import os
import numpy as np
import h5py
from utils import save_matv73
mat_path1 = './test_results1'
mat_path2 = './test_results2'
mat_path3 = './test_results3'
save_path = './final_results'
for mat_name in sorted(os.listdir(mat_path1)):
print(mat_name)
mat_path_name1 = os.path.join(mat_path1, mat_name)
hf1 = h5py.File(mat_path_name1)
data1 = hf1.get('rad')
res1 = np.transpose(np.array(data1), [2, 1, 0])
mat_path_name2 = os.path.join(mat_path2, mat_name)
hf2 = h5py.File(mat_path_name2)
data2 = hf2.get('rad')
res2 = np.transpose(np.array(data2), [2, 1, 0])
mat_path_name3 = os.path.join(mat_path3, mat_name)
hf3 = h5py.File(mat_path_name3)
data3 = hf3.get('rad')
res3 = np.transpose(np.array(data3), [2, 1, 0])
res = 0.4 * res1 + 0.3 * res2 + 0.3 * res3
mat_dir = os.path.join(save_path, mat_name)
save_matv73(mat_dir, 'rad', res)
from utils import save_matv73, reconstruction
model_path = './models/res_jpg_n16_64.pkl'
img_path = './test_imgs/'
result_path = './test_results1/'
var_name = 'rad'
save_point = torch.load(model_path)
model_param = save_point['state_dict']
model = resblock(conv_relu_res_relu_block, 16, 3, 31)
model.load_state_dict(model_param)
model = model.cuda()
model.eval()
for img_name in sorted(os.listdir(img_path)):
print(img_name)
img_path_name = os.path.join(img_path, img_name)
rgb = imread(img_path_name)
rgb = rgb / 255
rgb = np.expand_dims(np.transpose(rgb, [2, 1, 0]), axis=0).copy()
img_res1 = reconstruction(rgb, model)
img_res2 = np.flip(reconstruction(np.flip(rgb, 2).copy(), model), 1)
img_res3 = (img_res1 + img_res2) / 2
mat_name = img_name[:-11] + '.mat'
mat_dir = os.path.join(result_path, mat_name)
save_matv73(mat_dir, var_name, img_res3)
criterion_Div = torch.nn.KLDivLoss()
a = torch.log_softmax(output * 6.00, dim=1)
b = torch.softmax(gt * 6.00, dim=1)
a2 = torch.log_softmax(gt * 6.00, dim=1)
b2 = torch.softmax(output * 6.00, dim=1)
loss_Div = (criterion_Div(a, b) + criterion_Div(a2, b2))
ANG_loss = ANG_loss + criterion_Angle(output * 6.00,
gt * 6.00).item() / (512 * 512)
output = np.transpose(np.squeeze(output.cpu().detach().numpy()))
inputt = np.transpose(np.squeeze(inputt.cpu().detach().numpy()))
gt = np.transpose(np.squeeze(gt.cpu().detach().numpy()))
save_matv73(mat_dir, 'rad', output)
Loss_SID = Loss_SID + loss_Div.item()
mrae_error = mrae(output * 6.00, gt * 6.00)
# rrmse_error = rmse(inputt, gt)
Total_MRAE = Total_MRAE + mrae_error
# Total_RRMSE = Total_RRMSE + rrmse_error
print("[%s]" % (img_name))
print("#############################################")
print("Average SAM Loss is:")
print(ANG_loss / images_number)