def __call__(self, sample):
# Fixed range of scales
if self.resolutions is None:
return sample
elems = list(sample.keys())
for elem in elems:
if 'meta' in elem or 'bbox' in elem or ('extreme_points_coord' in elem and elem not in self.resolutions):
continue
if elem in self.resolutions:
if self.resolutions[elem] is None:
continue
if isinstance(sample[elem], list):
if sample[elem][0].ndim == 3:
output_size = np.append(self.resolutions[elem], [3, len(sample[elem])])
else:
output_size = np.append(self.resolutions[elem], len(sample[elem]))
tmp = sample[elem]
sample[elem] = np.zeros(output_size, dtype=np.float32)
for ii, crop in enumerate(tmp):
if self.flagvals is None:
sample[elem][..., ii] = utils.fixed_resize(crop, self.resolutions[elem])
else:
sample[elem][..., ii] = utils.fixed_resize(crop, self.resolutions[elem], flagval=self.flagvals[elem])
else:
if self.flagvals is None:
sample[elem] = utils.fixed_resize(sample[elem], self.resolutions[elem])
else:
sample[elem] = utils.fixed_resize(sample[elem], self.resolutions[elem], flagval=self.flagvals[elem])
return sample
def __call__(self, sample):
elems = list(sample.keys())
for elem in elems:
if self.flagvals is None:
sample[elem] = utils.fixed_resize(sample[elem], self.size)
else:
sample[elem] = utils.fixed_resize(sample[elem],
self.size,
flagval=self.flagvals[elem])
return sample
def __call__(self, sample):
i, j, h, w = self.get_params(sample['image'], self.scale, self.ratio)
elems = list(sample.keys())
for elem in elems:
if sample[elem].ndim == 2:
sample[elem] = sample[elem][i:i + h, j:j + w]
else:
sample[elem] = sample[elem][i:i + h, j:j + w, :]
if self.flagvals is None:
sample[elem] = utils.fixed_resize(sample[elem], self.size)
else:
sample[elem] = utils.fixed_resize(sample[elem], self.size,
flagval=self.flagvals[elem])
return sample
def main():
global image, output
cv2.namedWindow('draw', flags=cv2.WINDOW_NORMAL)
cv2.setMouseCallback('draw', interactive_drawing)
image_idx = 0
while (1):
cv2.imshow('draw', image)
k = cv2.waitKey(1) & 0xFF
if k != 255:
# print(k)
pass
if k == 100: # D
cv2.imwrite('./' + str(image_idx) + 'out.png', image)
if k == 115:
global left, right, up, down
left = 0xFFF
right = 0
up = 0xFFF
down = 0
drawing = False # true if mouse is pressed
image = cv2.imread(image_list[image_idx])
image = utils.fixed_resize(image, (450, 450)).astype(np.uint8)
sp = image.shape
w = sp[0]
h = sp[1]
output = np.zeros((w, h, 3), np.uint8)
while (1):
cv2.imshow('draw', image)
k = cv2.waitKey(1) & 0xFF
if k == 32:
break
if k == 27:
image = cv2.imread(image_list[image_idx])
image = utils.fixed_resize(image,
(450, 450)).astype(np.uint8)
output = np.zeros((w, h, 3), np.uint8)
tmp = (output[:, :, 0] > 0).astype(np.uint8)
tmp_ = deepcopy(tmp)
fill_mask = np.ones((tmp.shape[0] + 2, tmp.shape[1] + 2))
fill_mask[1:-1, 1:-1] = tmp_
fill_mask = fill_mask.astype(np.uint8)
cv2.floodFill(tmp_, fill_mask, (int(
(left + right) / 2), int((up + down) / 2)), 5)
tmp_ = tmp_.astype(np.int8)
output = cv2.resize(output, img_shape)
tmp_ = tmp_.astype(np.int8)
tmp_[tmp_ == 5] = -1 # pixel inside bounding box
tmp_[tmp_ == 0] = 1 # pixel on and outside bounding box
tmp = (tmp == 0).astype(np.uint8)
dismap = cv2.distanceTransform(
tmp, cv2.DIST_L2, cv2.DIST_MASK_PRECISE
) # compute distance inside and outside bounding box
dismap = tmp_ * dismap + 128
dismap[dismap > 255] = 255
dismap[dismap < 0] = 0
dismap = dismap
dismap = utils.fixed_resize(dismap, (450, 450)).astype(np.uint8)
dismap = np.expand_dims(dismap, axis=-1)
image = image[:, :, ::-1] # change to rgb
merge_input = np.concatenate((image, dismap),
axis=2).astype(np.float32)
inputs = torch.from_numpy(
merge_input.transpose((2, 0, 1))[np.newaxis, ...])
