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test_imageME.py
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# test phase
import torch
import os
from torch.autograd import Variable
from net import GhostFusion_net
import utils
from utils import gradient
from scipy.misc import imread, imsave, imresize
from args_fusion import args
import numpy as np
import time
import cv2
def load_model(path, input_nc, output_nc):
nest_model = GhostFusion_net(input_nc, output_nc)
nest_model.load_state_dict(torch.load(path))
para = sum([np.prod(list(p.size())) for p in nest_model.parameters()])
type_size = 4
print('Model {} : params: {:4f}M'.format(nest_model._get_name(), para * type_size / 1000 / 1000))
nest_model.eval()
#nest_model.cuda()
return nest_model
def _generate_fusion_image(model, strategy_type, img1, img2):
# encoder
en_r = model.encoder(img1)
en_v = model.encoder(img2)
f = model.fusion(en_r, en_v, strategy_type=strategy_type)
img_fusion = model.decoder(f);
return img_fusion[0]
def _generate_fusion_mf(model,imgBase1,imgBase2,imgDetail1,imgDetail2):
imgGradDetail1 = gradient(imgDetail1);
imgGradDetail2 = gradient(imgDetail2);
shape = imgBase1.shape;
imgGradDetail1 = torch.abs(imgGradDetail1);
imgGradDetail2 = torch.abs(imgGradDetail2);
en_GradDetail1 = model.encoder(imgGradDetail1);
en_GradDetail2 = model.encoder(imgGradDetail2);
focusMap1 = model.fusion(en_GradDetail1,en_GradDetail2,strategy_type='AGL1')[0];
ones = torch.ones(1,1,shape[2],shape[3]);
if (args.cuda):
ones = ones.cuda(args.device);
focusMap2 = ones-focusMap1;
fBase = imgBase1*focusMap1+imgBase2*focusMap2;
fDetail = imgDetail1*focusMap1+imgDetail2*focusMap2;
return fBase,fDetail
def run_demo(model, irBase_path,irDetail_path, visBase_path, visDetail_path, output_path_root, index, BS,DS, mode):
irBase_img = utils.get_test_images(irBase_path, height=None, width=None, mode=mode)
irDetail_img = utils.get_test_images(irDetail_path, height=None, width=None, mode=mode)
visBase_img = utils.get_test_images(visBase_path, height=None, width=None, mode=mode)
visDetail_img = utils.get_test_images(visDetail_path, height=None, width=None, mode=mode)
# dim = img_ir.shape
if args.cuda:
irBase_img = irBase_img.cuda(args.device)
irDetail_img = irDetail_img.cuda(args.device)
visBase_img = visBase_img.cuda(args.device)
visDetail_img = visDetail_img.cuda(args.device)
model = model.cuda(args.device);
irBase_img = Variable(irBase_img, requires_grad=False)
irDetail_img = Variable(irDetail_img, requires_grad=False)
visBase_img = Variable(visBase_img, requires_grad=False)
visDetail_img = Variable(visDetail_img, requires_grad=False)
if (BS=='AGL1' and DS=='AGL1'):
fusedBase,fusedDetail = _generate_fusion_mf(model,irBase_img,visBase_img,irDetail_img,visDetail_img);
else:
#strategy_type_list = strategy_type_list = ['AVG', 'L1','SC','MAX','AGL1']
#Base L1
fusedBase = _generate_fusion_image(model, BS, irBase_img, visBase_img)
fusedDetail = _generate_fusion_image(model, DS, irDetail_img, visDetail_img)
fusedBase = fusedBase[0].cpu();
fusedBase = fusedBase.squeeze().squeeze();
fusedBase = fusedBase.numpy();
fusedBase = fusedBase*255;
file_name = 'fuseBase'+str(index) + '.png'
output_path = output_path_root + file_name
imsave(output_path,fusedBase);
#Detail max
fusedDetail = fusedDetail[0].cpu();
fusedDetail = fusedDetail.squeeze().squeeze();
fusedDetail = fusedDetail.numpy();
fusedDetail = fusedDetail*255;
fusedDetail = fusedDetail - np.mean(fusedBase);
file_name = 'fuseDetail'+str(index) + '.png'
output_path = output_path_root + file_name
imsave(output_path,fusedDetail);
#finalFuseResult
fusedFinalResult = fusedDetail + fusedBase;
############################ multi outputs ##############################################
file_name = 'fuseME'+str(index) + '.png'
output_path = output_path_root + file_name
imsave(output_path,fusedFinalResult);
print(output_path)
def main():
test_path = "sample_input/"
fusion_type = 'auto' # auto, fusion_layer, fusion_all
strategy_type_list = ['AVG', 'L1','SC','MAX','AGL1'] # addition, attention_weight, attention_enhance, adain_fusion, channel_fusion, saliency_mask
BS = strategy_type_list[0];
DS = strategy_type_list[0];
output_path = './outputs/';
if os.path.exists(output_path) is False:
os.mkdir(output_path)
# in_c = 3 for RGB images; in_c = 1 for gray images
in_c = 1
if in_c == 1:
out_c = in_c
mode = 'L'
model_path = args.model_path_gray
else:
out_c = in_c
mode = 'RGB'
model_path = args.model_path_rgb
with torch.no_grad():
print('SSIM weight ----- ' + args.ssim_path[2])
ssim_weight_str = args.ssim_path[2]
model = load_model(model_path, in_c, out_c)
for i in range(1):
index = i + 1
irBase_path = test_path + 'ME_IRBase.png'
irDetail_path = test_path + 'ME_IRDetail.png'
visBase_path = test_path + 'ME_VISBase.png'
visDetail_path = test_path + 'ME_VISDetail.png'
run_demo(model,irBase_path,irDetail_path, visBase_path, visDetail_path, output_path, index, BS, DS, mode)
print('Done......')
if __name__ == '__main__':
main()