/
operators.py
1361 lines (1218 loc) · 50.8 KB
/
operators.py
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# function:
# operators to process sample,
# eg: decode/resize/crop image
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
try:
from collections.abc import Sequence
except Exception:
from collections import Sequence
from numbers import Number
import uuid
import logging
import random
import math
import numpy as np
import cv2
from PIL import Image, ImageEnhance
from ppdet.core.workspace import serializable
from .op_helper import (satisfy_sample_constraint, filter_and_process,
generate_sample_bbox, clip_bbox, data_anchor_sampling,
satisfy_sample_constraint_coverage, crop_image_sampling,
generate_sample_bbox_square, bbox_area_sampling)
logger = logging.getLogger(__name__)
registered_ops = []
def register_op(cls):
registered_ops.append(cls.__name__)
if not hasattr(BaseOperator, cls.__name__):
setattr(BaseOperator, cls.__name__, cls)
else:
raise KeyError("The {} class has been registered.".format(cls.__name__))
return serializable(cls)
class BboxError(ValueError):
pass
class ImageError(ValueError):
pass
class BaseOperator(object):
def __init__(self, name=None):
if name is None:
name = self.__class__.__name__
self._id = name + '_' + str(uuid.uuid4())[-6:]
def __call__(self, sample, context=None):
""" Process a sample.
Args:
sample (dict): a dict of sample, eg: {'image':xx, 'label': xxx}
context (dict): info about this sample processing
Returns:
result (dict): a processed sample
"""
return sample
def __str__(self):
return str(self._id)
@register_op
class DecodeImage(BaseOperator):
def __init__(self, to_rgb=True, with_mixup=False):
""" Transform the image data to numpy format.
Args:
to_rgb (bool): whether to convert BGR to RGB
with_mixup (bool): whether or not to mixup image and gt_bbbox/gt_score
"""
super(DecodeImage, self).__init__()
self.to_rgb = to_rgb
self.with_mixup = with_mixup
if not isinstance(self.to_rgb, bool):
raise TypeError("{}: input type is invalid.".format(self))
if not isinstance(self.with_mixup, bool):
raise TypeError("{}: input type is invalid.".format(self))
def __call__(self, sample, context=None):
""" load image if 'im_file' field is not empty but 'image' is"""
if 'image' not in sample:
with open(sample['im_file'], 'rb') as f:
sample['image'] = f.read()
im = sample['image']
data = np.frombuffer(im, dtype='uint8')
im = cv2.imdecode(data, 1) # BGR mode, but need RGB mode
if self.to_rgb:
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
sample['image'] = im
if 'h' not in sample:
sample['h'] = im.shape[0]
if 'w' not in sample:
sample['w'] = im.shape[1]
# make default im_info with [h, w, 1]
sample['im_info'] = np.array(
[im.shape[0], im.shape[1], 1.], dtype=np.float32)
# decode mixup image
if self.with_mixup and 'mixup' in sample:
self.__call__(sample['mixup'], context)
return sample
@register_op
class MultiscaleTestResize(BaseOperator):
def __init__(self,
origin_target_size=800,
origin_max_size=1333,
target_size=[],
max_size=2000,
interp=cv2.INTER_LINEAR,
use_flip=True):
"""
Rescale image to the each size in target size, and capped at max_size.
Args:
origin_target_size(int): original target size of image's short side.
origin_max_size(int): original max size of image.
target_size (list): A list of target sizes of image's short side.
max_size (int): the max size of image.
interp (int): the interpolation method.
use_flip (bool): whether use flip augmentation.
"""
super(MultiscaleTestResize, self).__init__()
self.origin_target_size = int(origin_target_size)
self.origin_max_size = int(origin_max_size)
self.max_size = int(max_size)
self.interp = int(interp)
self.use_flip = use_flip
if not isinstance(target_size, list):
raise TypeError(
"Type of target_size is invalid. Must be List, now is {}".
format(type(target_size)))
self.target_size = target_size
if not (isinstance(self.origin_target_size, int) and isinstance(
self.origin_max_size, int) and isinstance(self.max_size, int)
and isinstance(self.interp, int)):
raise TypeError("{}: input type is invalid.".format(self))
def __call__(self, sample, context=None):
""" Resize the image numpy for multi-scale test.