# Run a forward pass
inputs = inputs.to(device)
outputs = net.forward(inputs)
outputs = upsample(outputs,
size=(450, 450),
mode='bilinear',
align_corners=True)
outputs = outputs.to(torch.device('cpu'))
prediction = np.transpose(outputs.data.numpy()[0, ...], (1, 2, 0))
prediction = 1 / (1 + np.exp(-prediction))
prediction = np.squeeze(prediction)
prediction[prediction > thres] = 255
prediction[prediction <= thres] = 0
prediction = np.expand_dims(prediction, axis=-1).astype(np.uint8)
image = image[:, :, ::-1] # change to bgr
display_mask = np.concatenate([
np.zeros_like(prediction),
np.zeros_like(prediction), prediction
],
axis=-1)
image = cv2.addWeighted(image, 0.9, display_mask, 0.5, 0.1)
if k == 99:
break
if k == 110:
image_idx += 1
if image_idx >= len(image_list):
print('Already the last image. Starting from the beginning.')
image_idx = 0
image = cv2.imread(image_list[image_idx])
image = utils.fixed_resize(image, (450, 450)).astype(np.uint8)
sp = image.shape
w = sp[0]
h = sp[1]
output = np.zeros((w, h, 3), np.uint8)
if k == 112:
image_idx -= 1
if image_idx < 0:
print('Reached the first image. Starting from the end.')
image_idx = len(image_list) - 1
image = cv2.imread(image_list[image_idx])
image = utils.fixed_resize(image, (450, 450)).astype(np.uint8)
sp = image.shape
w = sp[0]
h = sp[1]
output = np.zeros((w, h, 3), np.uint8)
cv2.destroyAllWindows()
from torch.nn.functional import upsample
from dataloaders import utils
import networks.deeplab_resnet as resnet
from glob import glob
from copy import deepcopy
drawing = False
start = False
# TODO: import test image path
image_list = glob(os.path.join('ims/', '*.' + 'jpg'))
image = cv2.imread(image_list[0])
img_shape = (450, 450)
image = utils.fixed_resize(image, img_shape).astype(np.uint8)
w = img_shape[0]
h = img_shape[1]
output = np.zeros((w, h, 3), np.uint8)
thres = 0.8
left = 0xFFF
right = 0
up = 0xFFF
down = 0
gpu_id = 0
device = torch.device("cuda:" +
str(gpu_id) if torch.cuda.is_available() else "cpu")
'deepgc_pascal_epoch-99.pth'),
map_location=lambda storage, loc: storage)
net.load_state_dict(state_dict_checkpoint)
net.eval()
net.to(device)
files = os.listdir(img_dir)
imgs = [im for im in files if '_gt.png' not in im]
for im_name in imgs:
im_path = os.path.join(img_dir, im_name)
im = np.array(Image.open(im_path))
img_shape = (450, 450)
image = utils.fixed_resize(im, img_shape).astype(np.uint8)
gt = Image.open(im_path.replace('.png', '_gt.png')).convert('L')
gt = np.array(gt)
# image = np.array(im)
gt_bb = utils.get_bbox(gt)
# output = cv2.resize(output, img_shape)
# dismap = utils.compute_dismap(gt, gt_bb)
dismap, tmp = utils.distance_map(gt, v=0)
dismap[dismap > 255] = 255
dismap[dismap < 0] = 0
dismap = dismap