Source code for tensorpack.dataflow.imgaug.misc

# -*- coding: utf-8 -*-
# File:

import cv2

from ...utils import logger
from ...utils.argtools import shape2d
from .base import ImageAugmentor
from .transform import ResizeTransform, NoOpTransform, FlipTransform, TransposeTransform

__all__ = ['Flip', 'Resize', 'RandomResize', 'ResizeShortestEdge', 'Transpose']

[docs]class Flip(ImageAugmentor): """ Random flip the image either horizontally or vertically. """
[docs] def __init__(self, horiz=False, vert=False, prob=0.5): """ Args: horiz (bool): use horizontal flip. vert (bool): use vertical flip. prob (float): probability of flip. """ super(Flip, self).__init__() if horiz and vert: raise ValueError("Cannot do both horiz and vert. Please use two Flip instead.") if not horiz and not vert: raise ValueError("At least one of horiz or vert has to be True!") self._init(locals())
[docs] def get_transform(self, img): h, w = img.shape[:2] do = self._rand_range() < self.prob if not do: return NoOpTransform() else: return FlipTransform(h, w, self.horiz)
[docs]class Resize(ImageAugmentor): """ Resize image to a target size"""
[docs] def __init__(self, shape, interp=cv2.INTER_LINEAR): """ Args: shape: (h, w) tuple or a int interp: cv2 interpolation method """ shape = tuple(shape2d(shape)) self._init(locals())
[docs] def get_transform(self, img): return ResizeTransform( img.shape[0], img.shape[1], self.shape[0], self.shape[1], self.interp)
[docs]class ResizeShortestEdge(ImageAugmentor): """ Resize the shortest edge to a certain number while keeping the aspect ratio. """
[docs] def __init__(self, size, interp=cv2.INTER_LINEAR): """ Args: size (int): the size to resize the shortest edge to. """ size = int(size) self._init(locals())
[docs] def get_transform(self, img): h, w = img.shape[:2] scale = self.size * 1.0 / min(h, w) if h < w: newh, neww = self.size, int(scale * w + 0.5) else: newh, neww = int(scale * h + 0.5), self.size return ResizeTransform(h, w, newh, neww, self.interp)
[docs]class RandomResize(ImageAugmentor): """ Randomly rescale width and height of the image."""
[docs] def __init__(self, xrange, yrange=None, minimum=(0, 0), aspect_ratio_thres=0.15, interp=cv2.INTER_LINEAR): """ Args: xrange (tuple): a (min, max) tuple. If is floating point, the tuple defines the range of scaling ratio of new width, e.g. (0.9, 1.2). If is integer, the tuple defines the range of new width in pixels, e.g. (200, 350). yrange (tuple): similar to xrange, but for height. Should be None when aspect_ratio_thres==0. minimum (tuple): (xmin, ymin) in pixels. To avoid scaling down too much. aspect_ratio_thres (float): discard samples which change aspect ratio larger than this threshold. Set to 0 to keep aspect ratio. interp: cv2 interpolation method """ super(RandomResize, self).__init__() assert aspect_ratio_thres >= 0 self._init(locals()) def is_float(tp): return isinstance(tp[0], float) or isinstance(tp[1], float) if yrange is not None: assert is_float(xrange) == is_float(yrange), "xrange and yrange has different type!" self._is_scale = is_float(xrange) if aspect_ratio_thres == 0: if self._is_scale: assert xrange == yrange or yrange is None else: if yrange is not None: logger.warn("aspect_ratio_thres==0, yrange is not used!")
[docs] def get_transform(self, img): cnt = 0 h, w = img.shape[:2] def get_dest_size(): if self._is_scale: sx = self._rand_range(*self.xrange) if self.aspect_ratio_thres == 0: sy = sx else: sy = self._rand_range(*self.yrange) destX = max(sx * w, self.minimum[0]) destY = max(sy * h, self.minimum[1]) else: sx = self._rand_range(*self.xrange) if self.aspect_ratio_thres == 0: sy = sx * 1.0 / w * h else: sy = self._rand_range(*self.yrange) destX = max(sx, self.minimum[0]) destY = max(sy, self.minimum[1]) return (int(destX + 0.5), int(destY + 0.5)) while True: destX, destY = get_dest_size() if self.aspect_ratio_thres > 0: # don't check when thres == 0 oldr = w * 1.0 / h newr = destX * 1.0 / destY diff = abs(newr - oldr) / oldr if diff >= self.aspect_ratio_thres + 1e-5: cnt += 1 if cnt > 50: logger.warn("RandomResize failed to augment an image") return ResizeTransform(h, w, h, w, self.interp) continue return ResizeTransform(h, w, destY, destX, self.interp)
[docs]class Transpose(ImageAugmentor): """ Random transpose the image """
[docs] def __init__(self, prob=0.5): """ Args: prob (float): probability of transpose. """ super(Transpose, self).__init__() self.prob = prob
[docs] def get_transform(self, _): if self.rng.rand() < self.prob: return TransposeTransform() else: return NoOpTransform()