"""
origin_ims = {}
im = sample['image']
if not isinstance(im, np.ndarray):
raise TypeError("{}: image type is not numpy.".format(self))
if len(im.shape) != 3:
raise ImageError('{}: image is not 3-dimensional.'.format(self))
im_shape = im.shape
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])
if float(im_size_min) == 0:
raise ZeroDivisionError('{}: min size of image is 0'.format(self))
base_name_list = ['image']
origin_ims['image'] = im
if self.use_flip:
sample['flip_image'] = im[:, ::-1, :]
base_name_list.append('flip_image')
origin_ims['flip_image'] = sample['flip_image']
im_info = []
for base_name in base_name_list:
im_scale = float(self.origin_target_size) / float(im_size_min)
# Prevent the biggest axis from being more than max_size
if np.round(im_scale * im_size_max) > self.origin_max_size:
im_scale = float(self.origin_max_size) / float(im_size_max)
im_scale_x = im_scale
im_scale_y = im_scale
resize_w = np.round(im_scale_x * float(im_shape[1]))
resize_h = np.round(im_scale_y * float(im_shape[0]))
im_resize = cv2.resize(
origin_ims[base_name],
None,
None,
fx=im_scale_x,
fy=im_scale_y,
interpolation=self.interp)
im_info.extend([resize_h, resize_w, im_scale])
sample[base_name] = im_resize
for i, size in enumerate(self.target_size):
im_scale = float(size) / float(im_size_min)
if np.round(im_scale * im_size_max) > self.max_size:
im_scale = float(self.max_size) / float(im_size_max)
im_scale_x = im_scale
im_scale_y = im_scale
resize_w = np.round(im_scale_x * float(im_shape[1]))
resize_h = np.round(im_scale_y * float(im_shape[0]))
im_resize = cv2.resize(
origin_ims[base_name],
None,
None,
fx=im_scale_x,
fy=im_scale_y,
interpolation=self.interp)
im_info.extend([resize_h, resize_w, im_scale])
name = base_name + '_scale_' + str(i)
sample[name] = im_resize
sample['im_info'] = np.array(im_info, dtype=np.float32)
return sample
@register_op
class ResizeImage(BaseOperator):
def __init__(self,
target_size=0,
max_size=0,
interp=cv2.INTER_LINEAR,
use_cv2=True):
"""
Rescale image to the specified target size, and capped at max_size
if max_size != 0.
If target_size is list, selected a scale randomly as the specified
target size.
Args:
target_size (int|list): the target size of image's short side,
multi-scale training is adopted when type is list.
max_size (int): the max size of image
interp (int): the interpolation method
use_cv2 (bool): use the cv2 interpolation method or use PIL
interpolation method
"""
super(ResizeImage, self).__init__()
self.max_size = int(max_size)
self.interp = int(interp)
self.use_cv2 = use_cv2
if not (isinstance(target_size, int) or isinstance(target_size, list)):
raise TypeError(
"Type of target_size is invalid. Must be Integer or List, now is {}".
format(type(target_size)))
self.target_size = target_size
if not (isinstance(self.max_size, int) and isinstance(self.interp,
int)):
raise TypeError("{}: input type is invalid.".format(self))
def __call__(self, sample, context=None):
""" Resize the image numpy.
