bytetrack / tutorials /motr /transforms.py
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# ------------------------------------------------------------------------
# Copyright (c) 2021 megvii-model. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from DETR (https://github.com/facebookresearch/detr)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# ------------------------------------------------------------------------
"""
Transforms and data augmentation for both image + bbox.
"""
import copy
import random
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as F
from PIL import Image, ImageDraw
from util.box_ops import box_xyxy_to_cxcywh
from util.misc import interpolate
import numpy as np
import os
def crop_mot(image, target, region):
cropped_image = F.crop(image, *region)
target = target.copy()
i, j, h, w = region
# should we do something wrt the original size?
target["size"] = torch.tensor([h, w])
fields = ["labels", "area", "iscrowd"]
if 'obj_ids' in target:
fields.append('obj_ids')
if "boxes" in target:
boxes = target["boxes"]
max_size = torch.as_tensor([w, h], dtype=torch.float32)
cropped_boxes = boxes - torch.as_tensor([j, i, j, i])
for i, box in enumerate(cropped_boxes):
l, t, r, b = box
# if l < 0:
# l = 0
# if r < 0:
# r = 0
# if l > w:
# l = w
# if r > w:
# r = w
# if t < 0:
# t = 0
# if b < 0:
# b = 0
# if t > h:
# t = h
# if b > h:
# b = h
if l < 0 and r < 0:
l = r = 0
if l > w and r > w:
l = r = w
if t < 0 and b < 0:
t = b = 0
if t > h and b > h:
t = b = h
cropped_boxes[i] = torch.tensor([l, t, r, b], dtype=box.dtype)
cropped_boxes = torch.min(cropped_boxes.reshape(-1, 2, 2), max_size)
cropped_boxes = cropped_boxes.clamp(min=0)
area = (cropped_boxes[:, 1, :] - cropped_boxes[:, 0, :]).prod(dim=1)
target["boxes"] = cropped_boxes.reshape(-1, 4)
target["area"] = area
fields.append("boxes")
if "masks" in target:
# FIXME should we update the area here if there are no boxes?
target['masks'] = target['masks'][:, i:i + h, j:j + w]
fields.append("masks")
# remove elements for which the boxes or masks that have zero area
if "boxes" in target or "masks" in target:
# favor boxes selection when defining which elements to keep
# this is compatible with previous implementation
if "boxes" in target:
cropped_boxes = target['boxes'].reshape(-1, 2, 2)
keep = torch.all(cropped_boxes[:, 1, :] > cropped_boxes[:, 0, :], dim=1)
else:
keep = target['masks'].flatten(1).any(1)
for field in fields:
target[field] = target[field][keep]
return cropped_image, target
def random_shift(image, target, region, sizes):
oh, ow = sizes
# step 1, shift crop and re-scale image firstly
cropped_image = F.crop(image, *region)
cropped_image = F.resize(cropped_image, sizes)
target = target.copy()
i, j, h, w = region
# should we do something wrt the original size?
target["size"] = torch.tensor([h, w])
fields = ["labels", "area", "iscrowd"]
if 'obj_ids' in target:
fields.append('obj_ids')
if "boxes" in target:
boxes = target["boxes"]
max_size = torch.as_tensor([w, h], dtype=torch.float32)
cropped_boxes = boxes - torch.as_tensor([j, i, j, i])
for i, box in enumerate(cropped_boxes):
l, t, r, b = box
if l < 0:
l = 0
if r < 0:
r = 0
if l > w:
l = w
if r > w:
r = w
if t < 0:
t = 0
if b < 0:
b = 0
if t > h:
t = h
if b > h:
b = h
# step 2, re-scale coords secondly
ratio_h = 1.0 * oh / h
ratio_w = 1.0 * ow / w
cropped_boxes[i] = torch.tensor([ratio_w * l, ratio_h * t, ratio_w * r, ratio_h * b], dtype=box.dtype)
cropped_boxes = cropped_boxes.reshape(-1, 2, 2)
area = (cropped_boxes[:, 1, :] - cropped_boxes[:, 0, :]).prod(dim=1)
target["boxes"] = cropped_boxes.reshape(-1, 4)
target["area"] = area
fields.append("boxes")
if "masks" in target:
