defresize_image(image, min_dim=None, max_dim=None, min_scale=None, mode="square"):"""Resizes an image keeping the aspect ratio unchanged.
min_dim: if provided, resizes the image such that it's smaller
dimension == min_dim
max_dim: if provided, ensures that the image longest side doesn't
exceed this value.
min_scale: if provided, ensure that the image is scaled up by at least
this percent even if min_dim doesn't require it.
mode: Resizing mode.
none: No resizing. Return the image unchanged.
square: Resize and pad with zeros to get a square image
of size [max_dim, max_dim].
pad64: Pads width and height with zeros to make them multiples of 64.
If min_dim or min_scale are provided, it scales the image up
before padding. max_dim is ignored in this mode.
The multiple of 64 is needed to ensure smooth scaling of feature
maps up and down the 6 levels of the FPN pyramid (2**6=64).
crop: Picks random crops from the image. First, scales the image based
on min_dim and min_scale, then picks a random crop of
size min_dim x min_dim. Can be used in training only.
max_dim is not used in this mode.
Returns:
image: the resized image
window: (y1, x1, y2, x2). If max_dim is provided, padding might
be inserted in the returned image. If so, this window is the
coordinates of the image part of the full image (excluding
the padding). The x2, y2 pixels are not included.
scale: The scale factor used to resize the image
padding: Padding added to the image [(top, bottom), (left, right), (0, 0)]
"""# Keep track of image dtype and return results in the same dtype
image_dtype = image.dtype
# Default window (y1, x1, y2, x2) and default scale == 1.
h, w = image.shape[:2]
window = (0, 0, h, w)
scale = 1
padding = [(0, 0), (0, 0), (0, 0)]
crop = Noneif mode == "none":
return image, window, scale, padding, crop
# Scale?if min_dim:
# Scale up but not down
scale = max(1, min_dim / min(h, w))
if min_scale and scale < min_scale:
scale = min_scale
# Does it exceed max dim?if max_dim and mode == "square":
image_max = max(h, w)
if round(image_max * scale) > max_dim:
scale = max_dim / image_max
# Resize image using bilinear interpolationif scale != 1:
image = skimage.transform.resize(
image, (round(h * scale), round(w * scale)),
order=1, mode="constant", preserve_range=True)
# Need padding or cropping?if mode == "square":
# Get new height and width
h, w = image.shape[:2]
top_pad = (max_dim - h) // 2
bottom_pad = max_dim - h - top_pad
left_pad = (max_dim - w) // 2
right_pad = max_dim - w - left_pad
padding = [(top_pad, bottom_pad), (left_pad, right_pad), (0, 0)]
image = np.pad(image, padding, mode='constant', constant_values=0)
window = (top_pad, left_pad, h + top_pad, w + left_pad)
elif mode == "pad64":
h, w = image.shape[:2]
# Both sides must be divisible by 64assert min_dim % 64 == 0, "Minimum dimension must be a multiple of 64"# Heightif h % 64 > 0:
max_h = h - (h % 64) + 64
top_pad = (max_h - h) // 2
bottom_pad = max_h - h - top_pad
else:
top_pad = bottom_pad = 0# Widthif w % 64 > 0:
max_w = w - (w % 64) + 64
left_pad = (max_w - w) // 2
right_pad = max_w - w - left_pad
else:
left_pad = right_pad = 0
padding = [(top_pad, bottom_pad), (left_pad, right_pad), (0, 0)]
image = np.pad(image, padding, mode='constant', constant_values=0)
window = (top_pad, left_pad, h + top_pad, w + left_pad)
elif mode == "crop":
# Pick a random crop
h, w = image.shape[:2]
y = random.randint(0, (h - min_dim))
x = random.randint(0, (w - min_dim))
crop = (y, x, min_dim, min_dim)
image = image[y:y + min_dim, x:x + min_dim]
window = (0, 0, min_dim, min_dim)
else:
raise Exception("Mode {} not supported".format(mode))
return image.astype(image_dtype), window, scale, padding, crop
defresize_mask(mask, scale, padding, crop=None):"""Resizes a mask using the given scale and padding.
Typically, you get the scale and padding from resize_image() to
ensure both, the image and the mask, are resized consistently.
scale: mask scaling factor
padding: Padding to add to the mask in the form
[(top, bottom), (left, right), (0, 0)]
"""# Suppress warning from scipy 0.13.0, the output shape of zoom() is# calculated with round() instead of int()with warnings.catch_warnings():
warnings.simplefilter("ignore")
mask = scipy.ndimage.zoom(mask, zoom=[scale, scale, 1], order=0)
if crop isnotNone:
y, x, h, w = crop
mask = mask[y:y + h, x:x + w]
else:
mask = np.pad(mask, padding, mode='constant', constant_values=0)
return mask
defminimize_mask(bbox, mask, mini_shape):"""Resize masks to a smaller version to reduce memory load.
Mini-masks can be resized back to image scale using expand_masks()
See inspect_data.ipynb notebook for more details.
"""
mini_mask = np.zeros(mini_shape + (mask.shape[-1],), dtype=bool)
for i in range(mask.shape[-1]):
# Pick slice and cast to bool in case load_mask() returned wrong dtype
m = mask[:, :, i].astype(bool)
y1, x1, y2, x2 = bbox[i][:4]
m = m[y1:y2, x1:x2]
if m.size == 0:
raise Exception("Invalid bounding box with area of zero")
# Resize with bilinear interpolation
m = skimage.transform.resize(m, mini_shape, order=1, mode="constant")
mini_mask[:, :, i] = np.around(m).astype(np.bool)
return mini_mask