"""
im = sample['image']
if not isinstance(im, np.ndarray):
raise TypeError("{}: image type is not numpy.".format(self))
if len(im.shape) != 3:
raise ImageError('{}: image is not 3-dimensional.'.format(self))
im_shape = im.shape
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])
if isinstance(self.target_size, list):
# Case for multi-scale training
selected_size = random.choice(self.target_size)
else:
selected_size = self.target_size
if float(im_size_min) == 0:
raise ZeroDivisionError('{}: min size of image is 0'.format(self))
if self.max_size != 0:
im_scale = float(selected_size) / float(im_size_min)
# Prevent the biggest axis from being more than max_size
if np.round(im_scale * im_size_max) > self.max_size:
im_scale = float(self.max_size) / float(im_size_max)
im_scale_x = im_scale
im_scale_y = im_scale
resize_w = np.round(im_scale_x * float(im_shape[1]))
resize_h = np.round(im_scale_y * float(im_shape[0]))
im_info = [resize_h, resize_w, im_scale]
if 'im_info' in sample and sample['im_info'][2] != 1.:
sample['im_info'] = np.append(
list(sample['im_info']), im_info).astype(np.float32)
else:
sample['im_info'] = np.array(im_info).astype(np.float32)
else:
im_scale_x = float(selected_size) / float(im_shape[1])
im_scale_y = float(selected_size) / float(im_shape[0])
resize_w = selected_size
resize_h = selected_size
if self.use_cv2:
im = cv2.resize(
im,
None,
None,
fx=im_scale_x,
fy=im_scale_y,
interpolation=self.interp)
else:
im = Image.fromarray(im)
im = im.resize((resize_w, resize_h), self.interp)
im = np.array(im)
sample['image'] = im
return sample
@register_op
class RandomFlipImage(BaseOperator):
def __init__(self, prob=0.5, is_normalized=False, is_mask_flip=False):
"""
Args:
prob (float): the probability of flipping image
is_normalized (bool): whether the bbox scale to [0,1]
is_mask_flip (bool): whether flip the segmentation
"""
super(RandomFlipImage, self).__init__()
self.prob = prob
self.is_normalized = is_normalized
self.is_mask_flip = is_mask_flip
if not (isinstance(self.prob, float) and
isinstance(self.is_normalized, bool) and
isinstance(self.is_mask_flip, bool)):
raise TypeError("{}: input type is invalid.".format(self))
def flip_segms(self, segms, height, width):
def _flip_poly(poly, width):
flipped_poly = np.array(poly)
flipped_poly[0::2] = width - np.array(poly[0::2]) - 1
return flipped_poly.tolist()
def _flip_rle(rle, height, width):
if 'counts' in rle and type(rle['counts']) == list:
rle = mask_util.frPyObjects([rle], height, width)
mask = mask_util.decode(rle)
mask = mask[:, ::-1, :]
rle = mask_util.encode(np.array(mask, order='F', dtype=np.uint8))
return rle
def is_poly(segm):
assert isinstance(segm, (list, dict)), \
"Invalid segm type: {}".format(type(segm))
return isinstance(segm, list)
flipped_segms = []
for segm in segms:
if is_poly(segm):
# Polygon format
flipped_segms.append([_flip_poly(poly, width) for poly in segm])
else:
# RLE format
import pycocotools.mask as mask_util
flipped_segms.append(_flip_rle(segm, height, width))
return flipped_segms
def __call__(self, sample, context=None):
"""Filp the image and bounding box.
Operators:
1. Flip the image numpy.
2. Transform the bboxes' x coordinates.
(Must judge whether the coordinates are normalized!)
3. Transform the segmentations' x coordinates.
(Must judge whether the coordinates are normalized!)
Output:
sample: the image, bounding box and segmentation part
in sample are flipped.