# FIXME should we update the area here if there are no boxes?
target['masks'] = target['masks'][:, i:i + h, j:j + w]
fields.append("masks")
# remove elements for which the boxes or masks that have zero area
if "boxes" in target or "masks" in target:
# favor boxes selection when defining which elements to keep
# this is compatible with previous implementation
if "boxes" in target:
cropped_boxes = target['boxes'].reshape(-1, 2, 2)
keep = torch.all(cropped_boxes[:, 1, :] > cropped_boxes[:, 0, :], dim=1)
else:
keep = target['masks'].flatten(1).any(1)
for field in fields:
target[field] = target[field][keep]
return cropped_image, target
def crop(image, target, region):
cropped_image = F.crop(image, *region)
target = target.copy()
i, j, h, w = region
# should we do something wrt the original size?
target["size"] = torch.tensor([h, w])
fields = ["labels", "area", "iscrowd"]
if 'obj_ids' in target:
fields.append('obj_ids')
if "boxes" in target:
boxes = target["boxes"]
max_size = torch.as_tensor([w, h], dtype=torch.float32)
cropped_boxes = boxes - torch.as_tensor([j, i, j, i])
cropped_boxes = torch.min(cropped_boxes.reshape(-1, 2, 2), max_size)
cropped_boxes = cropped_boxes.clamp(min=0)
area = (cropped_boxes[:, 1, :] - cropped_boxes[:, 0, :]).prod(dim=1)
target["boxes"] = cropped_boxes.reshape(-1, 4)
target["area"] = area
fields.append("boxes")
if "masks" in target:
# FIXME should we update the area here if there are no boxes?
target['masks'] = target['masks'][:, i:i + h, j:j + w]
fields.append("masks")
# remove elements for which the boxes or masks that have zero area
if "boxes" in target or "masks" in target:
# favor boxes selection when defining which elements to keep
# this is compatible with previous implementation
if "boxes" in target:
cropped_boxes = target['boxes'].reshape(-1, 2, 2)
keep = torch.all(cropped_boxes[:, 1, :] > cropped_boxes[:, 0, :], dim=1)
else:
keep = target['masks'].flatten(1).any(1)
for field in fields:
target[field] = target[field][keep]
return cropped_image, target
def hflip(image, target):
flipped_image = F.hflip(image)
w, h = image.size
target = target.copy()
if "boxes" in target:
boxes = target["boxes"]
boxes = boxes[:, [2, 1, 0, 3]] * torch.as_tensor([-1, 1, -1, 1]) + torch.as_tensor([w, 0, w, 0])
target["boxes"] = boxes
if "masks" in target:
target['masks'] = target['masks'].flip(-1)
return flipped_image, target
def resize(image, target, size, max_size=None):
# size can be min_size (scalar) or (w, h) tuple
def get_size_with_aspect_ratio(image_size, size, max_size=None):
w, h = image_size
if max_size is not None:
min_original_size = float(min((w, h)))
max_original_size = float(max((w, h)))
if max_original_size / min_original_size * size > max_size:
size = int(round(max_size * min_original_size / max_original_size))
if (w <= h and w == size) or (h <= w and h == size):
return (h, w)
if w < h:
ow = size
oh = int(size * h / w)
else:
oh = size
ow = int(size * w / h)
return (oh, ow)
def get_size(image_size, size, max_size=None):
if isinstance(size, (list, tuple)):
return size[::-1]
else:
return get_size_with_aspect_ratio(image_size, size, max_size)
size = get_size(image.size, size, max_size)
rescaled_image = F.resize(image, size)
if target is None:
return rescaled_image, None
ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(rescaled_image.size, image.size))
ratio_width, ratio_height = ratios
target = target.copy()
if "boxes" in target:
boxes = target["boxes"]
scaled_boxes = boxes * torch.as_tensor([ratio_width, ratio_height, ratio_width, ratio_height])
target["boxes"] = scaled_boxes
if "area" in target:
area = target["area"]
scaled_area = area * (ratio_width * ratio_height)
target["area"] = scaled_area
h, w = size
target["size"] = torch.tensor([h, w])
if "masks" in target:
target['masks'] = interpolate(
target['masks'][:, None].float(), size, mode="nearest")[:, 0] > 0.5
return rescaled_image, target
def pad(image, target, padding):
# assumes that we only pad on the bottom right corners
padded_image = F.pad(image, (0, 0, padding[0], padding[1]))
if target is None:
return padded_image, None
target = target.copy()
# should we do something wrt the original size?
target["size"] = torch.tensor(padded_image[::-1])
if "masks" in target:
target['masks'] = torch.nn.functional.pad(target['masks'], (0, padding[0], 0, padding[1]))
return padded_image, target
class RandomCrop(object):
def __init__(self, size):
self.size = size
def __call__(self, img, target):
region = T.RandomCrop.get_params(img, self.size)
return crop(img, target, region)
class MotRandomCrop(RandomCrop):
def __call__(self, imgs: list, targets: list):
ret_imgs = []
ret_targets = []
region = T.RandomCrop.get_params(imgs[0], self.size)
for img_i, targets_i in zip(imgs, targets):
img_i, targets_i = crop(img_i, targets_i, region)
ret_imgs.append(img_i)
ret_targets.append(targets_i)
return ret_imgs, ret_targets
class FixedMotRandomCrop(object):
def __init__(self, min_size: int, max_size: int):
self.min_size = min_size
self.max_size = max_size
def __call__(self, imgs: list, targets: list):
ret_imgs = []
ret_targets = []
w = random.randint(self.min_size, min(imgs[0].width, self.max_size))
h = random.randint(self.min_size, min(imgs[0].height, self.max_size))
region = T.RandomCrop.get_params(imgs[0], [h, w])
for img_i, targets_i in zip(imgs, targets):
img_i, targets_i = crop_mot(img_i, targets_i, region)
ret_imgs.append(img_i)
ret_targets.append(targets_i)
return ret_imgs, ret_targets
class MotRandomShift(object):
def __init__(self, bs=1):
self.bs = bs
def __call__(self, imgs: list, targets: list):
ret_imgs = copy.deepcopy(imgs)
ret_targets = copy.deepcopy(targets)
n_frames = len(imgs)
select_i = random.choice(list(range(n_frames)))
w, h = imgs[select_i].size
xshift = (100 * torch.rand(self.bs)).int()
xshift *= (torch.randn(self.bs) > 0.0).int() * 2 - 1
yshift = (100 * torch.rand(self.bs)).int()
yshift *= (torch.randn(self.bs) > 0.0).int() * 2 - 1
ymin = max(0, -yshift[0])
ymax = min(h, h - yshift[0])
xmin = max(0, -xshift[0])
xmax = min(w, w - xshift[0])
region = (int(ymin), int(xmin), int(ymax-ymin), int(xmax-xmin))
ret_imgs[select_i], ret_targets[select_i] = random_shift(imgs[select_i], targets[select_i], region, (h,w))
return ret_imgs, ret_targets
class FixedMotRandomShift(object):
def __init__(self, bs=1, padding=50):
self.bs = bs
self.padding = padding
def __call__(self, imgs: list, targets: list):
ret_imgs = []
ret_targets = []
n_frames = len(imgs)
w, h = imgs[0].size
xshift = (self.padding * torch.rand(self.bs)).int() + 1
xshift *= (torch.randn(self.bs) > 0.0).int() * 2 - 1
yshift = (self.padding * torch.rand(self.bs)).int() + 1
yshift *= (torch.randn(self.bs) > 0.0).int() * 2 - 1
ret_imgs.append(imgs[0])
ret_targets.append(targets[0])
for i in range(1, n_frames):
ymin = max(0, -yshift[0])
ymax = min(h, h - yshift[0])
xmin = max(0, -xshift[0])
xmax = min(w, w - xshift[0])
prev_img = ret_imgs[i-1].copy()
prev_target = copy.deepcopy(ret_targets[i-1])
region = (int(ymin), int(xmin), int(ymax - ymin), int(xmax - xmin))
img_i, target_i = random_shift(prev_img, prev_target, region, (h, w))
ret_imgs.append(img_i)
ret_targets.append(target_i)
return ret_imgs, ret_targets
class RandomSizeCrop(object):
def __init__(self, min_size: int, max_size: int):
self.min_size = min_size
self.max_size = max_size
def __call__(self, img: PIL.Image.Image, target: dict):
w = random.randint(self.min_size, min(img.width, self.max_size))
h = random.randint(self.min_size, min(img.height, self.max_size))
region = T.RandomCrop.get_params(img, [h, w])
return crop(img, target, region)
class MotRandomSizeCrop(RandomSizeCrop):
def __call__(self, imgs, targets):
w = random.randint(self.min_size, min(imgs[0].width, self.max_size))
h = random.randint(self.min_size, min(imgs[0].height, self.max_size))
region = T.RandomCrop.get_params(imgs[0], [h, w])
ret_imgs = []
ret_targets = []
for img_i, targets_i in zip(imgs, targets):
img_i, targets_i = crop(img_i, targets_i, region)
ret_imgs.append(img_i)
ret_targets.append(targets_i)
return ret_imgs, ret_targets
class CenterCrop(object):
def __init__(self, size):
self.size = size
def __call__(self, img, target):
image_width, image_height = img.size
crop_height, crop_width = self.size
crop_top = int(round((image_height - crop_height) / 2.))
crop_left = int(round((image_width - crop_width) / 2.))
return crop(img, target, (crop_top, crop_left, crop_height, crop_width))
class MotCenterCrop(CenterCrop):
def __call__(self, imgs, targets):
image_width, image_height = imgs[0].size
crop_height, crop_width = self.size
crop_top = int(round((image_height - crop_height) / 2.))
crop_left = int(round((image_width - crop_width) / 2.))
ret_imgs = []
ret_targets = []
for img_i, targets_i in zip(imgs, targets):
img_i, targets_i = crop(img_i, targets_i, (crop_top, crop_left, crop_height, crop_width))
ret_imgs.append(img_i)
ret_targets.append(targets_i)
return ret_imgs, ret_targets
class RandomHorizontalFlip(object):
def __init__(self, p=0.5):
self.p = p
def __call__(self, img, target):
if random.random() < self.p:
return hflip(img, target)
return img, target
class MotRandomHorizontalFlip(RandomHorizontalFlip):
def __call__(self, imgs, targets):
if random.random() < self.p:
ret_imgs = []
ret_targets = []
for img_i, targets_i in zip(imgs, targets):
img_i, targets_i = hflip(img_i, targets_i)
ret_imgs.append(img_i)
ret_targets.append(targets_i)
return ret_imgs, ret_targets
return imgs, targets
class RandomResize(object):
def __init__(self, sizes, max_size=None):
assert isinstance(sizes, (list, tuple))
self.sizes = sizes
self.max_size = max_size
def __call__(self, img, target=None):
size = random.choice(self.sizes)
return resize(img, target, size, self.max_size)
class MotRandomResize(RandomResize):
def __call__(self, imgs, targets):
size = random.choice(self.sizes)
ret_imgs = []
ret_targets = []
for img_i, targets_i in zip(imgs, targets):
img_i, targets_i = resize(img_i, targets_i, size, self.max_size)
ret_imgs.append(img_i)
ret_targets.append(targets_i)
return ret_imgs, ret_targets
class RandomPad(object):
def __init__(self, max_pad):
self.max_pad = max_pad
def __call__(self, img, target):
pad_x = random.randint(0, self.max_pad)
pad_y = random.randint(0, self.max_pad)
return pad(img, target, (pad_x, pad_y))
class MotRandomPad(RandomPad):
def __call__(self, imgs, targets):
pad_x = random.randint(0, self.max_pad)
pad_y = random.randint(0, self.max_pad)
ret_imgs = []
ret_targets = []
for img_i, targets_i in zip(imgs, targets):
img_i, target_i = pad(img_i, targets_i, (pad_x, pad_y))
ret_imgs.append(img_i)
ret_targets.append(targets_i)
return ret_imgs, ret_targets
class RandomSelect(object):
"""
Randomly selects between transforms1 and transforms2,
with probability p for transforms1 and (1 - p) for transforms2
"""
def __init__(self, transforms1, transforms2, p=0.5):
self.transforms1 = transforms1
self.transforms2 = transforms2
self.p = p
def __call__(self, img, target):
if random.random() < self.p:
return self.transforms1(img, target)
return self.transforms2(img, target)
class MotRandomSelect(RandomSelect):
"""
Randomly selects between transforms1 and transforms2,
with probability p for transforms1 and (1 - p) for transforms2
"""
def __call__(self, imgs, targets):
if random.random() < self.p:
return self.transforms1(imgs, targets)
return self.transforms2(imgs, targets)
class ToTensor(object):
def __call__(self, img, target):
return F.to_tensor(img), target
class MotToTensor(ToTensor):
def __call__(self, imgs, targets):
ret_imgs = []
for img in imgs:
ret_imgs.append(F.to_tensor(img))
return ret_imgs, targets
class RandomErasing(object):
def __init__(self, *args, **kwargs):
self.eraser = T.RandomErasing(*args, **kwargs)
def __call__(self, img, target):
return self.eraser(img), target
class MotRandomErasing(RandomErasing):
def __call__(self, imgs, targets):
# TODO: Rewrite this part to ensure the data augmentation is same to each image.
ret_imgs = []
for img_i, targets_i in zip(imgs, targets):
ret_imgs.append(self.eraser(img_i))
return ret_imgs, targets
class MoTColorJitter(T.ColorJitter):
def __call__(self, imgs, targets):
transform = self.get_params(self.brightness, self.contrast,
self.saturation, self.hue)
ret_imgs = []
for img_i, targets_i in zip(imgs, targets):
ret_imgs.append(transform(img_i))
return ret_imgs, targets
class Normalize(object):
def __init__(self, mean, std):
self.mean = mean
self.std = std
def __call__(self, image, target=None):
if target is not None:
target['ori_img'] = image.clone()
image = F.normalize(image, mean=self.mean, std=self.std)
if target is None:
return image, None
target = target.copy()
h, w = image.shape[-2:]
if "boxes" in target:
boxes = target["boxes"]
boxes = box_xyxy_to_cxcywh(boxes)
boxes = boxes / torch.tensor([w, h, w, h], dtype=torch.float32)
target["boxes"] = boxes
return image, target
class MotNormalize(Normalize):
def __call__(self, imgs, targets=None):
ret_imgs = []
ret_targets = []
for i in range(len(imgs)):
img_i = imgs[i]
targets_i = targets[i] if targets is not None else None
img_i, targets_i = super().__call__(img_i, targets_i)
ret_imgs.append(img_i)
ret_targets.append(targets_i)
return ret_imgs, ret_targets
class Compose(object):
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, image, target):
for t in self.transforms:
image, target = t(image, target)
return image, target
def __repr__(self):
format_string = self.__class__.__name__ + "("
for t in self.transforms:
format_string += "\n"
format_string += " {0}".format(t)
format_string += "\n)"
return format_string
class MotCompose(Compose):
def __call__(self, imgs, targets):
for t in self.transforms:
imgs, targets = t(imgs, targets)
return imgs, targets