def plot(data, gs, mask, p_mask, use_log, custom_transform=lambda x: x):
d = to_np_img(data[0])
if use_log:
d = cv2.split(d)
d = np.dstack((d[0], d[1]))
gs = gs[0]
d = transform_from_log(d, gs)
if d.max() > 1:
d = d.astype(int)
mask = mask_to_image(to_np_img(mask[0]))
p_mask = custom_transform(mask_to_image(to_np_img(p_mask[0])))
visualize(d, p_mask, mask=mask)
def save_mask(name, mask):
image, _, _ = load_img_and_gt_crf_dataset(name,
path=path,
folder=folder,
dataset=datatype,
use_corrected=use_corrected,
rotate=False,
use_mask=False)
if image.shape[0] > image.shape[1]:
fx = image.shape[0] / mask.shape[3]
fy = image.shape[1] / mask.shape[2]
else:
fx = image.shape[1] / mask.shape[3]
fy = image.shape[0] / mask.shape[2]
rot_mask = cv2.resize(mask_to_image(to_np_img(mask)), (0, 0),
fx=fx,
fy=fy)
if image.shape[0] > image.shape[1]:
rot_mask = cv2.flip(rot_mask, -1)
rot_mask = cv2.flip(rot_mask, 0)
rot_mask = cv2.rotate(rot_mask, cv2.ROTATE_180)
fld = f'{path}/{folder}/pmasks6{"-cor" if use_corrected else ""}{"-custom" if custom else ""}{"-reg" if reg else ""}'
if not os.path.exists(fld):
os.mkdir(fld)
rot_mask = cv2.cvtColor(rot_mask, cv2.COLOR_RGB2BGR)
cv2.imwrite(f'{fld}/{name}', rot_mask)
def plot(data, gs, gt, p_mask, use_log, custom_transform=lambda x: x):
d = to_np_img(data[0])
gt = to_np_img(gt[0])
p_mask = to_np_img(p_mask[0])
if use_log:
d = cv2.split(d)
d = np.dstack((d[0], d[1]))
gs = gs[0]
d = transform_from_log(d, gs)
if d.max() > 1:
d = d.astype(int)
gt = gt.astype(np.uint8)
p_mask = np.clip(p_mask, 0, 255).astype(np.uint8)
p_mask = custom_transform(
cv2.cvtColor(to_np_img(p_mask), cv2.COLOR_LUV2RGB))
visualize(d, p_mask, mask=gt)
def plot(data,
gs,
mask,
p_mask,
use_log,
reg,
custom_transform=lambda x: x,
use_mixture=False):
d = to_np_img(data[0])
if use_log:
d = cv2.split(d)
d = np.dstack((d[0], d[1]))
gs = gs[0]
d = transform_from_log(d, gs)
if d.max() > 10:
d = d.astype(int)
if reg:
p_mask = to_np_img(p_mask[0])
p_mask = p_mask.astype(np.uint8)
p_mask = cv2.cvtColor(to_np_img(p_mask), cv2.COLOR_LUV2RGB)
mask = to_np_img(mask[0])
mask = mask.astype(np.uint8)
mask = cv2.cvtColor(to_np_img(mask), cv2.COLOR_LUV2RGB)
else:
mask = mask_to_image(to_np_img(mask[0]))
p_mask = custom_transform(
mask_to_image(to_np_img(p_mask[0]), use_mixture=use_mixture))
visualize(d, p_mask, mask=mask)
def color_correct_with_mask(img, mask, c1, c2, filter=lambda x: x):
mask = to_np_img(mask)
mask = mask / (np.max(mask))
mask = np.clip(mask, 0, 1)
# c1 = torch.tensor(c1)
# c2 = torch.tensor(c2)
# gt = np.array(
# [[c2 * pixel[0] + c1 * (1 - pixel[0]) for pixel in row] for row in mask])
gt = np.array([[c1 * (1 - pixel[0]) + c2 * pixel[0] for pixel in row]
for row in mask])
gt = filter(gt)
gt = torch.tensor(gt)
return color_correct_tensor(img[0], gt), gt
def plot(data, gt, out_a, gs, gt_gs):
gs = gs[0]
gt_gs = np.array([[np.array(gt_gs[0]) for i in range(64)]
for j in range(64)])
d = to_np_img(data[0])
d = cv2.split(d)
d = np.dstack((d[0], d[1]))
d = transform_from_log(d, gs)
gt_img = np.array([[np.array(gt[0].cpu()) for i in range(64)]
for j in range(64)])
gt_img = transform_from_log(gt_img, gt_gs)
pred_img = np.array(
[[out_a[0].detach().cpu().numpy() for i in range(64)]
for j in range(64)])
pred_img = transform_from_log(pred_img, gt_gs)
visualize(d, gt_img, pred_img)