def get_spixel_image(given_img,
spix_index,
n_spixels=600,
b_enforce_connect=False):
if not isinstance(given_img, np.ndarray):
given_img_np_ = given_img.detach().cpu().numpy().transpose(1, 2, 0)
else: # for cvt lab to rgb case
given_img_np_ = given_img
if not isinstance(spix_index, np.ndarray):
spix_index_np = spix_index.detach().cpu().numpy().transpose(0, 1)
else:
spix_index_np = spix_index
h, w = spix_index_np.shape
given_img_np = cv2.resize(given_img_np_,
dsize=(w, h),
interpolation=cv2.INTER_CUBIC)
if b_enforce_connect:
spix_index_np = spix_index_np.astype(np.int64)
segment_size = (given_img_np_.shape[0] *
given_img_np_.shape[1]) / (int(n_spixels) * 1.0)
min_size = int(0.06 * segment_size)
max_size = int(3 * segment_size)
spix_index_np = enforce_connectivity(spix_index_np[None, :, :],
min_size, max_size)[0]
cur_max = np.max(given_img_np)
spixel_bd_image = mark_boundaries(given_img_np / cur_max,
spix_index_np.astype(int),
color=(0, 1, 1)) #cyna
return (cur_max * spixel_bd_image).astype(np.float32).transpose(
2, 0, 1), spix_index_np #
def compute_spixels(data_type, n_spixels, num_steps,
caffe_model, out_folder, is_connected = True):
image_list = IMG_LIST[data_type]
if not os.path.exists(out_folder):
os.makedirs(out_folder)
p_scale = 0.40
color_scale = 0.26
with open(image_list) as list_f:
for imgname in list_f:
print(imgname)
imgname = imgname[:-1]
[inputs, height, width] = \
fetch_and_transform_data(imgname, data_type,
['img', 'label', 'problabel'],
int(n_spixels))
height = inputs['img'].shape[2]
width = inputs['img'].shape[3]
[spixel_initmap, feat_spixel_initmap, num_spixels_h, num_spixels_w] =\
transform_and_get_spixel_init(int(n_spixels), [height, width])
dinputs = {}
dinputs['img'] = inputs['img']
dinputs['spixel_init'] = spixel_initmap
dinputs['feat_spixel_init'] = feat_spixel_initmap
pos_scale_w = (1.0 * num_spixels_w) / (float(p_scale) * width)
pos_scale_h = (1.0 * num_spixels_h) / (float(p_scale) * height)
pos_scale = np.max([pos_scale_h, pos_scale_w])
net = load_ssn_net(height, width, int(num_spixels_w * num_spixels_h),
float(pos_scale), float(color_scale),
num_spixels_h, num_spixels_w, int(num_steps))
if caffe_model is not None:
net.copy_from(caffe_model)
else:
net = initialize_net_weight(net)
num_spixels = int(num_spixels_w * num_spixels_h)
result = net.forward_all(**dinputs)
given_img = np.array(Image.open(IMG_FOLDER[data_type] + imgname + '.jpg'))
spix_index = np.squeeze(net.blobs['new_spix_indices'].data).astype(int)
if enforce_connectivity:
segment_size = (given_img.shape[0] * given_img.shape[1]) / (int(n_spixels) * 1.0)
min_size = int(0.06 * segment_size)
max_size = int(3 * segment_size)
spix_index = enforce_connectivity(spix_index[None, :, :], min_size, max_size)[0]
spixel_image = get_spixel_image(given_img, spix_index)
out_img_file = out_folder + imgname + '_bdry.jpg'
imsave(out_img_file, spixel_image)
out_file = out_folder + imgname + '.npy'
np.save(out_file, spix_index)
return
def test(model, img_paths, save_path, spixeIds, idx, scale):
# Data loading code
input_transform = transforms.Compose([
flow_transforms.ArrayToTensor(),
transforms.Normalize(mean=[0, 0, 0], std=[255, 255, 255]),
transforms.Normalize(mean=[0.411, 0.432, 0.45], std=[1, 1, 1])
])
img_file = img_paths[idx]
load_path = str(img_file)
imgId = os.path.basename(img_file)[:-4]
# origin size 481*321 or 321*481
img_ = imread(load_path)
H_, W_, _ = img_.shape
# choose the right spixelIndx
spixl_map_idx_tensor = spixeIds[0]
img = cv2.resize(img_, (int(1024 * scale), int(512 * scale)),
interpolation=cv2.INTER_CUBIC)
