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【目标检测】数据增强：DOTA数据集

• 人工智能 • 阅读 94

前言

之前对于xml格式的YOLO数据集，之前记录过如何用imgaug对其进行数据增强。不过DOTA数据集采用的是txt格式的旋转框标注，因此不能直接套用，只能另辟蹊径。

DOTA数据集简介

DOTA数据集全称：Dataset for Object deTection in Aerial images
DOTA数据集v1.0共收录2806张4000 × 4000的图片，总共包含188282个目标。

【目标检测】数据增强：DOTA数据集

DOTA数据集论文介绍：https://arxiv.org/pdf/1711.10398.pdf
数据集官网：https://captain-whu.github.io/DOTA/dataset.html

DOTA数据集总共有3个版本

; DOTAV1.0

  • 类别数目：15
  • 类别名称：plane, ship, storage tank, baseball diamond, tennis court, basketball court, ground track field, harbor, bridge, large vehicle, small vehicle, helicopter, roundabout, soccer ball field , swimming pool

DOTAV1.5

  • 类别数目：16
  • 类别名称：plane, ship, storage tank, baseball diamond, tennis court, basketball court, ground track field, harbor, bridge, large vehicle, small vehicle, helicopter, roundabout, soccer ball field, swimming pool , container crane

DOTAV2.0

  • 类别数目：18
  • 类别名称：plane, ship, storage tank, baseball diamond, tennis court, basketball court, ground track field, harbor, bridge, large vehicle, small vehicle, helicopter, roundabout, soccer ball field, swimming pool, container crane, airport , helipad

DOTAV2.0版本，备份在我的GitHub上。
https://github.com/zstar1003/Dataset

本实验演示选择的是DOTA数据集中的一张图片，原图如下：

【目标检测】数据增强：DOTA数据集

标签如下：

每个对象有10个数值，前8个代表一个矩形框四个角的坐标，第9个表示对象类别，第10个表示识别难易程度，0表示简单，1表示困难。

【目标检测】数据增强：DOTA数据集

使用JDet中的 visualization.py将标签可视化，效果如下图所示：

【目标检测】数据增强：DOTA数据集
注：由于可视化代码需要坐标点输入为整数，因此后续给输出对象坐标点进行了取整操作，这会损失一些精度。

; 数据增强及可视化

数据增强代码主要参考的是这篇博文：目标识别小样本数据扩增

调整亮度

这里通过 skimage.exposure.adjust_gamma来调整亮度：


def changeLight(img, inputtxt, outputiamge, outputtxt):

    flag = random.uniform(0.5, 1.5)
    label = round(flag, 2)
    (filepath, tempfilename) = os.path.split(inputtxt)
    (filename, extension) = os.path.splitext(tempfilename)
    outputiamge = os.path.join(outputiamge + "/" + filename + "_" + str(label) + ".png")
    outputtxt = os.path.join(outputtxt + "/" + filename + "_" + str(label) + extension)

    ima_gamma = exposure.adjust_gamma(img, 0.5)

    shutil.copyfile(inputtxt, outputtxt)
    cv.imwrite(outputiamge, ima_gamma)

【目标检测】数据增强：DOTA数据集

添加高斯噪声

添加高斯噪声需要先将像素点进行归一化(/255）


def gasuss_noise(image, inputtxt, outputiamge, outputtxt, mean=0, var=0.01):
    '''
    mean : 均值
    var : 方差
    '''
    image = np.array(image / 255, dtype=float)
    noise = np.random.normal(mean, var ** 0.5, image.shape)
    out = image + noise

    if out.min() < 0:
        low_clip = -1.

    else:
        low_clip = 0.

    out = np.clip(out, low_clip, 1.0)
    out = np.uint8(out * 255)

    (filepath, tempfilename) = os.path.split(inputtxt)
    (filename, extension) = os.path.splitext(tempfilename)
    outputiamge = os.path.join(outputiamge + "/" + filename + "_gasunoise_" + str(mean) + "_" + str(var) + ".png")
    outputtxt = os.path.join(outputtxt + "/" + filename + "_gasunoise_" + str(mean) + "_" + str(var) + extension)

    shutil.copyfile(inputtxt, outputtxt)
    cv.imwrite(outputiamge, out)

