# 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) 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 """ 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] is not None: 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 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