person_monitor_fy/dependence/PaddleDetection/static/deploy/python/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

# Global dictionary
RESIZE_SCALE_SET = {
    'RCNN',
    'RetinaNet',
    'FCOS',
    'SOLOv2',
}


def decode_image(im_file, im_info):
    """read rgb image
    Args:
        im_file (str/np.ndarray): path of image/ np.ndarray read by cv2
        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)
        im_info['origin_shape'] = im.shape[:2]
        im_info['resize_shape'] = im.shape[:2]
    else:
        im = im_file
        im_info['origin_shape'] = im.shape[:2]
        im_info['resize_shape'] = im.shape[:2]
    return im, im_info


class Resize(object):
    """resize image by target_size and max_size
    Args:
        arch (str): model type
        target_size (int): the target size of image
        max_size (int): the max size of image
        use_cv2 (bool): whether us cv2
        image_shape (list): input shape of model
        interp (int): method of resize
    """

    def __init__(self,
                 arch,
                 target_size,
                 max_size,
                 use_cv2=True,
                 image_shape=None,
                 interp=cv2.INTER_LINEAR,
                 resize_box=False):
        self.target_size = target_size
        self.max_size = max_size
        self.image_shape = image_shape
        self.arch = arch
        self.use_cv2 = use_cv2
        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
        """
        im_channel = im.shape[2]
        im_scale_x, im_scale_y = self.generate_scale(im)
        im_info['resize_shape'] = [
            im_scale_x * float(im.shape[0]), im_scale_y * float(im.shape[1])
        ]
        if self.use_cv2:
            im = cv2.resize(
                im,
                None,
                None,
                fx=im_scale_x,
                fy=im_scale_y,
                interpolation=self.interp)
        else:
            resize_w = int(im_scale_x * float(im.shape[1]))
            resize_h = int(im_scale_y * float(im.shape[0]))
            if self.max_size != 0:
                raise TypeError(
                    'If you set max_size to cap the maximum size of image,'
                    'please set use_cv2 to True to resize the image.')
            im = im.astype('uint8')
            im = Image.fromarray(im)
            im = im.resize((int(resize_w), int(resize_h)), self.interp)
            im = np.array(im)

        # padding im when image_shape fixed by infer_cfg.yml
        if self.max_size != 0 and self.image_shape is not None:
            padding_im = np.zeros(
                (self.max_size, self.max_size, im_channel), dtype=np.float32)
            im_h, im_w = im.shape[:2]
            padding_im[:im_h, :im_w, :] = im
            im = padding_im

        im_info['scale'] = [im_scale_x, im_scale_y]
        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.max_size != 0 and self.arch in RESIZE_SCALE_SET:
            im_size_min = np.min(origin_shape[0:2])
            im_size_max = np.max(origin_shape[0:2])
            im_scale = float(self.target_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
        else:
            im_scale_x = float(self.target_size) / float(origin_shape[1])
            im_scale_y = float(self.target_size) / float(origin_shape[0])
        return im_scale_x, im_scale_y


class Normalize(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, is_channel_first=False):
        self.mean = mean
        self.std = std
        self.is_scale = is_scale
        self.is_channel_first = is_channel_first

    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)
        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
        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, to_bgr=False, channel_first=True):
        self.to_bgr = to_bgr
        self.channel_first = channel_first

    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
        """
        if self.channel_first:
            im = im.transpose((2, 0, 1)).copy()
        if self.to_bgr:
            im = im[[2, 1, 0], :, :]
        return im, im_info


class PadStride(object):
    """ padding image for model with FPN 
    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_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
        im_info['pad_shape'] = padding_im.shape[1:]
        return padding_im, im_info


def preprocess(im, preprocess_ops):
    # process image by preprocess_ops
    im_info = {
        'scale': [1., 1.],
        'origin_shape': None,
        'resize_shape': None,
        'pad_shape': None,
    }
    im, im_info = decode_image(im, im_info)
    for operator in preprocess_ops:
        im, im_info = operator(im, im_info)
    im = np.array((im, )).astype('float32')
    return im, im_info