目标检测图像数据增强（Data Augmentation）—— 旋转

<annotation>
   <folder>well</folder>
   <filename>15278480618780.jpg</filename>
   <path>15278480618780.jpg</path>
   <size>
       <width>828</width>
       <height>1104</height>
       <depth>3</depth>
   </size>
   <segmented>0</segmented>
   <object>
       <name>3</name>
       <pose>Unspecified</pose>
       <truncated>1</truncated>
       <difficult>0</difficult>
       <bndbox>
           <xmin>250</xmin>
           <ymin>672</ymin>
           <xmax>531</xmax>
           <ymax>1104</ymax>
       </bndbox>
   </object></annotation>

#!/usr/bin/env pythonimport cv2import mathimport numpy as npimport osimport pdbimport xml.etree.ElementTree as ETclass 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)

<annotation>
   <folder>well</folder>
   <filename>15278480618780.jpg</filename>
   <path>15278480618780.jpg</path>
   <size>
       <width>828</width>
       <height>1104</height>
       <depth>3</depth>
   </size>
   <segmented>0</segmented>
   <object>
       <name>3</name>
       <pose>Unspecified</pose>
       <truncated>1</truncated>
       <difficult>0</difficult>
       <bndbox updated="yes">
           <xmin>777</xmin>
           <ymin>701</ymin>
           <xmax>1152</xmax>
           <ymax>945</ymax>
       </bndbox>
   </object></annotation>

<annotation>
   <folder>well</folder>
   <filename>15278480618780.jpg</filename>
   <path>15278480618780.jpg</path>
   <source>
       <database>Unknown</database>
   </source>
   <size>
       <width>828</width>
       <height>1104</height>
       <depth>3</depth>
   </size>
   <segmented>0</segmented>
   <object>
       <name>3</name>
       <pose>Unspecified</pose>
       <truncated>1</truncated>
       <difficult>0</difficult>
       <bndbox updated="yes">
           <xmin>672</xmin>
           <ymin>297</ymin>
           <xmax>1105</xmax>
           <ymax>579</ymax>
       </bndbox>
   </object></annotation>