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""" COCO transforms (quick and dirty)
Hacked together by Ross Wightman
"""
import torch
from PIL import Image
import numpy as np
import random
import math
IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
IMAGENET_INCEPTION_MEAN = (0.5, 0.5, 0.5)
IMAGENET_INCEPTION_STD = (0.5, 0.5, 0.5)
class ImageToNumpy:
def __call__(self, pil_img, annotations: dict):
np_img = np.array(pil_img, dtype=np.uint8)
if np_img.ndim < 3:
np_img = np.expand_dims(np_img, axis=-1)
np_img = np.moveaxis(np_img, 2, 0) # HWC to CHW
return np_img, annotations
class ImageToTensor:
def __init__(self, dtype=torch.float32):
self.dtype = dtype
def __call__(self, pil_img, annotations: dict):
np_img = np.array(pil_img, dtype=np.uint8)
if np_img.ndim < 3:
np_img = np.expand_dims(np_img, axis=-1)
np_img = np.moveaxis(np_img, 2, 0) # HWC to CHW
return torch.from_numpy(np_img).to(dtype=self.dtype), annotations
def _pil_interp(method):
if method == 'bicubic':
return Image.BICUBIC
elif method == 'lanczos':
return Image.LANCZOS
elif method == 'hamming':
return Image.HAMMING
else:
# default bilinear, do we want to allow nearest?
return Image.BILINEAR
def clip_boxes_(boxes, img_size):
height, width = img_size
clip_upper = np.array([height, width] * 2, dtype=boxes.dtype)
np.clip(boxes, 0, clip_upper, out=boxes)
def clip_boxes(boxes, img_size):
clipped_boxes = boxes.copy()
clip_boxes_(clipped_boxes, img_size)
return clipped_boxes
def _size_tuple(size):
if isinstance(size, int):
return size, size
else:
assert len(size) == 2
return size
class ResizePad:
def __init__(self, target_size: int, interpolation: str = 'bilinear', fill_color: tuple = (0, 0, 0)):
self.target_size = _size_tuple(target_size)
self.interpolation = interpolation
self.fill_color = fill_color
def __call__(self, img, anno: dict):
width, height = img.size
img_scale_y = self.target_size[0] / height
img_scale_x = self.target_size[1] / width
img_scale = min(img_scale_y, img_scale_x)
scaled_h = int(height * img_scale)
scaled_w = int(width * img_scale)
new_img = Image.new("RGB", (self.target_size[1], self.target_size[0]), color=self.fill_color)
interp_method = _pil_interp(self.interpolation)
img = img.resize((scaled_w, scaled_h), interp_method)
new_img.paste(img)
if 'bbox' in anno:
# FIXME haven't tested this path since not currently using dataset annotations for train/eval
bbox = anno['bbox']
bbox[:, :4] *= img_scale
clip_boxes_(bbox, (scaled_h, scaled_w))
valid_indices = (bbox[:, :2] < bbox[:, 2:4]).all(axis=1)
anno['bbox'] = bbox[valid_indices, :]
anno['cls'] = anno['cls'][valid_indices]
anno['img_scale'] = 1. / img_scale # back to original
return new_img, anno
class RandomResizePad:
def __init__(self, target_size: int, scale: tuple = (0.1, 2.0), interpolation: str = 'bilinear',
fill_color: tuple = (0, 0, 0)):
self.target_size = _size_tuple(target_size)
self.scale = scale
self.interpolation = interpolation
self.fill_color = fill_color
def _get_params(self, img):
# Select a random scale factor.
scale_factor = random.uniform(*self.scale)
scaled_target_height = scale_factor * self.target_size[0]
scaled_target_width = scale_factor * self.target_size[1]
