图片数据增强 Data Augmentation

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YoloV5训练图片增强 python代码

Generate More Label – 生成配套的Label

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#!/usr/bin/python
# -*- coding=utf-8 -*-
from xml.etree.ElementTree import ElementTree, Element
import xml.etree.ElementTree as ET
import os
from tqdm import tqdm


def change_box(update_path, save_path, filename, box_n, new_value):
    update_tree = ET.parse(update_path + filename)
    root = update_tree.getroot()
    size = root.find('size')
    object = root.find('object')
    bndbox = object.find("bndbox")
    _box_n = bndbox.find(box_n)
    _box_n.text = new_value
    update_tree.write(save_path + filename, encoding='utf-8')


def change_name(update_path, save_path, filename, new_name):
    update_tree = ET.parse(update_path + filename)
    root = update_tree.getroot()
    size = root.find('size')
    object = root.find('object')
    filename = root.find("filename")
    filename.text = new_name
    update_tree.write(save_path + new_name, encoding='utf-8')


def rotate_90(update_path, save_path, filename, new_name):
    """generate rotate 90° new label"""
    update_tree = ET.parse(update_path + filename)
    root = update_tree.getroot()
    size = root.find('size')
    object = root.find('object')
    bndbox = root.find('object').find("bndbox")
    width = int(size.find('width').text)
    height = int(size.find('height').text)
    xmin, ymin, xmax, ymax = bndbox.find("xmin"), bndbox.find("ymin"), bndbox.find(
        "xmax"), bndbox.find("ymax")
    _xmin, _ymin, _xmax, _ymax = int(xmin.text), int(ymin.text), int(xmax.text), int(ymax.text)
    xmin.text = str(_ymin)
    ymin.text = str(width - _xmax)
    xmax.text = str(_ymax)
    ymax.text = str(height - _xmin)
    update_tree.write(save_path + new_name, encoding='utf-8')


def rotate_180(update_path, save_path, filename, new_name):
    """generate rotate 180° new label"""
    update_tree = ET.parse(update_path + filename)
    root = update_tree.getroot()
    size = root.find('size')
    bndbox = root.find('object').find("bndbox")
    width = int(size.find('width').text)
    height = int(size.find('height').text)
    xmin, ymin, xmax, ymax = bndbox.find("xmin"), bndbox.find("ymin"), bndbox.find(
        "xmax"), bndbox.find("ymax")
    _xmin, _ymin, _xmax, _ymax = int(xmin.text), int(ymin.text), int(xmax.text), int(ymax.text)
    xmin.text = str(height - _xmax)
    ymin.text = str(width - _ymax)
    xmax.text = str(height - _xmin)
    ymax.text = str(width - _ymin)
    update_tree.write(save_path + new_name, encoding='utf-8')


def gen_fli_label(update_path, save_path, filename, new_name):
    """generate fli new label"""
    update_tree = ET.parse(update_path + filename)
    root = update_tree.getroot()
    size = root.find('size')
    bndbox = root.find('object').find("bndbox")
    width = int(size.find('width').text)
    height = int(size.find('height').text)
    xmin, ymin, xmax, ymax = bndbox.find("xmin"), bndbox.find("ymin"), bndbox.find("xmax"), bndbox.find("ymax")
    _xmin, _ymin, _xmax, _ymax = int(xmin.text), int(ymin.text), int(xmax.text), int(ymax.text)
    xmin.text = str(width - _xmax)
    ymin.text = str(_ymin)
    xmax.text = str(height - _xmin)
    ymax.text = str(_ymax)
    update_tree.write(save_path + new_name, encoding='utf-8')


def modify_quality(update_path, save_path, filename, new_name):
    """generate rotate 180° new label"""
    change_name(update_path, save_path, filename, new_name)


