preprocess.py

"""
# Copyright (c) 2020 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.
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

from functools import partial
import six
import math
import random
import cv2
import numpy as np
import importlib
from PIL import Image
from paddle.vision.transforms import ToTensor, Normalize

from python.det_preprocess import DetNormalizeImage, DetPadStride, DetPermute, DetResize


def create_operators(params):
    """
    create operators based on the config

    Args:
        params(list): a dict list, used to create some operators
    """
    assert isinstance(params, list), ('operator config should be a list')
    mod = importlib.import_module(__name__)
    ops = []
    for operator in params:
        assert isinstance(operator,
                          dict) and len(operator) == 1, "yaml format error"
        op_name = list(operator)[0]
        param = {} if operator[op_name] is None else operator[op_name]
        op = getattr(mod, op_name)(**param)
        ops.append(op)

    return ops


class UnifiedResize(object):
    def __init__(self, interpolation=None, backend="cv2", return_numpy=True):
        _cv2_interp_from_str = {
            'nearest': cv2.INTER_NEAREST,
            'bilinear': cv2.INTER_LINEAR,
            'area': cv2.INTER_AREA,
            'bicubic': cv2.INTER_CUBIC,
            'lanczos': cv2.INTER_LANCZOS4,
            'random': (cv2.INTER_LINEAR, cv2.INTER_CUBIC)
        }
        _pil_interp_from_str = {
            'nearest': Image.NEAREST,
            'bilinear': Image.BILINEAR,
            'bicubic': Image.BICUBIC,
            'box': Image.BOX,
            'lanczos': Image.LANCZOS,
            'hamming': Image.HAMMING,
            'random': (Image.BILINEAR, Image.BICUBIC)
        }

        def _cv2_resize(src, size, resample):
            if isinstance(resample, tuple):
                resample = random.choice(resample)
            return cv2.resize(src, size, interpolation=resample)

        def _pil_resize(src, size, resample, return_numpy=True):
            if isinstance(resample, tuple):
                resample = random.choice(resample)
            if isinstance(src, np.ndarray):
                pil_img = Image.fromarray(src)
            else:
                pil_img = src
            pil_img = pil_img.resize(size, resample)
            if return_numpy:
                return np.asarray(pil_img)
            return pil_img

        if backend.lower() == "cv2":
            if isinstance(interpolation, str):
                interpolation = _cv2_interp_from_str[interpolation.lower()]
            # compatible with opencv < version 4.4.0
            elif interpolation is None:
                interpolation = cv2.INTER_LINEAR
            self.resize_func = partial(_cv2_resize, resample=interpolation)
        elif backend.lower() == "pil":
            if isinstance(interpolation, str):
                interpolation = _pil_interp_from_str[interpolation.lower()]
            self.resize_func = partial(
                _pil_resize, resample=interpolation, return_numpy=return_numpy)
        else:
            logger.warning(
                f"The backend of Resize only support \"cv2\" or \"PIL\". \"f{backend}\" is unavailable. Use \"cv2\" instead."
            )
            self.resize_func = cv2.resize

    def __call__(self, src, size):
        if isinstance(size, list):
            size = tuple(size)
        return self.resize_func(src, size)


class OperatorParamError(ValueError):
    """ OperatorParamError
    """
    pass


class DecodeImage(object):
    """ decode image """

    def __init__(self, to_rgb=True, to_np=False, channel_first=False):
        self.to_rgb = to_rgb
        self.to_np = to_np  # to numpy
        self.channel_first = channel_first  # only enabled when to_np is True

    def __call__(self, img):
        if six.PY2:
            assert type(img) is str and len(
                img) > 0, "invalid input 'img' in DecodeImage"
        else:
            assert type(img) is bytes and len(
                img) > 0, "invalid input 'img' in DecodeImage"
        data = np.frombuffer(img, dtype='uint8')
        img = cv2.imdecode(data, 1)
        if self.to_rgb:
            assert img.shape[2] == 3, 'invalid shape of image[%s]' % (
                img.shape)
            img = img[:, :, ::-1]

        if self.channel_first:
            img = img.transpose((2, 0, 1))

        return img


class ResizeImage(object):
    """ resize image """

    def __init__(self,
                 size=None,
                 resize_short=None,
                 interpolation=None,
                 backend="cv2",
                 return_numpy=True):
        if resize_short is not None and resize_short > 0:
            self.resize_short = resize_short
            self.w = None
            self.h = None
        elif size is not None:
            self.resize_short = None
            self.w = size if type(size) is int else size[0]
            self.h = size if type(size) is int else size[1]
        else:
            raise OperatorParamError("invalid params for ReisizeImage for '\
                'both 'size' and 'resize_short' are None")

        self._resize_func = UnifiedResize(
            interpolation=interpolation,
            backend=backend,
            return_numpy=return_numpy)

    def __call__(self, img):
        if isinstance(img, np.ndarray):
            img_h, img_w = img.shape[:2]
        else:
            img_w, img_h = img.size

