目标检测图像数据增强（Data Augmentation）—— 旋转

应用场景

由于业务需求，需要对部分不符合检测结果的图像进行过滤，因此需要对之前的检测项目进行优化。常见问题有如下亮点：

• 图像中检测目标是倾斜角度；
• 图像中是通过镜子自拍或者加了滤镜处理后的相片；
  这两种情况是由于训练样本中含有这两种情况的少，因此需要增加此类样本数。本文只针对第一种情况进行数据增强，解决办法——旋转。

素材

项目是对服装进行检测，样本图（来源于用户晒图）：

其对应的xml文件：

<annotation>
	<folder>wellfolder>
	<filename>15278480618780.jpgfilename>
	<path>15278480618780.jpgpath>
	<size>
		<width>828width>
		<height>1104height>
		<depth>3depth>
	size>
	<segmented>0segmented>
	<object>
		<name>3name>
		<pose>Unspecifiedpose>
		<truncated>1truncated>
		<difficult>0difficult>
		<bndbox>
			<xmin>250xmin>
			<ymin>672ymin>
			<xmax>531xmax>
			<ymax>1104ymax>
		bndbox>
	object>
annotation>

从xml信息中可以看见图像的具体信息，包括图像名称，尺寸以及检测方框的坐标范围。

处理程序

这里介绍处理批量处理文件夹中的情形，单张图像处理类似。

处理思想

• 读取对应的图像，解析对应的xml，根据旋转的角度来变换之前检测到的坐标，以及保存变换后的图像。

处理代码

#!/usr/bin/env python

import cv2
import math
import numpy as np
import os
import pdb
import xml.etree.ElementTree as ET


class ImgAugemention():
    def __init__(self):
        self.angle = 90

    # rotate_img
    def rotate_image(self, src, angle, scale=1.):
        w = src.shape[1]
        h = src.shape[0]
        # convet angle into rad
        rangle = np.deg2rad(angle)  # angle in radians
        # 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]
        # map
        return cv2.warpAffine(
            src, rot_mat, (int(math.ceil(nw)), int(math.ceil(nh))),
            flags=cv2.INTER_LANCZOS4)

    def rotate_xml(self, 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
        # get width and heigh of changed image
        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: the final rot matrix
        # get the four center of edges in the initial martix，and convert the coord
        point1 = np.dot(rot_mat, np.array([(xmin+xmax)/2, ymin, 1]))
        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.array
        concat = np.vstack((point1, point2, point3, point4))
        # change type
        concat = concat.astype(np.int32)
        print(concat)
        rx, ry, rw, rh = cv2.boundingRect(concat)
        return rx, ry, rw, rh

    def process_img(self, imgs_path, xmls_path, img_save_path, xml_save_path, angle_list):
        # assign the rot angles
        for angle in angle_list:
            for img_name in os.listdir(imgs_path):
                # split filename and suffix
                n, s = os.path.splitext(img_name)
                # for the sake of use yol model, only process '.jpg'
                if s == ".jpg":
                    img_path = os.path.join(imgs_path, img_name)
                    img = cv2.imread(img_path)
                    rotated_img = self.rotate_image(img, angle)
                    # 写入图像
                    cv2.imwrite(img_save_path + n + "_" + str(angle) + "d.jpg", rotated_img)
                    print("log: [%sd] %s is processed." % (angle, img))
                    xml_url = img_name.split('.')[0] + '.xml'
                    xml_path = os.path.join(xmls_path, xml_url)
                    tree = ET.parse(xml_path)
                    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 = self.rotate_xml(img, xmin, ymin, xmax, ymax, angle)
                        # change the coord
                        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)
                        box.set('updated', 'yes')
                    # write into new xml
                    tree.write(xml_save_path + n + "_" + str(angle) + "d.xml")
                print("[%s] %s is processed." % (angle, img_name))


if __name__ == '__main__':
    img_aug = ImgAugemention()
    imgs_path = './image/'
    xmls_path = './xml/'
    img_save_path = './rotate/'
    xml_save_path = './xml_rot/'
    angle_list = [60, 90, 120, 150, 210, 240, 300]
    img_aug.process_img(imgs_path, xmls_path, img_save_path, xml_save_path, angle_list)

处理结果

• 旋转60度

	well
	15278480618780.jpg
	15278480618780.jpg
	
		828
		1104
		3
	
	0
	

• 旋转90度

	well
	15278480618780.jpg
	15278480618780.jpg
	
		Unknown
	
	
		828
		1104
		3
	
	0
	

参考

• https://blog.csdn.net/u014540717/article/details/53301195
• https://www.cnblogs.com/wangguchangqing/p/4045150.html