def __getitem__(self, idx): if not len(self.origin_imgs[idx]): origin_img = read_img(self.filenames[idx]) if not (len(self.sigma_s) and len(self.sigma_c)): # all random min_log = np.log([0.000001]) sigma_s = min_log + np.random.rand(1) * (np.log([0.002]) - min_log) sigma_s = np.exp(sigma_s) sigma_c = min_log + np.random.rand(1) * (np.log([0.001]) - min_log) sigma_c = np.exp(sigma_c) else: sigma_s = self.sigma_s sigma_c = self.sigma_c self.origin_imgs[idx], self.noise_imgs[idx] = self.isp.cbdnet_noise_generate_srgb(origin_img, sigma_s, sigma_c) origin_img = self.origin_imgs[idx] noise_img = self.noise_imgs[idx] patch_origin_img, patch_noise_img = get_patch(origin_img, noise_img, self.patch_size, self.random) patch_origin_img_chw = hwc_to_chw(patch_origin_img) patch_noise_img_chw = hwc_to_chw(patch_noise_img) return patch_noise_img_chw, patch_origin_img_chw
def predict_img(net,
full_img,
scale_factor=0.5,
out_threshold=0.5,
use_dense_crf=True,
use_gpu=False):
img_height = full_img.size[1]
img_width = full_img.size[0]
img = resize_and_crop(full_img, scale=scale_factor)
img = normalize(img)
left_square, right_square = split_img_into_squares(img)
left_square = hwc_to_chw(left_square)
right_square = hwc_to_chw(right_square)
X_left = torch.from_numpy(left_square).unsqueeze(0)
X_right = torch.from_numpy(right_square).unsqueeze(0)
if use_gpu:
X_left = X_left.cuda()
X_right = X_right.cuda()
with torch.no_grad():
output_left = net(X_left)
output_right = net(X_right)
left_probs = F.sigmoid(output_left).squeeze(0)
right_probs = F.sigmoid(output_right).squeeze(0)
'''
tf = transforms.Compose(
[
transforms.ToPILImage(),
transforms.Resize(img_height),
transforms.ToTensor()
]
)
'''
tf = transforms.Compose([
transforms.ToPILImage(),
transforms.Scale(img_height),
transforms.ToTensor()
])
left_probs = tf(left_probs.cpu())
right_probs = tf(right_probs.cpu())
left_mask_np = left_probs.squeeze().cpu().numpy()
right_mask_np = right_probs.squeeze().cpu().numpy()
full_mask = merge_masks(left_mask_np, right_mask_np, img_width)
if use_dense_crf:
full_mask = dense_crf(np.array(full_img).astype(np.uint8), full_mask)
return full_mask > out_threshold
def __getitem__(self, idx):
clean_fn = random.choice(self.clean_fns[idx])
clean_img = read_img(clean_fn)
noise_img = read_img(clean_fn.replace('GT_SRGB', 'NOISY_SRGB'))
if self.patch_size > 0:
[clean_img, noise_img] = get_patch([clean_img, noise_img],
self.patch_size)
return hwc_to_chw(noise_img), hwc_to_chw(clean_img), np.zeros(
(3, self.patch_size, self.patch_size)), np.zeros(
(3, self.patch_size, self.patch_size))
def __getitem__(self, idx):
clean_fn = random.choice(self.clean_fns[idx])
clean_img = read_img(clean_fn)
noise_img = read_img(clean_fn.replace('GT_SRGB', 'NOISY_SRGB'))
if self.patch_size > 0:
clean_img, noise_img = get_patch(clean_img, noise_img,
self.patch_size)
clean_img_chw = hwc_to_chw(clean_img)
noise_img_chw = hwc_to_chw(noise_img)
return noise_img_chw, clean_img_chw
def __getitem__(self, idx):
clean_fn = random.choice(self.clean_fns[idx])
clean_img = read_img(clean_fn)
noise_img = read_img(clean_fn.replace('GT_SRGB', 'NOISY_SRGB'))
sigma_img = read_img(clean_fn.replace(
'GT_SRGB', 'SIGMA_SRGB')) / 15. # inverse scaling
if self.patch_size > 0:
[clean_img, noise_img,
sigma_img] = get_patch([clean_img, noise_img, sigma_img],
self.patch_size)
return hwc_to_chw(noise_img), hwc_to_chw(clean_img), hwc_to_chw(