        if self.resize_short is not None:
            percent = float(self.resize_short) / min(img_w, img_h)
            w = int(round(img_w * percent))
            h = int(round(img_h * percent))
        else:
            w = self.w
            h = self.h
        return self._resize_func(img, (w, h))


class CropImage(object):
    """ crop image """

    def __init__(self, size):
        if type(size) is int:
            self.size = (size, size)
        else:
            self.size = size  # (h, w)

    def __call__(self, img):
        w, h = self.size
        img_h, img_w = img.shape[:2]

        if img_h < h or img_w < w:
            raise Exception(
                f"The size({h}, {w}) of CropImage must be greater than size({img_h}, {img_w}) of image. Please check image original size and size of ResizeImage if used."
            )

        w_start = (img_w - w) // 2
        h_start = (img_h - h) // 2

        w_end = w_start + w
        h_end = h_start + h
        return img[h_start:h_end, w_start:w_end, :]


class RandCropImage(object):
    """ random crop image """

    def __init__(self,
                 size,
                 scale=None,
                 ratio=None,
                 interpolation=None,
                 backend="cv2"):
        if type(size) is int:
            self.size = (size, size)  # (h, w)
        else:
            self.size = size

        self.scale = [0.08, 1.0] if scale is None else scale
        self.ratio = [3. / 4., 4. / 3.] if ratio is None else ratio

        self._resize_func = UnifiedResize(
            interpolation=interpolation, backend=backend)

    def __call__(self, img):
        size = self.size
        scale = self.scale
        ratio = self.ratio

        aspect_ratio = math.sqrt(random.uniform(*ratio))
        w = 1. * aspect_ratio
        h = 1. / aspect_ratio

        img_h, img_w = img.shape[:2]

        bound = min((float(img_w) / img_h) / (w**2),
                    (float(img_h) / img_w) / (h**2))
        scale_max = min(scale[1], bound)
        scale_min = min(scale[0], bound)

        target_area = img_w * img_h * random.uniform(scale_min, scale_max)
        target_size = math.sqrt(target_area)
        w = int(target_size * w)
        h = int(target_size * h)

        i = random.randint(0, img_w - w)
        j = random.randint(0, img_h - h)

        img = img[j:j + h, i:i + w, :]

        return self._resize_func(img, size)


class RandFlipImage(object):
    """ random flip image
        flip_code:
            1: Flipped Horizontally
            0: Flipped Vertically
            -1: Flipped Horizontally & Vertically
    """

    def __init__(self, flip_code=1):
        assert flip_code in [-1, 0, 1
                             ], "flip_code should be a value in [-1, 0, 1]"
        self.flip_code = flip_code

    def __call__(self, img):
        if random.randint(0, 1) == 1:
            return cv2.flip(img, self.flip_code)
        else:
            return img


class AutoAugment(object):
    def __init__(self):
        self.policy = ImageNetPolicy()

    def __call__(self, img):
        from PIL import Image
        img = np.ascontiguousarray(img)
        img = Image.fromarray(img)
        img = self.policy(img)
        img = np.asarray(img)


class NormalizeImage(object):
    """ normalize image such as substract mean, divide std
    """

    def __init__(self,
                 scale=None,
                 mean=None,
                 std=None,
                 order='chw',
                 output_fp16=False,
                 channel_num=3):
        if isinstance(scale, str):
            scale = eval(scale)
        assert channel_num in [
            3, 4
        ], "channel number of input image should be set to 3 or 4."
        self.channel_num = channel_num
        self.output_dtype = 'float16' if output_fp16 else 'float32'
        self.scale = np.float32(scale if scale is not None else 1.0 / 255.0)
        self.order = order
        mean = mean if mean is not None else [0.485, 0.456, 0.406]
        std = std if std is not None else [0.229, 0.224, 0.225]

        shape = (3, 1, 1) if self.order == 'chw' else (1, 1, 3)
        self.mean = np.array(mean).reshape(shape).astype('float32')
        self.std = np.array(std).reshape(shape).astype('float32')

    def __call__(self, img):
        from PIL import Image
        if isinstance(img, Image.Image):
            img = np.array(img)

        assert isinstance(img,
                          np.ndarray), "invalid input 'img' in NormalizeImage"

        img = (img.astype('float32') * self.scale - self.mean) / self.std

        if self.channel_num == 4:
            img_h = img.shape[1] if self.order == 'chw' else img.shape[0]
            img_w = img.shape[2] if self.order == 'chw' else img.shape[1]
            pad_zeros = np.zeros(
                (1, img_h, img_w)) if self.order == 'chw' else np.zeros(
                    (img_h, img_w, 1))
            img = (np.concatenate(
                (img, pad_zeros), axis=0)
                   if self.order == 'chw' else np.concatenate(
                       (img, pad_zeros), axis=2))
        return img.astype(self.output_dtype)


class ToCHWImage(object):
    """ convert hwc image to chw image
    """

    def __init__(self):
        pass

    def __call__(self, img):
        from PIL import Image
        if isinstance(img, Image.Image):
            img = np.array(img)

        return img.transpose((2, 0, 1))