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# to you under the Apache License, Version 2.0 (the
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#
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#
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# KIND, either express or implied. See the License for the
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# pylint: disable=no-member, too-many-lines, redefined-builtin, protected-access, unused-import, invalid-name
# pylint: disable=too-many-arguments, too-many-locals, no-name-in-module, too-many-branches, too-many-statements
"""Read individual image files and perform augmentations."""
from __future__ import absolute_import, print_function
import os
import random
import logging
import json
import numpy as np
try:
import cv2
except ImportError:
cv2 = None
from ..base import numeric_types
from .. import ndarray as nd
from ..ndarray import _internal
from ..ndarray._internal import _cvimresize as imresize
from ..ndarray._internal import _cvcopyMakeBorder as copyMakeBorder
from .. import io
from .. import recordio
def imread(filename, *args, **kwargs):
"""Read and decode an image to an NDArray.
Note: `imread` uses OpenCV (not the CV2 Python library).
MXNet must have been built with USE_OPENCV=1 for `imdecode` to work.
Parameters
----------
filename : str
Name of the image file to be loaded.
flag : {0, 1}, default 1
1 for three channel color output. 0 for grayscale output.
to_rgb : bool, default True
True for RGB formatted output (MXNet default).
False for BGR formatted output (OpenCV default).
out : NDArray, optional
Output buffer. Use `None` for automatic allocation.
Returns
-------
NDArray
An `NDArray` containing the image.
Example
-------
>>> mx.img.imread("flower.jpg")
Set `flag` parameter to 0 to get grayscale output
>>> mx.img.imdecode("flower.jpg", flag=0)
Set `to_rgb` parameter to 0 to get output in OpenCV format (BGR)
>>> mx.img.imdecode(str_image, to_rgb=0)
"""
return _internal._cvimread(filename, *args, **kwargs)
def imdecode(buf, *args, **kwargs):
"""Decode an image to an NDArray.
Note: `imdecode` uses OpenCV (not the CV2 Python library).
MXNet must have been built with USE_OPENCV=1 for `imdecode` to work.
Parameters
----------
buf : str/bytes or numpy.ndarray
Binary image data as string or numpy ndarray.
flag : int, optional, default=1
1 for three channel color output. 0 for grayscale output.
to_rgb : int, optional, default=1
1 for RGB formatted output (MXNet default). 0 for BGR formatted output (OpenCV default).
out : NDArray, optional
Output buffer. Use `None` for automatic allocation.
Returns
-------
NDArray
An `NDArray` containing the image.
Example
-------
>>> with open("flower.jpg", 'rb') as fp:
... str_image = fp.read()
...
>>> image = mx.img.imdecode(str_image)
>>> image
Set `flag` parameter to 0 to get grayscale output
>>> with open("flower.jpg", 'rb') as fp:
... str_image = fp.read()
...
>>> image = mx.img.imdecode(str_image, flag=0)
>>> image
Set `to_rgb` parameter to 0 to get output in OpenCV format (BGR)
>>> with open("flower.jpg", 'rb') as fp:
... str_image = fp.read()
...
>>> image = mx.img.imdecode(str_image, to_rgb=0)
>>> image
"""
if not isinstance(buf, nd.NDArray):
buf = nd.array(np.frombuffer(buf, dtype=np.uint8), dtype=np.uint8)
return _internal._cvimdecode(buf, *args, **kwargs)
def scale_down(src_size, size):
"""Scales down crop size if it's larger than image size.
If width/height of the crop is larger than the width/height of the image,
sets the width/height to the width/height of the image.
Parameters
----------
src_size : tuple of int
Size of the image in (width, height) format.
size : tuple of int
Size of the crop in (width, height) format.
Returns
-------
tuple of int
A tuple containing the scaled crop size in (width, height) format.
