345 lines
9.7 KiB
Python
345 lines
9.7 KiB
Python
|
import cv2
|
||
|
|
import numpy as np
|
||
|
|
from PIL import Image
|
||
|
|
|
||
|
|
|
||
|
|
## aug functions
|
||
|
|
def identity_func(img):
|
||
|
|
return img
|
||
|
|
|
||
|
|
|
||
|
|
def autocontrast_func(img, cutoff=0):
|
||
|
|
'''
|
||
|
|
same output as PIL.ImageOps.autocontrast
|
||
|
|
'''
|
||
|
|
n_bins = 256
|
||
|
|
|
||
|
|
def tune_channel(ch):
|
||
|
|
n = ch.size
|
||
|
|
cut = cutoff * n // 100
|
||
|
|
if cut == 0:
|
||
|
|
high, low = ch.max(), ch.min()
|
||
|
|
else:
|
||
|
|
hist = cv2.calcHist([ch], [0], None, [n_bins], [0, n_bins])
|
||
|
|
low = np.argwhere(np.cumsum(hist) > cut)
|
||
|
|
low = 0 if low.shape[0] == 0 else low[0]
|
||
|
|
high = np.argwhere(np.cumsum(hist[::-1]) > cut)
|
||
|
|
high = n_bins - 1 if high.shape[0] == 0 else n_bins - 1 - high[0]
|
||
|
|
if high <= low:
|
||
|
|
table = np.arange(n_bins)
|
||
|
|
else:
|
||
|
|
scale = (n_bins - 1) / (high - low)
|
||
|
|
offset = -low * scale
|
||
|
|
table = np.arange(n_bins) * scale + offset
|
||
|
|
table[table < 0] = 0
|
||
|
|
table[table > n_bins - 1] = n_bins - 1
|
||
|
|
table = table.clip(0, 255).astype(np.uint8)
|
||
|
|
return table[ch]
|
||
|
|
|
||
|
|
channels = [tune_channel(ch) for ch in cv2.split(img)]
|
||
|
|
out = cv2.merge(channels)
|
||
|
|
return out
|
||
|
|
|
||
|
|
|
||
|
|
def equalize_func(img):
|
||
|
|
'''
|
||
|
|
same output as PIL.ImageOps.equalize
|
||
|
|
PIL's implementation is different from cv2.equalize
|
||
|
|
'''
|
||
|
|
n_bins = 256
|
||
|
|
|
||
|
|
def tune_channel(ch):
|
||
|
|
hist = cv2.calcHist([ch], [0], None, [n_bins], [0, n_bins])
|
||
|
|
non_zero_hist = hist[hist != 0].reshape(-1)
|
||
|
|
step = np.sum(non_zero_hist[:-1]) // (n_bins - 1)
|
||
|
|
if step == 0: return ch
|
||
|
|
n = np.empty_like(hist)
|
||
|
|
n[0] = step // 2
|
||
|
|
n[1:] = hist[:-1]
|
||
|
|
table = (np.cumsum(n) // step).clip(0, 255).astype(np.uint8)
|
||
|
|
return table[ch]
|
||
|
|
|
||
|
|
channels = [tune_channel(ch) for ch in cv2.split(img)]
|
||
|
|
out = cv2.merge(channels)
|
||
|
|
return out
|
||
|
|
|
||
|
|
|
||
|
|
def rotate_func(img, degree, fill=(0, 0, 0)):
|
||
|
|
'''
|
||
|
|
like PIL, rotate by degree, not radians
|
||
|
|
'''
|
||
|
|
H, W = img.shape[0], img.shape[1]
|
||
|
|
center = W / 2, H / 2
|
||
|
|
M = cv2.getRotationMatrix2D(center, degree, 1)
|
||
|
|
out = cv2.warpAffine(img, M, (W, H), borderValue=fill)
|
||
|
|
return out
|
||
|
|
|
||
|
|
|
||
|
|
def solarize_func(img, thresh=128):
|
||
|
|
'''
|
||
|
|
same output as PIL.ImageOps.posterize
|
||
|
|
'''
|
||
|
|
table = np.array([el if el < thresh else 255 - el for el in range(256)])
|
||
|
|
table = table.clip(0, 255).astype(np.uint8)
|
||
|
|
out = table[img]
|
||
|
|
return out
|
||
|
|
|
||
|
|
|
||
|
|
def color_func(img, factor):
|
||
|
|
'''
|
||
|
|
same output as PIL.ImageEnhance.Color
