图像增强等代码

数据增强
调用超级API： Auto Generated Documentation

增强代码参考链接

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
# import path_var

2.定义函数

def img_flip():
    # path = "F:/ad_samples/download_sample/14/8DB54D749B1D4A2D5FD3441C681D9A2C522453AC_s.jpg"
    path = r'C:\Users\aixin\Desktop\all_my_learning\match\niu_qu\original_path\A01_8.jpg'
    img = cv2.imread(path)
    horizontal_img = img.copy()
    vertical_img = img.copy()
    both_img = img.copy()
    horizontal_img = cv2.flip(img, 0)
    vertical_img = cv2.flip(img, 1)
    both_img = cv2.flip(img, -1)
    cv2.imshow("original img", img)
    cv2.imshow("horizontal img", horizontal_img)
    cv2.imshow("vertical img", vertical_img)
    cv2.imshow("both flip", both_img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
def flip_img_save2dir(file):
    img = cv2.imread(file)
    dst_dir = path_var.g_dst_dir
    h_img = img.copy()
    v_img = img.copy()
    b_img = img.copy()
    h_img = cv2.flip(img, 0)
    v_img = cv2.flip(img, 1)
    b_img = cv2.flip(img, -1)
    # file like F:/ad_samples/train_samples/ad_text_artifact/base_type/type_10.jpg
    # get file name "type_10"
    # type_10.jpg
    base_name = os.path.basename(file)
    # type_10
    base_name = os.path.splitext(base_name)[0]
    file_name = dst_dir + base_name + "_h" + ".jpg"
    cv2.imwrite(file_name, h_img)
    file_name = dst_dir + base_name + "_v" + ".jpg"
    cv2.imwrite(file_name, v_img)
    file_name = dst_dir + base_name + '_b' + ".jpg"
    cv2.imwrite(file_name, b_img)
def do_all_flip(base_dir="F:/ad_samples/train_samples/ad_web_2/"):
    """
    flip all the images in dir, and then save them
     to another dir
    :return:
    """
    # get all files
    files = glob.glob(base_dir + "/*.png")
    # like ['E:/img\\1.jpg', 'E:/img\\10.jpg']
    # start 3 process
    # pool = ProcessPool(3)
    pool = ThreadPool(3)
    rets = pool.map(flip_img_save2dir, files)
    pool.close()
    pool.join()
    print ('all images accomplish flip and save to dir')
def flip_all_in_dir():
    base_dir = 'F:/ad_samples/train_samples/others/'
    sub_dir_lst = glob.glob(base_dir + "*")
    # ['F:/dir1', 'F:/dir2']
    # print sub_dir_lst
    new_sub_dir = [os.path.join(base_dir, item + '_flip/') for item in os.listdir(base_dir)]
    # ['F:/dir1_flip', 'F:/dir2_flip']
    for dir_item, new_item in zip(sub_dir_lst[10:], new_sub_dir[10:]):
        global g_dst_dir
        if not os.path.exists(new_item):
            os.makedirs(new_item)
        # g_dst_dir = new_item
        # Path.g_dst_dir = new_item
        path_var.g_dst_dir = new_item
        print ('flip %s, flip dir %s' % (dir_item, new_item))
        do_all_flip(base_dir=dir_item)

3.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
    p.sample(10)
if __name__ == '__main__':
    # img_flip()
    # flip_all_in_dir()
    # do_all_flip()
    augmentation()
    # test single image flip and save
    # file = 'F:/ad_samples/train_samples/ad_text_artifact/base_type/type_10.jpg'
    # flip_img_save2dir(file=file)
    pass

4.opencv执行数据增强

import numpy as np
import cv2
# img_path = r'C:\Users\aixin\Desktop\Phebe.jpg'
img_path = r'C:\Users\aixin\Desktop\Bruce.png'
img = cv2.imread(img_path)
rows, cols, channels = img.shape
print(img.shape)
src = np.float32([[50,50],[200,50],[50,200]])
dst = np.float32([[10,100],[200,50],[100,250]])
pts1 = np.float32([[56,65],[368,52],[28,387],[389,390]])
pts2 = np.float32([[0,0],[300,0],[0,300],[300,300]])
M1 = cv2.getRotationMatrix2D((cols/2,rows/2),45,1)
M2 = cv2.getAffineTransform(src, dst)
M3 = cv2.getPerspectiveTransform(pts1, pts2)
dst1 = cv2.warpAffine(img,M1,(cols,rows))
dst2 = cv2.warpAffine(img,M2,(cols,rows))
dst3 = cv2.warpPerspective(img,M3,(cols,rows))
cv2.imshow('img', img)
cv2.imshow('dst1', dst1)
cv2.imshow('dst2', dst2)
cv2.imshow('dst3', dst3)
cv2.waitKey(0)
cv2.destroyAllWindows()

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