Example
--------
>>> src_size = (640,480)
>>> size = (720,120)
>>> new_size = mx.img.scale_down(src_size, size)
>>> new_size
(640,106)
"""
w, h = size
sw, sh = src_size
if sh < h:
w, h = float(w * sh) / h, sh
if sw < w:
w, h = sw, float(h * sw) / w
return int(w), int(h)
def _get_interp_method(interp, sizes=()):
"""Get the interpolation method for resize functions.
The major purpose of this function is to wrap a random interp method selection
and a auto-estimation method.
Parameters
----------
interp : int
interpolation method for all resizing operations
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.
sizes : tuple of int
(old_height, old_width, new_height, new_width), if None provided, auto(9)
will return Area(2) anyway.
Returns
-------
int
interp method from 0 to 4
"""
if interp == 9:
if sizes:
assert len(sizes) == 4
oh, ow, nh, nw = sizes
if nh > oh and nw > ow:
return 2
elif nh < oh and nw < ow:
return 3
else:
return 1
else:
return 2
if interp == 10:
return random.randint(0, 4)
if interp not in (0, 1, 2, 3, 4):
raise ValueError('Unknown interp method %d' % interp)
return interp
def resize_short(src, size, interp=2):
"""Resizes shorter 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.
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
>>> size = 640
>>> new_image = mx.img.resize_short(image, size)
>>> new_image
"""
h, w, _ = src.shape
if h > w:
new_h, new_w = size * h // w, size
else:
new_h, new_w = size, size * w // h
return imresize(src, new_w, new_h, interp=_get_interp_method(interp, (h, w, new_h, new_w)))
def fixed_crop(src, x0, y0, w, h, size=None, interp=2):
"""Crop src at fixed location, and (optionally) resize it to size.
Parameters
----------
src : NDArray
Input image
x0 : int
Left boundary of the cropping area
y0 : int
Top boundary of the cropping area
w : int
Width of the cropping area
h : int
Height of the cropping area
size : tuple of (w, h)
Optional, resize to new size after cropping
interp : int, optional, default=2
Interpolation method. See resize_short for details.
Returns
-------
NDArray
An `NDArray` containing the cropped image.
"""
out = nd.crop(src, begin=(y0, x0, 0), end=(y0 + h, x0 + w, int(src.shape[2])))
if size is not None and (w, h) != size:
sizes = (h, w, size[1], size[0])
out = imresize(out, *size, interp=_get_interp_method(interp, sizes))
return out
def random_crop(src, size, interp=2):
"""Randomly crop `src` with `size` (width, height).
Upsample result if `src` is smaller than `size`.
Parameters
----------
src: Source image `NDArray`
size: Size of the crop formatted as (width, height). If the `size` is larger
than the image, then the source image is upsampled to `size` and returned.
interp: int, optional, default=2
Interpolation method. See resize_short for details.
Returns
-------
NDArray
An `NDArray` containing the cropped image.
Tuple
A tuple (x, y, width, height) where (x, y) is top-left position of the crop in the
original image and (width, height) are the dimensions of the cropped image.
Example
-------
>>> im = mx.nd.array(cv2.imread("flower.jpg"))
>>> cropped_im, rect = mx.image.random_crop(im, (100, 100))
>>> print cropped_im
>>> print rect
(20, 21, 100, 100)
"""
h, w, _ = src.shape
new_w, new_h = scale_down((w, h), size)
x0 = random.randint(0, w - new_w)
y0 = random.randint(0, h - new_h)
out = fixed_crop(src, x0, y0, new_w, new_h, size, interp)
return out, (x0, y0, new_w, new_h)
def center_crop(src, size, interp=2):
"""Crops the image `src` to the given `size` by trimming on all four
sides and preserving the center of the image. Upsamples if `src` is smaller
than `size`.
.. note:: This requires MXNet to be compiled with USE_OPENCV.
Parameters
----------
src : NDArray
Binary source image data.
size : list or tuple of int
The desired output image size.
interp : int, optional, default=2
Interpolation method. See resize_short for details.
Returns
-------
NDArray
The cropped image.
Tuple
(x, y, width, height) where x, y are the positions of the crop in the
original image and width, height the dimensions of the crop.
