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feat: support multi image pasting

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Laurent Fainsin 2022-09-14 16:23:19 +02:00
parent c374673786
commit 57853be03e
3 changed files with 150 additions and 127 deletions
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31
src/comp.ipynb
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src/prerender.ipynb
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203
src/utils/paste.py
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@ -1,5 +1,9 @@
from __future__ import annotations
import random as rd
from dataclasses import dataclass
from pathlib import Path
from typing import List, Tuple
import albumentations as A
import numpy as np
@ -23,162 +27,201 @@ class RandomPaste(A.DualTransform):
def __init__(
self,
nb,
image_dir,
scale_range=(0.02, 0.3),
sphere_image_dir,
chrome_sphere_image_dir,
scale_range=(0.05, 0.3),
always_apply=True,
p=1.0,
):
super().__init__(always_apply, p)
self.images = []
self.images.extend(list(Path(image_dir).glob("**/*.jpg")))
self.images.extend(list(Path(image_dir).glob("**/*.png")))
self.sphere_images = []
self.sphere_images.extend(list(Path(sphere_image_dir).glob("**/*.jpg")))
self.sphere_images.extend(list(Path(sphere_image_dir).glob("**/*.png")))
self.chrome_sphere_images = []
self.chrome_sphere_images.extend(list(Path(chrome_sphere_image_dir).glob("**/*.jpg")))
self.chrome_sphere_images.extend(list(Path(chrome_sphere_image_dir).glob("**/*.png")))
self.scale_range = scale_range
self.nb = nb
self.augmentation_datas: List[AugmentationData] = []
@property
def targets_as_params(self):
return ["image"]
def apply(self, img, augmentations, paste_img, paste_mask, **params):
def apply(self, img, **params):
# convert img to Image, needed for `paste` function
img = Image.fromarray(img)
# copy paste_img and paste_mask
paste_mask = paste_mask.copy()
paste_img = paste_img.copy()
# paste spheres
for (x, y, shearx, sheary, shape, angle, brightness, contrast) in augmentations:
for augmentation in self.augmentation_datas:
paste_img_aug = T.functional.adjust_contrast(
paste_img,
contrast_factor=contrast,
augmentation.paste_img,
contrast_factor=augmentation.contrast,
)
paste_img_aug = T.functional.adjust_brightness(
paste_img_aug,
brightness_factor=brightness,
brightness_factor=augmentation.brightness,
)
paste_img_aug = T.functional.affine(
paste_img_aug,
scale=0.95,
angle=angle,
translate=(0, 0),
shear=(shearx, sheary),
angle=augmentation.angle,
shear=augmentation.shear,
interpolation=T.InterpolationMode.BICUBIC,
)
paste_img_aug = T.functional.resize(
paste_img_aug,
size=shape,
size=augmentation.shape,
interpolation=T.InterpolationMode.LANCZOS,
)
paste_mask_aug = T.functional.affine(
paste_mask,
augmentation.paste_mask,
scale=0.95,
angle=angle,
translate=(0, 0),
shear=(shearx, sheary),
angle=augmentation.angle,
shear=augmentation.shear,
interpolation=T.InterpolationMode.BICUBIC,
)
paste_mask_aug = T.functional.resize(
paste_mask_aug,
size=shape,
size=augmentation.shape,
interpolation=T.InterpolationMode.LANCZOS,
)
img.paste(paste_img_aug, (x, y), paste_mask_aug)
img.paste(paste_img_aug, augmentation.position, paste_mask_aug)
return np.array(img.convert("RGB"))
def apply_to_mask(self, mask, augmentations, paste_mask, **params):
def apply_to_mask(self, mask, **params):
# convert mask to Image, needed for `paste` function
mask = Image.fromarray(mask)
# copy paste_img and paste_mask
paste_mask = paste_mask.copy()
for i, (x, y, shearx, sheary, shape, angle, _, _) in enumerate(augmentations):
for augmentation in self.augmentation_datas:
paste_mask_aug = T.functional.affine(
paste_mask,
augmentation.paste_mask,
scale=0.95,
angle=angle,
translate=(0, 0),
shear=(shearx, sheary),
angle=augmentation.angle,
shear=augmentation.shear,
interpolation=T.InterpolationMode.BICUBIC,
)
paste_mask_aug = T.functional.resize(
paste_mask_aug,
size=shape,
size=augmentation.shape,
interpolation=T.InterpolationMode.LANCZOS,
)
# binarize the mask -> {0, 1}
paste_mask_aug_bin = paste_mask_aug.point(lambda p: i + 1 if p > 10 else 0)
paste_mask_aug_bin = paste_mask_aug.point(lambda p: augmentation.value if p > 10 else 0)
mask.paste(paste_mask_aug, (x, y), paste_mask_aug_bin)
mask.paste(paste_mask_aug, augmentation.position, paste_mask_aug_bin)
return np.array(mask.convert("L"))
def get_params_dependent_on_targets(self, params):
# choose a random image and its corresponding mask
img_path = rd.choice(self.images)
mask_path = img_path.parent.joinpath("MASK.PNG")
# load images (w/ transparency)
paste_img = Image.open(img_path).convert("RGBA")
paste_mask = Image.open(mask_path).convert("LA")
# load target image (w/ transparency)
target_img = params["image"]
# compute shapes
target_shape = np.array(target_img.shape[:2], dtype=np.uint)
paste_shape = np.array(paste_img.size, dtype=np.uint)
# compute minimum scaling to fit inside target
min_scale = np.min(target_shape / paste_shape)
# generate augmentations
augmentations = []
NB = rd.randint(1, self.nb)
ite = 0
while len(augmentations) < NB:
NB = rd.randint(1, self.nb)
while len(self.augmentation_datas) < NB:
if ite > 100:
break
else:
ite += 1
# choose a random sphere image and its corresponding mask
if rd.random() > 0.5:
img_path = rd.choice(self.sphere_images)
value = len(self.augmentation_datas) + 1
else:
img_path = rd.choice(self.chrome_sphere_images)
value = 255 - len(self.augmentation_datas)
mask_path = img_path.parent.joinpath("MASK.PNG")
# load paste assets
paste_img = Image.open(img_path).convert("RGBA")
paste_shape = np.array(paste_img.size, dtype=np.uint)
paste_mask = Image.open(mask_path).convert("LA")
# compute minimum scaling to fit inside target
min_scale = np.min(target_shape / paste_shape)
# randomly scale image inside target
scale = rd.uniform(*self.scale_range) * min_scale
shape = np.array(paste_shape * scale, dtype=np.uint)
x = rd.randint(0, target_shape[1] - shape[1])
y = rd.randint(0, target_shape[0] - shape[0])
# check for overlapping
if RandomPaste.overlap(augmentations, x, y, shape[1], shape[0]):
try:
self.augmentation_datas.append(
AugmentationData(
position=(
rd.randint(0, target_shape[1] - shape[1]),
rd.randint(0, target_shape[0] - shape[0]),
),
shear=(
rd.uniform(-2, 2),
rd.uniform(-2, 2),
),
shape=tuple(shape),
angle=rd.uniform(0, 360),
brightness=rd.uniform(0.8, 1.2),
contrast=rd.uniform(0.8, 1.2),
paste_img=paste_img,
paste_mask=paste_mask,
value=value,
other_augmentations=self.augmentation_datas,
)
)
except ValueError:
continue
shearx = rd.uniform(-2, 2)
sheary = rd.uniform(-2, 2)
angle = rd.uniform(0, 360)
brightness = rd.uniform(0.8, 1.2)
contrast = rd.uniform(0.8, 1.2)
augmentations.append((x, y, shearx, sheary, tuple(shape), angle, brightness, contrast))
ite += 1
params.update(
{
"augmentations": augmentations,
"paste_img": paste_img,
"paste_mask": paste_mask,
}
)
return params
@staticmethod
def overlap(positions, x1, y1, w1, h1):
for x2, y2, _, _, (w2, h2), _, _, _ in positions:
if x1 + w1 >= x2 and x1 <= x2 + w2 and y1 + h1 >= y2 and y1 <= y2 + h2:
return True
return False
@dataclass
class AugmentationData:
"""Store data for pasting augmentation."""
position: Tuple[int, int]
shape: Tuple[int, int]
angle: float
brightness: float
contrast: float
shear: Tuple[float, float]
paste_img: Image.Image
paste_mask: Image.Image
value: int
other_augmentations: List[AugmentationData]
def __post_init__(self) -> None:
# check for overlapping
if overlap(self.other_augmentations, self):
raise ValueError
def overlap(augmentations: List[AugmentationData], augmentation: AugmentationData) -> bool:
x1, y1 = augmentation.position
w1, h1 = augmentation.shape
for other_augmentation in augmentations:
x2, y2 = other_augmentation.position
w2, h2 = other_augmentation.shape
if x1 + w1 >= x2 and x1 <= x2 + w2 and y1 + h1 >= y2 and y1 <= y2 + h2:
return True
return False