|
||
|
|
'''
|
||
|
|
## implementation according to PIL definition, quite slow
|
||
|
|
# degenerate = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)[:, :, np.newaxis]
|
||
|
|
# out = blend(degenerate, img, factor)
|
||
|
|
# M = (
|
||
|
|
# np.eye(3) * factor
|
||
|
|
# + np.float32([0.114, 0.587, 0.299]).reshape(3, 1) * (1. - factor)
|
||
|
|
# )[np.newaxis, np.newaxis, :]
|
||
|
|
M = (
|
||
|
|
np.float32([
|
||
|
|
[0.886, -0.114, -0.114],
|
||
|
|
[-0.587, 0.413, -0.587],
|
||
|
|
[-0.299, -0.299, 0.701]]) * factor
|
||
|
|
+ np.float32([[0.114], [0.587], [0.299]])
|
||
|
|
)
|
||
|
|
out = np.matmul(img, M).clip(0, 255).astype(np.uint8)
|
||
|
|
return out
|
||
|
|
|
||
|
|
|
||
|
|
def contrast_func(img, factor):
|
||
|
|
"""
|
||
|
|
same output as PIL.ImageEnhance.Contrast
|
||
|
|
"""
|
||
|
|
mean = np.sum(np.mean(img, axis=(0, 1)) * np.array([0.114, 0.587, 0.299]))
|
||
|
|
table = np.array([(
|
||
|
|
el - mean) * factor + mean
|
||
|
|
for el in range(256)
|
||
|
|
]).clip(0, 255).astype(np.uint8)
|
||
|
|
out = table[img]
|
||
|
|
return out
|
||
|
|
|
||
|
|
|
||
|
|
def brightness_func(img, factor):
|
||
|
|
'''
|
||
|
|
same output as PIL.ImageEnhance.Contrast
|
||
|
|
'''
|
||
|
|
table = (np.arange(256, dtype=np.float32) * factor).clip(0, 255).astype(np.uint8)
|
||
|
|
out = table[img]
|
||
|
|
return out
|
||
|
|
|
||
|
|
|
||
|
|
def sharpness_func(img, factor):
|
||
|
|
'''
|
||
|
|
The differences the this result and PIL are all on the 4 boundaries, the center
|
||
|
|
areas are same
|
||
|
|
'''
|
||
|
|
kernel = np.ones((3, 3), dtype=np.float32)
|
||
|
|
kernel[1][1] = 5
|
||
|
|
kernel /= 13
|
||
|
|
degenerate = cv2.filter2D(img, -1, kernel)
|
||
|
|
if factor == 0.0:
|
||
|
|
out = degenerate
|
||
|
|
elif factor == 1.0:
|
||
|
|
out = img
|
||
|
|
else:
|
||
|
|
out = img.astype(np.float32)
|
||
|
|
degenerate = degenerate.astype(np.float32)[1:-1, 1:-1, :]
|
||
|
|
out[1:-1, 1:-1, :] = degenerate + factor * (out[1:-1, 1:-1, :] - degenerate)
|
||
|
|
out = out.astype(np.uint8)
|
||
|
|
return out
|
||
|
|
|
||
|
|
|
||
|
|
def shear_x_func(img, factor, fill=(0, 0, 0)):
|
||
|
|
H, W = img.shape[0], img.shape[1]
|
||
|
|
M = np.float32([[1, factor, 0], [0, 1, 0]])
|
||
|
|
out = cv2.warpAffine(img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR).astype(np.uint8)
|
||
|
|
return out
|
||
|
|
|
||
|
|
|
||
|
|
def translate_x_func(img, offset, fill=(0, 0, 0)):
|
||
|
|
'''
|
||
|
|
same output as PIL.Image.transform
|
||
|
|
'''
|
||
|
|
H, W = img.shape[0], img.shape[1]
|
||
|
|
M = np.float32([[1, 0, -offset], [0, 1, 0]])
|
||
|
|
out = cv2.warpAffine(img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR).astype(np.uint8)
|
||
|
|
return out
|
||
|
|
|
||
|
|
|
||
|
|
def translate_y_func(img, offset, fill=(0, 0, 0)):
|
||
|
|
'''
|
||
|
|
same output as PIL.Image.transform
|
||
|
|
'''
|
