def __call__(self, img, gt, hha, depth, coord, camera_params):
img, gt, depth, coord = random_mirror(img, gt, depth, coord)
if config.train_scale_array is not None:
img, gt, hha, depth, coord, scale = random_scale(
img, gt, hha, depth, coord, config.train_scale_array)
camera_params['scale'] = torch.from_numpy(
np.array(scale, dtype=np.float32)).float()
img = normalize(img, self.img_mean, self.img_std)
depth = normalize(depth, self.depth_mean, self.depth_var)
# hha = normalize(hha, self.hha_mean, self.hha_std)
crop_size = (config.image_height, config.image_width)
crop_pos = generate_random_crop_pos(img.shape[:2], crop_size)
p_img, _ = random_crop_pad_to_shape(img, crop_pos, crop_size, 0)
p_gt, _ = random_crop_pad_to_shape(gt, crop_pos, crop_size, 255)
p_hha, _ = random_crop_pad_to_shape(hha, crop_pos, crop_size, 0)
depth, _ = random_crop_pad_to_shape(depth, crop_pos, crop_size, 0)
coord, _ = random_crop_pad_to_shape(coord, crop_pos, crop_size, -1)
p_img = p_img.transpose(2, 0, 1)
p_hha = p_hha.transpose(2, 0, 1)
depth = depth[np.newaxis, ...]
coord = coord.transpose(2, 0, 1)
extra_dict = {
'hha_img': p_hha,
'depth_img': depth,
'coord_img': coord,
'camera_params': camera_params
}
return p_img, p_gt, extra_dict
def __call__(self, ref_img, cur_img, ref_mask, cur_mask):
common_bbox = generate_random_common_bbox(ref_mask, cur_mask)
ref_img = ref_img[common_bbox[1]:common_bbox[3],
common_bbox[0]:common_bbox[2], :]
cur_img = cur_img[common_bbox[1]:common_bbox[3],
common_bbox[0]:common_bbox[2], :]
cur_mask = cur_mask[common_bbox[1]:common_bbox[3],
common_bbox[0]:common_bbox[2]]
#ref_img, cur_img, ref_mask, cur_mask = random_scale_crop(ref_img, cur_img, ref_mask, cur_mask)
ref_img = cv2.resize(ref_img,
(config.image_width, config.image_height))
cur_img = cv2.resize(cur_img,
(config.image_width, config.image_height))
cur_mask = cv2.resize(cur_mask,
(config.image_width, config.image_height),
interpolation=cv2.INTER_NEAREST)
ref_img, cur_img, cur_mask = random_hflip_adnet(
ref_img, cur_img, cur_mask)
ref_img = normalize(ref_img, self.img_mean, self.img_std)
cur_img = normalize(cur_img, self.img_mean, self.img_std)
ref_img, cur_img, cur_mask = random_rotation_adnet(
ref_img, cur_img, cur_mask)
ref_img = ref_img.transpose(2, 0, 1)
cur_img = cur_img.transpose(2, 0, 1)
cur_mask = np.expand_dims(cur_mask, 0)
extra_dict = None
return ref_img, cur_img, cur_mask, extra_dict
def __call__(self, img, hha):
img = normalize(img, self.img_mean, self.img_std)
hha = normalize(hha, self.img_mean, self.img_std)
p_img = img.transpose(2, 0, 1)
p_hha = hha.transpose(2, 0, 1)
extra_dict = {'hha_img': p_hha}
return p_img, extra_dict
def __call__(self, img, gt):
img, gt = random_mirror(img, gt)
short_size = random.randint(int(config.base_size * 0.5),
int(config.base_size * 2.0))
h, w, c = img.shape
if h > w:
ow = short_size
oh = int(1.0 * h * ow / w)
else:
oh = short_size
ow = int(1.0 * w * oh / h)
img = cv2.resize(img, (ow, oh), interpolation=cv2.INTER_LINEAR)
gt = cv2.resize(gt, (ow, oh), interpolation=cv2.INTER_NEAREST)