【目标检测】数据增强：DOTA数据集

调整对比度


def ContrastAlgorithm(rgb_img, inputtxt, outputiamge, outputtxt):
    img_shape = rgb_img.shape
    temp_imag = np.zeros(img_shape, dtype=float)
    for num in range(0, 3):

        in_image = rgb_img[:, :, num]

        Imax = np.max(in_image)
        Imin = np.min(in_image)

        Omin, Omax = 0, 255

        a = float(Omax - Omin) / (Imax - Imin)
        b = Omin - a * Imin

        out_image = a * in_image + b

        out_image = out_image.astype(np.uint8)
        temp_imag[:, :, num] = out_image
    (filepath, tempfilename) = os.path.split(inputtxt)
    (filename, extension) = os.path.splitext(tempfilename)
    outputiamge = os.path.join(outputiamge + "/" + filename + "_contrastAlgorithm" + ".png")
    outputtxt = os.path.join(outputtxt + "/" + filename + "_contrastAlgorithm" + extension)
    shutil.copyfile(inputtxt, outputtxt)
    cv.imwrite(outputiamge, temp_imag)

【目标检测】数据增强：DOTA数据集

旋转

旋转本质上是利用仿射矩阵，这里预留了接口参数，可以自定义旋转角度


def rotate_img_bbox(img, inputtxt, temp_outputiamge, temp_outputtxt, angle, scale=1):
    nAgree = angle
    size = img.shape
    w = size[1]
    h = size[0]
    for numAngle in range(0, len(nAgree)):
        dRot = nAgree[numAngle] * np.pi / 180
        dSinRot = math.sin(dRot)
        dCosRot = math.cos(dRot)

        nw = (abs(np.sin(dRot) * h) + abs(np.cos(dRot) * w)) * scale
        nh = (abs(np.cos(dRot) * h) + abs(np.sin(dRot) * w)) * scale

        (filepath, tempfilename) = os.path.split(inputtxt)
        (filename, extension) = os.path.splitext(tempfilename)
        outputiamge = os.path.join(temp_outputiamge + "/" + filename + "_rotate_" + str(nAgree[numAngle]) + ".png")
        outputtxt = os.path.join(temp_outputtxt + "/" + filename + "_rotate_" + str(nAgree[numAngle]) + extension)

        rot_mat = cv.getRotationMatrix2D((nw * 0.5, nh * 0.5), nAgree[numAngle], scale)
        rot_move = np.dot(rot_mat, np.array([(nw - w) * 0.5, (nh - h) * 0.5, 0]))
        rot_mat[0, 2] += rot_move[0]
        rot_mat[1, 2] += rot_move[1]

        rotat_img = cv.warpAffine(img, rot_mat, (int(math.ceil(nw)), int(math.ceil(nh))), flags=cv.INTER_LANCZOS4)
        cv.imwrite(outputiamge, rotat_img)

        save_txt = open(outputtxt, 'w')
        f = open(inputtxt)
        for line in f.readlines():
            line = line.split(" ")
            x1 = float(line[0])
            y1 = float(line[1])
            x2 = float(line[2])
            y2 = float(line[3])
            x3 = float(line[4])
            y3 = float(line[5])
            x4 = float(line[6])
            y4 = float(line[7])
            category = str(line[8])
            dif = str(line[9])

            point1 = np.dot(rot_mat, np.array([x1, y1, 1]))
            point2 = np.dot(rot_mat, np.array([x2, y2, 1]))
            point3 = np.dot(rot_mat, np.array([x3, y3, 1]))
            point4 = np.dot(rot_mat, np.array([x4, y4, 1]))
            x1 = round(point1[0], 3)
            y1 = round(point1[1], 3)
            x2 = round(point2[0], 3)
            y2 = round(point2[1], 3)
            x3 = round(point3[0], 3)
            y3 = round(point3[1], 3)
            x4 = round(point4[0], 3)
            y4 = round(point4[1], 3)

            string = str(int(x1)) + " " + str(int(y1)) + " " + str(int(x2)) + " " + str(int(y2)) + " " + str(
                int(x3)) + " " + str(int(y3)) + " " + str(int(x4)) + " " + str(int(y4)) + " " + category + " " + dif
            save_txt.write(string)