"""
gt_bbox = sample['gt_bbox']
im = sample['image']
if not isinstance(im, np.ndarray):
raise TypeError("{}: image is not a numpy array.".format(self))
if len(im.shape) != 3:
raise ImageError("{}: image is not 3-dimensional.".format(self))
height, width, _ = im.shape
if np.random.uniform(0, 1) < self.prob:
im = im[:, ::-1, :]
if gt_bbox.shape[0] == 0:
return sample
oldx1 = gt_bbox[:, 0].copy()
oldx2 = gt_bbox[:, 2].copy()
if self.is_normalized:
gt_bbox[:, 0] = 1 - oldx2
gt_bbox[:, 2] = 1 - oldx1
else:
gt_bbox[:, 0] = width - oldx2 - 1
gt_bbox[:, 2] = width - oldx1 - 1
if gt_bbox.shape[0] != 0 and (gt_bbox[:, 2] < gt_bbox[:, 0]).all():
m = "{}: invalid box, x2 should be greater than x1".format(self)
raise BboxError(m)
sample['gt_bbox'] = gt_bbox
if self.is_mask_flip and len(sample['gt_poly']) != 0:
sample['gt_poly'] = self.flip_segms(sample['gt_poly'], height,
width)
sample['flipped'] = True
sample['image'] = im
return sample
@register_op
class NormalizeImage(BaseOperator):
def __init__(self,
mean=[0.485, 0.456, 0.406],
std=[1, 1, 1],
is_scale=True,
is_channel_first=True):
"""
Args:
mean (list): the pixel mean
std (list): the pixel variance
"""
super(NormalizeImage, self).__init__()
self.mean = mean
self.std = std
self.is_scale = is_scale
self.is_channel_first = is_channel_first
if not (isinstance(self.mean, list) and isinstance(self.std, list) and
isinstance(self.is_scale, bool)):
raise TypeError("{}: input type is invalid.".format(self))
from functools import reduce
if reduce(lambda x, y: x * y, self.std) == 0:
raise ValueError('{}: std is invalid!'.format(self))
def __call__(self, sample, context=None):
"""Normalize the image.
Operators:
1.(optional) Scale the image to [0,1]
2. Each pixel minus mean and is divided by std
"""
for k in sample.keys():
if 'image' in k:
im = sample[k]
im = im.astype(np.float32, copy=False)
if self.is_channel_first:
mean = np.array(self.mean)[:, np.newaxis, np.newaxis]
std = np.array(self.std)[:, np.newaxis, np.newaxis]
else:
mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
std = np.array(self.std)[np.newaxis, np.newaxis, :]
if self.is_scale:
im = im / 255.0
im -= mean
im /= std
sample[k] = im
return sample
@register_op
class RandomDistort(BaseOperator):
def __init__(self,
brightness_lower=0.5,
brightness_upper=1.5,
contrast_lower=0.5,
contrast_upper=1.5,
saturation_lower=0.5,
saturation_upper=1.5,
hue_lower=-18,
hue_upper=18,
brightness_prob=0.5,
contrast_prob=0.5,
saturation_prob=0.5,
hue_prob=0.5,
count=4,
is_order=False):
"""
Args:
brightness_lower/ brightness_upper (float): the brightness
between brightness_lower and brightness_upper
contrast_lower/ contrast_upper (float): the contrast between
contrast_lower and contrast_lower
saturation_lower/ saturation_upper (float): the saturation
between saturation_lower and saturation_upper
hue_lower/ hue_upper (float): the hue between
hue_lower and hue_upper
brightness_prob (float): the probability of changing brightness
contrast_prob (float): the probability of changing contrast
saturation_prob (float): the probability of changing saturation
hue_prob (float): the probability of changing hue
count (int): the kinds of doing distrot
is_order (bool): whether determine the order of distortion
"""
super(RandomDistort, self).__init__()
self.brightness_lower = brightness_lower
self.brightness_upper = brightness_upper
self.contrast_lower = contrast_lower
self.contrast_upper = contrast_upper
self.saturation_lower = saturation_lower
self.saturation_upper = saturation_upper
self.hue_lower = hue_lower
self.hue_upper = hue_upper
self.brightness_prob = brightness_prob
self.contrast_prob = contrast_prob
self.saturation_prob = saturation_prob
self.hue_prob = hue_prob
self.count = count
self.is_order = is_order
def random_brightness(self, img):
brightness_delta = np.random.uniform(self.brightness_lower,
self.brightness_upper)
prob = np.random.uniform(0, 1)
if prob < self.brightness_prob:
img = ImageEnhance.Brightness(img).enhance(brightness_delta)