sigma_img), np.ones((3, self.patch_size, self.patch_size))
def __getitem__(self, idx): batch_path = os.path.join(self.root_dir, self.batches[idx]) if not len(self.origin_imgs[idx]): origin_fns = glob.glob(batch_path + '/Reference.png') noise_fns = glob.glob(batch_path + '/Noisy.png') self.origin_imgs[idx] = read_img(origin_fns[0]) for noise_fn in noise_fns: self.noise_imgs[idx].append(read_img(noise_fn)) origin_img = self.origin_imgs[idx] noise_img = self.noise_imgs[idx][np.random.randint(len(self.noise_imgs[idx]))] patch_origin_img, patch_noise_img = get_patch(origin_img, noise_img, self.patch_size, self.random) patch_origin_img_chw = hwc_to_chw(patch_origin_img) patch_noise_img_chw = hwc_to_chw(patch_noise_img) return patch_noise_img_chw, patch_origin_img_chw
def predict_img(net,
full_img,
device,
scale_factor=1,
out_threshold=0.5,
use_dense_crf=False):
net.eval()
img_height = full_img.size[1]
img = resize_and_crop(full_img, scale=scale_factor)
img = normalize(img)
img = hwc_to_chw(img)
X = torch.from_numpy(img).unsqueeze(0)
X = X.to(device=device)
with torch.no_grad():
output = net(X)
if net.n_classes > 1:
probs = F.softmax(output, dim=1)
else:
probs = torch.sigmoid(output)
probs = probs.squeeze(0)
tf = transforms.Compose(
[
transforms.ToPILImage(),
transforms.Resize(img_height),
transforms.ToTensor()
]
)
probs = tf(probs.cpu())
full_mask = probs.squeeze().cpu().numpy()
if use_dense_crf:
full_mask = dense_crf(np.array(full_img).astype(np.uint8), full_mask)
return full_mask > out_threshold
def predict_img(net,
full_img,
scale_factor=0.5,
out_threshold=0.5,
use_dense_crf=True,
use_gpu=False):
net.eval()
img_height = full_img.size[1]
img_width = full_img.size[0]
img = resize_and_crop_y(full_img, scale=scale_factor)
img = normalize(img)
img = hwc_to_chw(img)
X_img = torch.from_numpy(img).unsqueeze(0)
if use_gpu:
X_img = X_img.cuda()
with torch.no_grad():
output_img = net(X_img)
img_probs = output_img.squeeze(0)
tf = transforms.Compose([
transforms.ToPILImage(),
#transforms.Resize(),
transforms.ToTensor()
])
img_probs = tf(img_probs.cpu())
img_mask = img_probs.squeeze().cpu().numpy()
#full_mask = merge_masks(left_mask_np, right_mask_np, img_width)
full_mask = img_mask
print(full_mask)
#if use_dense_crf:
# full_mask = dense_crf(np.array(full_img).astype(np.uint8), full_mask)
#return full_mask > out_threshold
return full_mask
def predict_img(net,
full_img,
scale_factor=0.5,
out_threshold=0.5,
use_dense_crf=True,
use_gpu=False):
net.eval()
img = resize_and_crop(full_img, scale=scale_factor)
img = normalize(img)
img = hwc_to_chw(img)
X = torch.from_numpy(img).unsqueeze(0)
if use_gpu:
X = X.cuda()
with torch.no_grad():
output = net(X)
probs = output.squeeze(0)
tf = transforms.Compose(
[
transforms.ToPILImage(),
transforms.ToTensor()
]
)
probs = tf(probs.cpu())
print("Probs - ", probs.shape)
mask = probs.squeeze().cpu().numpy()
print("Mask - ", mask.shape)
plt.imshow(mask)
plt.show()
if use_dense_crf:
mask = dense_crf(np.array(full_img).astype(np.uint8), mask)
return mask > out_threshold
def predict_img(net,
full_img,
scale_factor=0.5,
out_threshold=0.5,
use_dense_crf=True,
use_gpu=False):
net.eval()
img_height = full_img.size[0]
img_width = full_img.size[1]
# img = resize_and_crop(full_img, scale=scale_factor)
img = normalize(full_img)
# left_square, right_square = split_img_into_squares(img)
img = hwc_to_chw(img)
img = torch.from_numpy(img).unsqueeze(0)
