAutoEncoderSR(scale_factor)
elif model_type == "ddsr":
model = models.DeepDenoiseSR(scale_factor)
elif model_type == "rnsr":
model = models.ResNetSR(scale_factor)
else:
model = models.DeepDenoiseSR(scale_factor)
model.upscale(path,
save_intermediate=save,
mode=mode,
patch_size=patch_size,
suffix=suffix)
# esr = models.ExpantionSuperResolution(scale)
# esr.create_model()
# esr.fit(nb_epochs=250)
"""
Train DenoisingAutoEncoderSR
"""
# dsr = models.DenoisingAutoEncoderSR(scale)
# dsr.create_model()
# dsr.fit(nb_epochs=250)
"""
Train Deep Denoise SR
"""
ddsr = models.DeepDenoiseSR(scale)
ddsr.create_model()
ddsr.fit(nb_epochs=180)
"""
Train Res Net SR
"""
# rnsr = models.ResNetSR(scale)
# rnsr.create_model(load_weights=True)
# rnsr.fit(nb_epochs=50)
"""
Train ESPCNN SR
"""
# espcnn = models.EfficientSubPixelConvolutionalSR(scale)
# espcnn.create_model()
