83 lines
2.5 KiB
Python
83 lines
2.5 KiB
Python
import time
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from options.test_options import TestOptions
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from data import create_dataset
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from models import create_model
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import os
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from util.util import save_images
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import numpy as np
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from util.util import mkdir
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from PIL import Image
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def make_val_opt(opt):
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# hard-code some parameters for test
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opt.num_threads = 0 # test code only supports num_threads = 1
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opt.batch_size = 1 # test code only supports batch_size = 1
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opt.serial_batches = True # disable data shuffling; comment this line if results on randomly chosen images are needed.
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opt.no_flip = True # no flip; comment this line if results on flipped images are needed.
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opt.no_flip2 = True # no flip; comment this line if results on flipped images are needed.
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opt.display_id = -1 # no visdom display; the test code saves the results to a HTML file.
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opt.phase = 'val'
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opt.preprocess = 'none1'
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opt.isTrain = False
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opt.aspect_ratio = 1
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opt.eval = True
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return opt
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def val(opt):
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dataset = create_dataset(opt) # create a dataset given opt.dataset_mode and other options
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model = create_model(opt) # create a model given opt.model and other options
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model.setup(opt) # regular setup: load and print networks; create schedulers
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save_path = opt.results_dir
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mkdir(save_path)
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model.eval()
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for i, data in enumerate(dataset):
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if i >= opt.num_test: # only apply our model to opt.num_test images.
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break
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model.set_input(data) # unpack data from data loader
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pred = model.test(val=False) # run inference return pred
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img_path = model.get_image_paths() # get image paths
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if i % 5 == 0: # save images to an HTML file
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print('processing (%04d)-th image... %s' % (i, img_path))
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save_images(pred, save_path, img_path)
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def pred_image(data_root, results_dir):
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opt = TestOptions().parse() # get training options
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opt = make_val_opt(opt)
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opt.phase = 'test'
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opt.dataset_mode = 'changedetection'
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opt.n_class = 2
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opt.SA_mode = 'PAM'
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opt.arch = 'mynet3'
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opt.model = 'CDFA'
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opt.epoch = 'pam'
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opt.num_test = np.inf
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opt.name = 'pam'
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opt.dataroot = data_root
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opt.results_dir = results_dir
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val(opt)
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if __name__ == '__main__':
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# define the data_root and the results_dir
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# note:
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# data_root should have such structure:
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# ├─A
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# ├─B
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# A for before images
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# B for after images
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data_root = './samples'
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results_dir = './samples/output/'
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pred_image(data_root, results_dir)
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