def get(dataset_name):
base_dir = '/unsullied/sharefs/xqq/temp/ShanghaiTech_Crowd/'
rng = stable_rng(stable_rng)
items = pickle.load(
open(os.path.join(base_dir, 'ShanghaiTech_Crowd-mat.pkl'), 'rb'))
def data_gen(dataset_name, items):
if dataset_name == 'train':
items = [x for x in items if 'train' in x['group']]
else:
items = [x for x in items if 'test' in x['group']]
if dataset_name == 'train':
np.random.shuffle(items)
imgs = []
segmaps = []
for item in items:
img = cv2.imdecode(np.fromstring(item['nr_data'], np.uint8),
cv2.IMREAD_UNCHANGED)
if img.ndim == 2: #convert grey to bgr
img = img.reshape(img.shape + (1, ))
img = np.tile(img, (1, 1, 3))
else:
img = img[:, :, :3]
segmap = [np.load(BytesIO(item['mat_nori_data']))]
#img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
imgs.append(img)
segmaps.append(np.transpose(np.array(segmap), (1, 2, 0)))
#imgs = preprocess_input(np.array(imgs).astype(np.float64))
segmaps = np.array(segmaps)
return (imgs, segmaps)
return data_gen(dataset_name, items)
def get(dataset_name):
base_dir = '/unsullied/sharefs/xqq/temp/ShanghaiTech_Crowd/'
rng = stable_rng(stable_rng)
items = pickle.load(
open(os.path.join(base_dir, 'ShanghaiTech_Crowd-mat.pkl'), 'rb'))
def ensure_size(rng, img, hms, shape):
ch, cw = min(img.shape[0], shape[0]), min(img.shape[1], shape[1])
#random crop
dx, dy = img.shape[1] - cw, img.shape[0] - ch
x0, y0 = rng.randint(max(0, dx + 1)), rng.randint(max(0, dy + 1))
x1, y1 = x0 + cw, y0 + ch
img = img[y0:y1, x0:x1]
hms = [hm[y0:y1, x0:x1] for hm in hms]
#pad to target shape
img, pad = pad_image_to_shape(img, shape, return_padding=True)
hms = [pad_image_to_shape(hm, shape) for hm in hms]
return img, hms, (x0 - pad[1], y0 - pad[0])
def data_gen(dataset_name, items):
if dataset_name == 'train':
items = [x for x in items if 'train' in x['group']]
else:
items = [x for x in items if 'test' in x['group']]
while True:
if dataset_name == 'train':
np.random.shuffle(items)
imgs = []
segmaps = []
for item in items:
try:
img = cv2.imdecode(
np.fromstring(item['nr_data'], np.uint8),
cv2.IMREAD_UNCHANGED)[:, :, :3]
segmap = [np.load(BytesIO(item['mat_nori_data']))]
except Exception as e:
#print(e)
#drop grey image temporarily
continue
img, segmap, _ = ensure_size(rng, img, segmap,
config.image_shape)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
imgs.append(img)
segmaps.append(np.transpose(np.array(segmap), (1, 2, 0)))
if len(imgs) == config.minibatch_size:
imgs = preprocess_input(np.array(imgs).astype(np.float64))
segmaps = np.array(segmaps)
yield (imgs, segmaps)
imgs = []
segmaps = []
return data_gen(dataset_name, items)