# if H_ == 321 and W_==481:
# spixl_map_idx_tensor = spixeIds[0]
# img = cv2.resize(img_, (int(480 * scale), int(320 * scale)), interpolation=cv2.INTER_CUBIC)
# elif H_ == 481 and W_ == 321:
# spixl_map_idx_tensor = spixeIds[1]
# img = cv2.resize(img_, (int(320 * scale), int(480 * scale)), interpolation=cv2.INTER_CUBIC)
# else:
# print('The image size is wrong!')
# return
img1 = input_transform(img)
ori_img = input_transform(img_)
mean_values = torch.tensor([0.411, 0.432, 0.45],
dtype=img1.cuda().unsqueeze(0).dtype).view(
3, 1, 1)
# compute output
tic = time.time()
output = model(img1.cuda().unsqueeze(0))
# assign the spixel map and resize to the original size
curr_spixl_map = update_spixl_map(spixl_map_idx_tensor, output)
ori_sz_spixel_map = F.interpolate(curr_spixl_map.type(torch.float),
size=(H_, W_),
mode='nearest').type(torch.int)
spix_index_np = ori_sz_spixel_map.squeeze().detach().cpu().numpy(
).transpose(0, 1)
spix_index_np = spix_index_np.astype(np.int64)
segment_size = (spix_index_np.shape[0] *
spix_index_np.shape[1]) / (int(600 * scale * scale) * 1.0)
min_size = int(0.06 * segment_size)
max_size = int(3 * segment_size)
spixel_label_map = enforce_connectivity(spix_index_np[None, :, :],
min_size, max_size)[0]
torch.cuda.synchronize()
toc = time.time() - tic
n_spixel = len(np.unique(spixel_label_map))
given_img_np = (ori_img + mean_values).clamp(
0, 1).detach().cpu().numpy().transpose(1, 2, 0)
spixel_bd_image = mark_boundaries(given_img_np / np.max(given_img_np),
spixel_label_map.astype(int),
color=(0, 1, 1))
spixel_viz = spixel_bd_image.astype(np.float32).transpose(2, 0, 1)
# ************************ Save all result********************************************
# save img, uncomment it if needed
# if not os.path.isdir(os.path.join(save_path, 'img')):
# os.makedirs(os.path.join(save_path, 'img'))
# spixl_save_name = os.path.join(save_path, 'img', imgId + '.jpg')
# img_save = (ori_img + mean_values).clamp(0, 1)
# imsave(spixl_save_name, img_save.detach().cpu().numpy().transpose(1, 2, 0))
# save spixel viz
if not os.path.isdir(os.path.join(save_path, 'spixel_viz')):
os.makedirs(os.path.join(save_path, 'spixel_viz'))
spixl_save_name = os.path.join(save_path, 'spixel_viz',
imgId + '_sPixel.png')
imsave(spixl_save_name, spixel_viz.transpose(1, 2, 0))
# save the unique maps as csv for eval
# if not os.path.isdir(os.path.join(save_path, 'map_csv')):
# os.makedirs(os.path.join(save_path, 'map_csv'))
# output_path = os.path.join(save_path, 'map_csv', imgId + '.csv')
# # plus 1 to make it consistent with the toolkit format
# np.savetxt(output_path, (spixel_label_map + 1).astype(int), fmt='%i', delimiter=",")
if idx % 10 == 0:
print("processing %d" % idx)
return toc, n_spixel
def compute_spixels(data_type,
n_spixels,
num_steps,
caffe_model,
out_folder,
is_connected=True):
image_list = IMG_LIST[data_type]
if not os.path.exists(out_folder):
os.makedirs(out_folder)
p_scale = 0.40
color_scale = 0.26
asa_total = 0.0
asa_count = 0
with open(image_list) as list_f:
for imgname in list_f:
print(imgname)
imgname = imgname[:-1]
[inputs, height, width] = \
fetch_and_transform_data(imgname, data_type,
['img', 'label', 'problabel'],
int(n_spixels))
height = inputs['img'].shape[2]
width = inputs['img'].shape[3]
[spixel_initmap, feat_spixel_initmap, num_spixels_h, num_spixels_w] =\
transform_and_get_spixel_init(int(n_spixels), [height, width])
dinputs = {}
dinputs['img'] = inputs['img']
dinputs['spixel_init'] = spixel_initmap
dinputs['feat_spixel_init'] = feat_spixel_initmap
pos_scale_w = (1.0 * num_spixels_w) / (float(p_scale) * width)
pos_scale_h = (1.0 * num_spixels_h) / (float(p_scale) * height)
pos_scale = np.max([pos_scale_h, pos_scale_w])
net = load_ssn_net(height, width,
