def visualize_result(batch_data, pred, args):
colors = loadmat('datasets/mit_list/color150.mat')['colors']
(image, seg, info) = batch_data
for j in range(len(info)):
# recover image
img = image[j].clone()
for t, m, s in zip(img, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]):
t.mul_(s).add_(m)
img = (img.numpy().transpose((1, 2, 0)) * 255).astype(np.uint8)
img = img[:, :, ::-1]
# segmentation
seg_ = seg[j].numpy()
seg_color = colorEncode(seg_, colors)
# preddiction
pred_ = np.argmax(pred.data.cpu()[j].numpy(), axis=0)
pred_color = colorEncode(pred_, colors)
# aggregate images and save
im_vis = np.concatenate((img, seg_color, pred_color),
axis=1).astype(np.uint8)
img_name = info[j].split('/')[-1]
imsave(os.path.join(args.result, img_name.replace('.jpg', '.png')),
im_vis)
def visualize_tv(batch_data, pred1, pred2, args):
colors = loadmat('datasets/mit_list/color150.mat')['colors']
(imgs, segs, infos) = batch_data
for j in range(len(infos)):
# get/recover image
# img = imread(os.path.join(args.root_img, infos[j]))
img = imgs[j].clone()
for t, m, s in zip(img, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]):
t.mul_(s).add_(m)
img = (img.numpy().transpose((1, 2, 0)) * 255).astype(np.uint8)
img = imresize(img, (args.imgSize, args.imgSize), interp='bilinear')
# segmentation
lab = segs[j].numpy()
lab_color = colorEncode(lab, colors)
lab_color = imresize(lab_color, (args.imgSize, args.imgSize),
interp='nearest')
# prediction
#print('#############')
#print(pred1)
pred1_ = np.argmax(pred1.data.cpu()[j].numpy(), axis=0)
#print('**************')
#print(pred1_)
#print(pred1_.size())
pred1_color = colorEncode(pred1_, colors)
#print('&&&&&&&&&&&&&&&&&')
#print(pred1_color)
pred1_color = imresize(pred1_color, (args.imgSize, args.imgSize),
interp='nearest')
pred2_ = np.argmax(pred2.data.cpu()[j].numpy(), axis=0)
pred2_color = colorEncode(pred2_, colors)
pred2_color = imresize(pred2_color, (args.imgSize, args.imgSize),
interp='nearest')
#pred_out_ = np.argmax(pred_out.data.cpu()[j].numpy(), axis=0)
#pred_out_color = colorEncode(pred_out_, colors)
#pred_out_color = imresize(pred_out_color, (args.imgSize, args.imgSize), interp='nearest')
# aggregate images and save
im_vis = np.concatenate((img, lab_color, pred1_color, pred2_color),
axis=1).astype(np.uint8)
imsave(
os.path.join(args.result,
infos[j].replace('/', '_').replace('.jpg', '.png')),
im_vis)
def visualize_test_result(img, pred1, pred2, pred_outputs, args):
colors = loadmat('datasets/mit_list/color150.mat')['colors']
# recover image
img = img[0]
#pred1 = pred1.data.cpu()[0]
#pred2 = pred2.data.cpu()[0]
#pred_outputs = pred_outputs.data.cpu()[0]
for t, m, s in zip(img, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]):
t.mul_(s).add_(m)
img = (img.numpy().transpose((1, 2, 0)) * 255).astype(np.uint8)
#img = imresize(img, (args.imgSize, args.imgSize), interp='bilinear')
# prediction
#print('#############')
#print(pred1)
pred1_ = np.argmax(pred1.data.cpu()[0].numpy(), axis=0) + 1
#print('**************')
#print(pred1_)
#print(pred1_.size())
pred1_color = colorEncode(pred1_, colors)
#print('&&&&&&&&&&&&&&&&&')
#print(pred1_color)
#pred1_color = imresize(pred1_color, (args.imgSize, args.imgSize), interp='nearest')
pred2_ = np.argmax(pred2.data.cpu()[0].numpy(), axis=0) + 1
pred2_color = colorEncode(pred2_, colors)
#pred2_color = imresize(pred2_color, (args.imgSize, args.imgSize), interp='nearest')
pred_outputs_ = np.argmax(pred_outputs.data.cpu()[0].numpy(), axis=0) + 1
pred_outputs_color = colorEncode(pred_outputs_, colors)
#pred2_color = imresize(pred2_color, (args.imgSize, args.imgSize), interp='nearest')
# aggregate images and save
im_vis = np.concatenate(
(img, pred1_color, pred2_color, pred_outputs_color),
axis=1).astype(np.uint8)
imsave(os.path.join(args.result,
os.path.basename(args.test_img) + '.png'), im_vis)