Example
-------
>>> with open("flower.jpg", 'rb') as fp:
... str_image = fp.read()
...
>>> image = mx.image.imdecode(str_image)
>>> image
>>> cropped_image, (x, y, width, height) = mx.image.center_crop(image, (1000, 500))
>>> cropped_image
>>> x, y, width, height
(1241, 910, 1000, 500)
"""
h, w, _ = src.shape
new_w, new_h = scale_down((w, h), size)
x0 = int((w - new_w) / 2)
y0 = int((h - new_h) / 2)
out = fixed_crop(src, x0, y0, new_w, new_h, size, interp)
return out, (x0, y0, new_w, new_h)
def color_normalize(src, mean, std=None):
"""Normalize src with mean and std.
Parameters
----------
src : NDArray
Input image
mean : NDArray
RGB mean to be subtracted
std : NDArray
RGB standard deviation to be divided
Returns
-------
NDArray
An `NDArray` containing the normalized image.
"""
if mean is not None:
src -= mean
if std is not None:
src /= std
return src
def random_size_crop(src, size, min_area, ratio, interp=2):
"""Randomly crop src with size. Randomize area and aspect ratio.
Parameters
----------
src : NDArray
Input image
size : tuple of (int, int)
Size of the crop formatted as (width, height).
min_area : int
Minimum area to be maintained after cropping
ratio : tuple of (float, float)
Aspect ratio range as (min_aspect_ratio, max_aspect_ratio)
interp: int, optional, default=2
Interpolation method. See resize_short for details.
Returns
-------
NDArray
An `NDArray` containing the cropped image.
Tuple
A tuple (x, y, width, height) where (x, y) is top-left position of the crop in the
original image and (width, height) are the dimensions of the cropped image.
"""
h, w, _ = src.shape
area = h * w
for _ in range(10):
target_area = random.uniform(min_area, 1.0) * area
new_ratio = random.uniform(*ratio)
new_w = int(round(np.sqrt(target_area * new_ratio)))
new_h = int(round(np.sqrt(target_area / new_ratio)))
if random.random() < 0.5:
new_h, new_w = new_w, new_h
if new_w <= w and new_h <= h:
x0 = random.randint(0, w - new_w)
y0 = random.randint(0, h - new_h)
out = fixed_crop(src, x0, y0, new_w, new_h, size, interp)
return out, (x0, y0, new_w, new_h)
# fall back to center_crop
return center_crop(src, size, interp)
[docs]class Augmenter(object):
"""Image Augmenter base class"""
def __init__(self, **kwargs):
self._kwargs = kwargs
for k, v in self._kwargs.items():
if isinstance(v, nd.NDArray):
v = v.asnumpy()
if isinstance(v, np.ndarray):
v = v.tolist()
self._kwargs[k] = v
[docs] def dumps(self):
"""Saves the Augmenter to string
Returns
-------
str
JSON formatted string that describes the Augmenter.
"""
return json.dumps([self.__class__.__name__.lower(), self._kwargs])
def __call__(self, src):
"""Abstract implementation body"""
raise NotImplementedError("Must override implementation.")
class SequentialAug(Augmenter):
"""Composing a sequential augmenter list.
Parameters
----------
ts : list of augmenters
A series of augmenters to be applied in sequential order.
"""
def __init__(self, ts):
super(SequentialAug, self).__init__()
self.ts = ts
def dumps(self):
"""Override the default to avoid duplicate dump."""
return [self.__class__.__name__.lower(), [x.dumps() for x in self.ts]]
def __call__(self, src):
"""Augmenter body"""
for aug in self.ts:
src = aug(src)
return src
[docs]class ResizeAug(Augmenter):
"""Make resize shorter edge to size augmenter.
Parameters
----------
size : int
The length to be set for the shorter edge.
interp : int, optional, default=2
Interpolation method. See resize_short for details.