int(num_spixels_w * num_spixels_h),
float(pos_scale), float(color_scale),
num_spixels_h, num_spixels_w, int(num_steps))
if caffe_model is not None:
net.copy_from(caffe_model)
else:
net = initialize_net_weight(net)
num_spixels = int(num_spixels_w * num_spixels_h)
result = net.forward_all(**dinputs)
given_img = fromimage(
Image.open(IMG_FOLDER[data_type] + imgname + '.jpg'))
spix_index = np.squeeze(
net.blobs['new_spix_indices'].data).astype(int)
if enforce_connectivity:
segment_size = (given_img.shape[0] *
given_img.shape[1]) / (int(n_spixels) * 1.0)
min_size = int(0.06 * segment_size)
max_size = int(3 * segment_size)
spix_index = enforce_connectivity(spix_index[None, :, :],
min_size, max_size)[0]
# evaluate
spList = spix_index.flatten().tolist()
gt_filename = 'data/BSR/BSDS500/data/groundTruth/test/{}.mat'.format(
imgname)
from scipy.io import loadmat
gtseg_all = loadmat(gt_filename)['groundTruth'][0]
nlabel = len(gtseg_all)
for t in range(nlabel):
gtseg = gtseg_all[t][0][0][0]
gtList = gtseg.flatten().tolist()
asa = computeASA(spList, gtList, 0)
asa_total += asa
asa_count += 1
print('count: {}, asa: {}'.format(asa_count,
asa_total / asa_count))
# save image
# spixel_image = get_spixel_image(given_img, spix_index)
# out_img_file = out_folder + imgname + '_bdry.jpg'
# imsave(out_img_file, spixel_image)
# out_file = out_folder + imgname + '.npy'
# np.save(out_file, spix_index)
return
def compute_spixels(num_spixel, num_steps, pre_model, out_folder):
if not os.path.exists(out_folder):
os.makedirs(out_folder)
# os.makedirs(out_folder+'png')
# os.makedirs(out_folder + 'mat')
dtype = 'test'
dataloader = data.DataLoader(Dataset_T(num_spixel=num_spixel),
batch_size=1,
shuffle=False,
num_workers=1)
model = create_ssn_net(num_spixels=num_spixel,
num_iter=num_steps,
num_spixels_h=10,
num_spixels_w=10,
dtype=dtype,
ssn=0)
model = torch.nn.DataParallel(model)
if pre_model is not None:
if torch.cuda.is_available():
model.load_state_dict(torch.load(pre_model))
else:
model.load_state_dict(torch.load(pre_model, map_location='cpu'))
else:
raise ('no model')
criten = Loss()
device = torch.device('cpu')
if torch.cuda.is_available():
model.cuda()
device = torch.device('cuda')
for iter, [
inputs, num_h, num_w, init_index, cir_index, p2sp_index_,
invisible, file_name
] in enumerate(dataloader):
with torch.no_grad():
img = inputs['img'].to(device)
label = inputs['label'].to(device)
problabel = inputs['problabel'].to(device)
num_h = num_h.to(device)
num_w = num_w.to(device)
init_index = [x.to(device) for x in init_index]
cir_index = [x.to(device) for x in cir_index]
p2sp_index_ = p2sp_index_.to(device)
invisible = invisible.to(device)
recon_feat2, recon_label, new_spix_indices = model(
img, p2sp_index_, invisible, init_index, cir_index, problabel,
num_h, num_w, device)
# loss, loss_1, loss_2 = criten(recon_feat2, img, recon_label, label)
given_img = np.asarray(Image.open(file_name[0]))
h, w = given_img.shape[0], given_img.shape[1]
new_spix_indices = new_spix_indices[:, :h, :w].contiguous()
spix_index = new_spix_indices.cpu().numpy()[0]
spix_index = spix_index.astype(int)
if enforce_connectivity:
segment_size = (given_img.shape[0] * given_img.shape[1]) / (
int(num_h * num_w) * 1.0)
min_size = int(0.06 * segment_size)
max_size = int(3 * segment_size)
spix_index = enforce_connectivity(spix_index[np.newaxis, :, :],
min_size, max_size)[0]
# given_img_ = np.zeros([spix_index.shape[0], spix_index.shape[1], 3], dtype=np.int)
# h, w = given_img.shape[0], given_img.shape[1]
# given_img_[:h, :w] = given_img
counter_image = np.zeros_like(given_img)
counter_image = get_spixel_image(counter_image, spix_index)
spixel_image = get_spixel_image(given_img, spix_index)
