338 lines
10 KiB
Python
338 lines
10 KiB
Python
"""
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# 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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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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from functools import partial
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import six
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import math
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import random
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import cv2
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import numpy as np
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import importlib
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from PIL import Image
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from python.det_preprocess import DetNormalizeImage, DetPadStride, DetPermute, DetResize
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def create_operators(params):
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"""
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create operators based on the config
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Args:
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params(list): a dict list, used to create some operators
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"""
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assert isinstance(params, list), ('operator config should be a list')
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mod = importlib.import_module(__name__)
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ops = []
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for operator in params:
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assert isinstance(operator,
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dict) and len(operator) == 1, "yaml format error"
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op_name = list(operator)[0]
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param = {} if operator[op_name] is None else operator[op_name]
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op = getattr(mod, op_name)(**param)
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ops.append(op)
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return ops
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class UnifiedResize(object):
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def __init__(self, interpolation=None, backend="cv2"):
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_cv2_interp_from_str = {
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'nearest': cv2.INTER_NEAREST,
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'bilinear': cv2.INTER_LINEAR,
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'area': cv2.INTER_AREA,
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'bicubic': cv2.INTER_CUBIC,
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'lanczos': cv2.INTER_LANCZOS4
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}
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_pil_interp_from_str = {
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'nearest': Image.NEAREST,
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'bilinear': Image.BILINEAR,
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'bicubic': Image.BICUBIC,
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'box': Image.BOX,
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'lanczos': Image.LANCZOS,
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'hamming': Image.HAMMING
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}
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def _pil_resize(src, size, resample):
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pil_img = Image.fromarray(src)
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pil_img = pil_img.resize(size, resample)
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return np.asarray(pil_img)
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if backend.lower() == "cv2":
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if isinstance(interpolation, str):
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interpolation = _cv2_interp_from_str[interpolation.lower()]
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# compatible with opencv < version 4.4.0
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elif not interpolation:
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interpolation = cv2.INTER_LINEAR
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self.resize_func = partial(cv2.resize, interpolation=interpolation)
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elif backend.lower() == "pil":
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if isinstance(interpolation, str):
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interpolation = _pil_interp_from_str[interpolation.lower()]
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self.resize_func = partial(_pil_resize, resample=interpolation)
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else:
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logger.warning(
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f"The backend of Resize only support \"cv2\" or \"PIL\". \"f{backend}\" is unavailable. Use \"cv2\" instead."
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)
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self.resize_func = cv2.resize
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def __call__(self, src, size):
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return self.resize_func(src, size)
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class OperatorParamError(ValueError):
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""" OperatorParamError
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"""
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pass
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class DecodeImage(object):
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""" decode image """
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def __init__(self, to_rgb=True, to_np=False, channel_first=False):
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self.to_rgb = to_rgb
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self.to_np = to_np # to numpy
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self.channel_first = channel_first # only enabled when to_np is True
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def __call__(self, img):
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if six.PY2:
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assert type(img) is str and len(
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img) > 0, "invalid input 'img' in DecodeImage"
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else:
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assert type(img) is bytes and len(
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img) > 0, "invalid input 'img' in DecodeImage"
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data = np.frombuffer(img, dtype='uint8')
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img = cv2.imdecode(data, 1)
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if self.to_rgb:
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assert img.shape[2] == 3, 'invalid shape of image[%s]' % (
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img.shape)
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img = img[:, :, ::-1]
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if self.channel_first:
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img = img.transpose((2, 0, 1))
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return img
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class ResizeImage(object):
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""" resize image """
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def __init__(self,
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size=None,
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resize_short=None,
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interpolation=None,
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backend="cv2"):
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if resize_short is not None and resize_short > 0:
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self.resize_short = resize_short
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self.w = None
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self.h = None
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elif size is not None:
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self.resize_short = None
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self.w = size if type(size) is int else size[0]
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self.h = size if type(size) is int else size[1]
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else:
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raise OperatorParamError("invalid params for ReisizeImage for '\
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'both 'size' and 'resize_short' are None")
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self._resize_func = UnifiedResize(
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interpolation=interpolation, backend=backend)
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def __call__(self, img):
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img_h, img_w = img.shape[:2]
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if self.resize_short is not None:
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percent = float(self.resize_short) / min(img_w, img_h)
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w = int(round(img_w * percent))
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h = int(round(img_h * percent))
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else:
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w = self.w
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h = self.h
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return self._resize_func(img, (w, h))
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class CropImage(object):
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""" crop image """
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def __init__(self, size):
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if type(size) is int:
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self.size = (size, size)
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else:
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self.size = size # (h, w)
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def __call__(self, img):
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w, h = self.size
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img_h, img_w = img.shape[:2]
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if img_h < h or img_w < w:
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raise Exception(
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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."
