"""General-purpose test script for image-to-image translation.

Once you have trained your model with train.py, you can use this script to test the model.
It will load a saved model from '--checkpoints_dir' and save the results to '--results_dir'.

It first creates model and dataset given the option. It will hard-code some parameters.
It then runs inference for '--num_test' images and save results to an HTML file.
The results will be saved at ./results/.
Use '--results_dir <directory_path_to_save_result>' to specify the results directory.

Test a pix2pix model:
    python test.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix
"""
import os
from options.test_options import TestOptions
from data import create_dataset
from models import create_model
from util.evaluation import ExcelEvaluate
from util.visualizer import save_images, save_nifti_images, save_web_nifti
from util import html_handler
from util.util import postprocess_images, print_timestamped
import time

if __name__ == '__main__':
    opt = TestOptions().parse()  # get test options
    # hard-code some parameters for test
    opt.num_threads = 0  # test code only supports num_threads = 0
    opt.batch_size = 1  # test code only supports batch_size = 1
    opt.serial_batches = True  # disable data shuffling;
    # comment this line if results on randomly chosen images are needed.
    opt.no_flip = True  # no flip; comment this line if results on flipped images are needed.
    opt.display_id = -1  # no visdom display; the test code saves the results to a HTML file.
    dataset = create_dataset(opt)  # create a dataset given opt.dataset_mode and other options
    model = create_model(opt)  # create a model given opt.model and other options
    model.setup(opt)  # regular setup: load and print networks; create schedulers
    # Determine whether we are using nifti images
    nifti = True if opt.dataset_mode == "nifti" else False
    # create a website
    exc_eval = None
    if nifti:
        web_dir = os.path.join(opt.results_dir, opt.name,
                               '{}_{}_{}'.format(opt.phase, opt.epoch, opt.postprocess))  # define the website directory
        excel_filename = os.path.join(web_dir, (opt.phase + "_" + str(opt.name) + ".csv"))
        exc_eval = ExcelEvaluate(excel_filename, opt.excel)
    else:
        web_dir = os.path.join(opt.results_dir, opt.name,
                               '{}_{}'.format(opt.phase, opt.epoch))  # define the website directory
    if opt.load_iter > 0:  # load_iter is 0 by default
        web_dir = '{:s}_iter{:d}'.format(web_dir, opt.load_iter)
    print('creating web directory', web_dir)
    webpage = html_handler.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.epoch))
    # test with eval mode. This only affects layers like batchnorm and dropout.
    # For [pix2pix]: we use batchnorm and dropout in the original pix2pix.
    # You can experiment it with and without eval() mode.
    # For [CycleGAN]: It should not affect CycleGAN as CycleGAN uses instancenorm without dropout.
    if opt.eval:
        model.eval()
    init_time = time.time()
    for i, data in enumerate(dataset):
        if i >= opt.num_test:  # only apply our model to opt.num_test images.
            break
        model.set_input(data)  # unpack data from data loader
        model.test()  # run inference
        visuals = model.get_current_visuals()  # get image results
        img_path = model.get_image_paths()  # get image paths
        if i % 5 == 0:  # save images to an HTML file
            print('processing (%04d)-th image... %s' % (i, img_path))
        if nifti:
            query_name = os.path.basename(img_path[0])
            # Postprocess the results to numpy arrays
            np_post = postprocess_images(visuals, opt, dataset.dataset.original_shape)
            query_name_noext = query_name.split(".")[0]
            if 'real_B' in np_post:
                # Evaluate the results
                exc_eval.evaluate(np_post, query_name_noext, opt.smoothing)
            save_nifti_images(np_post, query_name_noext, opt, web_dir, dataset.dataset.affine)
            save_web_nifti(webpage, np_post, img_path, opt.show_plots, width=opt.display_winsize)
        else:
            save_images(webpage, visuals, img_path, aspect_ratio=opt.aspect_ratio, width=opt.display_winsize)
    webpage.save()  # save the HTML
    end_time = round(time.time() - init_time, 3)
    print_timestamped("The testing process took " + str(end_time) + "s.")
    if nifti:
        exc_eval.close()

"""General-purpose test script for image-to-image translation.

Once you have trained your model with train.py, you can use this script to test the model.
It will load a saved model from '--checkpoints_dir' and save the results to '--results_dir'.

It first creates model and dataset given the option. It will hard-code some parameters.
It then runs inference for '--num_test' images and save results to an HTML file.
The results will be saved at ./results/.
Use '--results_dir <directory_path_to_save_result>' to specify the results directory.