【目标检测】数据增强：DOTA数据集

翻转图像

翻转图像使用 cv.flip进行


def filp_pic_bboxes(img, inputtxt, outputiamge, outputtxt):
    (filepath, tempfilename) = os.path.split(inputtxt)
    (filename, extension) = os.path.splitext(tempfilename)
    output_vert_flip_img = os.path.join(outputiamge + "/" + filename + "_vert_flip" + ".png")
    output_vert_flip_txt = os.path.join(outputtxt + "/" + filename + "_vert_flip" + extension)
    output_horiz_flip_img = os.path.join(outputiamge + "/" + filename + "_horiz_flip" + ".png")
    output_horiz_flip_txt = os.path.join(outputtxt + "/" + filename + "_horiz_flip" + extension)

    h, w, _ = img.shape

    vert_flip_img = cv.flip(img, 1)
    cv.imwrite(output_vert_flip_img, vert_flip_img)

    horiz_flip_img = cv.flip(img, 0)
    cv.imwrite(output_horiz_flip_img, horiz_flip_img)

    save_vert_txt = open(output_vert_flip_txt, 'w')
    save_horiz_txt = open(output_horiz_flip_txt, 'w')
    f = open(inputtxt)
    for line in f.readlines():
        line = line.split(" ")
        x1 = float(line[0])
        y1 = float(line[1])
        x2 = float(line[2])
        y2 = float(line[3])
        x3 = float(line[4])
        y3 = float(line[5])
        x4 = float(line[6])
        y4 = float(line[7])
        category = str(line[8])
        dif = str(line[9])

        vert_string = str(int(round(w - x1, 3))) + " " + str(int(y1)) + " " + str(int(round(w - x2, 3))) + " " + str(
            int(y2)) + " " + str(int(round(w - x3, 3))) + " " + str(int(y3)) + " " + str(
            int(round(w - x4, 3))) + " " + str(int(y4)) + " " + category + " " + dif
        horiz_string = str(int(x1)) + " " + str(int(round(h - y1, 3))) + " " + str(int(x2)) + " " + str(
            int(round(h - y2, 3))) + " " + str(int(x3)) + " " + str(int(round(h - y3, 3))) + " " + str(
            int(x4)) + " " + str(int(round(h - y4, 3))) + " " + category + " " + dif

        save_horiz_txt.write(horiz_string)
        save_vert_txt.write(vert_string)

【目标检测】数据增强：DOTA数据集

平移图像

平移图像本质也是利用仿射矩阵


def shift_pic_bboxes(img, inputtxt, outputiamge, outputtxt):
    w = img.shape[1]
    h = img.shape[0]
    x_min = w
    x_max = 0
    y_min = h
    y_max = 0
    f = open(inputtxt)
    for line in f.readlines():
        line = line.split(" ")
        x1 = float(line[0])
        y1 = float(line[1])
        x2 = float(line[2])
        y2 = float(line[3])
        x3 = float(line[4])
        y3 = float(line[5])
        x4 = float(line[6])
        y4 = float(line[7])
        category = str(line[8])
        dif = str(line[9])

        x_min = min(x_min, x1, x2, x3, x4)
        y_min = min(y_min, y1, y2, y3, y4)
        x_max = max(x_max, x1, x2, x3, x4)
        y_max = max(y_max, y1, y2, y3, y4)

    d_to_left = x_min
    d_to_right = w - x_max
    d_to_top = y_min
    d_to_bottom = h - y_max

    x = random.uniform(-(d_to_left - 1) / 3, (d_to_right - 1) / 3)
    y = random.uniform(-(d_to_top - 1) / 3, (d_to_bottom - 1) / 3)

    (filepath, tempfilename) = os.path.split(inputtxt)
    (filename, extension) = os.path.splitext(tempfilename)
    if x >= 0 and y >= 0:
        outputiamge = os.path.join(
            outputiamge + "/" + filename + "_shift_" + str(round(x, 3)) + "_" + str(round(y, 3)) + ".png")
        outputtxt = os.path.join(
            outputtxt + "/" + filename + "_shift_" + str(round(x, 3)) + "_" + str(round(y, 3)) + extension)
    elif x >= 0 and y < 0:
        outputiamge = os.path.join(
            outputiamge + "/" + filename + "_shift_" + str(round(x, 3)) + "__" + str(round(abs(y), 3)) + ".png")
        outputtxt = os.path.join(
            outputtxt + "/" + filename + "_shift_" + str(round(x, 3)) + "__" + str(round(abs(y), 3)) + extension)
    elif x < 0 and y >= 0:
        outputiamge = os.path.join(
            outputiamge + "/" + filename + "_shift__" + str(round(abs(x), 3)) + "_" + str(round(y, 3)) + ".png")
        outputtxt = os.path.join(
            outputtxt + "/" + filename + "_shift__" + str(round(abs(x), 3)) + "_" + str(round(y, 3)) + extension)
    elif x < 0 and y < 0:
        outputiamge = os.path.join(
            outputiamge + "/" + filename + "_shift__" + str(round(abs(x), 3)) + "__" + str(round(abs(y), 3)) + ".png")
        outputtxt = os.path.join(
            outputtxt + "/" + filename + "_shift__" + str(round(abs(x), 3)) + "__" + str(round(abs(y), 3)) + extension)

