目标检测DOTA数据集
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。
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：
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。
使用JDet中的visualization.py将标签可视化，效果如下图所示：
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
调整亮度
这里通过skimage.exposure.adjust_gamma来调整亮度：
调整亮度
def changeLight(img, inputtxt, outputiamge, outputtxt):
# random.seed(int(time.time()))
flag = random.uniform(0.5, 1.5) # flag>1为调暗,小于1为调亮
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)
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）
添加高斯噪声
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)
调整对比度
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):
# 通过直方图正规化增强对比度
in_image = rgb_img[:, :, num]
# 求输入图片像素最大值和最小值
Imax = np.max(in_image)
Imin = np.min(in_image)
# 要输出的最小灰度级和最大灰度级
Omin, Omax = 0, 255
# 计算a 和 b的值
a = float(Omax - Omin) / (Imax - Imin)
b = Omin - a * Imin
# 矩阵的线性变化
out_image = a * in_image + b
# 数据类型的转化
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)
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
旋转
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]
# 仿射变换
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(x1) + " " + str(y1) + " " + str(x2) + " " + str(y2) + " " + str(x3) + " " + str(y3) + " " + str(x4) + " " + str(y4) + " " + category + " " + dif
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)
翻转图像
翻转图像使用cv.flip进行
翻转图像
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
# 垂直翻转
vert_flip_img = cv.flip(img, 1)
cv.imwrite(output_vert_flip_img, vert_flip_img)
# 水平翻转
horiz_flip_img = cv.flip(img, 0)
cv.imwrite(output_horiz_flip_img, horiz_flip_img)
# ---------------------- 调整boundingbox ----------------------
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)
平移图像
平移图像本质也是利用仿射矩阵
平移图像
def shift_pic_bboxes(img, inputtxt, outputiamge, outputtxt):
w = img.shape[1]
h = img.shape[0]
x_min = w # 裁剪后的包含所有目标框的最小的框
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 # 包含所有目标框的最大左移动距离
d_to_right = w - x_max # 包含所有目标框的最大右移动距离
d_to_top = y_min # 包含所有目标框的最大上移动距离
d_to_bottom = h - y_max # 包含所有目标框的最大下移动距离
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]]) # x为向左或右移动的像素值,正为向右负为向左; y为向上或者向下移动的像素值,正为向下负为向上
shift_img = cv.warpAffine(img, M, (img.shape[1], img.shape[0]))
cv.imwrite(outputiamge, shift_img)
# ---------------------- 平移boundingbox ----------------------
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
裁剪图像
def crop_img_bboxes(img, inputtxt, outputiamge, outputtxt):
# ---------------------- 裁剪图像 ----------------------
w = img.shape[1]
h = img.shape[0]
x_min = 0 # 裁剪后的包含所有目标框的最小的框
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 # 包含所有目标框的最小框到左边的距离
d_to_right = w - x_max # 包含所有目标框的最小框到右边的距离
d_to_top = y_min # 包含所有目标框的最小框到顶端的距离
d_to_bottom = h - y_max # 包含所有目标框的最小框到底部的距离
# 随机扩展这个最小框
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))
# 随机扩展这个最小框 , 防止别裁的太小
# crop_x_min = int(x_min - random.uniform(d_to_left//2, d_to_left))
# crop_y_min = int(y_min - random.uniform(d_to_top//2, d_to_top))
# crop_x_max = int(x_max + random.uniform(d_to_right//2, d_to_right))
# crop_y_max = int(y_max + random.uniform(d_to_bottom//2, d_to_bottom))
# 确保不要越界
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)
# ---------------------- 裁剪boundingbox ----------------------
# 裁剪后的boundingbox坐标计算
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)
完整代码
图片放置结构如下图所示：
import time
import random
import cv2 as cv
import os
import math
