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# -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
"""
See "Data Augmentation" tutorial for an overview of the system:
https://detectron2.readthedocs.io/tutorials/augmentation.html
"""
import numpy as np
import torch
import torch.nn.functional as F
from fvcore.transforms.transform import (
CropTransform,
HFlipTransform,
NoOpTransform,
Transform,
TransformList,
)
from PIL import Image
try:
import cv2 # noqa
except ImportError:
# OpenCV is an optional dependency at the moment
pass
__all__ = [
"ExtentTransform",
"ResizeTransform",
"RotationTransform",
"ColorTransform",
"PILColorTransform",
]
class ExtentTransform(Transform):
"""
Extracts a subregion from the source image and scales it to the output size.
The fill color is used to map pixels from the source rect that fall outside
the source image.
See: https://pillow.readthedocs.io/en/latest/PIL.html#PIL.ImageTransform.ExtentTransform
"""
def __init__(self, src_rect, output_size, interp=Image.BILINEAR, fill=0):
"""
Args:
src_rect (x0, y0, x1, y1): src coordinates
output_size (h, w): dst image size
interp: PIL interpolation methods
fill: Fill color used when src_rect extends outside image
"""
super().__init__()
self._set_attributes(locals())
def apply_image(self, img, interp=None):
h, w = self.output_size
if len(img.shape) > 2 and img.shape[2] == 1:
pil_image = Image.fromarray(img[:, :, 0], mode="L")
else:
pil_image = Image.fromarray(img)
pil_image = pil_image.transform(
size=(w, h),
method=Image.EXTENT,
data=self.src_rect,
resample=interp if interp else self.interp,
fill=self.fill,
)
ret = np.asarray(pil_image)
if len(img.shape) > 2 and img.shape[2] == 1:
ret = np.expand_dims(ret, -1)
return ret
def apply_coords(self, coords):
# Transform image center from source coordinates into output coordinates
# and then map the new origin to the corner of the output image.
h, w = self.output_size
x0, y0, x1, y1 = self.src_rect
new_coords = coords.astype(np.float32)
new_coords[:, 0] -= 0.5 * (x0 + x1)
new_coords[:, 1] -= 0.5 * (y0 + y1)
new_coords[:, 0] *= w / (x1 - x0)
new_coords[:, 1] *= h / (y1 - y0)
new_coords[:, 0] += 0.5 * w
new_coords[:, 1] += 0.5 * h
return new_coords
def apply_segmentation(self, segmentation):
segmentation = self.apply_image(segmentation, interp=Image.NEAREST)
return segmentation
class ResizeTransform(Transform):
"""
Resize the image to a target size.
"""
def __init__(self, h, w, new_h, new_w, interp=None):
"""
Args:
h, w (int): original image size
new_h, new_w (int): new image size
interp: PIL interpolation methods, defaults to bilinear.
"""
# TODO decide on PIL vs opencv
super().__init__()
if interp is None:
interp = Image.BILINEAR
self._set_attributes(locals())
def apply_image(self, img, interp=None):
assert img.shape[:2] == (self.h, self.w)
assert len(img.shape) <= 4
interp_method = interp if interp is not None else self.interp
if img.dtype == np.uint8:
if len(img.shape) > 2 and img.shape[2] == 1:
pil_image = Image.fromarray(img[:, :, 0], mode="L")
else:
pil_image = Image.fromarray(img)
pil_image = pil_image.resize((self.new_w, self.new_h), interp_method)
ret = np.asarray(pil_image)
if len(img.shape) > 2 and img.shape[2] == 1:
ret = np.expand_dims(ret, -1)
else:
# PIL only supports uint8
if any(x < 0 for x in img.strides):
img = np.ascontiguousarray(img)
img = torch.from_numpy(img)
shape = list(img.shape)
shape_4d = shape[:2] + [1] * (4 - len(shape)) + shape[2:]
img = img.view(shape_4d).permute(2, 3, 0, 1) # hw(c) -> nchw
_PIL_RESIZE_TO_INTERPOLATE_MODE = {
Image.NEAREST: "nearest",
Image.BILINEAR: "bilinear",
Image.BICUBIC: "bicubic",
}
mode = _PIL_RESIZE_TO_INTERPOLATE_MODE[interp_method]
align_corners = None if mode == "nearest" else False
img = F.interpolate(
img, (self.new_h, self.new_w), mode=mode, align_corners=align_corners
)
shape[:2] = (self.new_h, self.new_w)
ret = img.permute(2, 3, 0, 1).view(shape).numpy() # nchw -> hw(c)
return ret
def apply_coords(self, coords):
coords[:, 0] = coords[:, 0] * (self.new_w * 1.0 / self.w)
coords[:, 1] = coords[:, 1] * (self.new_h * 1.0 / self.h)
return coords
def apply_segmentation(self, segmentation):
segmentation = self.apply_image(segmentation, interp=Image.NEAREST)
return segmentation
def inverse(self):
return ResizeTransform(self.new_h, self.new_w, self.h, self.w, self.interp)
class RotationTransform(Transform):
"""
This method returns a copy of this image, rotated the given
number of degrees counter clockwise around its center.
