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transform.py
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transform.py
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import numpy as np
from numpy import random
import cv2
def rescale_pts(pts, down_ratio):
return np.asarray(pts, np.float32)/float(down_ratio)
class Compose(object):
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, img, pts):
for t in self.transforms:
img, pts = t(img, pts)
return img, pts
class ConvertImgFloat(object):
def __call__(self, img, pts):
return img.astype(np.float32), pts.astype(np.float32)
class RandomContrast(object):
def __init__(self, lower=0.5, upper=1.5):
self.lower = lower
self.upper = upper
assert self.upper >= self.lower, "contrast upper must be >= lower."
assert self.lower >= 0, "contrast lower must be non-negative."
def __call__(self, img, pts):
if random.randint(2):
alpha = random.uniform(self.lower, self.upper)
img *= alpha
return img, pts
class RandomBrightness(object):
def __init__(self, delta=32):
assert delta >= 0.0
assert delta <= 255.0
self.delta = delta
def __call__(self, img, pts):
if random.randint(2):
delta = random.uniform(-self.delta, self.delta)
img += delta
return img, pts
class SwapChannels(object):
def __init__(self, swaps):
self.swaps = swaps
def __call__(self, img):
img = img[:, :, self.swaps]
return img
class RandomLightingNoise(object):
def __init__(self):
self.perms = ((0, 1, 2), (0, 2, 1),
(1, 0, 2), (1, 2, 0),
(2, 0, 1), (2, 1, 0))
def __call__(self, img, pts):
if random.randint(2):
swap = self.perms[random.randint(len(self.perms))]
shuffle = SwapChannels(swap)
img = shuffle(img)
return img, pts
class PhotometricDistort(object):
def __init__(self):
self.pd = RandomContrast()
self.rb = RandomBrightness()
self.rln = RandomLightingNoise()
def __call__(self, img, pts):
img, pts = self.rb(img, pts)
if random.randint(2):
distort = self.pd
else:
distort = self.pd
img, pts = distort(img, pts)
img, pts = self.rln(img, pts)
return img, pts
class Expand(object):
def __init__(self, max_scale = 1.5, mean = (0.5, 0.5, 0.5)):
self.mean = mean
self.max_scale = max_scale
def __call__(self, img, pts):
if random.randint(2):
return img, pts
h,w,c = img.shape
ratio = random.uniform(1,self.max_scale)
y1 = random.uniform(0, h*ratio-h)
x1 = random.uniform(0, w*ratio-w)
if np.max(pts[:,0])+int(x1)>w-1 or np.max(pts[:,1])+int(y1)>h-1: # keep all the pts
return img, pts
else:
expand_img = np.zeros(shape=(int(h*ratio), int(w*ratio),c),dtype=img.dtype)
expand_img[:,:,:] = self.mean
expand_img[int(y1):int(y1+h), int(x1):int(x1+w)] = img
pts[:, 0] += int(x1)
pts[:, 1] += int(y1)
return expand_img, pts
class RandomSampleCrop(object):
def __init__(self, ratio=(0.5, 1.5), min_win = 0.9):
self.sample_options = (
# using entire original input image
None,
# sample a patch s.t. MIN jaccard w/ obj in .1,.3,.4,.7,.9
# (0.1, None),
# (0.3, None),
(0.7, None),
(0.9, None),
# randomly sample a patch
(None, None),
)
self.ratio = ratio
self.min_win = min_win
def __call__(self, img, pts):
height, width ,_ = img.shape
while True:
mode = random.choice(self.sample_options)
if mode is None:
return img, pts
for _ in range(50):
current_img = img
current_pts = pts
w = random.uniform(self.min_win*width, width)
h = random.uniform(self.min_win*height, height)
if h/w<self.ratio[0] or h/w>self.ratio[1]:
continue
y1 = random.uniform(height-h)
x1 = random.uniform(width-w)
rect = np.array([int(y1), int(x1), int(y1+h), int(x1+w)])
current_img = current_img[rect[0]:rect[2], rect[1]:rect[3], :]
current_pts[:, 0] -= rect[1]
current_pts[:, 1] -= rect[0]
pts_new = []
for pt in current_pts:
if any(pt)<0 or pt[0]>current_img.shape[1]-1 or pt[1]>current_img.shape[0]-1:
continue
else:
pts_new.append(pt)
return current_img, np.asarray(pts_new, np.float32)
class RandomMirror_w(object):
def __call__(self, img, pts):
_,w,_ = img.shape
if random.randint(2):
img = img[:,::-1,:]
pts[:,0] = w-pts[:,0]
return img, pts
class RandomMirror_h(object):
def __call__(self, img, pts):
h,_,_ = img.shape
if random.randint(2):
img = img[::-1,:,:]
pts[:,1] = h-pts[:,1]
return img, pts
class Resize(object):
def __init__(self, h, w):
self.dsize = (w,h)
def __call__(self, img, pts):
h,w,c = img.shape
pts[:, 0] = pts[:, 0]/w*self.dsize[0]
pts[:, 1] = pts[:, 1]/h*self.dsize[1]
img = cv2.resize(img, dsize=self.dsize)
return img, np.asarray(pts)