operators.py 27.0 KB
Newer Older
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84
# 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

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)

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
85
            with_mixup (bool): whether or not to mixup image and gt_bbbox/gt_score
86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129
        """

        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 ResizeImage(BaseOperator):
    def __init__(self,
                 target_size=0,
                 max_size=0,
                 interp=cv2.INTER_LINEAR,
                 use_cv2=True):
        """
130 131 132 133 134
        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.

135
        Args:
136 137
            target_size (int|list): the target size of image's short side, 
                multi-scale training is adopted when type is list.
138 139
            max_size (int): the max size of image
            interp (int): the interpolation method
140 141
            use_cv2 (bool): use the cv2 interpolation method or use PIL 
                interpolation method
142 143 144 145 146
        """
        super(ResizeImage, self).__init__()
        self.max_size = int(max_size)
        self.interp = int(interp)
        self.use_cv2 = use_cv2
147 148 149 150 151 152 153
        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)):
154 155 156
            raise TypeError("{}: input type is invalid.".format(self))

    def __call__(self, sample, context=None):
W
wangguanzhong 已提交
157
        """ Resize the image numpy.
158 159 160 161 162 163 164 165 166
        """
        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])
167 168 169 170 171
        if isinstance(self.target_size, list):
            # Case for multi-scale training
            selected_size = random.choice(self.target_size)
        else:
            selected_size = self.target_size
172 173 174
        if float(im_size_min) == 0:
            raise ZeroDivisionError('{}: min size of image is 0'.format(self))
        if self.max_size != 0:
175
            im_scale = float(selected_size) / float(im_size_min)
176 177 178 179 180
            # 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
181 182 183 184

            resize_w = np.round(im_scale_x * float(im_shape[1]))
            resize_h = np.round(im_scale_y * float(im_shape[0]))

185
            sample['im_info'] = np.array(
186
                [resize_h, resize_w, im_scale], dtype=np.float32)
187
        else:
188 189
            im_scale_x = float(selected_size) / float(im_shape[1])
            im_scale_y = float(selected_size) / float(im_shape[0])
190 191 192 193

            resize_w = selected_size
            resize_h = selected_size

194 195 196 197 198 199 200 201 202 203
        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)
204
            im = im.resize((resize_w, resize_h), self.interp)
205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462
            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
        """
        im = sample['image']
        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['image'] = 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:
463
            max_ratio (float): the ratio of expanding
464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563
            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 weight 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.
            avoid_no_bbox (bool): whether to to avoid the 
                                  situation where the box does not appear.
            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]
        """
        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 weight 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']
564 565 566
        gt_score = None
        if 'gt_score' in sample:
            gt_score = sample['gt_score']
567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738
        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 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"
        im = sample['image']
        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['image'] = 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)