data augmentation for object detecting目标检测xml文件扩增（旋转实现）

1. 背景描述：

在利用CNN做目标检测时，数据量不足时，旋转源图像进行数据的扩充。

例：
源图像如下图所示：

标记所得xml文件中目标信息如下：

<object>
        <name>airplanename>
        <pose>Unspecifiedpose>
        <truncated>0truncated>
        <difficult>0difficult>
        <bndbox>
            <xmin>431xmin>
            <ymin>367ymin>
            <xmax>607xmax>
            <ymax>453ymax>
        bndbox>
    object>
    <object>
        <name>airplanename>
        <pose>Unspecifiedpose>
        <truncated>0truncated>
        <difficult>0difficult>
        <bndbox>
            <xmin>570xmin>
            <ymin>419ymin>
            <xmax>768xmax>
            <ymax>512ymax>

想要将源图像旋转任意角度，相对应xml文件中的bndbox信息则需要更新。

2. 思路：

参考博客(http://blog.csdn.net/u014540717/article/details/53301195)

找到原图中标记方框的四个边中点坐标，计算其旋转后的坐标位置，然后利用cv2.boundingRect函数找到四个新坐标的外接矩形作为新的xml文件中的bndbox值写入。

3. 代码实现过程：

# coding:utf-8
# Copyright@hitzym, Dec,09,2017 at HIT
# blog:http://blog.csdn.net/yinhuan1649/article/category/7330626
import cv2
import math
import numpy as np
import xml.etree.ElementTree as ET
import os

def rotate_image(src, angle, scale=1):
    w = src.shape[1]
    h = src.shape[0]
    # 角度变弧度
    rangle = np.deg2rad(angle)  # angle in radians
    # now calculate new image width and height
    nw = (abs(np.sin(rangle) * h) + abs(np.cos(rangle) * w)) * scale
    nh = (abs(np.cos(rangle) * h) + abs(np.sin(rangle) * w)) * scale
    # ask OpenCV for the rotation matrix
    rot_mat = cv2.getRotationMatrix2D((nw * 0.5, nh * 0.5), angle, scale)
    # calculate the move from the old center to the new center combined
    # with the rotation
    rot_move = np.dot(rot_mat, np.array([(nw - w) * 0.5, (nh - h) * 0.5, 0]))
    # the move only affects the translation, so update the translation
    # part of the transform
    rot_mat[0, 2] += rot_move[0]
    rot_mat[1, 2] += rot_move[1]
    dst = cv2.warpAffine(src, rot_mat, (int(math.ceil(nw)), int(math.ceil(nh))), flags=cv2.INTER_LANCZOS4)
    # 仿射变换
    return dst

# 对应修改xml文件
def rotate_xml(src, xmin, ymin, xmax, ymax, angle, scale=1.):
    w = src.shape[1]
    h = src.shape[0]
    rangle = np.deg2rad(angle)  # angle in radians
    # now calculate new image width and height
    # 获取旋转后图像的长和宽
    nw = (abs(np.sin(rangle)*h) + abs(np.cos(rangle)*w))*scale
    nh = (abs(np.cos(rangle)*h) + abs(np.sin(rangle)*w))*scale
    # ask OpenCV for the rotation matrix
    rot_mat = cv2.getRotationMatrix2D((nw*0.5, nh*0.5), angle, scale)
    # calculate the move from the old center to the new center combined
    # with the rotation
    rot_move = np.dot(rot_mat, np.array([(nw-w)*0.5, (nh-h)*0.5,0]))
    # the move only affects the translation, so update the translation
    # part of the transform
    rot_mat[0, 2] += rot_move[0]
    rot_mat[1, 2] += rot_move[1]                                   # rot_mat是最终的旋转矩阵
    # point1 = np.dot(rot_mat, np.array([xmin, ymin, 1]))          #这种新画出的框大一圈
    # point2 = np.dot(rot_mat, np.array([xmax, ymin, 1]))
    # point3 = np.dot(rot_mat, np.array([xmax, ymax, 1]))
    # point4 = np.dot(rot_mat, np.array([xmin, ymax, 1]))
    point1 = np.dot(rot_mat, np.array([(xmin+xmax)/2, ymin, 1]))   # 获取原始矩形的四个中点，然后将这四个点转换到旋转后的坐标系下
    point2 = np.dot(rot_mat, np.array([xmax, (ymin+ymax)/2, 1]))
    point3 = np.dot(rot_mat, np.array([(xmin+xmax)/2, ymax, 1]))
    point4 = np.dot(rot_mat, np.array([xmin, (ymin+ymax)/2, 1]))
    concat = np.vstack((point1, point2, point3, point4))            # 合并np.array
    # 改变array类型
    concat = concat.astype(np.int32)
    rx, ry, rw, rh = cv2.boundingRect(concat)                        #rx,ry,为新的外接框左上角坐标，rw为框宽度，rh为高度，新的xmax=rx+rw,新的ymax=ry+rh
    return rx, ry, rw, rh