img = normalize(img, self.img_mean, self.img_std)
crop_size = (config.image_height, config.image_width)
crop_pos = generate_random_crop_pos(img.shape[:2], crop_size)
p_img, _ = random_crop_pad_to_shape(img, crop_pos, crop_size, 0)
p_gt, _ = random_crop_pad_to_shape(gt, crop_pos, crop_size, 255)
p_gt = cv2.resize(p_gt,
(config.image_width // config.gt_down_sampling,
config.image_height // config.gt_down_sampling),
interpolation=cv2.INTER_NEAREST)
p_img = p_img.transpose(2, 0, 1)
extra_dict = None
return p_img, p_gt, extra_dict
def __call__(self, img, gt):
#flip
img, gt = random_mirror(img, gt)
#according the paper
if config.train_scale_array is not None:
img, gt, scale = random_scale(img, gt, config.train_scale_array)
id255 = np.where(gt == 255)
no255_gt = np.array(gt)
no255_gt[id255] = 0
cgt = cv2.Canny(no255_gt, 5, 5, apertureSize=7)
#get border imformation from canny
cgt = cv2.dilate(cgt, self.edge_kernel)
cgt[cgt == 255] = 1
#img white
img = normalize(img, self.img_mean, self.img_std)
crop_size = (config.image_height, config.image_width)
crop_pos = generate_random_crop_pos(img.shape[:2], crop_size)
p_img, _ = random_crop_pad_to_shape(img, crop_pos, crop_size, 0)
p_gt, _ = random_crop_pad_to_shape(gt, crop_pos, crop_size, 255)
p_cgt, _ = random_crop_pad_to_shape(cgt, crop_pos, crop_size, 255)
p_img = p_img.transpose(2, 0, 1)
extra_dict = {'aux_label': p_cgt}
return p_img, p_gt, extra_dict
def __call__(self, img, gt):
img, gt = random_mirror(img, gt)
if config.train_scale_array is not None:
img, gt, scale = random_scale(img, gt, config.train_scale_array)
img = normalize(img, self.img_mean, self.img_std)
crop_size = (config.image_height, config.image_width)
crop_pos = generate_random_crop_pos(img.shape[:2], crop_size)
p_img, _ = random_crop_pad_to_shape(img, crop_pos, crop_size, 0)
p_gt, _ = random_crop_pad_to_shape(gt, crop_pos, crop_size, 0)
# scaled_gt = cv2.resize(p_gt, (
# config.image_width // 8, config.image_height // 8),
# interpolation=cv2.INTER_NEAREST)
#
# C = config.num_classes + 1
# one_hot_gt = convert_to_one_hot(scaled_gt.astype(np.int), C)
# similarity_gt = np.dot(one_hot_gt, one_hot_gt.T)
p_img = p_img.transpose(2, 0, 1)
# p_gt = p_gt - 1
extra_dict = None
return p_img, p_gt, extra_dict
def process_image_rgbd_coord(self, img, hha, depth, coord, crop_size=None):
p_img = img
p_hha = hha
p_depth = depth
p_coord = coord
if img.shape[2] < 3:
im_b = p_img
im_g = p_img
im_r = p_img
p_img = np.concatenate((im_b, im_g, im_r), axis=2)
p_img = normalize(p_img, self.image_mean, self.image_std)
# p_depth = normalize(p_depth, 0, 1)
if crop_size is not None:
p_img, margin = pad_image_to_shape(p_img, crop_size,
cv2.BORDER_CONSTANT, value=0)
p_hha, margin = pad_image_to_shape(p_hha, crop_size,
cv2.BORDER_CONSTANT, value=0)
p_depth, margin = pad_image_to_shape(p_depth, crop_size,
cv2.BORDER_CONSTANT, value=0)
p_coord, margin = pad_image_to_shape(p_coord, crop_size,
cv2.BORDER_CONSTANT, value=0)
p_img = p_img.transpose(2, 0, 1)
p_hha = p_hha.transpose(2, 0, 1)
p_depth = p_depth[np.newaxis,...]
