def __init__(self,data_file):
print('Reading data...')
self.fnames = []
f = open(data_file)
self.data = []
cnt = 0
for i in f:
i = i.strip().split('\t')
jpgfile = i[0]
# get jpg, seg, mask
jpg = img_reader.read_img(jpgfile,500,padding=True)
seg = data_provider.get_seg(jpgfile)
seg = img_reader.pad(seg,np.uint8,False)
mask = seg.copy()
seg[seg==255] = 0
mask[mask!=0] = 1
coords = data_provider.get_coord_map(jpgfile)
coords = img_reader.pad(coords,np.float32,6)
inst_num = data_provider.get_inst_num(jpgfile)
if seg.shape[0]!=500:
print(jpgfile)
continue
# if size is ok, add to data list
self.data.append([jpg,seg,mask,coords,inst_num])
self.fnames.append(jpgfile)
cnt += 1
if cnt%100==0:
print(cnt)
def __init__(self, data_file):
print('Reading data...')
self.fnames = []
f = open(data_file)
self.data = []
cnt = 0
for i in f:
i = i.strip()
jpgfile = './data/reform_img/' + i
segfile = './data/reform_annot/' + i.replace('.jpg', '.png')
jpg = img_reader.read_img(jpgfile, 500, padding=True)
seg = self.read_label(segfile)
seg = img_reader.pad(seg, np.uint8, False)
mask = seg.copy()
seg[seg == 255] = 0
mask[mask != 0] = 1
if seg.shape[0] != 500:
print(segfile)
continue
self.data.append([jpg, seg, mask])
self.fnames.append(jpgfile)
cnt += 1
if cnt % 100 == 0:
print(cnt)
print('Data length:', len(self.data))
def __init__(self, data_file):
print('Reading data...')
self.fnames = []
f = open(data_file)
self.data = []
cnt = 0
for i in f:
i = i.strip()
segfile = i
jpgfile = i.replace('SegmentationClass',
'JPEGImages').replace('.png', '.jpg')
jpg = img_reader.read_img(jpgfile, 500, padding=True)
seg = self.read_label(segfile)
seg = img_reader.pad(seg, np.uint8, False)
seg[seg != 15] = 0
seg[seg == 15] = 1
if seg.shape[0] != 500:
print(segfile)
continue
self.data.append([jpg, seg])
self.fnames.append(jpgfile)
cnt += 1
if cnt % 100 == 0:
print(cnt)
print('Data length:', len(self.data))
def __init__(self,data_file):
print('Reading data...')
self.fnames = []
f = open(data_file)
self.data = []
cnt = 0
for i in f:
try:
i = i.strip()
segfile = i.split('\t')[1]
jpgfile = i.split('\t')[0]
jpg = img_reader.read_img(jpgfile,500,padding=True)
seg = self.read_label(segfile)
seg = img_reader.pad(seg,np.uint8,False)
seg[seg!=15] = 0
seg[seg==15] = 1
if seg.shape[0]!=500:
print(segfile)
continue
self.data.append([jpg,seg])
self.fnames.append(jpgfile)
cnt += 1
if cnt%100==0:
print(cnt)
except:
pass
print('Data length:',len(self.data))
reader = data_provider('train_list.txt')
with tf.variable_scope('seg_part'):
net = network(21)
MAX_ITER = 100000
BSIZE = 1
for i in range(MAX_ITER):
img_batch, lab_batch, mask_batch = reader.next_batch(BSIZE)
loss = net.train(img_batch, lab_batch, mask_batch)
print('Iter:%d\tLoss:%.4f' % (i, loss))
# monitor the training process
if i % 10 == 0:
fname = random.sample(reader.fnames, 1)
img = cv2.imread(fname[0])
img = img_reader.pad(img, np.uint8)
img_inp = img_reader.read_img(fname[0], 500, True)
mask = read_label(fname[0].replace('reform_img',
'reform_annot').replace(
'jpg', 'png'))
mask = img_reader.pad(mask, np.uint8, False)
mask[mask != 0] = 1
img = img[20:480, 20:480]
img_inp = img_inp[20:480, 20:480]
mask = mask[20:480, 20:480]
res = net.eval([img_inp], [mask])
res = np.uint8(res)
res = cv2.resize(res, (img.shape[1], img.shape[0]),
with tf.variable_scope('bg_fg'):
net = network()
MAX_ITER = 100000
BSIZE = 1
for i in range(MAX_ITER):
img_batch, lab_batch = reader.next_batch(BSIZE)
loss = net.train(img_batch, lab_batch)
print('Iter:%d\tLoss:%.4f' % (i, loss))
# monitor the training process
if i % 10 == 0:
fname = random.sample(reader.fnames, 1)
img = cv2.imread(fname[0])
img = img_reader.pad(img, np.uint8)
img_inp = img_reader.read_img(fname[0], 500, True)
img = img[20:480, 20:480]
img_inp = img_inp[20:480, 20:480]
res = net.eval([img_inp])
res = np.uint8(res)
res = cv2.resize(res, (img.shape[1], img.shape[0]),
interpolation=cv2.INTER_NEAREST)
res[res == 1] = 255
cv2.imwrite('./sample_bgfg/%d.jpg' % (i), img)
cv2.imwrite('./sample_bgfg/%d_2.jpg' % (i), res)
# save model
if i % 1000 == 0 and i > 0: