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data_aug.py
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data_aug.py
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import random
import numpy as np
import cv2
import matplotlib.pyplot as plt
import sys
import os
from data_aug.bbox_util import *
lib_path = os.path.join(os.path.realpath("."), "data_aug")
sys.path.append(lib_path)
class RandomHorizontalFlip(object):
def __init__(self, p=0.5):
self.p = p
def __call__(self, img, bboxes):
img_center = np.array(img.shape[:2])[::-1]/2
img_center = np.hstack((img_center, img_center))
if random.random() < self.p:
img = img[:, ::-1, :]
bboxes[:, [0, 2]] += 2*(img_center[[0, 2]] - bboxes[:, [0, 2]])
box_w = abs(bboxes[:, 0] - bboxes[:, 2])
bboxes[:, 0] -= box_w
bboxes[:, 2] += box_w
return img, bboxes
class HorizontalFlip(object):
def __init__(self):
pass
def __call__(self, img, bboxes):
img_center = np.array(img.shape[:2])[::-1]/2
img_center = np.hstack((img_center, img_center))
img = img[:, ::-1, :]
bboxes[:, [0, 2]] += 2*(img_center[[0, 2]] - bboxes[:, [0, 2]])
box_w = abs(bboxes[:, 0] - bboxes[:, 2])
bboxes[:, 0] -= box_w
bboxes[:, 2] += box_w
return img, bboxes
class RandomScale(object):
def __init__(self, scale = 0.2, diff = False):
self.scale = scale
if type(self.scale) == tuple:
assert len(self.scale) == 2, "Invalid range"
assert self.scale[0] > -1, "Scale factor can't be less than -1"
assert self.scale[1] > -1, "Scale factor can't be less than -1"
else:
assert self.scale > 0, "Please input a positive float"
self.scale = (max(-1, -self.scale), self.scale)
self.diff = diff
def __call__(self, img, bboxes):
img_shape = img.shape
if self.diff:
scale_x = random.uniform(*self.scale)
scale_y = random.uniform(*self.scale)
else:
scale_x = random.uniform(*self.scale)
scale_y = scale_x
resize_scale_x = 1 + scale_x
resize_scale_y = 1 + scale_y
img= cv2.resize(img, None, fx = resize_scale_x, fy = resize_scale_y)
bboxes[:,:4] *= [resize_scale_x, resize_scale_y, resize_scale_x, resize_scale_y]
canvas = np.zeros(img_shape, dtype = np.uint8)
y_lim = int(min(resize_scale_y,1)*img_shape[0])
x_lim = int(min(resize_scale_x,1)*img_shape[1])
canvas[:y_lim,:x_lim,:] = img[:y_lim,:x_lim,:]
img = canvas
bboxes = clip_box(bboxes, [0,0,1 + img_shape[1], img_shape[0]], 0.25)
return img, bboxes
class Scale(object):
def __init__(self, scale_x = 0.2, scale_y = 0.2):
self.scale_x = scale_x
self.scale_y = scale_y
def __call__(self, img, bboxes):
img_shape = img.shape
resize_scale_x = 1 + self.scale_x
resize_scale_y = 1 + self.scale_y
img= cv2.resize(img, None, fx = resize_scale_x, fy = resize_scale_y)
bboxes[:,:4] *= [resize_scale_x, resize_scale_y, resize_scale_x, resize_scale_y]
canvas = np.zeros(img_shape, dtype = np.uint8)
y_lim = int(min(resize_scale_y,1)*img_shape[0])
x_lim = int(min(resize_scale_x,1)*img_shape[1])
canvas[:y_lim,:x_lim,:] = img[:y_lim,:x_lim,:]
img = canvas
bboxes = clip_box(bboxes, [0,0,1 + img_shape[1], img_shape[0]], 0.25)
return img, bboxes
class RandomTranslate(object):
def __init__(self, translate = 0.2, diff = False):
self.translate = translate
if type(self.translate) == tuple:
assert len(self.translate) == 2, "Invalid range"
assert self.translate[0] > 0 & self.translate[0] < 1
assert self.translate[1] > 0 & self.translate[1] < 1
else:
assert self.translate > 0 and self.translate < 1