# Recompute the accurate scale_factor using rounded scaled image size.
width, height = img.size
img_scale_y = scaled_target_height / height
img_scale_x = scaled_target_width / width
img_scale = min(img_scale_y, img_scale_x)
# Select non-zero random offset (x, y) if scaled image is larger than target size
scaled_h = int(height * img_scale)
scaled_w = int(width * img_scale)
offset_y = scaled_h - self.target_size[0]
offset_x = scaled_w - self.target_size[1]
offset_y = int(max(0.0, float(offset_y)) * random.uniform(0, 1))
offset_x = int(max(0.0, float(offset_x)) * random.uniform(0, 1))
return scaled_h, scaled_w, offset_y, offset_x, img_scale
def __call__(self, img, anno: dict):
scaled_h, scaled_w, offset_y, offset_x, img_scale = self._get_params(img)
interp_method = _pil_interp(self.interpolation)
img = img.resize((scaled_w, scaled_h), interp_method)
right, lower = min(scaled_w, offset_x + self.target_size[1]), min(scaled_h, offset_y + self.target_size[0])
img = img.crop((offset_x, offset_y, right, lower))
new_img = Image.new("RGB", (self.target_size[1], self.target_size[0]), color=self.fill_color)
new_img.paste(img)
if 'bbox' in anno:
# FIXME not fully tested
bbox = anno['bbox'].copy() # FIXME copy for debugger inspection, back to inplace
bbox[:, :4] *= img_scale
box_offset = np.stack([offset_y, offset_x] * 2)
bbox -= box_offset
clip_boxes_(bbox, (scaled_h, scaled_w))
valid_indices = (bbox[:, :2] < bbox[:, 2:4]).all(axis=1)
anno['bbox'] = bbox[valid_indices, :]
anno['cls'] = anno['cls'][valid_indices]
anno['img_scale'] = 1. / img_scale # back to original
return new_img, anno
class RandomFlip:
def __init__(self, horizontal=True, vertical=False, prob=0.5):
self.horizontal = horizontal
self.vertical = vertical
self.prob = prob
def _get_params(self):
do_horizontal = random.random() < self.prob if self.horizontal else False
do_vertical = random.random() < self.prob if self.vertical else False
return do_horizontal, do_vertical
def __call__(self, img, annotations: dict):
do_horizontal, do_vertical = self._get_params()
width, height = img.size
def _fliph(bbox):
x_max = width - bbox[:, 1]
x_min = width - bbox[:, 3]
bbox[:, 1] = x_min
bbox[:, 3] = x_max
def _flipv(bbox):
y_max = height - bbox[:, 0]
y_min = height - bbox[:, 2]
bbox[:, 0] = y_min
bbox[:, 2] = y_max
if do_horizontal and do_vertical:
img = img.transpose(Image.ROTATE_180)
if 'bbox' in annotations:
_fliph(annotations['bbox'])
_flipv(annotations['bbox'])
elif do_horizontal:
img = img.transpose(Image.FLIP_LEFT_RIGHT)
if 'bbox' in annotations:
_fliph(annotations['bbox'])
elif do_vertical:
img = img.transpose(Image.FLIP_TOP_BOTTOM)
if 'bbox' in annotations:
_flipv(annotations['bbox'])
return img, annotations
def resolve_fill_color(fill_color, img_mean=IMAGENET_DEFAULT_MEAN):
if isinstance(fill_color, tuple):
assert len(fill_color) == 3
fill_color = fill_color
else:
try:
int_color = int(fill_color)
fill_color = (int_color,) * 3
except ValueError:
assert fill_color == 'mean'
fill_color = tuple([int(round(255 * x)) for x in img_mean])
return fill_color
class Compose:
def __init__(self, transforms: list):
self.transforms = transforms
def __call__(self, img, annotations: dict):
for t in self.transforms:
img, annotations = t(img, annotations)
return img, annotations
def transforms_coco_eval(
img_size=224,
interpolation='bilinear',
use_prefetcher=False,
fill_color='mean',
mean=IMAGENET_DEFAULT_MEAN,
std=IMAGENET_DEFAULT_STD):
fill_color = resolve_fill_color(fill_color, mean)
image_tfl = [
ResizePad(
target_size=img_size, interpolation=interpolation, fill_color=fill_color),
ImageToNumpy(),
]
assert use_prefetcher, "Only supporting prefetcher usage right now"
image_tf = Compose(image_tfl)
return image_tf
def transforms_coco_train(
img_size=224,
interpolation='random',
use_prefetcher=False,
fill_color='mean',
mean=IMAGENET_DEFAULT_MEAN,
std=IMAGENET_DEFAULT_STD):
fill_color = resolve_fill_color(fill_color, mean)
image_tfl = [
RandomFlip(horizontal=True, prob=0.5),
RandomResizePad(
target_size=img_size, interpolation=interpolation, fill_color=fill_color),
ImageToNumpy(),
]
assert use_prefetcher, "Only supporting prefetcher usage right now"
image_tf = Compose(image_tfl)
return image_tf
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