p_coord = p_coord.transpose(2, 0, 1)
return p_img, p_hha, p_depth, p_coord, margin
def __call__(self, img, gt, edge, midline):
img, gt, edge, midline = random_mirror(img, gt, edge, midline)
gt = img_to_black(gt)
edge = img_to_black(edge)
midline = img_to_black(midline)
if config.train_scale_array is not None:
img, gt, scale, edge, midline = random_scale(
img, gt, config.train_scale_array, edge, midline)
img = normalize(img, self.img_mean, self.img_std)
p_img, p_gt, p_edge, p_midline = img, gt, edge, midline
p_img = cv2.resize(p_img,
(config.image_width // config.gt_down_sampling,
config.image_height // config.gt_down_sampling),
interpolation=cv2.INTER_NEAREST)
p_gt = cv2.resize(p_gt,
(config.image_width // config.gt_down_sampling,
config.image_height // config.gt_down_sampling),
interpolation=cv2.INTER_NEAREST)
p_edge = cv2.resize(p_edge,
(config.image_width // config.gt_down_sampling,
config.image_height // config.gt_down_sampling),
interpolation=cv2.INTER_NEAREST)
p_midline = cv2.resize(
p_midline, (config.image_width // config.gt_down_sampling,
config.image_height // config.gt_down_sampling),
interpolation=cv2.INTER_NEAREST)
p_img = p_img.transpose(2, 0, 1)
extra_dict = None
if p_gt.max() > 1:
print(p_gt)
return p_img, p_gt, extra_dict, p_edge, p_midline
def __call__(self, img, gt, edge, midline):
img, gt, edge, midline = random_mirror(
img, gt, edge,
midline) # images are randomly flipped to increase variance
gt = img_to_black(gt) # binary filter on gt.
edge = img_to_black(edge)
midline = img_to_black(midline)
if config.train_scale_array is not None:
img, gt, scale, edge, midline = random_scale(
img, gt, config.train_scale_array, edge,
midline) # scale the images with supplied list
img = normalize(img, self.img_mean, self.img_std)
crop_size = (200, 200)
crop_pos = generate_random_crop_pos(
img.shape[:2], crop_size) # obtain random location
p_img, _ = random_crop_pad_to_shape(
img, crop_pos, crop_size,
0) # get the cropped images and re-sized to crop-size
p_gt, _ = random_crop_pad_to_shape(gt, crop_pos, crop_size, -1)
p_edge, _ = random_crop_pad_to_shape(edge, crop_pos, crop_size, -1)
p_midline, _ = random_crop_pad_to_shape(midline, crop_pos, crop_size,
-1)
p_img = cv2.resize(
p_img, (config.image_width // config.gt_down_sampling,
config.image_height // config.gt_down_sampling),
interpolation=cv2.INTER_NEAREST) # resize by downsampling
p_gt = cv2.resize(p_gt,
(config.image_width // config.gt_down_sampling,
config.image_height // config.gt_down_sampling),
interpolation=cv2.INTER_NEAREST)
p_edge = cv2.resize(p_edge,
(config.image_width // config.gt_down_sampling,
config.image_height // config.gt_down_sampling),
interpolation=cv2.INTER_NEAREST)
p_midline = cv2.resize(
p_midline, (config.image_width // config.gt_down_sampling,
config.image_height // config.gt_down_sampling),
interpolation=cv2.INTER_NEAREST)
p_img = p_img.transpose(2, 0, 1)
extra_dict = None
if p_gt.max() > 1:
print(p_gt)
return p_img, p_gt, extra_dict, p_edge, p_midline
def process_image_rgbd(self, img, disp, crop_size=None):
p_img = img
p_disp = disp
if img.shape[2] < 3:
im_b = p_img
im_g = p_img
im_r = p_img
p_img = np.concatenate((im_b, im_g, im_r), axis=2)
p_img = normalize(p_img, self.image_mean, self.image_std)
if len(disp.shape) == 2:
p_disp = normalize(p_disp, 0, 1)
else:
p_disp = normalize(p_disp, self.image_mean, self.image_std)
if crop_size is not None:
p_img, margin = pad_image_to_shape(p_img,
crop_size,
cv2.BORDER_CONSTANT,
value=0)
p_disp, _ = pad_image_to_shape(p_disp,
crop_size,
cv2.BORDER_CONSTANT,
value=0)
p_img = p_img.transpose(2, 0, 1)
if len(disp.shape) == 2:
p_disp = p_disp[np.newaxis, ...]
else:
p_disp = p_disp.transpose(2, 0, 1)
return p_img, p_disp, margin
p_img = p_img.transpose(2, 0, 1)
if len(disp.shape) == 2:
p_disp = p_disp[np.newaxis, ...]