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)
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w_start = (img_w - w) // 2
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h_start = (img_h - h) // 2
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w_end = w_start + w
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h_end = h_start + h
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return img[h_start:h_end, w_start:w_end, :]
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class RandCropImage(object):
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""" random crop image """
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def __init__(self,
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size,
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scale=None,
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ratio=None,
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interpolation=None,
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backend="cv2"):
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if type(size) is int:
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self.size = (size, size) # (h, w)
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else:
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self.size = size
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self.scale = [0.08, 1.0] if scale is None else scale
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self.ratio = [3. / 4., 4. / 3.] if ratio is None else ratio
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self._resize_func = UnifiedResize(
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interpolation=interpolation, backend=backend)
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def __call__(self, img):
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size = self.size
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scale = self.scale
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ratio = self.ratio
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aspect_ratio = math.sqrt(random.uniform(*ratio))
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w = 1. * aspect_ratio
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h = 1. / aspect_ratio
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img_h, img_w = img.shape[:2]
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bound = min((float(img_w) / img_h) / (w**2),
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(float(img_h) / img_w) / (h**2))
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scale_max = min(scale[1], bound)
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scale_min = min(scale[0], bound)
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target_area = img_w * img_h * random.uniform(scale_min, scale_max)
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target_size = math.sqrt(target_area)
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w = int(target_size * w)
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h = int(target_size * h)
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i = random.randint(0, img_w - w)
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j = random.randint(0, img_h - h)
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img = img[j:j + h, i:i + w, :]
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return self._resize_func(img, size)
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class RandFlipImage(object):
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""" random flip image
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flip_code:
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1: Flipped Horizontally
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0: Flipped Vertically
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-1: Flipped Horizontally & Vertically
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"""
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def __init__(self, flip_code=1):
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assert flip_code in [-1, 0, 1
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], "flip_code should be a value in [-1, 0, 1]"
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self.flip_code = flip_code
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def __call__(self, img):
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if random.randint(0, 1) == 1:
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return cv2.flip(img, self.flip_code)
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else:
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return img
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class AutoAugment(object):
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def __init__(self):
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self.policy = ImageNetPolicy()
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def __call__(self, img):
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from PIL import Image
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img = np.ascontiguousarray(img)
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img = Image.fromarray(img)
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img = self.policy(img)
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img = np.asarray(img)
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class NormalizeImage(object):
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""" normalize image such as substract mean, divide std
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"""
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def __init__(self,
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scale=None,
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mean=None,
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std=None,
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order='chw',
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output_fp16=False,
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channel_num=3):
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if isinstance(scale, str):
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scale = eval(scale)
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assert channel_num in [
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3, 4
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], "channel number of input image should be set to 3 or 4."
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self.channel_num = channel_num
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self.output_dtype = 'float16' if output_fp16 else 'float32'
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self.scale = np.float32(scale if scale is not None else 1.0 / 255.0)
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self.order = order
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mean = mean if mean is not None else [0.485, 0.456, 0.406]
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std = std if std is not None else [0.229, 0.224, 0.225]
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shape = (3, 1, 1) if self.order == 'chw' else (1, 1, 3)
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self.mean = np.array(mean).reshape(shape).astype('float32')
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self.std = np.array(std).reshape(shape).astype('float32')
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def __call__(self, img):
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from PIL import Image
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if isinstance(img, Image.Image):
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img = np.array(img)
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assert isinstance(img,
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np.ndarray), "invalid input 'img' in NormalizeImage"
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img = (img.astype('float32') * self.scale - self.mean) / self.std
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if self.channel_num == 4:
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img_h = img.shape[1] if self.order == 'chw' else img.shape[0]
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img_w = img.shape[2] if self.order == 'chw' else img.shape[1]
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pad_zeros = np.zeros(
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(1, img_h, img_w)) if self.order == 'chw' else np.zeros(
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(img_h, img_w, 1))
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img = (np.concatenate(
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(img, pad_zeros), axis=0)
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if self.order == 'chw' else np.concatenate(
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(img, pad_zeros), axis=2))
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return img.astype(self.output_dtype)
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class ToCHWImage(object):
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""" convert hwc image to chw image
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"""
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def __init__(self):
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pass
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def __call__(self, img):
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from PIL import Image
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if isinstance(img, Image.Image):
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img = np.array(img)
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return img.transpose((2, 0, 1))
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