return img
def random_contrast(self, img):
contrast_delta = np.random.uniform(self.contrast_lower,
self.contrast_upper)
prob = np.random.uniform(0, 1)
if prob < self.contrast_prob:
img = ImageEnhance.Contrast(img).enhance(contrast_delta)
return img
def random_saturation(self, img):
saturation_delta = np.random.uniform(self.saturation_lower,
self.saturation_upper)
prob = np.random.uniform(0, 1)
if prob < self.saturation_prob:
img = ImageEnhance.Color(img).enhance(saturation_delta)
return img
def random_hue(self, img):
hue_delta = np.random.uniform(self.hue_lower, self.hue_upper)
prob = np.random.uniform(0, 1)
if prob < self.hue_prob:
img = np.array(img.convert('HSV'))
img[:, :, 0] = img[:, :, 0] + hue_delta
img = Image.fromarray(img, mode='HSV').convert('RGB')
return img
def __call__(self, sample, context):
"""random distort the image"""
ops = [
self.random_brightness, self.random_contrast,
self.random_saturation, self.random_hue
]
if self.is_order:
prob = np.random.uniform(0, 1)
if prob < 0.5:
ops = [
self.random_brightness,
self.random_saturation,
self.random_hue,
self.random_contrast,
]
else:
ops = random.sample(ops, self.count)
assert 'image' in sample, "image data not found"
im = sample['image']
im = Image.fromarray(im)
for id in range(self.count):
im = ops[id](im)
im = np.asarray(im)
sample['image'] = im
return sample
@register_op
class ExpandImage(BaseOperator):
def __init__(self, max_ratio, prob, mean=[127.5, 127.5, 127.5]):
"""
Args:
max_ratio (float): the ratio of expanding
prob (float): the probability of expanding image
mean (list): the pixel mean
"""
super(ExpandImage, self).__init__()
self.max_ratio = max_ratio
self.mean = mean
self.prob = prob
def __call__(self, sample, context):
"""
Expand the image and modify bounding box.
Operators:
1. Scale the image width and height.
2. Construct new images with new height and width.
3. Fill the new image with the mean.
4. Put original imge into new image.
5. Rescale the bounding box.
6. Determine if the new bbox is satisfied in the new image.
Returns:
sample: the image, bounding box are replaced.
"""
prob = np.random.uniform(0, 1)
assert 'image' in sample, 'not found image data'
im = sample['image']
gt_bbox = sample['gt_bbox']
gt_class = sample['gt_class']
im_width = sample['w']
im_height = sample['h']
if prob < self.prob:
if self.max_ratio - 1 >= 0.01:
expand_ratio = np.random.uniform(1, self.max_ratio)
height = int(im_height * expand_ratio)
width = int(im_width * expand_ratio)
h_off = math.floor(np.random.uniform(0, height - im_height))
w_off = math.floor(np.random.uniform(0, width - im_width))
expand_bbox = [
-w_off / im_width, -h_off / im_height,
(width - w_off) / im_width, (height - h_off) / im_height
]
expand_im = np.ones((height, width, 3))
expand_im = np.uint8(expand_im * np.squeeze(self.mean))
expand_im = Image.fromarray(expand_im)
im = Image.fromarray(im)
expand_im.paste(im, (int(w_off), int(h_off)))
expand_im = np.asarray(expand_im)
gt_bbox, gt_class, _ = filter_and_process(expand_bbox, gt_bbox,
gt_class)
sample['image'] = expand_im
sample['gt_bbox'] = gt_bbox
sample['gt_class'] = gt_class
sample['w'] = width
sample['h'] = height
return sample
@register_op
class CropImage(BaseOperator):
def __init__(self, batch_sampler, satisfy_all=False, avoid_no_bbox=True):
"""
Args:
batch_sampler (list): Multiple sets of different
parameters for cropping.
satisfy_all (bool): whether all boxes must satisfy.
e.g.[[1, 1, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.1, 1.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.3, 1.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.5, 1.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.7, 1.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.9, 1.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.0, 1.0]]
[max sample, max trial, min scale, max scale,
min aspect ratio, max aspect ratio,
min overlap, max overlap]
avoid_no_bbox (bool): whether to to avoid the
situation where the box does not appear.