if use_gpu:
img = img.cuda()
with torch.no_grad():
output_img = net(img)
img_probs = output_img.squeeze(0)
img_mask_np = img_probs.squeeze().cpu().numpy()
# img_mask_np=np.transpose(img_mask_np, axes=[1.txt, 2, 0])
mask_pred = (img_mask_np > 0.5).float()
# out_img=np.zeros((mask_pred.shape[0],mask_pred.shape[1.txt],3))
# for i in range(mask_pred.shape[0]):
# for j in range(mask_pred.shape[1.txt]):
# out_img[i,j]=colormap[np.argmax(mask_pred[i,j])]
return mask_pred
def predict_img(net,
full_img,
scale_factor=0.5,
out_threshold=0.5,
use_dense_crf=True,
use_gpu=False):
# eval mode fixes BN and dropout, which yields bad results
# net.eval()
img_tensor = torch.from_numpy(hwc_to_chw(full_img))
if use_gpu:
img_tensor = img_tensor.cuda()
with torch.no_grad():
input = img_tensor.unsqueeze(0)
print("input.shape: ", input.shape)
full_mask = net(input).squeeze().cpu().numpy()
print("full_mask.shape: ", full_mask.shape)
if out_threshold < 0:
return full_mask
return full_mask > out_threshold
def predict_img_batch(net,
full_img,
scale_factor=0.5,
out_threshold=0.5,
use_dense_crf=True,
use_gpu=True):
"""return fullmask with size (C, H, W)"""
net.eval()
img_height = full_img.size[1]
img_width = full_img.size[0]
print('imgheight', img_height)
print('imgwidth', img_width)
img = resize_and_crop(full_img, scale=scale_factor)
img = normalize(img)
if len(img.shape) == 2:
img = img[..., np.newaxis]
print('img.shape', img.shape)
left_square, right_square = split_img_into_squares(img)
left_square = hwc_to_chw(left_square)
right_square = hwc_to_chw(right_square)
X_left = torch.from_numpy(left_square).unsqueeze(0)
X_right = torch.from_numpy(right_square).unsqueeze(0)
if use_gpu:
X_left = X_left.cuda()
X_right = X_right.cuda()
with torch.no_grad():
output_left = net(X_left)
output_right = net(X_right)
print('output_left.shape', output_left.shape)
print('output_right.shape', output_right.shape)
left_probs = output_left.squeeze(0)
right_probs = output_right.squeeze(0)
#
# if not scale_factor==1:
# tf = transforms.Compose(
# [
# transforms.ToPILImage(),
# transforms.Resize(img_height),
# transforms.ToTensor()
# ]
# )
#
# left_probs = tf(left_probs.cpu())
# right_probs = tf(right_probs.cpu())
# print('left_probs', left_probs.shape)
# print('right_probs', right_probs.shape)
left_mask_np = left_probs.squeeze().cpu().numpy()
right_mask_np = right_probs.squeeze().cpu().numpy()
left_mask_np = np.transpose(left_mask_np, axes=[1, 2, 0])
right_mask_np = np.transpose(right_mask_np, axes=[1, 2, 0])
if not scale_factor == 1:
right_mask_np = resize_np(right_mask_np, 1 / scale_factor)
left_mask_np = resize_np(left_mask_np, 1 / scale_factor)
print('left_mask_np', left_mask_np.shape)
print('right_mask_np', right_mask_np.shape)
left_mask_np = np.transpose(left_mask_np, axes=[2, 0, 1])
right_mask_np = np.transpose(right_mask_np, axes=[2, 0, 1])
full_mask = merge_masks(left_mask_np, right_mask_np, img_width)
# if use_dense_crf:
if 0:
full_mask = dense_crf(np.array(full_img).astype(np.uint8), full_mask)
return full_mask
def predict_img(net,
full_img,
scale_factor=0.5,
out_threshold=0.5,
use_dense_crf=False,
use_gpu=True):
img_height = full_img.size[1]
img = full_img.resize((128, 128))
img = np.array(img, dtype=np.float32)
imgs_switched = hwc_to_chw(img)
imgs_normalized = normalize(imgs_switched)
imgs_normalized = torch.from_numpy(imgs_normalized).unsqueeze(0)
if use_gpu:
