forked from PulseFocusPlatform/PulseFocusPlatform
206 lines
6.6 KiB
Python
206 lines
6.6 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import cv2
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import numpy as np
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def decode_image(im_file, im_info):
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"""read rgb image
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Args:
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im_file (str|np.ndarray): input can be image path or np.ndarray
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im_info (dict): info of image
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Returns:
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im (np.ndarray): processed image (np.ndarray)
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im_info (dict): info of processed image
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"""
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if isinstance(im_file, str):
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with open(im_file, 'rb') as f:
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im_read = f.read()
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data = np.frombuffer(im_read, dtype='uint8')
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im = cv2.imdecode(data, 1) # BGR mode, but need RGB mode
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im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
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else:
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im = im_file
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im_info['im_shape'] = np.array(im.shape[:2], dtype=np.float32)
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im_info['scale_factor'] = np.array([1., 1.], dtype=np.float32)
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return im, im_info
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class Resize(object):
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"""resize image by target_size and max_size
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Args:
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target_size (int): the target size of image
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keep_ratio (bool): whether keep_ratio or not, default true
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interp (int): method of resize
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"""
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def __init__(self, target_size, keep_ratio=True, interp=cv2.INTER_LINEAR):
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if isinstance(target_size, int):
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target_size = [target_size, target_size]
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self.target_size = target_size
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self.keep_ratio = keep_ratio
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self.interp = interp
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def __call__(self, im, im_info):
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"""
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Args:
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im (np.ndarray): image (np.ndarray)
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im_info (dict): info of image
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Returns:
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im (np.ndarray): processed image (np.ndarray)
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im_info (dict): info of processed image
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"""
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assert len(self.target_size) == 2
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assert self.target_size[0] > 0 and self.target_size[1] > 0
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im_channel = im.shape[2]
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im_scale_y, im_scale_x = self.generate_scale(im)
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im = cv2.resize(
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im,
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None,
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None,
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fx=im_scale_x,
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fy=im_scale_y,
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interpolation=self.interp)
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im_info['im_shape'] = np.array(im.shape[:2]).astype('float32')
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im_info['scale_factor'] = np.array(
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[im_scale_y, im_scale_x]).astype('float32')
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return im, im_info
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def generate_scale(self, im):
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"""
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Args:
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im (np.ndarray): image (np.ndarray)
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Returns:
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im_scale_x: the resize ratio of X
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im_scale_y: the resize ratio of Y
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"""
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origin_shape = im.shape[:2]
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im_c = im.shape[2]
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if self.keep_ratio:
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im_size_min = np.min(origin_shape)
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im_size_max = np.max(origin_shape)
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target_size_min = np.min(self.target_size)
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target_size_max = np.max(self.target_size)
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im_scale = float(target_size_min) / float(im_size_min)
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if np.round(im_scale * im_size_max) > target_size_max:
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im_scale = float(target_size_max) / float(im_size_max)
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im_scale_x = im_scale
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im_scale_y = im_scale
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else:
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resize_h, resize_w = self.target_size
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im_scale_y = resize_h / float(origin_shape[0])
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im_scale_x = resize_w / float(origin_shape[1])
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return im_scale_y, im_scale_x
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class NormalizeImage(object):
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"""normalize image
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Args:
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mean (list): im - mean
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std (list): im / std
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is_scale (bool): whether need im / 255
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is_channel_first (bool): if True: image shape is CHW, else: HWC
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"""
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def __init__(self, mean, std, is_scale=True):
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self.mean = mean
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self.std = std
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self.is_scale = is_scale
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def __call__(self, im, im_info):
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"""
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Args:
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im (np.ndarray): image (np.ndarray)
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im_info (dict): info of image
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Returns:
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im (np.ndarray): processed image (np.ndarray)
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im_info (dict): info of processed image
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"""
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im = im.astype(np.float32, copy=False)
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mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
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std = np.array(self.std)[np.newaxis, np.newaxis, :]
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if self.is_scale:
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im = im / 255.0
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im -= mean
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im /= std
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return im, im_info
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class Permute(object):
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"""permute image
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Args:
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to_bgr (bool): whether convert RGB to BGR
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channel_first (bool): whether convert HWC to CHW
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"""
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def __init__(self, ):
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super(Permute, self).__init__()
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def __call__(self, im, im_info):
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"""
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Args:
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im (np.ndarray): image (np.ndarray)
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im_info (dict): info of image
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Returns:
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im (np.ndarray): processed image (np.ndarray)
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im_info (dict): info of processed image
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"""
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im = im.transpose((2, 0, 1)).copy()
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return im, im_info
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class PadStride(object):
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""" padding image for model with FPN, instead PadBatch(pad_to_stride) in original config
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Args:
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stride (bool): model with FPN need image shape % stride == 0
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"""
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def __init__(self, stride=0):
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self.coarsest_stride = stride
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def __call__(self, im, im_info):
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"""
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Args:
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im (np.ndarray): image (np.ndarray)
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im_info (dict): info of image
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Returns:
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im (np.ndarray): processed image (np.ndarray)
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im_info (dict): info of processed image
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"""
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coarsest_stride = self.coarsest_stride
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if coarsest_stride <= 0:
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return im, im_info
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im_c, im_h, im_w = im.shape
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pad_h = int(np.ceil(float(im_h) / coarsest_stride) * coarsest_stride)
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pad_w = int(np.ceil(float(im_w) / coarsest_stride) * coarsest_stride)
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padding_im = np.zeros((im_c, pad_h, pad_w), dtype=np.float32)
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padding_im[:, :im_h, :im_w] = im
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return padding_im, im_info
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def preprocess(im, preprocess_ops):
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# process image by preprocess_ops
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im_info = {
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'scale_factor': np.array(
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[1., 1.], dtype=np.float32),
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'im_shape': None,
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}
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im, im_info = decode_image(im, im_info)
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for operator in preprocess_ops:
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im, im_info = operator(im, im_info)
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return im, im_info
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