1、原始状态
最初的图像是这个样子的
.xml文件张下面这个样子
<annotation>
<object>
<name>face</name>
<difficult>0</difficult>
<bndbox>
<xmin>315.307918</xmin>
<ymin>240.234604</ymin>
<xmax>693.677419</xmax>
<ymax>699.683284</ymax>
</bndbox>
</object>
</annotation>
2、旋转图像并修改对应的xml文件
import cv2
import math
import numpy as np
import os
# pdb仅仅用于调试，不用管它
import pdb
#旋转图像的函数
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]
# 仿射变换
return cv2.warpAffine(src, rot_mat, (int(math.ceil(nw)), int(math.ceil(nh))), flags=cv2.INTER_LANCZOS4)
//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
# 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
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]))
# 合并np.array
concat = np.vstack((point1, point2, point3, point4))
# 改变array类型
concat = concat.astype(np.int32)
print concat
rx, ry, rw, rh = cv2.boundingRect(concat)
return rx, ry, rw, rh
# 使图像旋转60,90,120,150,210,240,300度
for angle in (60, 90, 120, 150, 210, 240, 300):
# 指向图片所在的文件夹
for i in os.listdir("/home/username/image"):
# 分离文件名与后缀
a, b = os.path.splitext(i)
# 如果后缀名是“.jpg”就旋转图像
if b == ".jpg":
img_path = os.path.join("/home/username/image", i)
img = cv2.imread(img_path)
rotated_img = rotate_image(img, angle)
# 写入图像
cv2.imwrite("/home/yourname/rotate/" + a + "_" + str(angle) +"d.jpg", rotated_img)
print "log: [%sd] %s is processed." % (angle, i)
else:
xml_path = os.path.join("/home/username/xml", i)
img_path = "/home/guoyana/varied_pose/" + a + ".jpg"
src = cv2.imread(img_path)
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 = rotate_xml(src, xmin, ymin, xmax, ymax, angle)
# 改变xml中的人脸坐标值
box.find('xmin').text = str(x)
box.find('ymin').text = str(y)
box.find('ymax').text = str(x+w)
box.find('ymax').text = str(y+h)
box.set('updated', 'yes')
# 写入新的xml
tree.write("/home/username/xml/" + a + "_" + str(angle) +".xml")
print "[%s] %s is processed." % (angle, i)
3、结果
我们来看下旋转后的图像和修改后的.xml文件
太多了，这里只放两张～
修改后的xml文件如下
<annotation>
<object>
<name>face</name>
<difficult>0</difficult>
<bndbox updated="yes">
<xmin>460</xmin>
<ymin>521</ymin>
<xmax>693.677419</xmax>
<ymax>849</ymax>
</bndbox>
</object>
</annotation>
效果怎么样呢？我们来写个小程序看一下
import cv2
import os
import xml.etree.ElementTree as ET
import pdb
for img in os.listdir("/home/mi/rotate/"):
a, b = os.path.splitext(img)
if b == ".jpg":
img = cv2.imread("/home/yourname/rotate/" + img)
tree = ET.parse("/home/yourname/xml/" + a + ".xml")
root = tree.getroot()
for box in root.iter('bndbox'):
x1 = float(box.find('xmin').text)
y1 = float(box.find('ymin').text)
x2 = float(box.find('xmax').text)
y2 = float(box.find('ymax').text)
x1 = int(x1)
y1 = int(y1)
x2 = int(x2)
y2 = int(y2)
cv2.rectangle(img, (x1, y1), (x2, y2), [0,255,0], 2)
cv2.imshow("test", img)
if 1 == cv2.waitKey(0):
pass
(end)