class ImageGenerator():
def __init__(self, path,img_path,image, label, output_shape=(256, 256), batch_size=32, translation=True, scale=True, rotation=True, mins=0.25,maxs=1.2,mina=-np.pi,maxa=np.pi, ilumination=0.0):
self.path = path
self.img_path = img_path
self.image = image
self.label = (label == 1)
self.output_shape = output_shape
self.batch_size = batch_size
self.translation = translation
self.scale = scale
self.rotation = rotation
self.mins=mins
self.maxs = maxs
self.mina = -np.pi
self.maxa = np.pi
self.ilumination = ilumination
self.ds = CocoPose(path, img_path, is_train)
self.ds_img = MapData(ds,read_image_url)
self.gen_ds = self.ds_img.get_data()
def next(self):
meta = next(gen_ds)[0]
new_meta = get_augmented_image(meta)
return get_augemented_image_and_label(self.image, self.label, output_shape=self.output_shape,
translation=self.translation, scale=self.scale,
rotation=self.rotation,mins=self.mins, maxs=self.maxs, ilumination=self.ilumination)
def __next__(self):
return self.next()
def __call__(self):
return self
def __iter__(self):
for i in range(self.batch_size):
# while True:
yield self.next()
def proceed_test():
ds = PascalVOC12(TEST_DATA_DIR, LIST_DIR, "test", shuffle=False)
imagelist = ds.imglist
def f(ds):
image = ds
m = np.array([104, 116, 122])
const_arr = np.resize(m, (1, 1, 3)) # NCHW
image = image - const_arr
return image
ds = MapData(ds, f)
ds = BatchData(ds, 1)
ctx = [mx.gpu(int(i)) for i in args.gpu.split(',')]
sym_instance = resnet101_deeplab_new()
# infer shape
val_provide_data = [[("data", (1, 3, tile_height, tile_width))]]
val_provide_label = [[("softmax_label", (1, 1, tile_height, tile_width))]]
data_shape_dict = {
'data': (1, 3, tile_height, tile_width),
'softmax_label': (1, 1, tile_height, tile_width)
}
eval_sym = sym_instance.get_symbol(NUM_CLASSES,
is_train=False,
use_global_stats=True)
sym_instance.infer_shape(data_shape_dict)
arg_params, aux_params = load_init_param(args.load, process=True)
sym_instance.check_parameter_shapes(arg_params,
aux_params,
data_shape_dict,
is_train=False)
data_names = ['data']
label_names = ['softmax_label']
# create predictor
predictor = Predictor(eval_sym,
data_names,
label_names,
context=ctx,
provide_data=val_provide_data,
provide_label=val_provide_label,
arg_params=arg_params,
aux_params=aux_params)
from mxnetgo.myutils.fs import mkdir_p
vis_dir = "deeplabv2_4gpu_test_result"
check_dir = os.path.join(vis_dir, "check")
import shutil
shutil.rmtree(vis_dir, ignore_errors=True)
mkdir_p(check_dir)
_itr = ds.get_data()
nbatch = 0
for i in tqdm(range(len(imagelist))):
data = next(_itr)
l = imagelist[i]
filename = os.path.basename(l).rsplit(".", 1)[0]
print filename
output_all = predict_scaler(data,
predictor,
scales=[0.5, 0.75, 1.0, 1.25, 1.5],
classes=NUM_CLASSES,
tile_size=(tile_height, tile_width),
is_densecrf=False,
nbatch=nbatch,
val_provide_data=val_provide_data,
val_provide_label=val_provide_label)
output_all = np.argmax(output_all, axis=0).astype(np.uint8)
result = output_all[:, :, None]
cv2.imwrite(os.path.join(vis_dir, "{}.png".format(filename)), result)
cv2.imwrite(
os.path.join(check_dir, "{}.png".format(filename)),
np.concatenate((data[0][0], visualize_label(output_all)), axis=1))
nbatch += 1