"""
def __init__(self, size, interp=2):
super(ResizeAug, self).__init__(size=size, interp=interp)
self.size = size
self.interp = interp
def __call__(self, src):
"""Augmenter body"""
return resize_short(src, self.size, self.interp)
[docs]class ForceResizeAug(Augmenter):
"""Force resize to size regardless of aspect ratio
Parameters
----------
size : tuple of (int, int)
The desired size as in (width, height)
interp : int, optional, default=2
Interpolation method. See resize_short for details.
"""
def __init__(self, size, interp=2):
super(ForceResizeAug, self).__init__(size=size, interp=interp)
self.size = size
self.interp = interp
def __call__(self, src):
"""Augmenter body"""
sizes = (src.shape[0], src.shape[1], self.size[1], self.size[0])
return imresize(src, *self.size, interp=_get_interp_method(self.interp, sizes))
[docs]class RandomCropAug(Augmenter):
"""Make random crop augmenter
Parameters
----------
size : int
The length to be set for the shorter edge.
interp : int, optional, default=2
Interpolation method. See resize_short for details.
"""
def __init__(self, size, interp=2):
super(RandomCropAug, self).__init__(size=size, interp=interp)
self.size = size
self.interp = interp
def __call__(self, src):
"""Augmenter body"""
return random_crop(src, self.size, self.interp)[0]
[docs]class RandomSizedCropAug(Augmenter):
"""Make random crop with random resizing and random aspect ratio jitter augmenter.
Parameters
----------
size : tuple of (int, int)
Size of the crop formatted as (width, height).
min_area : int
Minimum area to be maintained after cropping
ratio : tuple of (float, float)
Aspect ratio range as (min_aspect_ratio, max_aspect_ratio)
interp: int, optional, default=2
Interpolation method. See resize_short for details.
"""
def __init__(self, size, min_area, ratio, interp=2):
super(RandomSizedCropAug, self).__init__(size=size, min_area=min_area,
ratio=ratio, interp=interp)
self.size = size
self.min_area = min_area
self.ratio = ratio
self.interp = interp
def __call__(self, src):
"""Augmenter body"""
return random_size_crop(src, self.size, self.min_area, self.ratio, self.interp)[0]
[docs]class CenterCropAug(Augmenter):
"""Make center crop augmenter.
Parameters
----------
size : list or tuple of int
The desired output image size.
interp : int, optional, default=2
Interpolation method. See resize_short for details.
"""
def __init__(self, size, interp=2):
super(CenterCropAug, self).__init__(size=size, interp=interp)
self.size = size
self.interp = interp
def __call__(self, src):
"""Augmenter body"""
return center_crop(src, self.size, self.interp)[0]
[docs]class RandomOrderAug(Augmenter):
"""Apply list of augmenters in random order
Parameters
----------
ts : list of augmenters
A series of augmenters to be applied in random order
"""
def __init__(self, ts):
super(RandomOrderAug, self).__init__()
self.ts = ts
def dumps(self):
"""Override the default to avoid duplicate dump."""
return [self.__class__.__name__.lower(), [x.dumps() for x in self.ts]]
def __call__(self, src):
"""Augmenter body"""
random.shuffle(self.ts)
for t in self.ts:
src = t(src)
return src
[docs]class BrightnessJitterAug(Augmenter):
"""Random brightness jitter augmentation.
Parameters
----------
brightness : float
The brightness jitter ratio range, [0, 1]
"""
def __init__(self, brightness):
super(BrightnessJitterAug, self).__init__(brightness=brightness)
self.brightness = brightness
def __call__(self, src):
"""Augmenter body"""
alpha = 1.0 + random.uniform(-self.brightness, self.brightness)
src *= alpha
return src
[docs]class ContrastJitterAug(Augmenter):
"""Random contrast jitter augmentation.
Parameters
----------
contrast : float
The contrast jitter ratio range, [0, 1]
"""
def __init__(self, contrast):
super(ContrastJitterAug, self).__init__(contrast=contrast)
self.contrast = contrast
self.coef = nd.array([[[0.299, 0.587, 0.114]]])
def __call__(self, src):
"""Augmenter body"""
alpha = 1.0 + random.uniform(-self.contrast, self.contrast)
gray = src * self.coef
gray = (3.0 * (1.0 - alpha) / gray.size) * nd.sum(gray)
src *= alpha
src += gray
return src
[docs]class SaturationJitterAug(Augmenter):
"""Random saturation jitter augmentation.
