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augmentation.py
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augmentation.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
import cv2
from pysot.utils.bbox import corner2center, \
Center, center2corner, Corner
class Augmentation:
def __init__(self, shift, scale, blur, flip, color):
self.shift = shift
self.scale = scale
self.blur = blur
self.flip = flip
self.color = color
self.rgbVar = np.array(
[[-0.55919361, 0.98062831, - 0.41940627],
[1.72091413, 0.19879334, - 1.82968581],
[4.64467907, 4.73710203, 4.88324118]], dtype=np.float32)
@staticmethod
def random():
return np.random.random() * 2 - 1.0
def _crop_roi(self, image, bbox, out_sz, padding=(0, 0, 0)):
bbox = [float(x) for x in bbox]
a = (out_sz-1) / (bbox[2]-bbox[0])
b = (out_sz-1) / (bbox[3]-bbox[1])
c = -a * bbox[0]
d = -b * bbox[1]
mapping = np.array([[a, 0, c],
[0, b, d]]).astype(np.float)
crop = cv2.warpAffine(image, mapping, (out_sz, out_sz),
borderMode=cv2.BORDER_CONSTANT,
borderValue=padding)
return crop
def _blur_aug(self, image):
def rand_kernel():
sizes = np.arange(5, 46, 2)
size = np.random.choice(sizes)
kernel = np.zeros((size, size))
c = int(size/2)
wx = np.random.random()
kernel[:, c] += 1. / size * wx
kernel[c, :] += 1. / size * (1-wx)
return kernel
kernel = rand_kernel()
image = cv2.filter2D(image, -1, kernel)
return image
def _color_aug(self, image):
offset = np.dot(self.rgbVar, np.random.randn(3, 1))
offset = offset[::-1] # bgr 2 rgb
offset = offset.reshape(3)
image = image - offset
return image
def _gray_aug(self, image):
grayed = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
image = cv2.cvtColor(grayed, cv2.COLOR_GRAY2BGR)
return image
def _shift_scale_aug(self, image, bbox, crop_bbox, size):
im_h, im_w = image.shape[:2]
# adjust crop bounding box
crop_bbox_center = corner2center(crop_bbox)
if self.scale:
scale_x = (1.0 + Augmentation.random() * self.scale)
scale_y = (1.0 + Augmentation.random() * self.scale)
h, w = crop_bbox_center.h, crop_bbox_center.w
scale_x = min(scale_x, float(im_w) / w)
scale_y = min(scale_y, float(im_h) / h)
crop_bbox_center = Center(crop_bbox_center.x,
crop_bbox_center.y,
crop_bbox_center.w * scale_x,
crop_bbox_center.h * scale_y)
crop_bbox = center2corner(crop_bbox_center)
if self.shift:
sx = Augmentation.random() * self.shift
sy = Augmentation.random() * self.shift
x1, y1, x2, y2 = crop_bbox
sx = max(-x1, min(im_w - 1 - x2, sx))
sy = max(-y1, min(im_h - 1 - y2, sy))
crop_bbox = Corner(x1 + sx, y1 + sy, x2 + sx, y2 + sy)
# adjust target bounding box
x1, y1 = crop_bbox.x1, crop_bbox.y1
bbox = Corner(bbox.x1 - x1, bbox.y1 - y1,
bbox.x2 - x1, bbox.y2 - y1)
if self.scale:
bbox = Corner(bbox.x1 / scale_x, bbox.y1 / scale_y,
bbox.x2 / scale_x, bbox.y2 / scale_y)
image = self._crop_roi(image, crop_bbox, size)
return image, bbox
def _flip_aug(self, image, bbox):
image = cv2.flip(image, 1)
width = image.shape[1]
bbox = Corner(width - 1 - bbox.x2, bbox.y1,
width - 1 - bbox.x1, bbox.y2)
return image, bbox
def __call__(self, image, bbox, size, gray=False):
shape = image.shape
crop_bbox = center2corner(Center(shape[0]//2, shape[1]//2,
size-1, size-1))
# gray augmentation
if gray:
image = self._gray_aug(image)
# shift scale augmentation
image, bbox = self._shift_scale_aug(image, bbox, crop_bbox, size)
# color augmentation
if self.color > np.random.random():
image = self._color_aug(image)
# blur augmentation
if self.blur > np.random.random():
image = self._blur_aug(image)
# flip augmentation
if self.flip and self.flip > np.random.random():
image, bbox = self._flip_aug(image, bbox)
return image, bbox