else:
p_disp = p_disp.transpose(2, 0, 1)
return p_img, p_disp
def pre_processing(img_path):
img = read_image(img_path)
img = cv2.resize(img, size)
img = Image.fromarray(img)
img = transforms.ToTensor()(img)
# img = normalize(img, tuple([0.5, 0.5, 0.5]), tuple([0.5, 0.5, 0.5]),
# inplace=True)
img = normalize(img,
tuple([0.408, 0.447, 0.47]),
tuple([0.289, 0.274, 0.278]),
inplace=True)
return img
def __getitem__(self, index):
datafiles = self.files[index]
image = cv2.imread(datafiles["img"], cv2.IMREAD_COLOR)
label = cv2.imread(datafiles["label"], cv2.IMREAD_GRAYSCALE)
size = image.shape
name = datafiles["name"]
image = normalize(image, np.array(self.mean), np.array(self.std))
image = image.transpose(2, 0, 1)
return image.copy(), label.copy(), np.array(size), name
def __call__(self, img, gt):
img, gt = random_mirror(img, gt)
if config.train_scale_array is not None:
img, gt, scale = random_scale(img, gt, config.train_scale_array)
img = normalize(img, self.img_mean, self.img_std)
crop_size = (config.image_height, config.image_width)
crop_pos = generate_random_crop_pos(img.shape[:2], crop_size)
p_img, _ = random_crop_pad_to_shape(img, crop_pos, crop_size, 0)
p_gt, _ = random_crop_pad_to_shape(gt, crop_pos, crop_size, 0)
p_img = p_img.transpose(2, 0, 1)
extra_dict = None
return p_img, p_gt, extra_dict
def __call__(self, img, gt):
img, gt = random_mirror(img, gt)
if self.config.train_scale_array is not None:
img, gt, scale = random_scale(img, gt, self.config.train_scale_array)
img = normalize(img, self.img_mean, self.img_std)
crop_size = (self.config.image_height, self.config.image_width)
crop_pos = generate_random_crop_pos(img.shape[:2], crop_size)
p_img, _ = random_crop_pad_to_shape(img, crop_pos, crop_size, 0)
p_gt, _ = random_crop_pad_to_shape(gt, crop_pos, crop_size, 255)
p_gt = cv2.resize(p_gt, (self.config.image_width // self.config.gt_down_sampling, self.config.image_height // self.config.gt_down_sampling), interpolation=cv2.INTER_NEAREST)
p_img = p_img.transpose(2, 0, 1)
extra_dict = None
return p_img, p_gt, extra_dict
def __call__(self, img, gt):
(img, gt) = random_mirror(img, gt)
gt = img_to_black(gt)
if config.train_scale_array is not None:
(img, gt, scale) = random_scale(img, gt, config.train_scale_array)
img = normalize(img, self.img_mean, self.img_std)
(p_img, p_gt) = (img, gt)
p_img = cv2.resize(p_img,
(config.image_width // config.gt_down_sampling,
config.image_height // config.gt_down_sampling),
interpolation=cv2.INTER_NEAREST)
p_gt = cv2.resize(p_gt,
(config.image_width // config.gt_down_sampling,
config.image_height // config.gt_down_sampling),
interpolation=cv2.INTER_NEAREST)
p_img = p_img.transpose(2, 0, 1)
extra_dict = None
return (p_img, p_gt, extra_dict)
def process_image(self, img, crop_size=None):
p_img = img
if img.shape[2] < 3:
im_b = p_img
im_g = p_img
im_r = p_img
p_img = np.concatenate((im_b, im_g, im_r), axis=2)
p_img = normalize(p_img, self.image_mean, self.image_std)
if crop_size is not None:
p_img, margin = pad_image_to_shape(p_img, crop_size, cv2.BORDER_CONSTANT, value=0)
p_img = p_img.transpose(2, 0, 1)
return p_img, margin
p_img = p_img.transpose(2, 0, 1)
return p_img
def __call__(self, img, gt):
# img, gt = random_mirror(img, gt)
# if self.config.train_scale_array is not None:
# img, gt, scale = random_scale(img, gt, self.config.train_scale_array)
#
# img = normalize(img, self.img_mean, self.img_std)
#
# crop_size = (self.config.image_height, self.config.image_width)
# crop_pos = generate_random_crop_pos(img.shape[:2], crop_size)
# p_img, _ = random_crop_pad_to_shape(img, crop_pos, crop_size, 0)
# p_gt, _ = random_crop_pad_to_shape(gt, crop_pos, crop_size, 255)
# p_gt = cv2.resize(p_gt, (self.config.image_width // self.config.gt_down_sampling, self.config.image_height // self.config.gt_down_sampling), interpolation=cv2.INTER_NEAREST)
augment = self.augmenter(image=img, mask=gt)
p_img, p_gt = augment['image'], augment['mask']
img = normalize(p_img, self.img_mean, self.img_std)
p_img = img.transpose(2, 0, 1)
extra_dict = None
return p_img, p_gt, extra_dict
def __call__(self, img, gt):
img, gt = random_mirror(
img, gt) # images are randomly flipped to increase variance
gt = img_to_black(gt) # binary filter on gt.
