def resize_long(src, size, interp=2):
"""Resizes longer edge to size.
Note: `resize_short` uses OpenCV (not the CV2 Python library).
MXNet must have been built with OpenCV for `resize_short` to work.
Resizes the original image by setting the shorter edge to size
and setting the longer edge accordingly. This will ensure the new image will
fit into the `size` specified.
Resizing function is called from OpenCV.
Parameters
----------
src : NDArray
The original image.
size : int
The length to be set for the shorter edge.
interp : int, optional, default=2
Interpolation method used for resizing the image.
Possible values:
0: Nearest Neighbors Interpolation.
1: Bilinear interpolation.
2: Area-based (resampling using pixel area relation). It may be a
preferred method for image decimation, as it gives moire-free
results. But when the image is zoomed, it is similar to the Nearest
Neighbors method. (used by default).
3: Bicubic interpolation over 4x4 pixel neighborhood.
4: Lanczos interpolation over 8x8 pixel neighborhood.
9: Cubic for enlarge, area for shrink, bilinear for others
10: Random select from interpolation method metioned above.
Note:
When shrinking an image, it will generally look best with AREA-based
interpolation, whereas, when enlarging an image, it will generally look best
with Bicubic (slow) or Bilinear (faster but still looks OK).
More details can be found in the documentation of OpenCV, please refer to
http://docs.opencv.org/master/da/d54/group__imgproc__transform.html.
Returns
-------
NDArray
An 'NDArray' containing the resized image.
Example
-------
>>> with open("flower.jpeg", 'rb') as fp:
... str_image = fp.read()
...
>>> image = mx.img.imdecode(str_image)
>>> image
<NDArray 2321x3482x3 @cpu(0)>
>>> size = 640
>>> new_image = mx.img.resize_long(image, size)
>>> new_image
<NDArray 2321x3482x3 @cpu(0)>
"""
from mxnet.image.image import _get_interp_method as get_interp
h, w, _ = src.shape
if h > w:
new_h, new_w = size, size * w // h
else:
new_h, new_w = size * h // w, size
return imresize(src,
new_w,
new_h,
interp=get_interp(interp, (h, w, new_h, new_w)))
def resize_long(src, size, interp=2):
"""Resizes longer edge to size.
Note: `resize_long` uses OpenCV (not the CV2 Python library).
MXNet must have been built with OpenCV for `resize_long` to work.
Resizes the original image by setting the longer edge to size
and setting the shorter edge accordingly. This will ensure the new image will
fit into the `size` specified.
Resizing function is called from OpenCV.
Parameters
----------
src : NDArray
The original image.
size : int
The length to be set for the shorter edge.
interp : int, optional, default=2
Interpolation method used for resizing the image.
Possible values:
0: Nearest Neighbors Interpolation.
1: Bilinear interpolation.
2: Area-based (resampling using pixel area relation). It may be a
preferred method for image decimation, as it gives moire-free
results. But when the image is zoomed, it is similar to the Nearest
Neighbors method. (used by default).
3: Bicubic interpolation over 4x4 pixel neighborhood.
4: Lanczos interpolation over 8x8 pixel neighborhood.
9: Cubic for enlarge, area for shrink, bilinear for others
10: Random select from interpolation method mentioned above.
Note:
When shrinking an image, it will generally look best with AREA-based
interpolation, whereas, when enlarging an image, it will generally look best
with Bicubic (slow) or Bilinear (faster but still looks OK).
More details can be found in the documentation of OpenCV, please refer to
http://docs.opencv.org/master/da/d54/group__imgproc__transform.html.
Returns
-------
NDArray
An 'NDArray' containing the resized image.
Example
-------
>>> with open("flower.jpeg", 'rb') as fp:
... str_image = fp.read()
...