||
|
|
H, W = img.shape[0], img.shape[1]
|
||
|
|
M = np.float32([[1, 0, 0], [0, 1, -offset]])
|
||
|
|
out = cv2.warpAffine(img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR).astype(np.uint8)
|
||
|
|
return out
|
||
|
|
|
||
|
|
|
||
|
|
def posterize_func(img, bits):
|
||
|
|
'''
|
||
|
|
same output as PIL.ImageOps.posterize
|
||
|
|
'''
|
||
|
|
out = np.bitwise_and(img, np.uint8(255 << (8 - bits)))
|
||
|
|
return out
|
||
|
|
|
||
|
|
|
||
|
|
def shear_y_func(img, factor, fill=(0, 0, 0)):
|
||
|
|
H, W = img.shape[0], img.shape[1]
|
||
|
|
M = np.float32([[1, 0, 0], [factor, 1, 0]])
|
||
|
|
out = cv2.warpAffine(img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR).astype(np.uint8)
|
||
|
|
return out
|
||
|
|
|
||
|
|
|
||
|
|
def cutout_func(img, pad_size, replace=(0, 0, 0)):
|
||
|
|
replace = np.array(replace, dtype=np.uint8)
|
||
|
|
H, W = img.shape[0], img.shape[1]
|
||
|
|
rh, rw = np.random.random(2)
|
||
|
|
pad_size = pad_size // 2
|
||
|
|
ch, cw = int(rh * H), int(rw * W)
|
||
|
|
x1, x2 = max(ch - pad_size, 0), min(ch + pad_size, H)
|
||
|
|
y1, y2 = max(cw - pad_size, 0), min(cw + pad_size, W)
|
||
|
|
out = img.copy()
|
||
|
|
out[x1:x2, y1:y2, :] = replace
|
||
|
|
return out
|
||
|
|
|
||
|
|
|
||
|
|
### level to args
|
||
|
|
def enhance_level_to_args(MAX_LEVEL):
|
||
|
|
def level_to_args(level):
|
||
|
|
return ((level / MAX_LEVEL) * 1.8 + 0.1,)
|
||
|
|
return level_to_args
|
||
|
|
|
||
|
|
|
||
|
|
def shear_level_to_args(MAX_LEVEL, replace_value):
|
||
|
|
def level_to_args(level):
|
||
|
|
level = (level / MAX_LEVEL) * 0.3
|
||
|
|
if np.random.random() > 0.5: level = -level
|
||
|
|
return (level, replace_value)
|
||
|
|
|
||
|
|
return level_to_args
|
||
|
|
|
||
|
|
|
||
|
|
def translate_level_to_args(translate_const, MAX_LEVEL, replace_value):
|
||
|
|
def level_to_args(level):
|
||
|
|
level = (level / MAX_LEVEL) * float(translate_const)
|
||
|
|
if np.random.random() > 0.5: level = -level
|
||
|
|
return (level, replace_value)
|
||
|
|
|
||
|
|
return level_to_args
|
||
|
|
|
||
|
|
|
||
|
|
def cutout_level_to_args(cutout_const, MAX_LEVEL, replace_value):
|
||
|
|
def level_to_args(level):
|
||
|
|
level = int((level / MAX_LEVEL) * cutout_const)
|
||
|
|
return (level, replace_value)
|
||
|
|
|
||
|
|
return level_to_args
|
||
|
|
|
||
|
|
|
||
|
|
def solarize_level_to_args(MAX_LEVEL):
|
||
|
|
def level_to_args(level):
|
||
|
|
level = int((level / MAX_LEVEL) * 256)
|
||
|
|
return (level, )
|
||
|
|
return level_to_args
|
||
|
|
|
||
|
|
|
||
|
|
def none_level_to_args(level):
|
||
|
|
return ()
|
||
|
|
|
||
|
|
|
||
|
|
def posterize_level_to_args(MAX_LEVEL):
|
||
|
|
def level_to_args(level):
|
||
|
|
level = int((level / MAX_LEVEL) * 4)
|
||
|
|
return (level, )
|
||
|
|
return level_to_args
|
||
|
|
|
||
|
|
|
||
|
|
def rotate_level_to_args(MAX_LEVEL, replace_value):
|
||
|
|
