def __init__(self):
print("开始进行测试")
self.param_gpu_id = 0
self.param_single_channel = True
self.param_threshold = 3
self.param_batch_size = 1
self.param_use_cvpr = False
self.param_use_cnn = True
self.param_use_traditional = True
self.param_use_combine = True
self.param_save_mat = False
# 加载模型
os.environ["CUDA_VISIBLE_DEVICES"] = str(self.param_gpu_id)
self.use_cuda = torch.cuda.is_available()
print(self.param_gpu_id, self.param_single_channel,
self.param_threshold, self.param_batch_size)
if self.param_single_channel:
if self.use_cuda:
param_checkpoint_path = '/home/zlk/project/registration_cnn_ntg/trained_weight/output/voc2012_coco2014_NTG_resnet101.pth.tar'
else:
param_checkpoint_path = '/Users/zale/project/myself/registration_cnn_ntg/trained_weight/output/voc2012_coco2014_NTG_resnet101.pth.tar'
else:
param_checkpoint_path = '/mnt/4T/zlk/trained_weights/best_checkpoint_coco2017_multi_gpu_paper30_NTG_resnet101.pth.tar'
if self.param_use_cnn:
self.ntg_model = createModel(
param_checkpoint_path,
use_cuda=self.use_cuda,
single_channel=self.param_single_channel)
self.ntg_model.eval()
else:
self.ntg_model = None
if self.param_use_cvpr:
self.cvpr_model = createCVPRModel(use_cuda=self.use_cuda)
self.cvpr_model.eval()
else:
self.cvpr_model = None
def main():
single_channel = False
print("开始进行测试")
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
ntg_checkpoint_path = '/mnt/4T/zlk/trained_weights/best_checkpoint_coco2017_multi_gpu_paper30_NTG_resnet101.pth.tar'
#test_image_path = '/home/zlk/datasets/coco_test2017'
# test_image_path = '/mnt/4T/zlk/datasets/mulitspectral/Harvard'
test_image_path = '/mnt/4T/zlk/datasets/mulitspectral/complete_ms_data_mat'
threshold = 3
batch_size = 1
# 加载模型
use_cuda = torch.cuda.is_available()
# vis = VisdomHelper(env_name='Harvard_test')
vis = VisdomHelper(env_name='CAVE_test')
ntg_model = createModel(ntg_checkpoint_path,
use_cuda=use_cuda,
single_channel=single_channel)
cvpr_model = createCVPRModel(use_cuda=use_cuda)
print('测试harvard网格点损失')
dataloader, pair_generator = createDataloader(
test_image_path,
batch_size,
use_cuda=use_cuda,
single_channel=single_channel)
iterDataset(dataloader,
pair_generator,
ntg_model,
cvpr_model,
vis,
threshold=threshold,
use_cuda=use_cuda)
param_test_image_path = '/mnt/4T/zlk/datasets/mulitspectral/complete_ms_data_mat'
# param_test_image_path = '/home/zale/datasets/complete_ms_data_mat'
# param_test_image_path = '/Users/zale/project/datasets/complete_ms_data_mat'
# 加载模型
os.environ["CUDA_VISIBLE_DEVICES"] = str(param_gpu_id)
use_cuda = torch.cuda.is_available()
if param_use_cnn:
ntg_model = createModel(param_checkpoint_path,
use_cuda=use_cuda,
single_channel=param_single_channel)
else:
ntg_model = None
cvpr_model = createCVPRModel(use_cuda=use_cuda)
print('测试cave网格点损失')
dataloader, pair_generator = createDataloader(
param_test_image_path,
batch_size=param_batch_size,
single_channel=param_single_channel,
use_cuda=use_cuda)
iterDataset(dataloader,
pair_generator,
ntg_model,
cvpr_model,
vis,
threshold=param_threshold,
use_cuda=use_cuda,
def register_showVisdom(self):
print("开始进行测试")
param_gpu_id = 0
param_single_channel = True
param_threshold = 3
param_batch_size = 1
param_use_cvpr = True
param_use_cnn = True
param_use_traditional = True
param_use_combine = True
param_save_mat = False
print(param_gpu_id, param_single_channel, param_threshold,
param_batch_size)
vis = VisdomHelper(env_name='CAVE_common', port=8098)
if param_single_channel:
param_checkpoint_path = '/home/zlk/project/registration_cnn_ntg/trained_weight/output/voc2012_coco2014_NTG_resnet101.pth.tar'
else:
param_checkpoint_path = '/mnt/4T/zlk/trained_weights/best_checkpoint_coco2017_multi_gpu_paper30_NTG_resnet101.pth.tar'
param_test_image_path = '/mnt/4T/zlk/datasets/mulitspectral/complete_ms_data_mat'
# param_test_image_path = '/home/zale/datasets/complete_ms_data_mat'
# param_test_image_path = '/Users/zale/project/datasets/complete_ms_data_mat'
# 加载模型
os.environ["CUDA_VISIBLE_DEVICES"] = str(param_gpu_id)
use_cuda = torch.cuda.is_available()
if param_use_cnn:
ntg_model = createModel(param_checkpoint_path,
use_cuda=use_cuda,
single_channel=param_single_channel)
else:
ntg_model = None
cvpr_model = createCVPRModel(use_cuda=use_cuda)
source_image_path = '../datasets/row_data/multispectral/door2.jpg'
target_image_path = '../datasets/row_data/multispectral/door1.jpg'
source_image_raw = io.imread(source_image_path)
target_image_raw = io.imread(target_image_path)
source_image = source_image_raw[:, :, 0:1]
target_image = target_image_raw[:, :, 2:3]
source_image_var = preprocess_image(source_image,
resize=True,
use_cuda=use_cuda)
target_image_var = preprocess_image(target_image,
resize=True,
use_cuda=use_cuda)
batch = {
'source_image': source_image_var,
'target_image': target_image_var
}
ntg_model.eval()
theta = ntg_model(batch)
# theta_opencv = theta2param(theta.view(-1, 2, 3), 240, 240, use_cuda=use_cuda)
# cnn_ntg_param_batch = estimate_param_batch(source_image_var[:, 0, :, :], target_image_var[:, 2, :, :], theta_opencv)
cnn_image_warped_batch = affine_transform_pytorch(
source_image_var, theta)
vis.showImageBatch(source_image_var,
normailze=True,
win='source_image_batch',
title='source_image_batch')
vis.showImageBatch(target_image_var,
normailze=True,
win='target_image_batch',
title='target_image_batch')
vis.showImageBatch(cnn_image_warped_batch,
normailze=True,
win='cnn_image_warped_batch',
title='cnn_image_warped_batch')