    M = np.float32([[1, 0, x], [0, 1, y]])
    shift_img = cv.warpAffine(img, M, (img.shape[1], img.shape[0]))
    cv.imwrite(outputiamge, shift_img)

    save_txt = open(outputtxt, "w")
    f = open(inputtxt)
    for line in f.readlines():
        line = line.split(" ")
        x1 = float(line[0])
        y1 = float(line[1])
        x2 = float(line[2])
        y2 = float(line[3])
        x3 = float(line[4])
        y3 = float(line[5])
        x4 = float(line[6])
        y4 = float(line[7])
        category = str(line[8])
        dif = str(line[9])
        shift_str = str(int(round(x1 + x, 3))) + " " + str(int(round(y1 + y, 3))) + " " + str(int(round(x2 + x, 3))) + " " + str(int(round(y2 + y, 3))) + " " + str(int(round(x3 + x, 3))) + " " + str(int(round(y3 + y, 3))) + " " + str(int(round(x4 + x, 3))) + " " + str(int(round(y4 + y, 3))) + " " + category + " " + dif
        save_txt.write(shift_str)

裁剪图像

这里的裁剪图像指的是裁剪目标周围的背景，从外面向内收缩，直到碰到第一个对象框停止


def crop_img_bboxes(img, inputtxt, outputiamge, outputtxt):

    w = img.shape[1]
    h = img.shape[0]
    x_min = 0
    x_max = w
    y_min = 0
    y_max = h
    f = open(inputtxt)
    for line in f.readlines():
        line = line.split(" ")
        x1 = float(line[0])
        y1 = float(line[1])
        x2 = float(line[2])
        y2 = float(line[3])
        x3 = float(line[4])
        y3 = float(line[5])
        x4 = float(line[6])
        y4 = float(line[7])
        category = str(line[8])
        dif = str(line[9])

        x_min = min(x_min, x1, x2, x3, x4)
        y_min = min(y_min, y1, y2, y3, y4)
        x_max = max(x_max, x1, x2, x3, x4)
        y_max = max(y_max, y1, y2, y3, y4)

    d_to_left = x_min
    d_to_right = w - x_max
    d_to_top = y_min
    d_to_bottom = h - y_max

    crop_x_min = int(x_min - random.uniform(0, d_to_left))
    crop_y_min = int(y_min - random.uniform(0, d_to_top))
    crop_x_max = int(x_max + random.uniform(0, d_to_right))
    crop_y_max = int(y_max + random.uniform(0, d_to_bottom))

    crop_x_min = max(0, crop_x_min)
    crop_y_min = max(0, crop_y_min)
    crop_x_max = min(w, crop_x_max)
    crop_y_max = min(h, crop_y_max)
    crop_img = img[crop_y_min:crop_y_max, crop_x_min:crop_x_max]

    (filepath, tempfilename) = os.path.split(inputtxt)
    (filename, extension) = os.path.splitext(tempfilename)
    outputiamge = os.path.join(outputiamge + "/" + filename + "_crop_" + str(crop_x_min) + "_" +
                               str(crop_y_min) + "_" + str(crop_x_max) + "_" +
                               str(crop_y_max) + ".png")
    outputtxt = os.path.join(outputtxt + "/" + filename + "_crop_" + str(crop_x_min) + "_" +
                             str(crop_y_min) + "_" + str(crop_x_max) + "_" +
                             str(crop_y_max) + extension)
    cv.imwrite(outputiamge, crop_img)