imgname = file_name[0].split('/')[-1][:-4]
out_img_file = out_folder + imgname + '_bdry_.jpg'
imageio.imwrite(out_img_file, spixel_image)
# out_file = out_folder + imgname + '.npy'
# np.save(out_file, spix_index)
# validation code only for sp_pix 400
# out_file_mat = out_folder + 'mat/'+ imgname + '.mat'
# scio.savemat(out_file_mat, {'segs': spix_index})
# out_count_file = out_folder + 'png/' + imgname + '.png'
# imageio.imwrite(out_count_file, counter_image)
print(iter)
def compute_spixels(data_type,
n_spixels,
num_steps,
caffe_model,
out_folder,
is_connected=True):
image_list = IMG_LIST[data_type]
if not os.path.exists(out_folder):
os.makedirs(out_folder)
p_scale = 0.23
color_scale = 0.26
start_time = time.time()
bound = range(25, 33, 2)
# bound = range(31, 71, 2)
for i in range(len(bound)):
threshold = bound[i] / 100.0
if threshold <= 0.3:
min_size = [0]
else:
# min_size = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
min_size = [0]
for j in range(len(min_size)):
new_save_root = os.path.join(
out_folder,
'threshold_{:.2f}_{}'.format(threshold, min_size[j]))
if not os.path.exists(new_save_root):
os.mkdir(new_save_root)
with open(image_list) as list_f:
for imgname in list_f:
print(imgname)
imgname = imgname[:-1]
[inputs, height, width] = \
fetch_and_transform_data(imgname, data_type,
['img', 'label', 'problabel'],
int(n_spixels))
height = inputs['img'].shape[2]
width = inputs['img'].shape[3]
[spixel_initmap, feat_spixel_initmap, num_spixels_h, num_spixels_w] = \
transform_and_get_spixel_init(int(n_spixels), [height, width])
dinputs = {}
dinputs['img'] = inputs['img']
dinputs['spixel_init'] = spixel_initmap
dinputs['feat_spixel_init'] = feat_spixel_initmap
print j
# 新加
thresholda = np.random.randint(bound[i],
bound[i] + 1,
size=(1, 1, 1, 1))
thresholda = thresholda / 100.0
min_sizea = np.random.randint(min_size[j],
min_size[j] + 1,
size=(1, 1, 1, 1))
dinputs['bound_param'] = thresholda
dinputs['minsize_param'] = min_sizea
pos_scale_w = (1.0 * num_spixels_w) / (float(p_scale) *
width)
pos_scale_h = (1.0 * num_spixels_h) / (float(p_scale) *
height)
pos_scale = np.max([pos_scale_h, pos_scale_w])
net = load_ssn_net(height, width,
int(num_spixels_w * num_spixels_h),
float(pos_scale), float(color_scale),
num_spixels_h, num_spixels_w,
int(num_steps))
if caffe_model is not None:
net.copy_from(caffe_model)
else:
net = initialize_net_weight(net)
num_spixels = int(num_spixels_w * num_spixels_h)
result = net.forward_all(**dinputs)
given_img = fromimage(
Image.open(IMG_FOLDER[data_type] + imgname + '.jpg'))
spix_index = np.squeeze(
net.blobs['new_spix_indices'].data).astype(int)
# spix_index = spix_index[None, :, :]
print spix_index.shape
if enforce_connectivity:
segment_size = (given_img.shape[0] * given_img.shape[1]
) / (int(n_spixels) * 1.0)
min_size_sp = int(0.06 * segment_size)
max_size_sp = int(3 * segment_size)
spix_index = enforce_connectivity(
spix_index[None, :, :], min_size_sp,
max_size_sp)[0]
net.blobs['new_spix_indices'].data[0] = spix_index
out = net.forward(start='segmentation')
# 保存mat格式
# out2 = np.squeeze(net.blobs['segmentation'].data).astype(int)
# # enforce_connectivity
out2 = net.blobs['segmentation'].data[0].copy().astype(int)
# #
# #
if enforce_connectivity:
segment_size = (given_img.shape[0] * given_img.shape[1]
) / (int(n_spixels) * 1.0)
min_size1 = int(0.24 * segment_size)
max_size1 = int(200 * segment_size)
out2 = enforce_connectivity(out2, min_size1,
max_size1)[0]
out2 = out2[None, :, :]
out2 = out2.transpose((1, 2, 0)).astype(dtype=np.uint16)
sio.savemat(new_save_root + '/' + imgname + '.mat',
{'Segmentation': out2})
diff_time = time.time() - start_time
print('Detection took {:.3f}s per image'.format(diff_time))
return
