def make_grid(batch_img, batch_mask, img_denormalize_fn, batch_gt_mask=None):
"""Create a grid from batch image and mask as
img1 | img2 | img3 | img4 | ...
i+m1 | i+m2 | i+m3 | i+m4 | ...
mask1 | mask2 | mask3 | mask4 | ...
i+M1 | i+M2 | i+M3 | i+M4 | ...
Mask1 | Mask2 | Mask3 | Mask4 | ...
i+m = image + mask blended with alpha=0.4
- maskN is predicted mask
- MaskN is ground-truth mask if given
Args:
batch_img (torch.Tensor) batch of images of any type
batch_mask (torch.Tensor) batch of masks
img_denormalize_fn (Callable): function to denormalize batch of images
batch_gt_mask (torch.Tensor, optional): batch of ground truth masks.
"""
assert isinstance(batch_img, torch.Tensor) and isinstance(
batch_mask, torch.Tensor)
assert len(batch_img) == len(batch_mask)
if batch_gt_mask is not None:
assert isinstance(batch_gt_mask, torch.Tensor)
assert len(batch_mask) == len(batch_gt_mask)
b = batch_img.shape[0]
h, w = batch_img.shape[2:]
le = 3 if batch_gt_mask is None else 3 + 2
out_image = np.zeros((h * le, w * b, 3), dtype="uint8")
for i in range(b):
img = batch_img[i]
mask = batch_mask[i]
img = img_denormalize_fn(img)
img = tensor_to_rgb(img)
mask = mask.cpu().numpy()
mask = render_mask(mask)
out_image[0:h, i * w:(i + 1) * w, :] = img
out_image[1 * h:2 * h,
i * w:(i + 1) * w, :] = render_datapoint(img,
mask,
blend_alpha=0.4)
out_image[2 * h:3 * h, i * w:(i + 1) * w, :] = mask
if batch_gt_mask is not None:
gt_mask = batch_gt_mask[i]
gt_mask = gt_mask.cpu().numpy()
gt_mask = render_mask(gt_mask)
out_image[3 * h:4 * h,
i * w:(i + 1) * w, :] = render_datapoint(img,
gt_mask,
blend_alpha=0.4)
out_image[4 * h:5 * h, i * w:(i + 1) * w, :] = gt_mask
return out_image
def write_prediction_on_image2(mask_predicted, im, filepath):
assert isinstance(mask_predicted, np.ndarray) and mask_predicted.ndim == 2, \
"{} and {}".format(type(mask_predicted), mask_predicted.shape if isinstance(mask_predicted, np.ndarray) else None)
assert isinstance(im, np.ndarray) and im.ndim == 3, \
"{} and {}".format(type(im), im.shape if isinstance(im, np.ndarray) else None)
# Normalize for rendering
x = render_x(im)
# Save the images and masks
im = Image.fromarray(x).convert('RGB')
pil_pred = Image.fromarray(mask_predicted.astype('uint8'))
pil_pred.putpalette(vocpallete)
pil_pred = pil_pred.convert('RGB')
res_pred = render_datapoint(im, pil_pred)
size_image = (mask_predicted.shape[0], mask_predicted.shape[1])
tiles = [[im, res_pred, pil_pred]]
nb_cols = len(tiles)
nb_rows = len(tiles[0])
cvs = Image.new('RGB', (nb_cols * size_image[0], nb_rows * size_image[1]))
for i_row in range(nb_cols):
for i_col in range(nb_rows):
px, py = (i_row * size_image[0], i_col * size_image[1])
cvs.paste(tiles[i_row][i_col], (px, py))
cvs.save(filepath)
def wrapper(engine, writer, state_attr):
output = engine.state.output
x, y_pred, y = output['x'], output['y_pred'], output['y']
_, y_pred = torch.max(y_pred, dim=1)
indices = torch.randperm(x.shape[0])[:n_images]
bg_imgs = render_x(x[indices, ...], nrow=n_rows)
pred_imgs = render_y(y_pred[indices, ...], nrow=n_rows)
gt_imgs = render_y(y[indices, ...], nrow=n_rows)
img1 = np.asarray(render_datapoint(bg_imgs, pred_imgs, output_size=single_img_size))
img2 = np.asarray(render_datapoint(bg_imgs, gt_imgs, output_size=single_img_size))
state = engine.state if another_engine is None else another_engine.state
global_step = getattr(state, state_attr)
writer.add_image(tag="predictions", img_tensor=img1, global_step=global_step, dataformats='HWC')
writer.add_image(tag="ground-truth", img_tensor=img2, global_step=global_step, dataformats='HWC')
def make_grid(
batch_img: torch.Tensor,
batch_preds: torch.Tensor,
img_denormalize_fn: Callable,
batch_gt: Optional[torch.Tensor] = None,
):
"""Create a grid from batch image and mask as
i+l1+gt1 | i+l2+gt2 | i+l3+gt3 | i+l4+gt4 | ...