"""
super(CropImage, self).__init__()
self.batch_sampler = batch_sampler
self.satisfy_all = satisfy_all
self.avoid_no_bbox = avoid_no_bbox
def __call__(self, sample, context):
"""
Crop the image and modify bounding box.
Operators:
1. Scale the image width and height.
2. Crop the image according to a radom sample.
3. Rescale the bounding box.
4. Determine if the new bbox is satisfied in the new image.
Returns:
sample: the image, bounding box are replaced.
"""
assert 'image' in sample, "image data not found"
im = sample['image']
gt_bbox = sample['gt_bbox']
gt_class = sample['gt_class']
im_width = sample['w']
im_height = sample['h']
gt_score = None
if 'gt_score' in sample:
gt_score = sample['gt_score']
sampled_bbox = []
gt_bbox = gt_bbox.tolist()
for sampler in self.batch_sampler:
found = 0
for i in range(sampler[1]):
if found >= sampler[0]:
break
sample_bbox = generate_sample_bbox(sampler)
if satisfy_sample_constraint(sampler, sample_bbox, gt_bbox,
self.satisfy_all):
sampled_bbox.append(sample_bbox)
found = found + 1
im = np.array(im)
while sampled_bbox:
idx = int(np.random.uniform(0, len(sampled_bbox)))
sample_bbox = sampled_bbox.pop(idx)
sample_bbox = clip_bbox(sample_bbox)
crop_bbox, crop_class, crop_score = \
filter_and_process(sample_bbox, gt_bbox, gt_class, gt_score)
if self.avoid_no_bbox:
if len(crop_bbox) < 1:
continue
xmin = int(sample_bbox[0] * im_width)
xmax = int(sample_bbox[2] * im_width)
ymin = int(sample_bbox[1] * im_height)
ymax = int(sample_bbox[3] * im_height)
im = im[ymin:ymax, xmin:xmax]
sample['image'] = im
sample['gt_bbox'] = crop_bbox
sample['gt_class'] = crop_class
sample['gt_score'] = crop_score
return sample
return sample
@register_op
class CropImageWithDataAchorSampling(BaseOperator):
def __init__(self,
batch_sampler,
anchor_sampler=None,
target_size=None,
das_anchor_scales=[16, 32, 64, 128],
sampling_prob=0.5,
min_size=8.,
avoid_no_bbox=True):
"""
Args:
anchor_sampler (list): anchor_sampling sets of different
parameters for cropping.
batch_sampler (list): Multiple sets of different
parameters for cropping.
e.g.[[1, 10, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.2, 0.0]]
[[1, 50, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0],
[1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0],
[1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0],
[1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0],
[1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]]
[max sample, max trial, min scale, max scale,
min aspect ratio, max aspect ratio,
min overlap, max overlap, min coverage, max coverage]
target_size (bool): target image size.
das_anchor_scales (list[float]): a list of anchor scales in data
anchor smapling.
min_size (float): minimum size of sampled bbox.
avoid_no_bbox (bool): whether to to avoid the
situation where the box does not appear.
"""
super(CropImageWithDataAchorSampling, self).__init__()
self.anchor_sampler = anchor_sampler
self.batch_sampler = batch_sampler
self.target_size = target_size
self.sampling_prob = sampling_prob
self.min_size = min_size
self.avoid_no_bbox = avoid_no_bbox
self.das_anchor_scales = np.array(das_anchor_scales)
def __call__(self, sample, context):
"""
Crop the image and modify bounding box.
Operators:
1. Scale the image width and height.
2. Crop the image according to a radom sample.
3. Rescale the bounding box.
4. Determine if the new bbox is satisfied in the new image.
Returns:
sample: the image, bounding box are replaced.