    save_txt = open(outputtxt, "w")
    f = open(inputtxt)
    for line in f.readlines():
        line = line.split(" ")
        x1 = float(line[0])
        y1 = float(line[1])
        x2 = float(line[2])
        y2 = float(line[3])
        x3 = float(line[4])
        y3 = float(line[5])
        x4 = float(line[6])
        y4 = float(line[7])
        category = str(line[8])
        dif = str(line[9])
        crop_str = str(int(round(x1 - crop_x_min, 3))) + " " + str(int(round(y1 - crop_y_min, 3))) + " " + str(int(round(x2 - crop_x_min, 3))) + " " + str(int(round(y2 - crop_y_min, 3))) + " " + str(int(round(x3 - crop_x_min, 3))) + " " + str(int(round(y3 - crop_y_min, 3))) + " " + str(int(round(x4 - crop_x_min, 3))) + " " + str(int(round(y4 - crop_y_min, 3))) + " " + category + " " + dif
        save_txt.write(crop_str)

【目标检测】数据增强：DOTA数据集

完整代码

图片放置结构如下图所示：

【目标检测】数据增强：DOTA数据集 
import time
import random
import cv2 as cv
import os
import math
import numpy as np
from skimage import exposure
import shutil

def changeLight(img, inputtxt, outputiamge, outputtxt):

    flag = random.uniform(0.5, 1.5)
    label = round(flag, 2)
    (filepath, tempfilename) = os.path.split(inputtxt)
    (filename, extension) = os.path.splitext(tempfilename)
    outputiamge = os.path.join(outputiamge + "/" + filename + "_" + str(label) + ".png")
    outputtxt = os.path.join(outputtxt + "/" + filename + "_" + str(label) + extension)

    ima_gamma = exposure.adjust_gamma(img, 0.5)

    shutil.copyfile(inputtxt, outputtxt)
    cv.imwrite(outputiamge, ima_gamma)

def gasuss_noise(image, inputtxt, outputiamge, outputtxt, mean=0, var=0.01):
    '''
    mean : 均值
    var : 方差
    '''
    image = np.array(image / 255, dtype=float)
    noise = np.random.normal(mean, var ** 0.5, image.shape)
    out = image + noise

    if out.min() < 0:
        low_clip = -1.

    else:
        low_clip = 0.

    out = np.clip(out, low_clip, 1.0)
    out = np.uint8(out * 255)

    (filepath, tempfilename) = os.path.split(inputtxt)
    (filename, extension) = os.path.splitext(tempfilename)
    outputiamge = os.path.join(outputiamge + "/" + filename + "_gasunoise_" + str(mean) + "_" + str(var) + ".png")
    outputtxt = os.path.join(outputtxt + "/" + filename + "_gasunoise_" + str(mean) + "_" + str(var) + extension)

    shutil.copyfile(inputtxt, outputtxt)
    cv.imwrite(outputiamge, out)

def ContrastAlgorithm(rgb_img, inputtxt, outputiamge, outputtxt):
    img_shape = rgb_img.shape
    temp_imag = np.zeros(img_shape, dtype=float)
    for num in range(0, 3):

        in_image = rgb_img[:, :, num]

        Imax = np.max(in_image)
        Imin = np.min(in_image)

        Omin, Omax = 0, 255

        a = float(Omax - Omin) / (Imax - Imin)
        b = Omin - a * Imin

        out_image = a * in_image + b

        out_image = out_image.astype(np.uint8)
        temp_imag[:, :, num] = out_image
    (filepath, tempfilename) = os.path.split(inputtxt)
    (filename, extension) = os.path.splitext(tempfilename)
    outputiamge = os.path.join(outputiamge + "/" + filename + "_contrastAlgorithm" + ".png")
    outputtxt = os.path.join(outputtxt + "/" + filename + "_contrastAlgorithm" + extension)
    shutil.copyfile(inputtxt, outputtxt)
    cv.imwrite(outputiamge, temp_imag)

def rotate_img_bbox(img, inputtxt, temp_outputiamge, temp_outputtxt, angle, scale=1):
    nAgree = angle
    size = img.shape
    w = size[1]
    h = size[0]
    for numAngle in range(0, len(nAgree)):
        dRot = nAgree[numAngle] * np.pi / 180
        dSinRot = math.sin(dRot)
        dCosRot = math.cos(dRot)

        nw = (abs(np.sin(dRot) * h) + abs(np.cos(dRot) * w)) * scale
        nh = (abs(np.cos(dRot) * h) + abs(np.sin(dRot) * w)) * scale