>>> image = mx.img.imdecode(str_image)
>>> image
<NDArray 2321x3482x3 @cpu(0)>
>>> size = 640
>>> new_image = mx.img.resize_long(image, size)
>>> new_image
<NDArray 386x640x3 @cpu(0)>
"""
from mxnet.image.image import _get_interp_method as get_interp
h, w, _ = src.shape
if h > w:
new_h, new_w = size, size * w // h
else:
new_h, new_w = size * h // w, size
return imresize(src, new_w, new_h, interp=get_interp(interp, (h, w, new_h, new_w)))
def resize_short_within(src, short, max_size, mult_base=1, interp=2):
h, w, _ = src.shape # _ = len
im_size_min, im_size_max = (h, w) if w > h else (w, h)
scale = float(short) / float(im_size_min)
if np.round(scale * im_size_max / mult_base) * mult_base > max_size:
# fit in max_size
scale = float(
np.floor(max_size / mult_base) * mult_base) / float(im_size_max)
new_w, new_h = (int(np.round(w * scale / mult_base) * mult_base),
int(np.round(h * scale / mult_base) * mult_base))
return imresize(src,
new_w,
new_h,
interp=get_interp(interp, (h, w, new_h, new_w)))
def imresize(src, w, h, interp=1):
"""Resize image with OpenCV.
This is a duplicate of mxnet.image.imresize for name space consistancy.
Parameters
----------
src : mxnet.nd.NDArray
source image
w : int, required
Width of resized image.
h : int, required
Height of resized image.
interp : int, optional, default='1'
Interpolation method (default=cv2.INTER_LINEAR).
out : NDArray, optional
The output NDArray to hold the result.
Returns
-------
out : NDArray or list of NDArrays
The output of this function.
Examples
--------
>>> import mxnet as mx
>>> from gluoncv import data as gdata
>>> img = mx.random.uniform(0, 255, (300, 300, 3)).astype('uint8')
>>> print(img.shape)
(300, 300, 3)
>>> img = gdata.transforms.image.imresize(img, 200, 200)
>>> print(img.shape)
(200, 200, 3)
"""
from mxnet.image.image import _get_interp_method as get_interp
oh, ow, _ = src.shape
return mx.image.imresize(src,
w,
h,
interp=get_interp(interp, (oh, ow, h, w)))
def imresize(src, w, h, interp=1):
"""Resize image with OpenCV.
This is a duplicate of mxnet.image.imresize for name space consistency.
Parameters
----------
src : mxnet.nd.NDArray
source image
w : int, required
Width of resized image.
h : int, required
Height of resized image.
interp : int, optional, default='1'
Interpolation method (default=cv2.INTER_LINEAR).
out : NDArray, optional
The output NDArray to hold the result.
Returns
-------
out : NDArray or list of NDArrays
The output of this function.
Examples
--------
>>> import mxnet as mx
>>> from gluoncv import data as gdata
>>> img = mx.random.uniform(0, 255, (300, 300, 3)).astype('uint8')
>>> print(img.shape)
(300, 300, 3)
>>> img = gdata.transforms.image.imresize(img, 200, 200)
>>> print(img.shape)
(200, 200, 3)
"""
from mxnet.image.image import _get_interp_method as get_interp
oh, ow, _ = src.shape
return mx.image.imresize(src, w, h, interp=get_interp(interp, (oh, ow, h, w)))
def imresize(src, w, h, interp=1):
oh, ow, _ = src.shape # len
return mx.image.imresize(src,
w,
h,
interp=get_interp(interp, (oh, ow, h, w)))
def resize_short_within(src, short, max_size, interp=2):
"""Resizes shorter edge to size but make sure it's capped at maximum size.
Note: `resize_short_within` uses OpenCV (not the CV2 Python library).
MXNet must have been built with OpenCV for `resize_short_within` to work.
Resizes the original image by setting the shorter edge to size
and setting the longer edge accordingly. Also this function will ensure
the new image will not exceed ``max_size`` even at the longer side.
Resizing function is called from OpenCV.
Parameters
----------
src : NDArray
The original image.
short : int
Resize shorter side to ``short``.
max_size : int
Make sure the longer side of new image is smaller than ``max_size``.
interp : int, optional, default=2
Interpolation method used for resizing the image.
Possible values:
0: Nearest Neighbors Interpolation.
1: Bilinear interpolation.
2: Area-based (resampling using pixel area relation). It may be a
preferred method for image decimation, as it gives moire-free
results. But when the image is zoomed, it is similar to the Nearest
Neighbors method. (used by default).
3: Bicubic interpolation over 4x4 pixel neighborhood.
4: Lanczos interpolation over 8x8 pixel neighborhood.
9: Cubic for enlarge, area for shrink, bilinear for others
10: Random select from interpolation method metioned above.
Note:
When shrinking an image, it will generally look best with AREA-based
interpolation, whereas, when enlarging an image, it will generally look best
with Bicubic (slow) or Bilinear (faster but still looks OK).