where i+l+gt = image + predicted label + ground truth
Args:
batch_img (torch.Tensor) batch of images of any type
batch_preds (torch.Tensor) batch of masks
img_denormalize_fn (Callable): function to denormalize batch of images
batch_gt (torch.Tensor, optional): batch of ground truth masks.
"""
assert isinstance(batch_img, torch.Tensor) and isinstance(batch_preds, torch.Tensor)
assert len(batch_img) == len(batch_preds), "{} vs {}".format(
len(batch_img), len(batch_preds)
)
assert batch_preds.ndim == 1, "{}".format(batch_preds.ndim)
if batch_gt is not None:
assert isinstance(batch_gt, torch.Tensor)
assert len(batch_preds) == len(batch_gt)
assert batch_gt.ndim == 1, "{}".format(batch_gt.ndim)
b = batch_img.shape[0]
h, w = batch_img.shape[2:]
le = 1
out_image = np.zeros((h * le, w * b, 3), dtype="uint8")
for i in range(b):
img = batch_img[i]
y_preds = batch_preds[i]
img = img_denormalize_fn(img)
img = tensor_to_numpy(img)
pred_label = y_preds.cpu().item()
target = "p={}".format(pred_label)
if batch_gt is not None:
gt_label = batch_gt[i]
gt_label = gt_label.cpu().item()
target += " | gt={}".format(gt_label)
out_image[0:h, i * w : (i + 1) * w, :] = render_datapoint(
img, target, text_size=12
)
return out_image
def write_prediction_on_image(y_pred, x, filepath, palette=default_palette):
assert isinstance(y_pred, np.ndarray) and y_pred.ndim == 2, \
"{} and {}".format(type(y_pred), y_pred.shape if isinstance(y_pred, np.ndarray) else None)
assert isinstance(x, np.ndarray) and x.ndim == 3, \
"{} and {}".format(type(x), x.shape if isinstance(x, np.ndarray) else None)
pil_pred = Image.fromarray(y_pred)
pil_pred.putpalette(palette)
res = render_datapoint(x, pil_pred.convert('RGB'), blend_alpha=0.5)
filepath = filepath + ".png"
res.save(filepath)
def visualize(image_name, image_dir, mask_dir, pre_dir, save_name, dice=None):
print("The {} visualization".format(save_name))
fontsize = 8
if 'mask' in image_name[0]:
image_name = [i.split('_')[0] for i in image_name]
length = len(image_name)
images_path = find_imagenames(image_dir, image_name)
masks_path = find_imagenames(mask_dir, image_name)
pres_path = find_imagenames(pre_dir, image_name)
f, ax = plt.subplots(length, 3, figsize=(10, 40))
if dice:
for i in range(length):
name = images_path[i].split('/')[-1].split('.')[0]
original_image = cv2.imread(images_path[i])
original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
dice_score = dice[name]
w, h = original_image.shape[:2]
ax[i, 0].imshow(original_image)
ax[i, 0].set_title('Original image ' + name + ' ' + str(w) + '*' +
str(h),
fontsize=fontsize)
original_mask = cv2.imread(masks_path[i])
# original_mask = cv2.cvtColor(original_mask,cv2.COLOR_BGR2GRAY)
rimg = render_datapoint(original_mask,
original_image,
blend_alpha=0.8)
ax[i, 1].imshow(rimg)
ax[i, 1].set_title('Original mask', fontsize=fontsize)
pre_mask = cv2.imread(pres_path[i])
# pre_mask = cv2.cvtColor(pre_mask,cv2.COLOR_BGR2GRAY)
rimg2 = render_datapoint(pre_mask, original_image, blend_alpha=0.8)
ax[i, 2].imshow(rimg2)
ax[i,
2].set_title('pred mask' + ' dice:' + str(round(dice_score, 3)),
fontsize=fontsize)
plt.savefig(save_name + '.png')
plt.show()