if config.train_scale_array is not None:
img, gt, scale = random_scale(
img, gt, config.train_scale_array
) # scale the images with supplied list
img = normalize(img, self.img_mean, self.img_std)
crop_size = (200, 200)
crop_pos = generate_random_crop_pos(img.shape[:2], crop_size)
p_img, _ = random_crop_pad_to_shape(
img, crop_pos, crop_size,
0) # get the cropped images and re-sized to crop-size
p_gt, _ = random_crop_pad_to_shape(gt, crop_pos, crop_size,
255) # value=
p_img = cv2.resize(p_img,
(config.image_width // config.gt_down_sampling,
config.image_height // config.gt_down_sampling),
interpolation=cv2.INTER_NEAREST)
p_gt = cv2.resize(p_gt,
(config.image_width // config.gt_down_sampling,
config.image_height // config.gt_down_sampling),
interpolation=cv2.INTER_NEAREST)
p_img = p_img.transpose(2, 0, 1)
extra_dict = None
return p_img, p_gt, extra_dict
def __getitem__(self, index):
datafiles = self.files[index]
image = cv2.imread(datafiles["img"], cv2.IMREAD_COLOR)
label = cv2.imread(datafiles["label"], cv2.IMREAD_GRAYSCALE)
size = image.shape
name = datafiles["name"]
image = normalize(image, np.array(self.mean), np.array(self.std))
image, label = random_mirror(image, label)
if self.scale:
image, label, scale = random_scale(image, label,
[0.75, 1, 1.25, 1.5, 1.75, 2.0])
crop_pos = generate_random_crop_pos(image.shape[:2],
self.crop_size)
image, _ = random_crop_pad_to_shape(image, crop_pos,
self.crop_size, 0)
label, _ = random_crop_pad_to_shape(label, crop_pos,
self.crop_size, 255)
image = image.transpose(2, 0, 1)
return image.copy(), label.copy(), np.array(size), name
def predict(models: nn.ModuleList, img_path, path2save, thresh=0.5):
"""
Perfrom prediction for single image
Params:
models : NN models
img_path : path to an image
path2save :
thresh : preiction threshold
"""
img_path = Path(img_path)
if not img_path.exists():
raise FileNotFoundError("File '{}' not found.".format(str(img_path)))
src_img = cv2.imread(str(img_path))
transform = test_trasformations()
augmented = transform(image=src_img)
src_img = augmented["image"]
img2predict = src_img.copy()
img2predict = cv2.cvtColor(img2predict,
cv2.COLOR_BGR2RGB).astype(dtype=np.float32)
img2predict = normalize(img2predict)
img2predict = utils.to_gpu(
numpy_to_tensor(img2predict).unsqueeze(0).contiguous()).float()
if len(models) == 1:
#evaluate mode
model = models[0].eval()
with torch.set_grad_enabled(False):
predict = model(img2predict)
#Probs
predict = F.sigmoid(predict).squeeze(0).squeeze(0)
mask = (predict > thresh).cpu().numpy().astype(dtype=np.uint8)
overlayed_img = alpha_overlay(src_img, mask)
else:
#Averaging all predictions for one point of test data
sum_predicts = utils.to_gpu(
torch.zeros((1, 1, src_img.shape[0], src_img.shape[1])).float())
for model in models:
model.eval()
with torch.set_grad_enabled(False):
predict = model(img2predict)
sum_predicts += F.sigmoid(predict)
predict = (sum_predicts / len(models)).squeeze(0).squeeze(0).float()
mask = (predict > thresh).cpu().numpy().astype(dtype=np.uint8)
overlayed_img = alpha_overlay(src_img, mask)
#save
cv2.imwrite(path2save, overlayed_img)
#show
cv2.imshow("Predicted", overlayed_img)
cv2.waitKey(0)
cv2.destroyAllWindows()
print("Image '{}' was processed successfully.".format(str(img_path)))
def predict_batch(models: nn.ModuleList, path2images, path2save, thresh=0.5):
"""
Perfrom prediction for a batch images
Params:
models : NN models
path2images : path to an image
path2save : should be a dir
thresh : preiction threshold
"""
path2images = Path(path2images)
path2save = Path(path2save)
if not path2images.is_dir():