Parameters
----------
saturation : float
The saturation jitter ratio range, [0, 1]
"""
def __init__(self, saturation):
super(SaturationJitterAug, self).__init__(saturation=saturation)
self.saturation = saturation
self.coef = nd.array([[[0.299, 0.587, 0.114]]])
def __call__(self, src):
"""Augmenter body"""
alpha = 1.0 + random.uniform(-self.saturation, self.saturation)
gray = src * self.coef
gray = nd.sum(gray, axis=2, keepdims=True)
gray *= (1.0 - alpha)
src *= alpha
src += gray
return src
[docs]class HueJitterAug(Augmenter):
"""Random hue jitter augmentation.
Parameters
----------
hue : float
The hue jitter ratio range, [0, 1]
"""
def __init__(self, hue):
super(HueJitterAug, self).__init__(hue=hue)
self.hue = hue
self.tyiq = np.array([[0.299, 0.587, 0.114],
[0.596, -0.274, -0.321],
[0.211, -0.523, 0.311]])
self.ityiq = np.array([[1.0, 0.956, 0.621],
[1.0, -0.272, -0.647],
[1.0, -1.107, 1.705]])
def __call__(self, src):
"""Augmenter body.
Using approximate linear transfomation described in:
https://beesbuzz.biz/code/hsv_color_transforms.php
"""
alpha = random.uniform(-self.hue, self.hue)
u = np.cos(alpha * np.pi)
w = np.sin(alpha * np.pi)
bt = np.array([[1.0, 0.0, 0.0],
[0.0, u, -w],
[0.0, w, u]])
t = np.dot(np.dot(self.ityiq, bt), self.tyiq).T
src = nd.dot(src, nd.array(t))
return src
[docs]class ColorJitterAug(RandomOrderAug):
"""Apply random brightness, contrast and saturation jitter in random order.
Parameters
----------
brightness : float
The brightness jitter ratio range, [0, 1]
contrast : float
The contrast jitter ratio range, [0, 1]
saturation : float
The saturation jitter ratio range, [0, 1]
"""
def __init__(self, brightness, contrast, saturation):
ts = []
if brightness > 0:
ts.append(BrightnessJitterAug(brightness))
if contrast > 0:
ts.append(ContrastJitterAug(contrast))
if saturation > 0:
ts.append(SaturationJitterAug(saturation))
super(ColorJitterAug, self).__init__(ts)
[docs]class LightingAug(Augmenter):
"""Add PCA based noise.
Parameters
----------
alphastd : float
Noise level
eigval : 3x1 np.array
Eigen values
eigvec : 3x3 np.array
Eigen vectors
"""
def __init__(self, alphastd, eigval, eigvec):
super(LightingAug, self).__init__(alphastd=alphastd, eigval=eigval, eigvec=eigvec)
self.alphastd = alphastd
self.eigval = eigval
self.eigvec = eigvec
def __call__(self, src):
"""Augmenter body"""
alpha = np.random.normal(0, self.alphastd, size=(3,))
rgb = np.dot(self.eigvec * alpha, self.eigval)
src += nd.array(rgb)
return src
[docs]class ColorNormalizeAug(Augmenter):
"""Mean and std normalization.
Parameters
----------
mean : NDArray
RGB mean to be subtracted
std : NDArray
RGB standard deviation to be divided
"""
def __init__(self, mean, std):
super(ColorNormalizeAug, self).__init__(mean=mean, std=std)
self.mean = nd.array(mean) if mean is not None else None
self.std = nd.array(std) if std is not None else None
def __call__(self, src):
"""Augmenter body"""
return color_normalize(src, self.mean, self.std)
[docs]class RandomGrayAug(Augmenter):
"""Randomly convert to gray image.