        (filepath, tempfilename) = os.path.split(inputtxt)
        (filename, extension) = os.path.splitext(tempfilename)
        outputiamge = os.path.join(temp_outputiamge + "/" + filename + "_rotate_" + str(nAgree[numAngle]) + ".png")
        outputtxt = os.path.join(temp_outputtxt + "/" + filename + "_rotate_" + str(nAgree[numAngle]) + extension)

        rot_mat = cv.getRotationMatrix2D((nw * 0.5, nh * 0.5), nAgree[numAngle], scale)
        rot_move = np.dot(rot_mat, np.array([(nw - w) * 0.5, (nh - h) * 0.5, 0]))
        rot_mat[0, 2] += rot_move[0]
        rot_mat[1, 2] += rot_move[1]

        rotat_img = cv.warpAffine(img, rot_mat, (int(math.ceil(nw)), int(math.ceil(nh))), flags=cv.INTER_LANCZOS4)
        cv.imwrite(outputiamge, rotat_img)

        save_txt = open(outputtxt, 'w')
        f = open(inputtxt)
        for line in f.readlines():
            line = line.split(" ")
            x1 = float(line[0])
            y1 = float(line[1])
            x2 = float(line[2])
            y2 = float(line[3])
            x3 = float(line[4])
            y3 = float(line[5])
            x4 = float(line[6])
            y4 = float(line[7])
            category = str(line[8])
            dif = str(line[9])

            point1 = np.dot(rot_mat, np.array([x1, y1, 1]))
            point2 = np.dot(rot_mat, np.array([x2, y2, 1]))
            point3 = np.dot(rot_mat, np.array([x3, y3, 1]))
            point4 = np.dot(rot_mat, np.array([x4, y4, 1]))
            x1 = round(point1[0], 3)
            y1 = round(point1[1], 3)
            x2 = round(point2[0], 3)
            y2 = round(point2[1], 3)
            x3 = round(point3[0], 3)
            y3 = round(point3[1], 3)
            x4 = round(point4[0], 3)
            y4 = round(point4[1], 3)

            string = str(int(x1)) + " " + str(int(y1)) + " " + str(int(x2)) + " " + str(int(y2)) + " " + str(
                int(x3)) + " " + str(int(y3)) + " " + str(int(x4)) + " " + str(int(y4)) + " " + category + " " + dif
            save_txt.write(string)

def filp_pic_bboxes(img, inputtxt, outputiamge, outputtxt):
    (filepath, tempfilename) = os.path.split(inputtxt)
    (filename, extension) = os.path.splitext(tempfilename)
    output_vert_flip_img = os.path.join(outputiamge + "/" + filename + "_vert_flip" + ".png")
    output_vert_flip_txt = os.path.join(outputtxt + "/" + filename + "_vert_flip" + extension)
    output_horiz_flip_img = os.path.join(outputiamge + "/" + filename + "_horiz_flip" + ".png")
    output_horiz_flip_txt = os.path.join(outputtxt + "/" + filename + "_horiz_flip" + extension)

    h, w, _ = img.shape

    vert_flip_img = cv.flip(img, 1)
    cv.imwrite(output_vert_flip_img, vert_flip_img)

    horiz_flip_img = cv.flip(img, 0)
    cv.imwrite(output_horiz_flip_img, horiz_flip_img)

    save_vert_txt = open(output_vert_flip_txt, 'w')
    save_horiz_txt = open(output_horiz_flip_txt, 'w')
    f = open(inputtxt)
    for line in f.readlines():
        line = line.split(" ")
        x1 = float(line[0])
        y1 = float(line[1])
        x2 = float(line[2])
        y2 = float(line[3])
        x3 = float(line[4])
        y3 = float(line[5])
        x4 = float(line[6])
        y4 = float(line[7])
        category = str(line[8])
        dif = str(line[9])

        vert_string = str(int(round(w - x1, 3))) + " " + str(int(y1)) + " " + str(int(round(w - x2, 3))) + " " + str(
            int(y2)) + " " + str(int(round(w - x3, 3))) + " " + str(int(y3)) + " " + str(
            int(round(w - x4, 3))) + " " + str(int(y4)) + " " + category + " " + dif
        horiz_string = str(int(x1)) + " " + str(int(round(h - y1, 3))) + " " + str(int(x2)) + " " + str(
            int(round(h - y2, 3))) + " " + str(int(x3)) + " " + str(int(round(h - y3, 3))) + " " + str(
            int(x4)) + " " + str(int(round(h - y4, 3))) + " " + category + " " + dif

