def fit_reflectance_model(model_type, L, ori_r, r, ori_ndotl, ndotl,
fit_falloff, width, height, *extra_params):
# determine which reflectance model function to use
if model_type == sfs.LAMBERTIAN_MODEL:
model_func = reflectance_models.fit_lambertian
elif model_type == sfs.OREN_NAYAR_MODEL:
model_func = reflectance_models.fit_oren_nayar
elif model_type == sfs.PHONG_MODEL:
model_func = reflectance_models.fit_phong
elif model_type == sfs.COOK_TORRANCE_MODEL:
model_func = reflectance_models.fit_cook_torrance
elif model_type == sfs.POWER_MODEL:
model_func = reflectance_models.fit_power_model
if not fit_falloff:
falloff = 1.5
model_params, residual = model_func(L, ori_r, r, ori_ndotl, ndotl,
width, height, *extra_params)
else:
#import pdb;pdb.set_trace()
falloff, model_params, residual = reflectance_models.fit_with_falloff(
L, ori_r, r, ori_ndotl, ndotl, width, height, model_func,
*extra_params)
print 'Residual =', residual
print 'Falloff = ', falloff
return falloff, model_params, residual
def fit_reflectance_model(model_type, L, r, ndotl, fit_falloff, *extra_params):
# determine which reflectance model function to use
if model_type == sfs.LAMBERTIAN_MODEL:
model_func = reflectance_models.fit_lambertian
elif model_type == sfs.OREN_NAYAR_MODEL:
model_func = reflectance_models.fit_oren_nayar
elif model_type == sfs.PHONG_MODEL:
model_func = reflectance_models.fit_phong
elif model_type == sfs.COOK_TORRANCE_MODEL:
model_func = reflectance_models.fit_cook_torrance
elif model_type == sfs.POWER_MODEL:
model_func = reflectance_models.fit_power_model
if not fit_falloff:
falloff = 2.
model_params, residual = model_func(L, r, ndotl, *extra_params)
else:
falloff, model_params, residual = reflectance_models.fit_with_falloff(
L, r, ndotl, model_func, *extra_params)
print 'Residual =', residual
return falloff, model_params, residual
def fit_reflectance_model(model_type, L, r, ndotl, fit_falloff, *extra_params):
# determine which reflectance model function to use
if model_type == sfs.LAMBERTIAN_MODEL:
model_func = reflectance_models.fit_lambertian
elif model_type == sfs.OREN_NAYAR_MODEL:
model_func = reflectance_models.fit_oren_nayar
elif model_type == sfs.PHONG_MODEL:
model_func = reflectance_models.fit_phong
elif model_type == sfs.COOK_TORRANCE_MODEL:
model_func = reflectance_models.fit_cook_torrance
elif model_type == sfs.POWER_MODEL:
model_func = reflectance_models.fit_power_model
if not fit_falloff:
falloff = 2.
model_params, residual = model_func(L, r, ndotl, *extra_params)
else:
falloff, model_params, residual = reflectance_models.fit_with_falloff(
L, r, ndotl, model_func, *extra_params)
print 'Residual =', residual
return falloff, model_params, residual
def fit_reflectance_model(model_type, L, ori_r, r, ori_ndotl,ndotl, fit_falloff,width,height, *extra_params):
# determine which reflectance model function to use
if model_type == sfs.LAMBERTIAN_MODEL:
model_func = reflectance_models.fit_lambertian
elif model_type == sfs.OREN_NAYAR_MODEL:
model_func = reflectance_models.fit_oren_nayar
elif model_type == sfs.PHONG_MODEL:
model_func = reflectance_models.fit_phong
elif model_type == sfs.COOK_TORRANCE_MODEL:
model_func = reflectance_models.fit_cook_torrance
elif model_type == sfs.POWER_MODEL:
model_func = reflectance_models.fit_power_model
if not fit_falloff:
falloff =1.5
model_params, residual = model_func(L,ori_r, r,ori_ndotl,ndotl,width,height, *extra_params)
else:
#import pdb;pdb.set_trace()
falloff, model_params, residual = reflectance_models.fit_with_falloff(L,ori_r, r,ori_ndotl,ndotl,width,height, model_func, *extra_params)
print 'Residual =', residual
print 'Falloff = ', falloff
return falloff, model_params, residual