# 使图像旋转60,90,120,150,210,240,300度
xmlpath = './xml/'          #源图像路径
imgpath = './imgs/'         #源图像所对应的xml文件路径
rotated_imgpath = './rotatedimg/'
rotated_xmlpath = './rotatedxml/'
for angle in (60, 90, 120, 150, 180, 210, 240, 300):
    for i in os.listdir(imgpath):
        a, b = os.path.splitext(i)                            #分离出文件名a
        img = cv2.imread(imgpath + a + '.jpg')
        rotated_img = rotate_image(img,angle)
        cv2.imwrite(rotated_imgpath + a + '_'+ str(angle) +'d.jpg',rotated_img)
        print str(i) + ' has been rotated for '+ str(angle)+'°'
        tree = ET.parse(xmlpath + a + '.xml')
        root = tree.getroot()
        for box in root.iter('bndbox'):
            xmin = float(box.find('xmin').text)
            ymin = float(box.find('ymin').text)
            xmax = float(box.find('xmax').text)
            ymax = float(box.find('ymax').text)
            x, y, w, h = rotate_xml(img, xmin, ymin, xmax, ymax, angle)
            # cv2.rectangle(rotated_img, (x, y), (x+w, y+h), [0, 0, 255], 2)   #可在该步骤测试新画的框位置是否正确
            # cv2.imshow('xmlbnd',rotated_img)
            # cv2.waitKey(200)
            box.find('xmin').text = str(x)
            box.find('ymin').text = str(y)
            box.find('xmax').text = str(x+w)
            box.find('ymax').text = str(y+h)
        tree.write(rotated_xmlpath + a + '_'+ str(angle) +'d.xml')
        print str(a) + '.xml has been rotated for '+ str(angle)+'°'

4. 测试旋转结果

将xml中的bounding box 显示在图片上用来测试旋转后结果是否正确

注：
- xml文件需要和其对应的jpg文件文件名一样
- e.g. monkey001.jpg 对应 monkey001.xml
- 上代码

# coding:utf-8
# Copyright@hitzym, Dec,09,2017 at HIT
# blog:http://blog.csdn.net/yinhuan1649/article/category/7330626
import cv2
import xml.etree.ElementTree as ET
import os

imgpath = './testimgs/'          #旋转后的图像路径
xmlpath = './testxml/'           #旋转后的xml文件路径
for img in os.listdir(imgpath):
    a, b = os.path.splitext(img)
    img = cv2.imread(imgpath + a +'.jpg')
    tree = ET.parse(xmlpath + a + '.xml')
    root = tree.getroot()
    for box in root.iter('bndbox'):
        x1 = int(box.find('xmin').text)
        y1 = int(box.find('ymin').text)
        x2 = int(box.find('xmax').text)
        y2 = int(box.find('ymax').text)
        cv2.rectangle(img,(x1,y1),(x2, y2), [0,255,0], 2)
    cv2.imshow("test", img)
    # cv2.waitKey(1000)
    if 1 == cv2.waitKey(0):
        pass

原图：
结果图：
这是旋转60°的结果图

主要参考了博客(http://blog.csdn.net/u014540717/article/details/53301195)

稍有改动

感谢！