imgs_normalized = imgs_normalized.cuda()
with torch.no_grad():
output = net(imgs_normalized)
prob = torch.sigmoid(output).squeeze(0)
tf = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize(img_height),
transforms.ToTensor()
])
prob = tf(prob.cpu())
full_mask = prob.squeeze().cpu().numpy()
'''#full_img = full_img.resize((128,128))
img_height = full_img.size[1]
img_width = full_img.size[0]
img = resize_and_crop(full_img, scale=scale_factor)
img = normalize(img)
left_square, right_square = split_img_into_squares(img)
left_square = hwc_to_chw(left_square)
right_square = hwc_to_chw(right_square)
X_left = torch.from_numpy(left_square).unsqueeze(0)
X_right = torch.from_numpy(right_square).unsqueeze(0)
if use_gpu:
X_left = X_left.cuda()
X_right = X_right.cuda()
with torch.no_grad():
output_left = net(X_left)
output_right = net(X_right)
left_probs = torch.sigmoid(output_left).squeeze(0)
right_probs = torch.sigmoid(output_right).squeeze(0)
tf = transforms.Compose(
[
transforms.ToPILImage(),
transforms.Resize(img_height),
transforms.ToTensor()
]
)
left_probs = tf(left_probs.cpu())
right_probs = tf(right_probs.cpu())
left_mask_np = left_probs.squeeze().cpu().numpy()
right_mask_np = right_probs.squeeze().cpu().numpy()
#print(left_mask_np.shape, right_mask_np.shape)
full_mask = merge_masks(left_mask_np, right_mask_np, img_width)'''
ipdb.set_trace()
if use_dense_crf:
full_mask = dense_crf(np.array(full_img).astype(np.uint8), full_mask)
return full_mask > out_threshold
parser.add_argument('input_filename', type=str)
parser.add_argument('output_filename', type=str)
args = parser.parse_args()
save_dir = './save_model/'
model = Network()
model.cuda()
model = nn.DataParallel(model)
model.eval()
if os.path.exists(os.path.join(save_dir, 'checkpoint.pth.tar')):
# load existing model
model_info = torch.load(os.path.join(save_dir, 'checkpoint.pth.tar'))
model.load_state_dict(model_info['state_dict'])
else:
print('Error: no trained model detected!')
exit(1)
input_image = read_img(args.input_filename)
input_var = torch.from_numpy(hwc_to_chw(input_image)).unsqueeze(0).cuda()
with torch.no_grad():
_, output = model(input_var)
output_image = chw_to_hwc(output[0, ...].cpu().numpy())
output_image = np.uint8(np.round(np.clip(output_image, 0, 1) *
255.))[:, :, ::-1]
cv2.imwrite(args.output_filename, output_image)
def predict_img(net,
full_img,
scale_factor=0.5,
out_threshold=0.5,
use_dense_crf=True,
use_gpu=False):
net.eval()
img_height = full_img.size[1]
img_width = full_img.size[0]
# img = resize_and_crop(full_img, scale=scale_factor)
# img = normalize(img)
img = np.array(full_img, dtype=np.float32)
img = normalize(img)
# left_square, right_square = split_img_into_squares(img)
#
# left_square = hwc_to_chw(left_square)
# right_square = hwc_to_chw(right_square)
square = hwc_to_chw(img)
#
# X_left = torch.from_numpy(left_square).unsqueeze(0)
# X_right = torch.from_numpy(right_square).unsqueeze(0)
img = torch.from_numpy(square).unsqueeze(0)
if use_gpu:
# X_left = X_left.cuda()
# X_right = X_right.cuda()
img = img.cuda()
with torch.no_grad():
# output_left = net(X_left)
# output_right = net(X_right)
output = net(img)
# left_probs = output_left.squeeze(0)
# right_probs = output_right.squeeze(0)
# probs = output # .squeeze(0)
# arr_probs = np.array(probs.cpu())
# arr_probs = np.transpose
# tf = transforms.Compose(
# [
# # transforms.ToPILImage(),
# # transforms.Resize(img_height),
# transforms.ToTensor()
# ]
# )
# left_probs = tf(left_probs.cpu())
# right_probs = tf(right_probs.cpu())
# probs = tf(arr_probs)
# left_mask_np = left_probs.squeeze().cpu().numpy()
# right_mask_np = right_probs.squeeze().cpu().numpy()
mask_np = output.squeeze().cpu().numpy()
