# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import import os import os.path as osp import random import cv2 import time import numpy as np import xml.etree.ElementTree as ET import paddlex.utils.logging as logging def write_xml(im_info, label_info, anno_dir): im_fname = im_info['file_name'] im_h, im_w, im_c = im_info['image_shape'] is_crowd = label_info['is_crowd'] gt_class = label_info['gt_class'] gt_bbox = label_info['gt_bbox'] gt_score = label_info['gt_score'] gt_poly = label_info['gt_poly'] difficult = label_info['difficult'] import xml.dom.minidom as minidom xml_doc = minidom.Document() root = xml_doc.createElement("annotation") xml_doc.appendChild(root) node_filename = xml_doc.createElement("filename") node_filename.appendChild(xml_doc.createTextNode(im_fname)) root.appendChild(node_filename) node_size = xml_doc.createElement("size") node_width = xml_doc.createElement("width") node_width.appendChild(xml_doc.createTextNode(str(im_w))) node_size.appendChild(node_width) node_height = xml_doc.createElement("height") node_height.appendChild(xml_doc.createTextNode(str(im_h))) node_size.appendChild(node_height) node_depth = xml_doc.createElement("depth") node_depth.appendChild(xml_doc.createTextNode(str(im_c))) node_size.appendChild(node_depth) root.appendChild(node_size) for i in range(len(label_info['gt_class'])): node_obj = xml_doc.createElement("object") node_name = xml_doc.createElement("name") label = gt_class[i] node_name.appendChild(xml_doc.createTextNode(label)) node_obj.appendChild(node_name) node_diff = xml_doc.createElement("difficult") node_diff.appendChild(xml_doc.createTextNode(str(difficult[i][0]))) node_obj.appendChild(node_diff) node_box = xml_doc.createElement("bndbox") node_xmin = xml_doc.createElement("xmin") node_xmin.appendChild(xml_doc.createTextNode(str(gt_bbox[i][0]))) node_box.appendChild(node_xmin) node_ymin = xml_doc.createElement("ymin") node_ymin.appendChild(xml_doc.createTextNode(str(gt_bbox[i][1]))) node_box.appendChild(node_ymin) node_xmax = xml_doc.createElement("xmax") node_xmax.appendChild(xml_doc.createTextNode(str(gt_bbox[i][2]))) node_box.appendChild(node_xmax) node_ymax = xml_doc.createElement("ymax") node_ymax.appendChild(xml_doc.createTextNode(str(gt_bbox[i][3]))) node_box.appendChild(node_ymax) node_obj.appendChild(node_box) root.appendChild(node_obj) img_name_part = im_fname.split('.')[0] with open(osp.join(anno_dir, img_name_part + ".xml"), 'w') as fxml: xml_doc.writexml( fxml, indent='\t', addindent='\t', newl='\n', encoding="utf-8") def paste_objects(templates, background, save_dir='dataset_clone'): """将目标物体粘贴在背景图片上生成新的图片，并加入到数据集中 Args: templates (list|tuple)：可以将多张图像上的目标物体同时粘贴在同一个背景图片上， 因此templates是一个列表，其中每个元素是一个dict，表示一张图片的目标物体。 一张图片的目标物体有`image`和`annos`两个关键字，`image`的键值是图像的路径， 或者是解码后的排列格式为（H, W, C）且类型为uint8且为BGR格式的数组。 图像上可以有多个目标物体，因此`annos`的键值是一个列表，列表中每个元素是一个dict， 表示一个目标物体的信息。该dict包含`polygon`和`category`两个关键字， 其中`polygon`表示目标物体的边缘坐标，例如[[0, 0], [0, 1], [1, 1], [1, 0]]， `category`表示目标物体的类别，例如'dog'。 background (dict): 背景图片可以有真值，因此background是一个dict，包含`image`和`annos` 两个关键字，`image`的键值是背景图像的路径，或者是解码后的排列格式为（H, W, C） 且类型为uint8且为BGR格式的数组。若背景图片上没有真值，则`annos`的键值是空列表[]， 若有，则`annos`的键值是由多个dict组成的列表，每个dict表示一个物体的信息， 包含`bbox`和`category`两个关键字，`bbox`的键值是物体框左上角和右下角的坐标，即 [x1, y1, x2, y2]，`category`表示目标物体的类别，例如'dog'。 save_dir (str)：新图片及其标注文件的存储目录。默认值为`dataset_clone`。 """ if not osp.exists(save_dir): os.makedirs(save_dir) image_dir = osp.join(save_dir, 'JPEGImages_clone') anno_dir = osp.join(save_dir, 'Annotations_clone') json_path = osp.join(save_dir, "annotations.json") if not osp.exists(image_dir): os.makedirs(image_dir) if not osp.exists(anno_dir): os.makedirs(anno_dir) num_objs = len(background['annos']) for temp in templates: num_objs += len(temp['annos']) gt_bbox = np.zeros((num_objs, 4), dtype=np.float32) gt_class = list() gt_score = np.ones((num_objs, 1), dtype=np.float32) is_crowd = np.zeros((num_objs, 1), dtype=np.int32) difficult = np.zeros((num_objs, 1), dtype=np.int32) i = -1 for i, back_anno in enumerate(background['annos']): gt_bbox[i] = back_anno['bbox'] gt_class.append(back_anno['category']) back_im = background['image'] if isinstance(back_im, np.ndarray): if len(back_im.shape) != 3: raise Exception( "background image should be 3-dimensions, but now is {}-dimensions". format(len(back_im.shape))) else: try: back_im = cv2.imread(back_im, cv2.IMREAD_UNCHANGED) except: raise TypeError('Can\'t read The image file {}!'.format(back_im)) back_annos = background['annos'] im_h, im_w, im_c = back_im.shape for temp in templates: temp_im = temp['image'] if isinstance(temp_im, np.ndarray): if len(temp_im.shape) != 3: raise Exception( "template image should be 3-dimensions, but now is {}-dimensions". format(len(temp_im.shape))) else: try: temp_im = cv2.imread(temp_im, cv2.IMREAD_UNCHANGED) except: raise TypeError('Can\'t read The image file {}!'.format( temp_im)) if im_c != temp_im.shape[-1]: raise Exception( "The channels of template({}) and background({}) images are not same. Objects cannot be pasted normally! Please check your images.". format(temp_im.shape[-1], im_c)) temp_annos = temp['annos'] for temp_anno in temp_annos: temp_mask = np.zeros(temp_im.shape, temp_im.dtype) temp_poly = np.array(temp_anno['polygon'], np.int32) temp_category = temp_anno['category'] cv2.fillPoly(temp_mask, [temp_poly], (255, 255, 255)) x_list = [temp_poly[i][0] for i in range(len(temp_poly))] y_list = [temp_poly[i][1] for i in range(len(temp_poly))] temp_poly_w = max(x_list) - min(x_list) temp_poly_h = max(y_list) - min(y_list) found = False while not found: center_x = random.randint(1, im_w - 1) center_y = random.randint(1, im_h - 1) if center_x < temp_poly_w / 2 or center_x > im_w - temp_poly_w / 2 - 1 or \ center_y < temp_poly_h / 2 or center_y > im_h - temp_poly_h / 2 - 1: found = False continue if len(back_annos) == 0: found = True for back_anno in back_annos: x1, y1, x2, y2 = back_anno['bbox'] if center_x > x1 and center_x < x2 and center_y > y1 and center_y < y2: found = False continue found = True center = (center_x, center_y) back_im = cv2.seamlessClone(temp_im, back_im, temp_mask, center, cv2.MIXED_CLONE) i += 1 x1 = center[0] - temp_poly_w / 2 x2 = center[0] + temp_poly_w / 2 y1 = center[1] - temp_poly_h / 2 y2 = center[1] + temp_poly_h / 2 gt_bbox[i] = [x1, y1, x2, y2] gt_class.append(temp_category) im_fname = str(int(time.time() * 1000)) + '.jpg' im_info = { 'file_name': im_fname, 'image_shape': [im_h, im_w, im_c], } label_info = { 'is_crowd': is_crowd, 'gt_class': gt_class, 'gt_bbox': gt_bbox, 'gt_score': gt_score, 'difficult': difficult, 'gt_poly': [], } cv2.imwrite(osp.join(image_dir, im_fname), back_im.astype('uint8')) write_xml(im_info, label_info, anno_dir) logging.info("Gegerated image is saved in {}".format(image_dir)) logging.info("Generated Annotation is saved as xml files in {}".format( anno_dir))