More details can be found in the documentation of OpenCV, please refer to
http://docs.opencv.org/master/da/d54/group__imgproc__transform.html.
Returns
-------
NDArray
An 'NDArray' containing the resized image.
Example
-------
>>> with open("flower.jpeg", 'rb') as fp:
... str_image = fp.read()
...
>>> image = mx.img.imdecode(str_image)
>>> image
<NDArray 2321x3482x3 @cpu(0)>
>>> new_image = mx.img.resize_short_within(image, size=600, max_size=1000)
>>> new_image
<NDArray 604x1000x3 @cpu(0)>
>>> new_image = mx.img.resize_short_within(image, size=600, max_size=1200)
>>> new_image
<NDArray 600x993x3 @cpu(0)>
"""
from mxnet.image.image import _get_interp_method as get_interp
h, w, _ = src.shape
im_size_min, im_size_max = (h, w) if w > h else (w, h)
scale = float(short) / float(im_size_min)
if np.round(scale * im_size_max) > max_size:
# fit in max_size
scale = float(max_size) / float(im_size_max)
new_w, new_h = int(np.round(w * scale)), int(np.round(h * scale))
return imresize(src,
new_w,
new_h,
interp=get_interp(interp, (h, w, new_h, new_w)))
def resize_short_within(src, short, max_size, mult_base=1, interp=2):
"""Resizes shorter edge to size but make sure it's capped at maximum size.
Note: `resize_short_within` uses OpenCV (not the CV2 Python library).
MXNet must have been built with OpenCV for `resize_short_within` to work.
Resizes the original image by setting the shorter edge to size
and setting the longer edge accordingly. Also this function will ensure
the new image will not exceed ``max_size`` even at the longer side.
Resizing function is called from OpenCV.
Parameters
----------
src : NDArray
The original image.
short : int
Resize shorter side to ``short``.
max_size : int
Make sure the longer side of new image is smaller than ``max_size``.
mult_base : int, default is 1
Width and height are rounded to multiples of `mult_base`.
interp : int, optional, default=2
Interpolation method used for resizing the image.
Possible values:
0: Nearest Neighbors Interpolation.
1: Bilinear interpolation.
2: Area-based (resampling using pixel area relation). It may be a
preferred method for image decimation, as it gives moire-free
results. But when the image is zoomed, it is similar to the Nearest
Neighbors method. (used by default).
3: Bicubic interpolation over 4x4 pixel neighborhood.
4: Lanczos interpolation over 8x8 pixel neighborhood.
9: Cubic for enlarge, area for shrink, bilinear for others
10: Random select from interpolation method mentioned above.
Note:
When shrinking an image, it will generally look best with AREA-based
interpolation, whereas, when enlarging an image, it will generally look best
with Bicubic (slow) or Bilinear (faster but still looks OK).
More details can be found in the documentation of OpenCV, please refer to
http://docs.opencv.org/master/da/d54/group__imgproc__transform.html.
Returns
-------
NDArray
An 'NDArray' containing the resized image.
Example
-------
>>> with open("flower.jpeg", 'rb') as fp:
... str_image = fp.read()
...
>>> image = mx.img.imdecode(str_image)
>>> image
<NDArray 2321x3482x3 @cpu(0)>
>>> new_image = resize_short_within(image, short=800, max_size=1000)
>>> new_image
<NDArray 667x1000x3 @cpu(0)>
>>> new_image = resize_short_within(image, short=800, max_size=1200)
>>> new_image
<NDArray 800x1200x3 @cpu(0)>
>>> new_image = resize_short_within(image, short=800, max_size=1200, mult_base=32)
>>> new_image
<NDArray 800x1184x3 @cpu(0)>
"""
from mxnet.image.image import _get_interp_method as get_interp
h, w, _ = src.shape
im_size_min, im_size_max = (h, w) if w > h else (w, h)
scale = float(short) / float(im_size_min)
if np.round(scale * im_size_max / mult_base) * mult_base > max_size:
# fit in max_size
scale = float(np.floor(max_size / mult_base) * mult_base) / float(im_size_max)
new_w, new_h = (int(np.round(w * scale / mult_base) * mult_base),
int(np.round(h * scale / mult_base) * mult_base))
return imresize(src, new_w, new_h, interp=get_interp(interp, (h, w, new_h, new_w)))