# full_mask = merge_masks(left_mask_np, right_mask_np, img_width)
if use_dense_crf:
# full_mask = dense_crf(np.array(full_img).astype(np.uint8), full_mask)
full_mask = dense_crf(np.array(full_img).astype(np.uint8), mask_np)
# return full_mask > out_threshold
return mask_np > out_threshold
def predict_img(net,
full_img,
scale_factor=0.5,
out_threshold=0.5,
use_gpu=False):
pst = time.perf_counter()
net.eval()
#print(' 0 running time: %s seconds ' %(( time.clock() -pst)))
img_height = full_img.size[1]
# print(img_height)
img_width = full_img.size[0]
# print(full_img.size) # 2048,1229
img = resize_and_crop(full_img, scale=scale_factor)
#pdb.set_trace()
#print(' 1 running time: %s seconds ' %(( time.clock() -pst)))
img = normalize(img)
#print(' 2 running time: %s seconds ' %(( time.clock() -pst)))
# print(img.shape) # 614,1024,3
left_square, right_square = split_img_into_squares(img)
# print(right_square.shape) # 614,614,3
left_square = hwc_to_chw(left_square)
right_square = hwc_to_chw(right_square)
#print(' 3 running time: %s seconds ' %(( time.clock() -pst)))
X_left = torch.from_numpy(left_square).unsqueeze(0)
X_right = torch.from_numpy(right_square).unsqueeze(0)
#print(' 4 running time: %s seconds ' %(( time.clock() -pst)))
#outstart = time.clock()
if use_gpu:
X_left = X_left.cuda()
X_right = X_right.cuda()
#print(' 5 running time: %s seconds ' %(( time.clock() -pst)))
with torch.no_grad():
torch.cuda.synchronize()
st = time.perf_counter()
# print(X_left.shape) # 1,3,614,614
output_left = net(X_left)
output_right = net(X_right)
torch.cuda.synchronize()
st1 = time.perf_counter()
#outend = time.clock()
print(' Unet++ --------------------> running time: %s seconds ' %
(st1 - st))
left_probs = output_left.squeeze(0)
right_probs = output_right.squeeze(0)
# print(' squeeze running time: %s seconds ' %((time.perf_counter()-st1)))
if (left_probs.shape[1] != img_height):
tf = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize(img_height),
transforms.ToTensor()
])
left_probs = tf(left_probs.cpu())
right_probs = tf(right_probs.cpu())
print("Transform done!")
#print(' 8 running time: %s seconds ' %(( time.clock() -pst)))
#lstart = time.clock()
#left_probs.cpu()
#print(' transforms running time: %s seconds ' %(( time.time() -pst)))
st = time.perf_counter()
left_mask_np = left_probs.squeeze().cpu().numpy()
end1 = time.perf_counter() - st
#print(left_probs.shape)
#pdb.set_trace()
# print(' tonumpy1 running time: %s seconds ' %(end1))
st = time.perf_counter()
right_mask_np = right_probs.squeeze().cpu().numpy()
end2 = time.perf_counter() - st
# print(' tonumpy2 running time: %s seconds ' %(end2))
full_mask = merge_masks(left_mask_np, right_mask_np, img_width)
# print(' 9 running time: %s seconds ' %(( time.perf_counter() -pst)))
#pdb.set_trace()
full_mask[full_mask >= out_threshold] = 1
full_mask[full_mask < out_threshold] = 0
#-------------------------------------------------------------------------------
#newmask = dense_crf(np.array(full_img).astype(np.uint8), full_mask)
#lend = time.clock()
#print(' running time: %s seconds ' %((lend-pst)))
#pdb.set_trace()
return full_mask > out_threshold