Parameters
----------
p : float
Probability to convert to grayscale
"""
def __init__(self, p):
super(RandomGrayAug, self).__init__(p=p)
self.p = p
self.mat = nd.array([[0.21, 0.21, 0.21],
[0.72, 0.72, 0.72],
[0.07, 0.07, 0.07]])
def __call__(self, src):
"""Augmenter body"""
if random.random() < self.p:
src = nd.dot(src, self.mat)
return src
[docs]class HorizontalFlipAug(Augmenter):
"""Random horizontal flip.
Parameters
----------
p : float
Probability to flip image horizontally
"""
def __init__(self, p):
super(HorizontalFlipAug, self).__init__(p=p)
self.p = p
def __call__(self, src):
"""Augmenter body"""
if random.random() < self.p:
src = nd.flip(src, axis=1)
return src
[docs]class CastAug(Augmenter):
"""Cast to float32"""
def __init__(self, typ='float32'):
super(CastAug, self).__init__(type=typ)
self.typ = typ
def __call__(self, src):
"""Augmenter body"""
src = src.astype(self.typ)
return src
def CreateAugmenter(data_shape, resize=0, rand_crop=False, rand_resize=False, rand_mirror=False,
mean=None, std=None, brightness=0, contrast=0, saturation=0, hue=0,
pca_noise=0, rand_gray=0, inter_method=2):
"""Creates an augmenter list.
Parameters
----------
data_shape : tuple of int
Shape for output data
resize : int
Resize shorter edge if larger than 0 at the begining
rand_crop : bool
Whether to enable random cropping other than center crop
rand_resize : bool
Whether to enable random sized cropping, require rand_crop to be enabled
rand_gray : float
[0, 1], probability to convert to grayscale for all channels, the number
of channels will not be reduced to 1
rand_mirror : bool
Whether to apply horizontal flip to image with probability 0.5
mean : np.ndarray or None
Mean pixel values for [r, g, b]
std : np.ndarray or None
Standard deviations for [r, g, b]
brightness : float
Brightness jittering range (percent)
contrast : float
Contrast jittering range (percent)
saturation : float
Saturation jittering range (percent)
hue : float
Hue jittering range (percent)
pca_noise : float
Pca noise level (percent)
inter_method : int, default=2(Area-based)
Interpolation method for all resizing operations
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).
Examples
--------
>>> # An example of creating multiple augmenters
>>> augs = mx.image.CreateAugmenter(data_shape=(3, 300, 300), rand_mirror=True,
... mean=True, brightness=0.125, contrast=0.125, rand_gray=0.05,
... saturation=0.125, pca_noise=0.05, inter_method=10)
>>> # dump the details
>>> for aug in augs:
... aug.dumps()
"""
auglist = []
if resize > 0:
auglist.append(ResizeAug(resize, inter_method))
crop_size = (data_shape[2], data_shape[1])
if rand_resize:
assert rand_crop
auglist.append(RandomSizedCropAug(crop_size, 0.08, (3.0 / 4.0, 4.0 / 3.0), inter_method))
elif rand_crop:
auglist.append(RandomCropAug(crop_size, inter_method))
else:
auglist.append(CenterCropAug(crop_size, inter_method))
if rand_mirror:
auglist.append(HorizontalFlipAug(0.5))
auglist.append(CastAug())
if brightness or contrast or saturation:
auglist.append(ColorJitterAug(brightness, contrast, saturation))
if hue:
auglist.append(HueJitterAug(hue))
if pca_noise > 0:
eigval = np.array([55.46, 4.794, 1.148])
eigvec = np.array([[-0.5675, 0.7192, 0.4009],
[-0.5808, -0.0045, -0.8140],
[-0.5836, -0.6948, 0.4203]])
auglist.append(LightingAug(pca_noise, eigval, eigvec))
if rand_gray > 0:
auglist.append(RandomGrayAug(rand_gray))
if mean is True:
mean = np.array([123.68, 116.28, 103.53])
elif mean is not None:
assert isinstance(mean, np.ndarray) and mean.shape[0] in [1, 3]
if std is True:
std = np.array([58.395, 57.12, 57.375])
elif std is not None:
assert isinstance(std, np.ndarray) and std.shape[0] in [1, 3]
if mean is not None or std is not None:
auglist.append(ColorNormalizeAug(mean, std))
return auglist
[docs]class ImageIter(io.DataIter):
"""Image data iterator with a large number of augmentation choices.