        save_horiz_txt.write(horiz_string)
        save_vert_txt.write(vert_string)

def shift_pic_bboxes(img, inputtxt, outputiamge, outputtxt):
    w = img.shape[1]
    h = img.shape[0]
    x_min = w
    x_max = 0
    y_min = h
    y_max = 0
    f = open(inputtxt)
    for line in f.readlines():
        line = line.split(" ")
        x1 = float(line[0])
        y1 = float(line[1])
        x2 = float(line[2])
        y2 = float(line[3])
        x3 = float(line[4])
        y3 = float(line[5])
        x4 = float(line[6])
        y4 = float(line[7])
        category = str(line[8])
        dif = str(line[9])

        x_min = min(x_min, x1, x2, x3, x4)
        y_min = min(y_min, y1, y2, y3, y4)
        x_max = max(x_max, x1, x2, x3, x4)
        y_max = max(y_max, y1, y2, y3, y4)

    d_to_left = x_min
    d_to_right = w - x_max
    d_to_top = y_min
    d_to_bottom = h - y_max

    x = random.uniform(-(d_to_left - 1) / 3, (d_to_right - 1) / 3)
    y = random.uniform(-(d_to_top - 1) / 3, (d_to_bottom - 1) / 3)

    (filepath, tempfilename) = os.path.split(inputtxt)
    (filename, extension) = os.path.splitext(tempfilename)
    if x >= 0 and y >= 0:
        outputiamge = os.path.join(
            outputiamge + "/" + filename + "_shift_" + str(round(x, 3)) + "_" + str(round(y, 3)) + ".png")
        outputtxt = os.path.join(
            outputtxt + "/" + filename + "_shift_" + str(round(x, 3)) + "_" + str(round(y, 3)) + extension)
    elif x >= 0 and y < 0:
        outputiamge = os.path.join(
            outputiamge + "/" + filename + "_shift_" + str(round(x, 3)) + "__" + str(round(abs(y), 3)) + ".png")
        outputtxt = os.path.join(
            outputtxt + "/" + filename + "_shift_" + str(round(x, 3)) + "__" + str(round(abs(y), 3)) + extension)
    elif x < 0 and y >= 0:
        outputiamge = os.path.join(
            outputiamge + "/" + filename + "_shift__" + str(round(abs(x), 3)) + "_" + str(round(y, 3)) + ".png")
        outputtxt = os.path.join(
            outputtxt + "/" + filename + "_shift__" + str(round(abs(x), 3)) + "_" + str(round(y, 3)) + extension)
    elif x < 0 and y < 0:
        outputiamge = os.path.join(
            outputiamge + "/" + filename + "_shift__" + str(round(abs(x), 3)) + "__" + str(round(abs(y), 3)) + ".png")
        outputtxt = os.path.join(
            outputtxt + "/" + filename + "_shift__" + str(round(abs(x), 3)) + "__" + str(round(abs(y), 3)) + extension)

    M = np.float32([[1, 0, x], [0, 1, y]])
    shift_img = cv.warpAffine(img, M, (img.shape[1], img.shape[0]))
    cv.imwrite(outputiamge, shift_img)

    save_txt = open(outputtxt, "w")
    f = open(inputtxt)
    for line in f.readlines():
        line = line.split(" ")
        x1 = float(line[0])
        y1 = float(line[1])
        x2 = float(line[2])
        y2 = float(line[3])
        x3 = float(line[4])
        y3 = float(line[5])
        x4 = float(line[6])
        y4 = float(line[7])
        category = str(line[8])
        dif = str(line[9])
        shift_str = str(int(round(x1 + x, 3))) + " " + str(int(round(y1 + y, 3))) + " " + str(int(round(x2 + x, 3))) + " " + str(int(round(y2 + y, 3))) + " " + str(int(round(x3 + x, 3))) + " " + str(int(round(y3 + y, 3))) + " " + str(int(round(x4 + x, 3))) + " " + str(int(round(y4 + y, 3))) + " " + category + " " + dif
        save_txt.write(shift_str)

def crop_img_bboxes(img, inputtxt, outputiamge, outputtxt):

