In depth learning training sample amplification and tag name modification

The previous one is in different folders , Modify by yourself

# -*- coding:utf-8 -*-
import sys
# sys.path.append('E:\\Anaconda\\libs')
import os  # os： Operating system related information modules 
import shutil
from os.path import join
import cv2
import numpy as np
import random  #  Import random functions 

os.environ["CUDA_VISIBLE_DEVICES"] = "0"
#  The address where the original picture is stored 
data_base_dir = '../switch/images/train2017'
label_path = '../switch/labels/train2017'
file_dir = '../switch/images/train2017'
dest_dir = "./VINS"
# dest_dir1 = r"E:\VINS\train01"
# dest_dir2 = r"E:\VINS\train02"
# dest_dir3 = r"E:\VINS\train03"
# dest_dir4 = r"E:\VINS\train04"
# dest_dir5 = r"E:\VINS\train05"
# dest_dir6 = r"E:\VINS\train06"
# dest_dir7 = r"E:\VINS\train07"
# dest_dir8 = r"E:\VINS\train08"
# dest_dir9 = r"E:\VINS\train09"
# dest_dir10 = r"E:\VINS\train10"
# dest_dir11 = r"E:\VINS\train11"
# dest_dir12 = r"E:\VINS\train12"

if not os.path.exists(dest_dir): # Judge whether there is a folder with this file name under the directory 
    os.mkdir(dest_dir)

file_list = []  #  Create a list , Used to save picture information 


#  Read picture file , And send the picture address 、 The picture name and label write txt In file 

def imgBrightness(img1, c, b):
    rows, cols, channels = img1.shape
    blank = np.zeros([rows, cols, channels], img1.dtype)
    rst = cv2.addWeighted(img1, c, blank, 1 - c, b)
    return rst


def noise(img, snr):
    h = img.shape[0]
    w = img.shape[1]
    img1 = img.copy()
    sp = h * w  #  Calculate the number of image pixels 
    NP = int(sp * (1 - snr))  #  Calculate the number of salt and pepper noise points in the image 
    for i in range(NP):
        randx = np.random.randint(1, h - 1)  #  Generate a  1  to  h-1  Random integer between 
        randy = np.random.randint(1, w - 1)  #  Generate a  1  to  w-1  Random integer between 
        if np.random.random() <= 0.5:  # np.random.random() Generate a  0  to  1  The floating point number between 
            img1[randx, randy] = 0
        else:
            img1[randx, randy] = 255
    return img1


for file in os.listdir(data_base_dir):  # file by current_dir Picture name in current directory 
    if file.endswith(".jpg"):  #  If file With jpg ending 
        for item in range(13):
            # print(file)
            imageName = "VIN" + str(item) + file[:-4] + '.jpg'
            labelName = "VIN" + str(item) + file[:-4] + '.txt'

            if item == 0:
                #  The original picture is not copied 
                scr_dir = join(file_dir, file)
                shutil.copyfile(scr_dir, os.path.join(dest_dir, imageName))
                # shutil.copyfile(scr_dir, det)

            #  Enhance the picture 
            if item == 1:
                img = cv2.imread(scr_dir, cv2.IMREAD_GRAYSCALE)

                gaussianBlur = cv2.GaussianBlur(img, (3, 3), 3)
                blur = cv2.blur(gaussianBlur, (5, 5))       #  Blur the picture 

                # cv2.imshow("img1", blur)
                # cv2.waitKey()
                det = join(dest_dir, imageName)
                cv2.imwrite(det, blur)

            if item == 2:
                img = cv2.imread(scr_dir)
                boxblur = cv2.boxFilter(img, -1, (9, 9))     #  Blur the picture *2
                # cv2.imshow("img2", boxblur)
                # cv2.waitKey()
                det = join(dest_dir, imageName)
                cv2.imwrite(det, boxblur)

            #  Add salt and pepper and make less noise 
            if item == 3:
                img = cv2.imread(scr_dir, cv2.IMREAD_GRAYSCALE)
                noiseimage = noise(img, 0.98)  #  Set the signal-to-noise ratio to 0.6
                # cv2.imshow("img3", noiseimage)
                # cv2.waitKey()
                det = join(dest_dir, imageName)
                cv2.imwrite(det, noiseimage)