self.translate = (-self.translate, self.translate)
self.diff = diff
def __call__(self, img, bboxes):
img_shape = img.shape
translate_factor_x = random.uniform(*self.translate)
translate_factor_y = random.uniform(*self.translate)
if not self.diff:
translate_factor_y = translate_factor_x
canvas = np.zeros(img_shape).astype(np.uint8)
corner_x = int(translate_factor_x*img.shape[1])
corner_y = int(translate_factor_y*img.shape[0])
orig_box_cords = [max(0,corner_y), max(corner_x,0), min(img_shape[0], corner_y + img.shape[0]), min(img_shape[1],corner_x + img.shape[1])]
mask = img[max(-corner_y, 0):min(img.shape[0], -corner_y + img_shape[0]), max(-corner_x, 0):min(img.shape[1], -corner_x + img_shape[1]),:]
canvas[orig_box_cords[0]:orig_box_cords[2], orig_box_cords[1]:orig_box_cords[3],:] = mask
img = canvas
bboxes[:,:4] += [corner_x, corner_y, corner_x, corner_y]
bboxes = clip_box(bboxes, [0,0,img_shape[1], img_shape[0]], 0.25)
return img, bboxes
class Translate(object):
def __init__(self, translate_x = 0.2, translate_y = 0.2, diff = False):
self.translate_x = translate_x
self.translate_y = translate_y
assert self.translate_x > 0 and self.translate_x < 1
assert self.translate_y > 0 and self.translate_y < 1
def __call__(self, img, bboxes):
img_shape = img.shape
translate_factor_x = self.translate_x
translate_factor_y = self.translate_y
canvas = np.zeros(img_shape).astype(np.uint8)
corner_x = int(translate_factor_x*img.shape[1])
corner_y = int(translate_factor_y*img.shape[0])
orig_box_cords = [max(0,corner_y), max(corner_x,0), min(img_shape[0], corner_y + img.shape[0]), min(img_shape[1],corner_x + img.shape[1])]
mask = img[max(-corner_y, 0):min(img.shape[0], -corner_y + img_shape[0]), max(-corner_x, 0):min(img.shape[1], -corner_x + img_shape[1]),:]
canvas[orig_box_cords[0]:orig_box_cords[2], orig_box_cords[1]:orig_box_cords[3],:] = mask
img = canvas
bboxes[:,:4] += [corner_x, corner_y, corner_x, corner_y]
bboxes = clip_box(bboxes, [0,0,img_shape[1], img_shape[0]], 0.25)
return img, bboxes
class RandomRotate(object):
def __init__(self, angle = 10):
self.angle = angle
if type(self.angle) == tuple:
assert len(self.angle) == 2, "Invalid range"
else:
self.angle = (-self.angle, self.angle)
def __call__(self, img, bboxes):
angle = random.uniform(*self.angle)
w,h = img.shape[1], img.shape[0]
cx, cy = w//2, h//2
img = rotate_im(img, angle)
corners = get_corners(bboxes)
corners = np.hstack((corners, bboxes[:,4:]))
corners[:,:8] = rotate_box(corners[:,:8], angle, cx, cy, h, w)
new_bbox = get_enclosing_box(corners)
scale_factor_x = img.shape[1] / w
scale_factor_y = img.shape[0] / h
img = cv2.resize(img, (w,h))
new_bbox[:,:4] /= [scale_factor_x, scale_factor_y, scale_factor_x, scale_factor_y]
bboxes = new_bbox
bboxes = clip_box(bboxes, [0,0,w, h], 0.25)
return img, bboxes
class Rotate(object):
def __init__(self, angle):
self.angle = angle
def __call__(self, img, bboxes):
angle = self.angle
print(self.angle)
w,h = img.shape[1], img.shape[0]
cx, cy = w//2, h//2
corners = get_corners(bboxes)
corners = np.hstack((corners, bboxes[:,4:]))
img = rotate_im(img, angle)
corners[:,:8] = rotate_box(corners[:,:8], angle, cx, cy, h, w)
new_bbox = get_enclosing_box(corners)
scale_factor_x = img.shape[1] / w
scale_factor_y = img.shape[0] / h
img = cv2.resize(img, (w,h))
new_bbox[:,:4] /= [scale_factor_x, scale_factor_y, scale_factor_x, scale_factor_y]