def level_to_args(level):
|
||
|
|
level = (level / MAX_LEVEL) * 30
|
||
|
|
if np.random.random() < 0.5:
|
||
|
|
level = -level
|
||
|
|
return (level, replace_value)
|
||
|
|
|
||
|
|
return level_to_args
|
||
|
|
|
||
|
|
|
||
|
|
func_dict = {
|
||
|
|
'Identity': identity_func,
|
||
|
|
'AutoContrast': autocontrast_func,
|
||
|
|
'Equalize': equalize_func,
|
||
|
|
'Rotate': rotate_func,
|
||
|
|
'Solarize': solarize_func,
|
||
|
|
'Color': color_func,
|
||
|
|
'Contrast': contrast_func,
|
||
|
|
'Brightness': brightness_func,
|
||
|
|
'Sharpness': sharpness_func,
|
||
|
|
'ShearX': shear_x_func,
|
||
|
|
'TranslateX': translate_x_func,
|
||
|
|
'TranslateY': translate_y_func,
|
||
|
|
'Posterize': posterize_func,
|
||
|
|
'ShearY': shear_y_func,
|
||
|
|
}
|
||
|
|
|
||
|
|
translate_const = 10
|
||
|
|
MAX_LEVEL = 10
|
||
|
|
replace_value = (128, 128, 128)
|
||
|
|
arg_dict = {
|
||
|
|
'Identity': none_level_to_args,
|
||
|
|
'AutoContrast': none_level_to_args,
|
||
|
|
'Equalize': none_level_to_args,
|
||
|
|
'Rotate': rotate_level_to_args(MAX_LEVEL, replace_value),
|
||
|
|
'Solarize': solarize_level_to_args(MAX_LEVEL),
|
||
|
|
'Color': enhance_level_to_args(MAX_LEVEL),
|
||
|
|
'Contrast': enhance_level_to_args(MAX_LEVEL),
|
||
|
|
'Brightness': enhance_level_to_args(MAX_LEVEL),
|
||
|
|
'Sharpness': enhance_level_to_args(MAX_LEVEL),
|
||
|
|
'ShearX': shear_level_to_args(MAX_LEVEL, replace_value),
|
||
|
|
'TranslateX': translate_level_to_args(
|
||
|
|
translate_const, MAX_LEVEL, replace_value
|
||
|
|
),
|
||
|
|
'TranslateY': translate_level_to_args(
|
||
|
|
translate_const, MAX_LEVEL, replace_value
|
||
|
|
),
|
||
|
|
'Posterize': posterize_level_to_args(MAX_LEVEL),
|
||
|
|
'ShearY': shear_level_to_args(MAX_LEVEL, replace_value),
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
class RandomAugment(object):
|
||
|
|
|
||
|
|
def __init__(self, N=2, M=10, isPIL=False, returnPIL=False, augs=[]):
|
||
|
|
self.N = N
|
||
|
|
self.M = M
|
||
|
|
self.isPIL = isPIL
|
||
|
|
self.returnPIL = returnPIL
|
||
|
|
if augs:
|
||
|
|
self.augs = augs
|
||
|
|
else:
|
||
|
|
self.augs = list(arg_dict.keys())
|
||
|
|
|
||
|
|
def get_random_ops(self):
|
||
|
|
sampled_ops = np.random.choice(self.augs, self.N)
|
||
|
|
return [(op, 0.5, self.M) for op in sampled_ops]
|
||
|
|
|
||
|
|
def __call__(self, img):
|
||
|
|
if self.isPIL:
|
||
|
|
img = np.array(img)
|
||
|
|
ops = self.get_random_ops()
|
||
|
|
for name, prob, level in ops:
|
||
|
|
if np.random.random() > prob:
|
||
|
|
continue
|
||
|
|
args = arg_dict[name](level)
|
||
|
|
img = func_dict[name](img, *args)
|
||
|
|
if self.returnPIL:
|
||
|
|
img = img.astype('uint8')
|
||
|
|
img = Image.fromarray(img)
|
||
|
|
return img
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == '__main__':
|
||
|
|
a = RandomAugment()
|
||
|
|
img = np.random.randn(32, 32, 3)
|
||
|
|
a(img)
|