"""
def __init__(self, h, w, angle, expand=True, center=None, interp=None):
"""
Args:
h, w (int): original image size
angle (float): degrees for rotation
expand (bool): choose if the image should be resized to fit the whole
rotated image (default), or simply cropped
center (tuple (width, height)): coordinates of the rotation center
if left to None, the center will be fit to the center of each image
center has no effect if expand=True because it only affects shifting
interp: cv2 interpolation method, default cv2.INTER_LINEAR
"""
super().__init__()
image_center = np.array((w / 2, h / 2))
if center is None:
center = image_center
if interp is None:
interp = cv2.INTER_LINEAR
abs_cos, abs_sin = (abs(np.cos(np.deg2rad(angle))), abs(np.sin(np.deg2rad(angle))))
if expand:
# find the new width and height bounds
bound_w, bound_h = np.rint(
[h * abs_sin + w * abs_cos, h * abs_cos + w * abs_sin]
).astype(int)
else:
bound_w, bound_h = w, h
self._set_attributes(locals())
self.rm_coords = self.create_rotation_matrix()
# Needed because of this problem https://github.com/opencv/opencv/issues/11784
self.rm_image = self.create_rotation_matrix(offset=-0.5)
def apply_image(self, img, interp=None):
"""
img should be a numpy array, formatted as Height * Width * Nchannels
"""
if len(img) == 0 or self.angle % 360 == 0:
return img
assert img.shape[:2] == (self.h, self.w)
interp = interp if interp is not None else self.interp
return cv2.warpAffine(img, self.rm_image, (self.bound_w, self.bound_h), flags=interp)
def apply_coords(self, coords):
"""
coords should be a N * 2 array-like, containing N couples of (x, y) points
"""
coords = np.asarray(coords, dtype=float)
if len(coords) == 0 or self.angle % 360 == 0:
return coords
return cv2.transform(coords[:, np.newaxis, :], self.rm_coords)[:, 0, :]
def apply_segmentation(self, segmentation):
segmentation = self.apply_image(segmentation, interp=cv2.INTER_NEAREST)
return segmentation
def create_rotation_matrix(self, offset=0):
center = (self.center[0] + offset, self.center[1] + offset)
rm = cv2.getRotationMatrix2D(tuple(center), self.angle, 1)
if self.expand:
# Find the coordinates of the center of rotation in the new image
# The only point for which we know the future coordinates is the center of the image
rot_im_center = cv2.transform(self.image_center[None, None, :] + offset, rm)[0, 0, :]
new_center = np.array([self.bound_w / 2, self.bound_h / 2]) + offset - rot_im_center
# shift the rotation center to the new coordinates
rm[:, 2] += new_center
return rm
def inverse(self):
"""
The inverse is to rotate it back with expand, and crop to get the original shape.
"""
if not self.expand: # Not possible to inverse if a part of the image is lost
raise NotImplementedError()
rotation = RotationTransform(
self.bound_h, self.bound_w, -self.angle, True, None, self.interp
)
crop = CropTransform(
(rotation.bound_w - self.w) // 2, (rotation.bound_h - self.h) // 2, self.w, self.h
)
return TransformList([rotation, crop])
class ColorTransform(Transform):
"""
Generic wrapper for any photometric transforms.
These transformations should only affect the color space and
not the coordinate space of the image (e.g. annotation
coordinates such as bounding boxes should not be changed)
"""
def __init__(self, op):
"""
Args:
op (Callable): operation to be applied to the image,
which takes in an ndarray and returns an ndarray.
"""
if not callable(op):
raise ValueError("op parameter should be callable")
super().__init__()
self._set_attributes(locals())
def apply_image(self, img):
return self.op(img)
def apply_coords(self, coords):
return coords
def inverse(self):
return NoOpTransform()
def apply_segmentation(self, segmentation):
return segmentation
class PILColorTransform(ColorTransform):
"""
Generic wrapper for PIL Photometric image transforms,
which affect the color space and not the coordinate
space of the image
"""
def __init__(self, op):
"""
Args:
op (Callable): operation to be applied to the image,
which takes in a PIL Image and returns a transformed
PIL Image.
For reference on possible operations see:
- https://pillow.readthedocs.io/en/stable/
"""
if not callable(op):
raise ValueError("op parameter should be callable")
super().__init__(op)
def apply_image(self, img):
img = Image.fromarray(img)
return np.asarray(super().apply_image(img))
def HFlip_rotated_box(transform, rotated_boxes):
"""
Apply the horizontal flip transform on rotated boxes.
Args:
rotated_boxes (ndarray): Nx5 floating point array of
(x_center, y_center, width, height, angle_degrees) format
in absolute coordinates.
"""
# Transform x_center
rotated_boxes[:, 0] = transform.width - rotated_boxes[:, 0]
# Transform angle
rotated_boxes[:, 4] = -rotated_boxes[:, 4]
return rotated_boxes
def Resize_rotated_box(transform, rotated_boxes):
"""
Apply the resizing transform on rotated boxes. For details of how these (approximation)
formulas are derived, please refer to :meth:`RotatedBoxes.scale`.
Args:
rotated_boxes (ndarray): Nx5 floating point array of
(x_center, y_center, width, height, angle_degrees) format
in absolute coordinates.
"""
scale_factor_x = transform.new_w * 1.0 / transform.w
scale_factor_y = transform.new_h * 1.0 / transform.h
rotated_boxes[:, 0] *= scale_factor_x
rotated_boxes[:, 1] *= scale_factor_y
theta = rotated_boxes[:, 4] * np.pi / 180.0
c = np.cos(theta)
s = np.sin(theta)
rotated_boxes[:, 2] *= np.sqrt(np.square(scale_factor_x * c) + np.square(scale_factor_y * s))
rotated_boxes[:, 3] *= np.sqrt(np.square(scale_factor_x * s) + np.square(scale_factor_y * c))
rotated_boxes[:, 4] = np.arctan2(scale_factor_x * s, scale_factor_y * c) * 180 / np.pi
return rotated_boxes
HFlipTransform.register_type("rotated_box", HFlip_rotated_box)
ResizeTransform.register_type("rotated_box", Resize_rotated_box)
# not necessary any more with latest fvcore
NoOpTransform.register_type("rotated_box", lambda t, x: x)
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