raise RuntimeError("File '{}' is not dir.".format(str(path2images)))
if not path2save.is_dir():
raise RuntimeError("File '{}' is not dir.".format(str(path2save)))
imgs_paths = sorted(list(path2images.glob("*")))
count_processed = 0
for idx, ip in enumerate(imgs_paths):
src_img = cv2.imread(str(ip))
transform = test_trasformations()
augmented = transform(image=src_img)
src_img = augmented["image"]
img2predict = src_img.copy()
img2predict = cv2.cvtColor(img2predict,
cv2.COLOR_BGR2RGB).astype(dtype=np.float32)
img2predict = normalize(img2predict)
img2predict = utils.to_gpu(
numpy_to_tensor(img2predict).unsqueeze(0).contiguous()).float()
if len(models) == 1:
model = models[0].eval()
with torch.set_grad_enabled(False):
predict = model(img2predict)
#Probs
predict = F.sigmoid(predict).squeeze(0).squeeze(0)
mask = (predict > thresh).cpu().numpy().astype(dtype=np.uint8)
overlayed_img = alpha_overlay(src_img, mask)
else:
#Averaging all predictions for one point of test data
sum_predicts = utils.to_gpu(
torch.zeros(
(1, 1, src_img.shape[0], src_img.shape[1])).float())
for model in models:
model.eval()
with torch.set_grad_enabled(False):
predict = model(img2predict)
sum_predicts += F.sigmoid(predict)
predict = (sum_predicts /
len(models)).squeeze(0).squeeze(0).float()
mask = (predict > thresh).cpu().numpy().astype(dtype=np.uint8)
overlayed_img = alpha_overlay(src_img, mask)
#save
cv2.imwrite(str(path2save / "{}".format(ip.name)), overlayed_img)
print("Image '{}' was processed successfully.".format(str(ip)))
count_processed += 1
print("{} images were processed.".format(count_processed))
def main():
cudnn.enabled = True
if args.model == 'base':
model = ResNetDeepLabv3(backbone=args.backbone)
elif args.model == 'intra':
model = IntraFrameNet(backbone=args.backbone, pyramid_pooling=args.pyramid_pooling,
embedding=args.embedding_size, batch_mode='sync')
elif args.model == 'inter':
model = InterFrameNet(backbone=args.backbone, pyramid_pooling=args.pyramid_pooling,
embedding=args.embedding_size, batch_mode='sync')
elif args.model == 'concat':
model = ConcatNet(backbone=args.backbone, pyramid_pooling=args.pyramid_pooling,
embedding=args.embedding_size, batch_mode='sync')
elif args.model == 'ad':
cfg = get_cfg()
cfg.merge_from_file('./fpn_config/semantic_R_50_FPN_1x.yaml')
model = AnchorDiffNet(cfg,
embedding=args.embedding_size, batch_mode='sync')
model.load_state_dict(torch.load('./log/snapshot/epoch-last.pth')['model'])
model.eval()
model.float()
model.cuda()
with torch.no_grad():
video_mean_iou_list = []
model.eval()
videos = [i_id.strip() for i_id in open(osp.join(args.data_dir, 'ImageSets', '2016', 'val.txt'))]
if args.video and args.video in videos:
videos = [args.video]
for vid, video in enumerate(videos, start=1):
curr_video_iou_list = []
img_files = sorted(glob.glob(osp.join(args.data_dir, 'JPEGImages', '480p', video, '*.jpg')))
ann_files = sorted(glob.glob(osp.join(args.data_dir, 'Annotations', '480p', video, '*.png')))
#img_files = img_files[:10]
#ann_files = ann_files[:10]
if args.ms_mirror:
w = 512
h = 256
resize_shape = [(w*0.5, h*0.5), (w, h), (w*1.5, h*1.5)]
resize_shape = [(int((s[0])), int((s[1]))) for s in resize_shape]
mirror = True
else:
#resize_shape = [(857, 481)]
resize_shape = [(704, 480)]
#resize_shape = [(720, 432)]
mirror = False
reference_img = []
for s in resize_shape:
reference_img.append(normalize(np.asarray(cv2.resize(cv2.imread(img_files[0], cv2.IMREAD_COLOR)[:,:,::-1], s),
np.float32), config.image_mean, config.image_std).transpose((2, 0, 1)))
if mirror:
for r in range(len(reference_img)):
