PaddleClas/ppcls/data/preprocess/ops/random_erasing.py

56 lines
1.9 KiB
Python

# Copyright (c) 2021 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.
#This code is based on https://github.com/zhunzhong07/Random-Erasing
import math
import random
import numpy as np
class RandomErasing(object):
def __init__(self, EPSILON=0.5, sl=0.02, sh=0.4, r1=0.3,
mean=[0., 0., 0.]):
self.EPSILON = EPSILON
self.mean = mean
self.sl = sl
self.sh = sh
self.r1 = r1
def __call__(self, img):
if random.uniform(0, 1) > self.EPSILON:
return img
for _ in range(100):
area = img.shape[0] * img.shape[1]
target_area = random.uniform(self.sl, self.sh) * area
aspect_ratio = random.uniform(self.r1, 1 / self.r1)
h = int(round(math.sqrt(target_area * aspect_ratio)))
w = int(round(math.sqrt(target_area / aspect_ratio)))
if w < img.shape[1] and h < img.shape[0]:
x1 = random.randint(0, img.shape[0] - h)
y1 = random.randint(0, img.shape[1] - w)
if img.shape[0] == 3:
img[x1:x1 + h, y1:y1 + w, 0] = self.mean[0]
img[x1:x1 + h, y1:y1 + w, 1] = self.mean[1]
img[x1:x1 + h, y1:y1 + w, 2] = self.mean[2]
else:
img[0, x1:x1 + h, y1:y1 + w] = self.mean[1]
return img
return img