This iterator supports reading from both .rec files and raw image files.
To load input images from .rec files, use `path_imgrec` parameter and to load from raw image
files, use `path_imglist` and `path_root` parameters.
To use data partition (for distributed training) or shuffling, specify `path_imgidx` parameter.
Parameters
----------
batch_size : int
Number of examples per batch.
data_shape : tuple
Data shape in (channels, height, width) format.
For now, only RGB image with 3 channels is supported.
label_width : int, optional
Number of labels per example. The default label width is 1.
path_imgrec : str
Path to image record file (.rec).
Created with tools/im2rec.py or bin/im2rec.
path_imglist : str
Path to image list (.lst).
Created with tools/im2rec.py or with custom script.
Format: Tab separated record of index, one or more labels and relative_path_from_root.
imglist: list
A list of images with the label(s).
Each item is a list [imagelabel: float or list of float, imgpath].
path_root : str
Root folder of image files.
path_imgidx : str
Path to image index file. Needed for partition and shuffling when using .rec source.
shuffle : bool
Whether to shuffle all images at the start of each iteration or not.
Can be slow for HDD.
part_index : int
Partition index.
num_parts : int
Total number of partitions.
data_name : str
Data name for provided symbols.
label_name : str
Label name for provided symbols.
kwargs : ...
More arguments for creating augmenter. See mx.image.CreateAugmenter.
"""
def __init__(self, batch_size, data_shape, label_width=1,
path_imgrec=None, path_imglist=None, path_root=None, path_imgidx=None,
shuffle=False, part_index=0, num_parts=1, aug_list=None, imglist=None,
data_name='data', label_name='softmax_label', **kwargs):
super(ImageIter, self).__init__()
assert path_imgrec or path_imglist or (isinstance(imglist, list))
num_threads = os.environ.get('MXNET_CPU_WORKER_NTHREADS', 1)
logging.info('Using %s threads for decoding...', str(num_threads))
logging.info('Set enviroment variable MXNET_CPU_WORKER_NTHREADS to a'
' larger number to use more threads.')
class_name = self.__class__.__name__
if path_imgrec:
logging.info('%s: loading recordio %s...',
class_name, path_imgrec)
if path_imgidx:
self.imgrec = recordio.MXIndexedRecordIO(path_imgidx, path_imgrec, 'r') # pylint: disable=redefined-variable-type
self.imgidx = list(self.imgrec.keys)
else:
self.imgrec = recordio.MXRecordIO(path_imgrec, 'r') # pylint: disable=redefined-variable-type
self.imgidx = None
else:
self.imgrec = None
if path_imglist:
logging.info('%s: loading image list %s...', class_name, path_imglist)
with open(path_imglist) as fin:
imglist = {}
imgkeys = []
for line in iter(fin.readline, ''):
line = line.strip().split('\t')
label = nd.array([float(i) for i in line[1:-1]])
key = int(line[0])
imglist[key] = (label, line[-1])
imgkeys.append(key)
self.imglist = imglist
elif isinstance(imglist, list):
logging.info('%s: loading image list...', class_name)
result = {}
imgkeys = []
index = 1
for img in imglist:
key = str(index) # pylint: disable=redefined-variable-type
index += 1
if len(img) > 2:
label = nd.array(img[:-1])
elif isinstance(img[0], numeric_types):
label = nd.array([img[0]])
else:
label = nd.array(img[0])
result[key] = (label, img[-1])
imgkeys.append(str(key))
self.imglist = result
else:
self.imglist = None
self.path_root = path_root
self.check_data_shape(data_shape)
self.provide_data = [(data_name, (batch_size,) + data_shape)]
if label_width > 1:
self.provide_label = [(label_name, (batch_size, label_width))]
else:
self.provide_label = [(label_name, (batch_size,))]
self.batch_size = batch_size
self.data_shape = data_shape
self.label_width = label_width
self.shuffle = shuffle
if self.imgrec is None:
self.seq = imgkeys
elif shuffle or num_parts > 1:
assert self.imgidx is not None
self.seq = self.imgidx
else:
self.seq = None
if num_parts > 1:
assert part_index < num_parts
N = len(self.seq)
C = N // num_parts
self.seq = self.seq[part_index * C:(part_index + 1) * C]
if aug_list is None:
self.auglist = CreateAugmenter(data_shape, **kwargs)
else:
self.auglist = aug_list
self.cur = 0
self.reset()
[docs] def reset(self):
"""Resets the iterator to the beginning of the data."""