    w = img.shape[1]
    h = img.shape[0]
    x_min = 0
    x_max = w
    y_min = 0
    y_max = h
    f = open(inputtxt)
    for line in f.readlines():
        line = line.split(" ")
        x1 = float(line[0])
        y1 = float(line[1])
        x2 = float(line[2])
        y2 = float(line[3])
        x3 = float(line[4])
        y3 = float(line[5])
        x4 = float(line[6])
        y4 = float(line[7])
        category = str(line[8])
        dif = str(line[9])

        x_min = min(x_min, x1, x2, x3, x4)
        y_min = min(y_min, y1, y2, y3, y4)
        x_max = max(x_max, x1, x2, x3, x4)
        y_max = max(y_max, y1, y2, y3, y4)

    d_to_left = x_min
    d_to_right = w - x_max
    d_to_top = y_min
    d_to_bottom = h - y_max

    crop_x_min = int(x_min - random.uniform(0, d_to_left))
    crop_y_min = int(y_min - random.uniform(0, d_to_top))
    crop_x_max = int(x_max + random.uniform(0, d_to_right))
    crop_y_max = int(y_max + random.uniform(0, d_to_bottom))

    crop_x_min = max(0, crop_x_min)
    crop_y_min = max(0, crop_y_min)
    crop_x_max = min(w, crop_x_max)
    crop_y_max = min(h, crop_y_max)
    crop_img = img[crop_y_min:crop_y_max, crop_x_min:crop_x_max]

    (filepath, tempfilename) = os.path.split(inputtxt)
    (filename, extension) = os.path.splitext(tempfilename)
    outputiamge = os.path.join(outputiamge + "/" + filename + "_crop_" + str(crop_x_min) + "_" +
                               str(crop_y_min) + "_" + str(crop_x_max) + "_" +
                               str(crop_y_max) + ".png")
    outputtxt = os.path.join(outputtxt + "/" + filename + "_crop_" + str(crop_x_min) + "_" +
                             str(crop_y_min) + "_" + str(crop_x_max) + "_" +
                             str(crop_y_max) + extension)
    cv.imwrite(outputiamge, crop_img)

    save_txt = open(outputtxt, "w")
    f = open(inputtxt)
    for line in f.readlines():
        line = line.split(" ")
        x1 = float(line[0])
        y1 = float(line[1])
        x2 = float(line[2])
        y2 = float(line[3])
        x3 = float(line[4])
        y3 = float(line[5])
        x4 = float(line[6])
        y4 = float(line[7])
        category = str(line[8])
        dif = str(line[9])
        crop_str = str(int(round(x1 - crop_x_min, 3))) + " " + str(int(round(y1 - crop_y_min, 3))) + " " + str(int(round(x2 - crop_x_min, 3))) + " " + str(int(round(y2 - crop_y_min, 3))) + " " + str(int(round(x3 - crop_x_min, 3))) + " " + str(int(round(y3 - crop_y_min, 3))) + " " + str(int(round(x4 - crop_x_min, 3))) + " " + str(int(round(y4 - crop_y_min, 3))) + " " + category + " " + dif
        save_txt.write(crop_str)

if __name__ == '__main__':
    inputiamge = "dataset/ceshi/images"
    inputtxt = "dataset/ceshi/labelTxt"
    outputiamge = "dataset/ceshi_aug/images"
    outputtxt = "dataset/ceshi_aug/labelTxt"
    angle = [30, 60, 90, 120, 150, 180]
    tempfilename = os.listdir(inputiamge)
    for file in tempfilename:
        (filename, extension) = os.path.splitext(file)
        input_image = os.path.join(inputiamge + "/" + file)
        input_txt = os.path.join(inputtxt + "/" + filename + ".txt")

        img = cv.imread(input_image)

        changeLight(img,input_txt,outputiamge,outputtxt)

        gasuss_noise(img, input_txt, outputiamge, outputtxt, mean=0, var=0.001)

        ContrastAlgorithm(img, input_txt, outputiamge, outputtxt)

        rotate_img_bbox(img, input_txt, outputiamge, outputtxt, angle)

        filp_pic_bboxes(img, input_txt, outputiamge, outputtxt)

        shift_pic_bboxes(img, input_txt, outputiamge, outputtxt)

        crop_img_bboxes(img, input_txt, outputiamge, outputtxt)
    print("finished!")

Original: https://blog.csdn.net/qq1198768105/article/details/127027065
Author: zstar-_
Title: 【目标检测】数据增强：DOTA数据集

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