def invert_color_func(params_dict):
params01_vec_dst = []
for key in FurParam.ParamsColor:
value = param_color_dict[key]
val01 = FurParam.ConvertFurParam(key, value)
params01_vec_dst.append(val01)
return params01_vec_dst
## invert initial parameter dictionary as vector
############################################################
## 1) random initial parameters [NOT SO USEFUL]
#params01_vec_dst = np.random.random(15)
## 2) use initial parameters from Bayesian optimization
#'''
csv_ref_path = os.path.splitext(img_ref_path)[0]+"_bayesopt.csv"
init_params_dict = FurParam.csv2dict(csv_ref_path)
params01_vec_dst = []
#'''
for key in FurParam.ParamsGeom:
value = init_params_dict[key]
val01 = FurParam.ConvertFurParam(key, value)
params01_vec_dst.append(val01)
############################################################
## enter optimization routine
############################################################
## initialize elapsed time for each component as 0:00:00.000
t_total_start = datetime.now()
## run optimization on GEOMETRY parameters
succeeded, shape_param_dict = GradientDescent(
vgg_max_gray_gram, calc_cost_func, furRenderer.RenderFur,
convert_param_func, params01_vec_dst,
folder_root, imgFileExt
)