"""
assert 'image' in sample, "image data not found"
im = sample['image']
gt_bbox = sample['gt_bbox']
gt_class = sample['gt_class']
image_width = sample['w']
image_height = sample['h']
gt_score = None
if 'gt_score' in sample:
gt_score = sample['gt_score']
sampled_bbox = []
gt_bbox = gt_bbox.tolist()
prob = np.random.uniform(0., 1.)
if prob > self.sampling_prob: # anchor sampling
assert self.anchor_sampler
for sampler in self.anchor_sampler:
found = 0
for i in range(sampler[1]):
if found >= sampler[0]:
break
sample_bbox = data_anchor_sampling(
gt_bbox, image_width, image_height,
self.das_anchor_scales, self.target_size)
if sample_bbox == 0:
break
if satisfy_sample_constraint_coverage(sampler, sample_bbox,
gt_bbox):
sampled_bbox.append(sample_bbox)
found = found + 1
im = np.array(im)
while sampled_bbox:
idx = int(np.random.uniform(0, len(sampled_bbox)))
sample_bbox = sampled_bbox.pop(idx)
crop_bbox, crop_class, crop_score = filter_and_process(
sample_bbox, gt_bbox, gt_class, gt_score)
crop_bbox, crop_class, crop_score = bbox_area_sampling(
crop_bbox, crop_class, crop_score, self.target_size,
self.min_size)
if self.avoid_no_bbox:
if len(crop_bbox) < 1:
continue
im = crop_image_sampling(im, sample_bbox, image_width,
image_height, self.target_size)
sample['image'] = im
sample['gt_bbox'] = crop_bbox
sample['gt_class'] = crop_class
sample['gt_score'] = crop_score
return sample
return sample
else:
for sampler in self.batch_sampler:
found = 0
for i in range(sampler[1]):
if found >= sampler[0]:
break
sample_bbox = generate_sample_bbox_square(
sampler, image_width, image_height)
if satisfy_sample_constraint_coverage(sampler, sample_bbox,
gt_bbox):
sampled_bbox.append(sample_bbox)
found = found + 1
im = np.array(im)
while sampled_bbox:
idx = int(np.random.uniform(0, len(sampled_bbox)))
sample_bbox = sampled_bbox.pop(idx)
sample_bbox = clip_bbox(sample_bbox)
crop_bbox, crop_class, crop_score = filter_and_process(
sample_bbox, gt_bbox, gt_class, gt_score)
# sampling bbox according the bbox area
crop_bbox, crop_class, crop_score = bbox_area_sampling(
crop_bbox, crop_class, crop_score, self.target_size,
self.min_size)
if self.avoid_no_bbox:
if len(crop_bbox) < 1:
continue
xmin = int(sample_bbox[0] * image_width)
xmax = int(sample_bbox[2] * image_width)
ymin = int(sample_bbox[1] * image_height)
ymax = int(sample_bbox[3] * image_height)
im = im[ymin:ymax, xmin:xmax]
sample['image'] = im
sample['gt_bbox'] = crop_bbox
sample['gt_class'] = crop_class
sample['gt_score'] = crop_score
return sample
return sample
@register_op
class NormalizeBox(BaseOperator):
"""Transform the bounding box's coornidates to [0,1]."""
def __init__(self):
super(NormalizeBox, self).__init__()
def __call__(self, sample, context):
gt_bbox = sample['gt_bbox']
width = sample['w']
height = sample['h']
for i in range(gt_bbox.shape[0]):
gt_bbox[i][0] = gt_bbox[i][0] / width
gt_bbox[i][1] = gt_bbox[i][1] / height
gt_bbox[i][2] = gt_bbox[i][2] / width
gt_bbox[i][3] = gt_bbox[i][3] / height
sample['gt_bbox'] = gt_bbox
return sample
@register_op
class Permute(BaseOperator):
def __init__(self, to_bgr=True, channel_first=True):
"""
Change the channel.