reference_img.append(reference_img[r][:, :, ::-1].copy())
reference_img = [torch.from_numpy(np.expand_dims(r, axis=0)).float().cuda() for r in reference_img]
reference_mask = np.array(Image.open(ann_files[0])) > 0
reference_mask = torch.from_numpy(np.expand_dims(np.expand_dims(reference_mask.astype(np.float32),
axis=0), axis=0)).cuda()
H, W = reference_mask.size(2), reference_mask.size(3)
if args.visualize:
colors = np.random.randint(128, 255, size=(1, 3), dtype="uint8")
colors = np.vstack([[0, 0, 0], colors]).astype("uint8")
last_mask_num = 0
last_mask = None
last_mask_final = None
kernel1 = np.ones((15, 15), np.uint8)
kernel2 = np.ones((101, 101), np.uint8)
kernel3 = np.ones((31, 31), np.uint8)
predictions_all = []
gt_all = []
for f, (img_file, ann_file) in enumerate(zip(img_files, ann_files)):
current_img = []
for s in resize_shape:
current_img.append(normalize(np.asarray(cv2.resize(
cv2.imread(img_file, cv2.IMREAD_COLOR)[:,:,::-1], s),
np.float32), config.image_mean, config.image_std).transpose((2, 0, 1)))
if mirror:
for c in range(len(current_img)):
current_img.append(current_img[c][:, :, ::-1].copy())
current_img = [torch.from_numpy(np.expand_dims(c, axis=0)).float().cuda() for c in current_img]
#current_mask = np.array(Image.open(ann_file)) > 0
current_mask = Image.open(ann_file)
current_mask = np.atleast_3d(current_mask)[...,0]
current_mask = current_mask.copy()
current_mask[current_mask > 0] = 1
current_mask = torch.from_numpy(np.expand_dims(np.expand_dims(current_mask.astype(np.float32), axis=0), axis=0)).cuda()
if args.model in ['base']:
predictions = [model(cur) for ref, cur in zip(reference_img, current_img)]
predictions = [F.interpolate(input=p[0], size=(H, W), mode='bilinear', align_corners=True) for p in predictions]
elif args.model in ['intra']:
predictions = [model(cur) for ref, cur in zip(reference_img, current_img)]
predictions = [F.interpolate(input=p, size=(H, W), mode='bilinear', align_corners=True) for p in predictions]
elif args.model in ['inter', 'concat', 'ad']:
predictions = [model(ref, cur) for ref, cur in zip(reference_img, current_img)]
predictions = [F.interpolate(input=p, size=(H, W), mode='bilinear', align_corners=True) for p in predictions]
if mirror:
for r in range(len(predictions)//2, len(predictions)):
predictions[r] = torch.flip(predictions[r], [3])
predictions = torch.mean(torch.stack(predictions, dim=0), 0)
predictions_all.append(predictions.sigmoid().data.cpu().numpy()[0, 0].copy())
gt_all.append(current_mask.data.cpu().numpy()[0, 0].astype(np.uint8).copy())
if args.inst_prune:
result_dir = os.path.join('inst_prune', video)
if os.path.exists(os.path.join(result_dir, img_file.split('/')[-1].split('.')[0] + '.png')):
detection_mask = np.array(
Image.open(os.path.join(result_dir, img_file.split('/')[-1].split('.')[0] + '.png'))) > 0
detection_mask = torch.from_numpy(
np.expand_dims(np.expand_dims(detection_mask.astype(np.float32), axis=0), axis=0)).cuda()
predictions = predictions * detection_mask
process_now = (predictions > args.threshold).data.cpu().numpy().astype(np.uint8)[0, 0]
if 100000 > process_now.sum() > 40000:
last_mask_numpy = (predictions > args.threshold).data.cpu().numpy().astype(np.uint8)[0, 0]
last_mask_numpy = cv2.morphologyEx(last_mask_numpy, cv2.MORPH_OPEN, kernel1)
dilation = cv2.dilate(last_mask_numpy, kernel3, iterations=1)
contours, _ = cv2.findContours(dilation, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
cnt_area = [cv2.contourArea(cnt) for cnt in contours]
if len(contours) > 1:
contour = contours[np.argmax(cnt_area)]
