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