if self.shuffle:
random.shuffle(self.seq)
if self.imgrec is not None:
self.imgrec.reset()
self.cur = 0
[docs] def next_sample(self):
"""Helper function for reading in next sample."""
if self.seq is not None:
if self.cur >= len(self.seq):
raise StopIteration
idx = self.seq[self.cur]
self.cur += 1
if self.imgrec is not None:
s = self.imgrec.read_idx(idx)
header, img = recordio.unpack(s)
if self.imglist is None:
return header.label, img
else:
return self.imglist[idx][0], img
else:
label, fname = self.imglist[idx]
return label, self.read_image(fname)
else:
s = self.imgrec.read()
if s is None:
raise StopIteration
header, img = recordio.unpack(s)
return header.label, img
[docs] def next(self):
"""Returns the next batch of data."""
batch_size = self.batch_size
c, h, w = self.data_shape
batch_data = nd.empty((batch_size, c, h, w))
batch_label = nd.empty(self.provide_label[0][1])
i = 0
try:
while i < batch_size:
label, s = self.next_sample()
data = self.imdecode(s)
try:
self.check_valid_image(data)
except RuntimeError as e:
logging.debug('Invalid image, skipping: %s', str(e))
continue
data = self.augmentation_transform(data)
assert i < batch_size, 'Batch size must be multiples of augmenter output length'
batch_data[i] = self.postprocess_data(data)
batch_label[i] = label
i += 1
except StopIteration:
if not i:
raise StopIteration
return io.DataBatch([batch_data], [batch_label], batch_size - i)
[docs] def check_data_shape(self, data_shape):
"""Checks if the input data shape is valid"""
if not len(data_shape) == 3:
raise ValueError('data_shape should have length 3, with dimensions CxHxW')
if not data_shape[0] == 3:
raise ValueError('This iterator expects inputs to have 3 channels.')
[docs] def check_valid_image(self, data):
"""Checks if the input data is valid"""
if len(data[0].shape) == 0:
raise RuntimeError('Data shape is wrong')
[docs] def imdecode(self, s):
"""Decodes a string or byte string to an NDArray.
See mx.img.imdecode for more details."""
def locate():
"""Locate the image file/index if decode fails."""
if self.seq is not None:
idx = self.seq[self.cur - 1]
else:
idx = self.cur - 1
if self.imglist is not None:
_, fname = self.imglist[idx]
msg = "filename: {}".format(fname)
else:
msg = "index: {}".format(idx)
return "Broken image " + msg
try:
img = imdecode(s)
except Exception as e:
raise RuntimeError("{}, {}".format(locate(), e))
return img
[docs] def read_image(self, fname):
"""Reads an input image `fname` and returns the decoded raw bytes.
Example usage:
----------
>>> dataIter.read_image('Face.jpg') # returns decoded raw bytes.
"""
with open(os.path.join(self.path_root, fname), 'rb') as fin:
img = fin.read()
return img
[docs] def postprocess_data(self, datum):
"""Final postprocessing step before image is loaded into the batch."""
return nd.transpose(datum, axes=(2, 0, 1))