def gen_label_square(update_path, save_path, filename):
    update_tree = ET.parse(update_path + filename)
    root = update_tree.getroot()
    bndbox = root.find('object').find("bndbox")
    width = root.find('size').find('width')
    height = root.find('size').find('height')
    xmin, ymin, xmax, ymax = bndbox.find("xmin"), bndbox.find("ymin"), bndbox.find("xmax"), bndbox.find("ymax")
    _xmin, _ymin, _xmax, _ymax = int(xmin.text), int(ymin.text), int(xmax.text), int(ymax.text)
    _width, _height = int(width.text), int(height.text)
    max_w = max(_width, _height)
    width.text, height.text = str(max_w), str(max_w)
    if _height > _width:
        xmin.text = str(_xmin + (_height - _width) // 2)
        xmax.text = str(_xmax + (_height - _width) // 2)
    elif _width > _height:
        ymin.text = str(_ymin + (_width - _height) // 2)
        ymax.text = str(_ymax + (_width - _height) // 2)
    update_tree.write(save_path + filename, encoding='utf-8')


def gen_rotate_label(update_path, save_path, filename):
    new_name_90 = filename.split('.')[0] + "_r90" + ".xml"
    new_name_180 = filename.split('.')[0] + "_r180" + ".xml"
    modify_fli = filename.split('.')[0] + "_fli" + ".xml"
    rotate_90(update_path, save_path, filename, new_name=new_name_90)
    rotate_180(update_path, save_path, filename, new_name=new_name_180)
    gen_fli_label(update_path, save_path, filename, new_name=modify_fli)


def gen_modify_label(update_path, save_path, filename):
    modify_blur = filename.split('.')[0] + "_blur" + ".xml"
    modify_brighter = filename.split('.')[0] + "_brighter" + ".xml"
    modify_darker = filename.split('.')[0] + "_darker" + ".xml"
    modify_nosie = filename.split('.')[0] + "_nosie" + ".xml"
    modify_quality(update_path, save_path, filename, new_name=modify_blur)
    modify_quality(update_path, save_path, filename, new_name=modify_brighter)
    modify_quality(update_path, save_path, filename, new_name=modify_darker)
    modify_quality(update_path, save_path, filename, new_name=modify_nosie)


if __name__ == "__main__":
    update_path = "../data/picture/label/"
    # save_path = "../yolov5/data/VOCdevkit/VOC2007/pic_train20221110/label/"
    for label_name in tqdm(os.listdir(update_path)):
        update_tree = ET.parse(update_path + label_name)
        root = update_tree.getroot()
        bndbox = root.find('object').find("bndbox")
        width = root.find('size').find('width')
        height = root.find('size').find('height')
        _width, _height = int(width.text), int(height.text)
        if _width != _height:
            gen_label_square(update_path, update_path, label_name)
    for label_name in tqdm(os.listdir(update_path)):
        gen_rotate_label(update_path, update_path, label_name)
    for label_name in tqdm(os.listdir(update_path)):
        gen_modify_label(update_path, update_path, label_name)

Generate More Picture – 生成增强图片

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# -*- coding: utf-8 -*-

import cv2
import numpy as np
import os.path
import copy
from tqdm import tqdm
import xml.etree.ElementTree as ET

from PIL import Image


def trans_square(image, save_name):
    r"""Open the image using PIL."""
    image = image.convert('RGB')
    w, h = image.size
    background = Image.new('RGB', size=(max(w, h), max(w, h)), color=(255, 255, 255))  # 创建背景图，颜色值为127
    length = int(abs(w - h) // 2)  # 一侧需要填充的长度
    box = (length, 0) if w < h else (0, length)  # 粘贴的位置
    background.paste(image, box)
    print(save_name)
    background.save(save_name, quality=95)
    return background


def salt_and_pepper(src, percetage):
    """椒盐噪声"""
    SP_NoiseImg = src.copy()
    SP_NoiseNum = int(percetage * src.shape[0] * src.shape[1])
    for i in range(SP_NoiseNum):
        randR = np.random.randint(0, src.shape[0] - 1)
        randG = np.random.randint(0, src.shape[1] - 1)
        randB = np.random.randint(0, 3)
        if np.random.randint(0, 1) == 0:
            SP_NoiseImg[randR, randG, randB] = 0
        else:
            SP_NoiseImg[randR, randG, randB] = 255
    return SP_NoiseImg