Args:
to_bgr (bool): confirm whether to convert RGB to BGR
channel_first (bool): confirm whether to change channel
"""
super(Permute, self).__init__()
self.to_bgr = to_bgr
self.channel_first = channel_first
if not (isinstance(self.to_bgr, bool) and
isinstance(self.channel_first, bool)):
raise TypeError("{}: input type is invalid.".format(self))
def __call__(self, sample, context=None):
assert 'image' in sample, "image data not found"
for k in sample.keys():
if 'image' in k:
im = sample[k]
if self.channel_first:
im = np.swapaxes(im, 1, 2)
im = np.swapaxes(im, 1, 0)
if self.to_bgr:
im = im[[2, 1, 0], :, :]
sample[k] = im
return sample
@register_op
class MixupImage(BaseOperator):
def __init__(self, alpha=1.5, beta=1.5):
""" Mixup image and gt_bbbox/gt_score
Args:
alpha (float): alpha parameter of beta distribute
beta (float): beta parameter of beta distribute
"""
super(MixupImage, self).__init__()
self.alpha = alpha
self.beta = beta
if self.alpha <= 0.0:
raise ValueError("alpha shold be positive in {}".format(self))
if self.beta <= 0.0:
raise ValueError("beta shold be positive in {}".format(self))
def _mixup_img(self, img1, img2, factor):
h = max(img1.shape[0], img2.shape[0])
w = max(img1.shape[1], img2.shape[1])
img = np.zeros((h, w, img1.shape[2]), 'float32')
img[:img1.shape[0], :img1.shape[1], :] = \
img1.astype('float32') * factor
img[:img2.shape[0], :img2.shape[1], :] += \
img2.astype('float32') * (1.0 - factor)
return img.astype('uint8')
def __call__(self, sample, context=None):
if 'mixup' not in sample:
return sample
factor = np.random.beta(self.alpha, self.beta)
factor = max(0.0, min(1.0, factor))
if factor >= 1.0:
sample.pop('mixup')
return sample
if factor <= 0.0:
return sample['mixup']
im = self._mixup_img(sample['image'], sample['mixup']['image'], factor)
gt_bbox1 = sample['gt_bbox']
gt_bbox2 = sample['mixup']['gt_bbox']
gt_bbox = np.concatenate((gt_bbox1, gt_bbox2), axis=0)
gt_class1 = sample['gt_class']
gt_class2 = sample['mixup']['gt_class']
gt_class = np.concatenate((gt_class1, gt_class2), axis=0)
gt_score1 = sample['gt_score']
gt_score2 = sample['mixup']['gt_score']
gt_score = np.concatenate(
(gt_score1 * factor, gt_score2 * (1. - factor)), axis=0)
sample['image'] = im
sample['gt_bbox'] = gt_bbox
sample['gt_score'] = gt_score
sample['gt_class'] = gt_class
sample['h'] = im.shape[0]
sample['w'] = im.shape[1]
sample.pop('mixup')
return sample
@register_op
class RandomInterpImage(BaseOperator):
def __init__(self, target_size=0, max_size=0):
"""
Random reisze image by multiply interpolate method.
Args:
target_size (int): the taregt size of image's short side
max_size (int): the max size of image
"""
super(RandomInterpImage, self).__init__()
self.target_size = target_size
self.max_size = max_size
if not (isinstance(self.target_size, int) and
isinstance(self.max_size, int)):
raise TypeError('{}: input type is invalid.'.format(self))
interps = [
cv2.INTER_NEAREST,
cv2.INTER_LINEAR,
cv2.INTER_AREA,
cv2.INTER_CUBIC,
cv2.INTER_LANCZOS4,
]
self.resizers = []
for interp in interps:
self.resizers.append(ResizeImage(target_size, max_size, interp))
def __call__(self, sample, context=None):
"""Resise the image numpy by random resizer."""
resizer = random.choice(self.resizers)
return resizer(sample, context)