polygon = contour.reshape(-1, 2)
x, y, w, h = cv2.boundingRect(polygon)
x0, y0 = x, y
x1 = x + w
y1 = y + h
mask_rect = torch.from_numpy(np.zeros_like(dilation).astype(np.float32)).cuda()
mask_rect[y0:y1, x0:x1] = 1
mask_rect = mask_rect.unsqueeze(0).unsqueeze(0)
if np.max(cnt_area) > 30000:
if last_mask_final is None or get_iou(last_mask_final, mask_rect, thresh=args.threshold) > 0.3:
predictions = predictions * mask_rect
last_mask_final = predictions.clone()
if 100000 > last_mask_num > 5000:
last_mask_numpy = (last_mask > args.threshold).data.cpu().numpy().astype(np.uint8)[0, 0]
last_mask_numpy = cv2.morphologyEx(last_mask_numpy, cv2.MORPH_OPEN, kernel1)
dilation = cv2.dilate(last_mask_numpy, kernel2, iterations=1)
dilation = torch.from_numpy(dilation.astype(np.float32)).cuda()
last_mask = predictions.clone()
last_mask_num = (predictions > args.threshold).sum()
predictions = predictions*dilation
else:
last_mask = predictions.clone()
last_mask_num = (predictions > args.threshold).sum()
iou_temp = get_iou(predictions, current_mask, thresh=args.threshold)
if 0 < f < (len(ann_files)-1):
curr_video_iou_list.append(iou_temp)
if args.visualize:
mask = colors[(predictions.cpu().numpy().squeeze() > args.threshold).astype(np.uint8)]
output = ((0.4 * cv2.imread(img_file)) + (0.6 * mask)).astype("uint8")
cv2.putText(output, "%.3f" % (iou_temp.item()),
(50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2, cv2.LINE_AA)
cv2.imshow(video, output)
cv2.waitKey(1)
suffix = args.ms_mirror*'ms_mirror'+(not args.ms_mirror)*'single'+args.inst_prune*'_prune'
visual_path = osp.join('visualization', args.model + '_' + suffix, img_file.split('/')[-2])
if not osp.exists(visual_path):
os.makedirs(visual_path)
cv2.imwrite(osp.join(visual_path, ann_file.split('/')[-1]), output)
if args.save_mask:
suffix = args.ms_mirror*'ms_mirror'+(not args.ms_mirror)*'single'+args.inst_prune*'_prune'
if not osp.exists(osp.join(args.save_mask_dir, args.model, suffix, video)):
os.makedirs(osp.join(args.save_mask_dir, args.model, suffix, video))
cv2.imwrite(osp.join(args.save_mask_dir, args.model, suffix, video, ann_file.split('/')[-1]),
(predictions.squeeze() > args.threshold).cpu().numpy().astype(np.uint8))
cv2.destroyAllWindows()
video_mean_iou_list.append(sum(curr_video_iou_list)/len(curr_video_iou_list))
print('{} {} {}'.format(vid, video, video_mean_iou_list[-1]))
if args.eval_sal:
if not osp.exists(args.save_heatmap_dir):
os.makedirs(args.save_heatmap_dir)
with open(args.save_heatmap_dir + video + '.pkl', 'wb') as f:
pickle.dump({'pred': np.array(predictions_all), 'gt': np.array(gt_all)}, f, pickle.HIGHEST_PROTOCOL)
mean_iou = sum(video_mean_iou_list)/len(video_mean_iou_list)
print('mean_iou {}'.format(mean_iou))
end = timeit.default_timer()
print(end-start, 'seconds')
# ==========================
if args.eval_sal:
pkl_files = glob.glob(args.save_heatmap_dir + '*.pkl')
heatmap_gt = []
heatmap_pred = []
for i, pkl_file in enumerate(pkl_files):
with open(pkl_file, 'rb') as f:
info = pickle.load(f)
heatmap_gt.append(np.array(info['gt'][1:-1]).flatten())
heatmap_pred.append(np.array(info['pred'][1:-1]).flatten())
heatmap_gt = np.hstack(heatmap_gt).flatten()
heatmap_pred = np.hstack(heatmap_pred).flatten()
precision, recall, _ = precision_recall_curve(heatmap_gt, heatmap_pred)
Fmax = 2 * (precision * recall) / (precision + recall)
print('MAE', np.mean(abs(heatmap_pred - heatmap_gt)))
print('F_max', Fmax.max())
n_sample = len(precision)//1000
import scipy.io
scipy.io.savemat('davis.mat', {'recall': recall[0::n_sample], 'precision': precision[0::n_sample]})