            # #  Add salt and pepper to the noise 
            # if item == 4:
            #     img = cv2.imread(scr_dir)
            #     noiseimage4 = noise(img, 0.90)  #  Set the signal-to-noise ratio to 0.6
            #     # cv2.imshow("img4", noiseimage4)
            #     # cv2.waitKey()
            #     det = join(dest_dir, imageName)
            #     cv2.imwrite(det, noiseimage4)
            #
            # #  Add more salt and pepper 
            # if item == 5:
            #     img = cv2.imread(scr_dir, cv2.IMREAD_GRAYSCALE)
            #     noiseimage5 = noise(img, 0.86)  #  Set the signal-to-noise ratio to 0.6
            #     cv2.imshow("img5", noiseimage5)
            #     cv2.waitKey()
            #     det = join(dest_dir, imageName)
            #     cv2.imwrite(det, noiseimage5)

            #  Add salt and pepper noise and Gaussian Blur 
            if item == 6:
                img = cv2.imread(scr_dir)
                noiseimage = noise(img, 0.98)  #  Set the signal-to-noise ratio to 0.6
                gaussianBlur6 = cv2.GaussianBlur(noiseimage, (3, 3), 3)

                # cv2.imshow("img3", gaussianBlur6)
                # cv2.waitKey()
                det = join(dest_dir, imageName)
                cv2.imwrite(det, gaussianBlur6)

            #  Add salt and pepper noise and Gaussian Blur 
            if item == 7:
                img = cv2.imread(scr_dir, cv2.IMREAD_GRAYSCALE)
                noiseimage = noise(img, 0.98)  #  Set the signal-to-noise ratio to 0.6
                boxblur7 = cv2.boxFilter(noiseimage, -1, (9, 9))

                # cv2.imshow("img7", boxblur7)
                # cv2.waitKey()
                det = join(dest_dir, imageName)
                cv2.imwrite(det, boxblur7)

            #  Gaussian blur plus salt and pepper noise 
            if item == 8:
                img = cv2.imread(scr_dir)

                gaussianBlur = cv2.GaussianBlur(img, (5, 5), 3)
                noiseimage8 = noise(gaussianBlur, 0.98)  #  Set the signal-to-noise ratio to 0.6

                # cv2.imshow("img8", noiseimage8)
                # cv2.waitKey()
                det = join(dest_dir, imageName)
                cv2.imwrite(det, noiseimage8)

            # #  Bilateral blur plus salt and pepper noise 
            # if item == 9:
            #     img = cv2.imread(scr_dir, cv2.IMREAD_GRAYSCALE)
            #
            #     bilBlur = cv2.bilateralFilter(img, 1, 25, 10)
            #     noiseimage9 = noise(bilBlur, 0.92)  #  Set the signal-to-noise ratio to 0.6
            #     cv2.imshow("img9", noiseimage9)
            #     cv2.waitKey()
            #     det = join(dest_dir, imageName)
            #     cv2.imwrite(det, noiseimage9)

            #  Dimming 
            if item == 10:
                img = cv2.imread(scr_dir)
                #  The second parameter adjusts the brightness , The bigger, the brighter , The smaller, the darker 
                rst = imgBrightness(img, 1.6, 1)

                # cv2.imshow("img10", rst)
                # cv2.waitKey()
                det = join(dest_dir, imageName)
                cv2.imwrite(det, rst)

            #  Brighten and blur 
            if item == 11:
                img = cv2.imread(scr_dir)
                #  The second parameter adjusts the brightness , The bigger, the brighter , The smaller, the darker 
                rst = imgBrightness(img, 1.4, 4)
                boxblur11 = cv2.boxFilter(rst, -1, (3, 3))

                # cv2.imshow("img11", boxblur11)
                # cv2.waitKey()
                det = join(dest_dir, imageName)
                cv2.imwrite(det, boxblur11)

            #  Brighten and add salt and pepper noise 
            if item == 12:
                img = cv2.imread(scr_dir)
                #  The second parameter adjusts the brightness , The bigger, the brighter , The smaller, the darker 
                rst = imgBrightness(img, 1.28, 4)
                noiseimage12 = noise(rst, 0.98)  #  Set the signal-to-noise ratio to 0.6

                cv2.imshow("img12", noiseimage12)
                cv2.waitKey(3)
                det = join(dest_dir, imageName)
                cv2.imwrite(det, noiseimage12)

            shutil.copyfile(os.path.join(label_path, file[:-4] + '.txt'), os.path.join(dest_dir, labelName))

print("all down")