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 PIL import Image
import cv2
import numpy as np


def decode_image(im_file, im_info):
    """read rgb image
    Args:
        im_file (str|np.ndarray): input can be image path or np.ndarray
        im_info (dict): info of image
    Returns:
        im (np.ndarray):  processed image (np.ndarray)
        im_info (dict): info of processed image
    """
    if isinstance(im_file, str):
        with open(im_file, 'rb') as f:
            im_read = f.read()
        data = np.frombuffer(im_read, dtype='uint8')
        im = cv2.imdecode(data, 1)  # BGR mode, but need RGB mode
        im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
    else:
        im = im_file
    im_info['im_shape'] = np.array(im.shape[:2], dtype=np.float32)
    im_info['scale_factor'] = np.array([1., 1.], dtype=np.float32)
    return im, im_info


class Resize(object):
    """resize image by target_size and max_size
    Args:
        target_size (int): the target size of image
        keep_ratio (bool): whether keep_ratio or not, default true
        interp (int): method of resize
    """

    def __init__(
            self,
            target_size,
            keep_ratio=True,
            interp=cv2.INTER_LINEAR, ):
        if isinstance(target_size, int):
            target_size = [target_size, target_size]
        self.target_size = target_size
        self.keep_ratio = keep_ratio
        self.interp = interp

    def __call__(self, im, im_info):
        """
        Args:
            im (np.ndarray): image (np.ndarray)
            im_info (dict): info of image
        Returns:
            im (np.ndarray):  processed image (np.ndarray)
            im_info (dict): info of processed image
        """
        assert len(self.target_size) == 2
        assert self.target_size[0] > 0 and self.target_size[1] > 0
        im_channel = im.shape[2]
        im_scale_y, im_scale_x = self.generate_scale(im)
        im = cv2.resize(
            im,
            None,
            None,
            fx=im_scale_x,
            fy=im_scale_y,
            interpolation=self.interp)
        im_info['im_shape'] = np.array(im.shape[:2]).astype('float32')
        im_info['scale_factor'] = np.array(
            [im_scale_y, im_scale_x]).astype('float32')
        # padding im when image_shape fixed by infer_cfg.yml
        if self.keep_ratio and im_info['input_shape'][1] != -1:
            max_size = im_info['input_shape'][1]
            padding_im = np.zeros(
                (max_size, max_size, im_channel), dtype=np.float32)
            im_h, im_w = im.shape[:2]
            padding_im[:im_h, :im_w, :] = im
            im = padding_im
        return im, im_info

    def generate_scale(self, im):
        """
        Args:
            im (np.ndarray): image (np.ndarray)
        Returns:
            im_scale_x: the resize ratio of X
            im_scale_y: the resize ratio of Y
        """
        origin_shape = im.shape[:2]
        im_c = im.shape[2]
        if self.keep_ratio:
            im_size_min = np.min(origin_shape)
            im_size_max = np.max(origin_shape)
            target_size_min = np.min(self.target_size)
            target_size_max = np.max(self.target_size)
            im_scale = float(target_size_min) / float(im_size_min)
            if np.round(im_scale * im_size_max) > target_size_max:
                im_scale = float(target_size_max) / float(im_size_max)
            im_scale_x = im_scale
            im_scale_y = im_scale
        else:
            resize_h, resize_w = self.target_size
            im_scale_y = resize_h / float(origin_shape[0])
            im_scale_x = resize_w / float(origin_shape[1])
        return im_scale_y, im_scale_x


class NormalizeImage(object):
    """normalize image
    Args:
        mean (list): im - mean
        std (list): im / std
        is_scale (bool): whether need im / 255
        is_channel_first (bool): if True: image shape is CHW, else: HWC
    """

    def __init__(self, mean, std, is_scale=True):
        self.mean = mean
        self.std = std
        self.is_scale = is_scale

    def __call__(self, im, im_info):
        """
        Args:
            im (np.ndarray): image (np.ndarray)
            im_info (dict): info of image
        Returns:
            im (np.ndarray):  processed image (np.ndarray)
            im_info (dict): info of processed image
        """
        im = im.astype(np.float32, copy=False)
        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
        return im, im_info


class Permute(object):
    """permute image
    Args:
        to_bgr (bool): whether convert RGB to BGR 
        channel_first (bool): whether convert HWC to CHW
    """

    def __init__(self, ):
        super(Permute, self).__init__()

    def __call__(self, im, im_info):
        """
        Args:
            im (np.ndarray): image (np.ndarray)
            im_info (dict): info of image
        Returns:
            im (np.ndarray):  processed image (np.ndarray)
            im_info (dict): info of processed image
        """
        im = im.transpose((2, 0, 1)).copy()
        return im, im_info


class PadStride(object):
    """ padding image for model with FPN, instead PadBatch(pad_to_stride) in original config
    Args:
        stride (bool): model with FPN need image shape % stride == 0
    """

    def __init__(self, stride=0):
        self.coarsest_stride = stride

    def __call__(self, im, im_info):
        """
        Args:
            im (np.ndarray): image (np.ndarray)
            im_info (dict): info of image
        Returns:
            im (np.ndarray):  processed image (np.ndarray)
            im_info (dict): info of processed image
        """
        coarsest_stride = self.coarsest_stride
        if coarsest_stride <= 0:
            return im, im_info
        im_c, im_h, im_w = im.shape
        pad_h = int(np.ceil(float(im_h) / coarsest_stride) * coarsest_stride)
        pad_w = int(np.ceil(float(im_w) / coarsest_stride) * coarsest_stride)
        padding_im = np.zeros((im_c, pad_h, pad_w), dtype=np.float32)
        padding_im[:, :im_h, :im_w] = im
        return padding_im, im_info


def preprocess(im, preprocess_ops, input_shape):
    # process image by preprocess_ops
    im_info = {
        'scale_factor': np.array(
            [1., 1.], dtype=np.float32),
        'im_shape': None,
        'input_shape': input_shape,
    }
    im, im_info = decode_image(im, im_info)
    for operator in preprocess_ops:
        im, im_info = operator(im, im_info)
    return im, im_info