def addGaussianNoise(image, percetage):
    """给图片增加高斯噪声"""
    G_Noiseimg = image.copy()
    w = image.shape[1]
    h = image.shape[0]
    G_NoiseNum = int(percetage * image.shape[0] * image.shape[1])
    for i in range(G_NoiseNum):
        temp_x = np.random.randint(0, h)
        temp_y = np.random.randint(0, w)
        G_Noiseimg[temp_x][temp_y][np.random.randint(3)] = np.random.randn(1)[0]
    return G_Noiseimg


def darker(image, percetage=0.9):
    """降低图片亮度"""
    image_copy = image.copy()
    w = image.shape[1]
    h = image.shape[0]
    # get darker
    for xi in range(0, w):
        for xj in range(0, h):
            image_copy[xj, xi, 0] = int(image[xj, xi, 0] * percetage)
            image_copy[xj, xi, 1] = int(image[xj, xi, 1] * percetage)
            image_copy[xj, xi, 2] = int(image[xj, xi, 2] * percetage)
    return image_copy


def brighter(image, percetage=1.5):
    """增加图片亮度"""
    image_copy = image.copy()
    w = image.shape[1]
    h = image.shape[0]
    # get brighter
    for xi in range(0, w):
        for xj in range(0, h):
            image_copy[xj, xi, 0] = np.clip(int(image[xj, xi, 0] * percetage), a_max=255, a_min=0)
            image_copy[xj, xi, 1] = np.clip(int(image[xj, xi, 1] * percetage), a_max=255, a_min=0)
            image_copy[xj, xi, 2] = np.clip(int(image[xj, xi, 2] * percetage), a_max=255, a_min=0)
    return image_copy


def rotate(image, angle, center=None, scale=1.0):
    """生成旋转图片"""
    (h, w) = image.shape[:2]
    # If no rotation center is specified, the center of the image is set as the rotation center
    if center is None:
        center = (w / 2, h / 2)
    m = cv2.getRotationMatrix2D(center, angle, scale)
    rotated = cv2.warpAffine(image, m, (w, h))
    return rotated


def flip(image):
    """生成翻转图片"""
    flipped_image = np.fliplr(image)
    return flipped_image


def run(file_dir):
    # # 图片文件夹路径
    # file_dir = '../data/meishigewen-img/'

    for img_name in tqdm(os.listdir(file_dir)):
        image = Image.open(file_dir + img_name)
        if image.size[0] != image.size[1]:
            trans_square(image, file_dir + img_name)

    for img_name in tqdm(os.listdir(file_dir)):
        img_path = file_dir + img_name
        img = cv2.imread(img_path)
        rotated_90 = rotate(img, 90)
        cv2.imwrite(file_dir + img_name[0:-4] + '_r90.jpg', rotated_90)

        rotated_180 = rotate(img, 180)
        cv2.imwrite(file_dir + img_name[0:-4] + '_r180.jpg', rotated_180)

        flipped_img = flip(img)
        cv2.imwrite(file_dir + img_name[0:-4] + '_fli.jpg', flipped_img)

    for img_name in tqdm(os.listdir(file_dir)):
        img_path = file_dir + img_name
        img = cv2.imread(img_path)

        img_gauss = addGaussianNoise(img, 0.3)
        cv2.imwrite(file_dir + img_name[0:-4] + '_noise.jpg', img_gauss)

        img_darker = darker(img)
        cv2.imwrite(file_dir + img_name[0:-4] + '_darker.jpg', img_darker)

        img_brighter = brighter(img)
        cv2.imwrite(file_dir + img_name[0:-4] + '_brighter.jpg', img_brighter)

        blur = cv2.GaussianBlur(img, (7, 7), 1.5)
        cv2.imwrite(file_dir + img_name[0:-4] + '_blur.jpg', blur)


if __name__ == '__main__':
    run(file_dir='../data/picture/img/')

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