图像扭曲代码

数据增强
[TOC]

1.图像增强

# _*_ coding:utf-8 _*_
"""
Deep learning image augmentation
cited from https://scottontechnology.com/flip-image-opencv-python/
http://augmentor.readthedocs.io/en/master/userguide/mainfeatures.html
"""
import cv2
import glob
import random
import os
from multiprocessing import Pool as ProcessPool
from multiprocessing.dummy import Pool as ThreadPool
import Augmentor
import numpy as np

2.终极执行扩增函数

def augmentation():
    path = r'C:\Users\aixin\Desktop\all_my_learning\match\niu_qu\original_path'
    output_path = r'C:\Users\aixin\Desktop\all_my_learning\match\niu_qu\niuqu_path'
    p = Augmentor.Pipeline(path, output_directory=output_path)
    # p.flip_left_right(probability=0.4)
    # p.flip_top_bottom(probability=0.6)
    # p.flip_random(probability=0.5)
    # p.crop_centre(probability=0.2, percentage_area=0.8)
    # p.crop_random(probability=0.6, percentage_area=0.7)
    # p.rotate(probability=0.2, max_left_rotation=10, max_right_rotation=16)
    # p.rotate_random_90(probability=0.5)
    # p.rotate180(probability=0.4)
    # p.rotate270(probability=0.3)
    p.zoom(probability=0.3, min_factor=1.1, max_factor=1.5)
    p.random_distortion(probability=0.5, grid_height=4, grid_width=4, magnitude=4)
    p.shear(probability=0.2, max_shear_left=15, max_shear_right=15)
    p.shear(probability=0.5, max_shear_left=15, max_shear_right=15)
    p.skew(probability=0.1, magnitude=0.6)
    p.skew_tilt(probability=0.2, magnitude=0.6)
    p.skew_corner(probability=0.2, magnitude=0.6)
    p.skew_top_bottom(probability=0.3, magnitude=0.6)
    p.skew_left_right(probability=0.2, magnitude=0.6)
    # SIZE = 4 * 5
    # 这里的size表示的是random_distortion随机产生的扩展个数
    p.sample(10)
if __name__ == '__main__':
    augmentation()
    pass

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