import numpy as np
from skimage import exposure
import shutil
# 调整亮度
def changeLight(img, inputtxt, outputiamge, outputtxt):
# random.seed(int(time.time()))
flag = random.uniform(0.5, 1.5) # flag>1为调暗,小于1为调亮
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)
# 添加高斯噪声
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)
# 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):
# 通过直方图正规化增强对比度
in_image = rgb_img[:, :, num]
# 求输入图片像素最大值和最小值
Imax = np.max(in_image)
Imin = np.min(in_image)
# 要输出的最小灰度级和最大灰度级
Omin, Omax = 0, 255
# 计算a 和 b的值
a = float(Omax - Omin) / (Imax - Imin)
b = Omin - a * Imin
# 矩阵的线性变化
out_image = a * in_image + b
# 数据类型的转化
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)
# 旋转
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]
# 仿射变换
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(x1) + " " + str(y1) + " " + str(x2) + " " + str(y2) + " " + str(x3) + " " + str(y3) + " " + str(x4) + " " + str(y4) + " " + category + " " + dif
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)
# 翻转图像
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
# 垂直翻转
vert_flip_img = cv.flip(img, 1)
cv.imwrite(output_vert_flip_img, vert_flip_img)
# 水平翻转
horiz_flip_img = cv.flip(img, 0)
cv.imwrite(output_horiz_flip_img, horiz_flip_img)
# ---------------------- 调整boundingbox ----------------------
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)
# 平移图像
def shift_pic_bboxes(img, inputtxt, outputiamge, outputtxt):
w = img.shape[1]
h = img.shape[0]
x_min = w # 裁剪后的包含所有目标框的最小的框
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 # 包含所有目标框的最大左移动距离
d_to_right = w - x_max # 包含所有目标框的最大右移动距离
d_to_top = y_min # 包含所有目标框的最大上移动距离
d_to_bottom = h - y_max # 包含所有目标框的最大下移动距离
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]]) # x为向左或右移动的像素值,正为向右负为向左; y为向上或者向下移动的像素值,正为向下负为向上
shift_img = cv.warpAffine(img, M, (img.shape[1], img.shape[0]))
cv.imwrite(outputiamge, shift_img)
# ---------------------- 平移boundingbox ----------------------
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
def crop_img_bboxes(img, inputtxt, outputiamge, outputtxt):
# ---------------------- 裁剪图像 ----------------------
w = img.shape[1]
h = img.shape[0]
x_min = 0 # 裁剪后的包含所有目标框的最小的框
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 # 包含所有目标框的最小框到左边的距离
d_to_right = w - x_max # 包含所有目标框的最小框到右边的距离
d_to_top = y_min # 包含所有目标框的最小框到顶端的距离
d_to_bottom = h - y_max # 包含所有目标框的最小框到底部的距离
# 随机扩展这个最小框
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))
# 随机扩展这个最小框 , 防止别裁的太小
# crop_x_min = int(x_min - random.uniform(d_to_left//2, d_to_left))
# crop_y_min = int(y_min - random.uniform(d_to_top//2, d_to_top))
# crop_x_max = int(x_max + random.uniform(d_to_right//2, d_to_right))
# crop_y_max = int(y_max + random.uniform(d_to_bottom//2, d_to_bottom))
# 确保不要越界
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)
# ---------------------- 裁剪boundingbox ----------------------
# 裁剪后的boundingbox坐标计算
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)
# 图像亮度变化
changeLight(img,input_txt,outputiamge,outputtxt)
# 加高斯噪声
gasuss_noise(img, input_txt, outputiamge, outputtxt, mean=0, var=0.001)
# 改变图像对比度
ContrastAlgorithm(img, input_txt, outputiamge, outputtxt)
# 图像旋转
rotate_img_bbox(img, input_txt, outputiamge, outputtxt, angle)
# 图像镜像
filp_pic_bboxes(img, input_txt, outputiamge, outputtxt)
# 平移
shift_pic_bboxes(img, input_txt, outputiamge, outputtxt)
# 剪切
crop_img_bboxes(img, input_txt, outputiamge, outputtxt)
print("finished!")