bboxes = new_bbox
bboxes = clip_box(bboxes, [0,0,w, h], 0.25)
return img, bboxes
class RandomShear(object):
def __init__(self, shear_factor = 0.2):
self.shear_factor = shear_factor
if type(self.shear_factor) == tuple:
assert len(self.shear_factor) == 2, "Invalid range for scaling factor"
else:
self.shear_factor = (-self.shear_factor, self.shear_factor)
shear_factor = random.uniform(*self.shear_factor)
def __call__(self, img, bboxes):
shear_factor = random.uniform(*self.shear_factor)
w,h = img.shape[1], img.shape[0]
if shear_factor < 0:
img, bboxes = HorizontalFlip()(img, bboxes)
M = np.array([[1, abs(shear_factor), 0],[0,1,0]])
nW = img.shape[1] + abs(shear_factor*img.shape[0])
bboxes[:,[0,2]] += ((bboxes[:,[1,3]]) * abs(shear_factor) ).astype(int)
img = cv2.warpAffine(img, M, (int(nW), img.shape[0]))
if shear_factor < 0:
img, bboxes = HorizontalFlip()(img, bboxes)
img = cv2.resize(img, (w,h))
scale_factor_x = nW / w
bboxes[:,:4] /= [scale_factor_x, 1, scale_factor_x, 1]
return img, bboxes
class Shear(object):
def __init__(self, shear_factor = 0.2):
self.shear_factor = shear_factor
def __call__(self, img, bboxes):
shear_factor = self.shear_factor
if shear_factor < 0:
img, bboxes = HorizontalFlip()(img, bboxes)
M = np.array([[1, abs(shear_factor), 0],[0,1,0]])
nW = img.shape[1] + abs(shear_factor*img.shape[0])
bboxes[:,[0,2]] += ((bboxes[:,[1,3]])*abs(shear_factor)).astype(int)
img = cv2.warpAffine(img, M, (int(nW), img.shape[0]))
if shear_factor < 0:
img, bboxes = HorizontalFlip()(img, bboxes)
return img, bboxes
class Resize(object):
def __init__(self, inp_dim):
self.inp_dim = inp_dim
def __call__(self, img, bboxes):
w,h = img.shape[1], img.shape[0]
img = letterbox_image(img, self.inp_dim)
scale = min(self.inp_dim/h, self.inp_dim/w)
bboxes[:,:4] *= (scale)
new_w = scale*w
new_h = scale*h
inp_dim = self.inp_dim
del_h = (inp_dim - new_h)/2
del_w = (inp_dim - new_w)/2
add_matrix = np.array([[del_w, del_h, del_w, del_h]]).astype(int)
bboxes[:,:4] += add_matrix
img = img.astype(np.uint8)
return img, bboxes
class RandomHSV(object):
def __init__(self, hue = None, saturation = None, brightness = None):
if hue:
self.hue = hue
else:
self.hue = 0
if saturation:
self.saturation = saturation
else:
self.saturation = 0
if brightness:
self.brightness = brightness
else:
self.brightness = 0
if type(self.hue) != tuple:
self.hue = (-self.hue, self.hue)
if type(self.saturation) != tuple:
self.saturation = (-self.saturation, self.saturation)
if type(brightness) != tuple:
self.brightness = (-self.brightness, self.brightness)
def __call__(self, img, bboxes):
hue = random.randint(*self.hue)
saturation = random.randint(*self.saturation)
brightness = random.randint(*self.brightness)
img = img.astype(int)
a = np.array([hue, saturation, brightness]).astype(int)
img += np.reshape(a, (1,1,3))
img = np.clip(img, 0, 255)
img[:,:,0] = np.clip(img[:,:,0],0, 179)
img = img.astype(np.uint8)
return img, bboxes
class Sequence(object):
def __init__(self, augmentations, probs = 1):
self.augmentations = augmentations
self.probs = probs
def __call__(self, images, bboxes):
for i, augmentation in enumerate(self.augmentations):
if type(self.probs) == list:
prob = self.probs[i]
else:
prob = self.probs
if random.random() < prob:
images, bboxes = augmentation(images, bboxes)
return images, bboxes