Test a pix2pix model:
    python test.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix
"""
import os
from options.test_options import TestOptions
from data import create_dataset
from models import create_model
from util.evaluation import ExcelEvaluate
from util.visualizer import save_images, save_nifti_images, save_web_nifti
from util import html_handler
from util.util import postprocess_images, print_timestamped
import time

if __name__ == '__main__':
    opt = TestOptions().parse()  # get test options
    # hard-code some parameters for test
    opt.num_threads = 0  # test code only supports num_threads = 0
    opt.batch_size = 1  # test code only supports batch_size = 1
    opt.serial_batches = True  # disable data shuffling;
    # comment this line if results on randomly chosen images are needed.
    opt.no_flip = True  # no flip; comment this line if results on flipped images are needed.
    opt.display_id = -1  # no visdom display; the test code saves the results to a HTML file.
    dataset = create_dataset(opt)  # create a dataset given opt.dataset_mode and other options
    model = create_model(opt)  # create a model given opt.model and other options
    model.setup(opt)  # regular setup: load and print networks; create schedulers
    # Determine whether we are using nifti images
    nifti = True if opt.dataset_mode == "nifti" else False
    # create a website
    exc_eval = None
    if nifti:
        web_dir = os.path.join(opt.results_dir, opt.name,
                               '{}_{}_{}'.format(opt.phase, opt.epoch, opt.postprocess))  # define the website directory
        excel_filename = os.path.join(web_dir, (opt.phase + "_" + str(opt.name) + ".csv"))
        exc_eval = ExcelEvaluate(excel_filename, opt.excel)
    else:
        web_dir = os.path.join(opt.results_dir, opt.name,
                               '{}_{}'.format(opt.phase, opt.epoch))  # define the website directory
    if opt.load_iter > 0:  # load_iter is 0 by default
        web_dir = '{:s}_iter{:d}'.format(web_dir, opt.load_iter)
    print('creating web directory', web_dir)
    webpage = html_handler.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.epoch))
    # test with eval mode. This only affects layers like batchnorm and dropout.
    # For [pix2pix]: we use batchnorm and dropout in the original pix2pix.
    # You can experiment it with and without eval() mode.
    # For [CycleGAN]: It should not affect CycleGAN as CycleGAN uses instancenorm without dropout.
    if opt.eval:
        model.eval()
    init_time = time.time()
    for i, data in enumerate(dataset):
        if i >= opt.num_test:  # only apply our model to opt.num_test images.
            break
        model.set_input(data)  # unpack data from data loader
        model.test()  # run inference
        visuals = model.get_current_visuals()  # get image results
        img_path = model.get_image_paths()  # get image paths
        if i % 5 == 0:  # save images to an HTML file
            print('processing (%04d)-th image... %s' % (i, img_path))
        if nifti:
            query_name = os.path.basename(img_path[0])
            # Postprocess the results to numpy arrays
            np_post = postprocess_images(visuals, opt, dataset.dataset.original_shape)
            query_name_noext = query_name.split(".")[0]
            if 'real_B' in np_post:
                # Evaluate the results
                exc_eval.evaluate(np_post, query_name_noext, opt.smoothing)
            save_nifti_images(np_post, query_name_noext, opt, web_dir, dataset.dataset.affine)
            save_web_nifti(webpage, np_post, img_path, opt.show_plots, width=opt.display_winsize)
        else:
            save_images(webpage, visuals, img_path, aspect_ratio=opt.aspect_ratio, width=opt.display_winsize)
    webpage.save()  # save the HTML
    end_time = round(time.time() - init_time, 3)
    print_timestamped("The testing process took " + str(end_time) + "s.")
    if nifti:
        exc_eval.close()

General-purpose test script for image-to-image translation.  Once you have trained your model with train.py, you can use this script to test the model. It will load a saved model from '--checkpoints_dir' and save the results to '--results_dir'.  It first creates model and dataset given the option. It will hard-code some parameters. It then runs inference for '--num_test' images and save results to an HTML file. The results will be saved at ./results/. Use '--results_dir <directory_path_to_save_result>' to specify the results directory.  Test a pix2pix model:     python test.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix # get test options # hard-code some parameters for test # test code only supports num_threads = 0 # test code only supports batch_size = 1 # disable data shuffling; # comment this line if results on randomly chosen images are needed. # no flip; comment this line if results on flipped images are needed. # no visdom display; the test code saves the results to a HTML file. # create a dataset given opt.dataset_mode and other options # create a model given opt.model and other options # regular setup: load and print networks; create schedulers # Determine whether we are using nifti images # create a website # define the website directory # define the website directory # load_iter is 0 by default # test with eval mode. This only affects layers like batchnorm and dropout. # For [pix2pix]: we use batchnorm and dropout in the original pix2pix. # You can experiment it with and without eval() mode. # For [CycleGAN]: It should not affect CycleGAN as CycleGAN uses instancenorm without dropout. # only apply our model to opt.num_test images. # unpack data from data loader # run inference # get image results # get image paths # save images to an HTML file # Postprocess the results to numpy arrays # Evaluate the results # save the HTML