Skip to content

Y
yolov5-seg-opencv-dnn-cpp

FL1623863129 / yolov5-seg-opencv-dnn-cpp
与 Fork 源项目一致

从无法访问的项目Fork

通知 1
Star 0
Fork 0
Y
yolov5-seg-opencv-dnn-cpp

前往新版Gitcode，体验更适合开发者的 AI 搜索 >>

提交 c46410eb 编写于 作者: U UNeedCryDear
浏览文件
操作

update yolov5-seg

上级
隐藏空白更改
内联 并排
Showing with 607 addition and 0 deletion
LICENSE 0 → 100644
浏览文件 @ c46410eb
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
to the Licensor or its representatives, including but not limited to
communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
Licensor for the purpose of discussing and improving the Work, but
excluding communication that is conspicuously marked or otherwise
designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity
on behalf of whom a Contribution has been received by Licensor and
subsequently incorporated within the Work.
2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare Derivative Works of,
publicly display, publicly perform, sublicense, and distribute the
Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
comment syntax for the file format. We also recommend that a
file or class name and description of purpose be included on the
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright [yyyy] [name of copyright owner]
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.
README.md 0 → 100644
浏览文件 @ c46410eb
# yolov5-seg-opencv-dnn-cpp
使用opencv-dnn部署yolov5实例分割模型
基于6.2版本的yolov5:https://github.com/ultralytics/yolov5
**OpenCV>=4.5.0**
```
python export.py --weights yolov5s-seg.pt --img [640,640] --include onnx --dnn
```
#### 2022.10.10 更新：
+ 0.opencv不支持动态推理，请不要加--dymanic导出onnx。
+ 1.关于换行符，windows下面需要设置为CRLF，上传到github会自动切换成LF，windows下面切换一下即可<br>
+ 2.有些小伙伴用版本为1.12.x的pytorch的时候，需要将
https://github.com/ultralytics/yolov5/blob/c98128fe71a8676037a0605ab389c7473c743d07/export.py#L155
这里的标志位改成```do_constant_folding=False, ```,否者opencv用dnn读取不了onnx文件
以下为yolov5-seg.onnx运行结果：
![](res/bus.bmp)
![](res/zidane.bmp)
\ No newline at end of file
main.cpp 0 → 100644
浏览文件 @ c46410eb
#include <iostream>
#include<opencv2//opencv.hpp>
#include<math.h>
#include "yolo_seg.h"
//#include"yolov5.h"
using namespace std;
using namespace cv;
using namespace dnn;
int yolov5_seg()
{
string img_path = "./images/bus.jpg";
string model_path = "./models/yolov5s-seg.onnx";
YoloSeg test;
Net net;
if (test.ReadModel(net, model_path, false)) {
cout << "read net ok!" << endl;
}
else {
return -1;
}
//生成随机颜色
vector<Scalar> color;
srand(time(0));
for (int i = 0; i < 80; i++) {
int b = rand() % 256;
int g = rand() % 256;
int r = rand() % 256;
color.push_back(Scalar(b, g, r));
}
vector<OutputSeg> result;
Mat img = imread(img_path);
if (test.Detect(img, net, result)) {
test.DrawPred(img, result, color);
}
else {
cout << "Detect Failed!"<<endl;
}
system("pause");
return 0;
}
//int yolov5()
//{
// string img_path = "./images/bus.jpg";
// string model_path = "./models/yolov5s.onnx";
// Yolo test;
// Net net;
// if (test.ReadModel(net, model_path, false)) {
// cout << "read net ok!" << endl;
// }
// else {
// return -1;
// }
// //生成随机颜色
// vector<Scalar> color;
// srand(time(0));
// for (int i = 0; i < 80; i++) {
// int b = rand() % 256;
// int g = rand() % 256;
// int r = rand() % 256;
// color.push_back(Scalar(b, g, r));
// }
// vector<OutputSeg> result;
// Mat img = imread(img_path);
// if (test.Detect(img, net, result)) {
// test.DrawPred(img, result, color);
//
// }
// else {
// cout << "Detect Failed!" << endl;
// }
// system("pause");
// return 0;
//}
int main() {
//yolov5(); //https://github.com/UNeedCryDear/yolov5-opencv-dnn-cpp
yolov5_seg();
return 0;
}
yolo_seg.cpp 0 → 100644
浏览文件 @ c46410eb
#include"yolo_seg.h"
using namespace std;
using namespace cv;
using namespace cv::dnn;
bool YoloSeg::ReadModel(Net& net, string& netPath, bool isCuda = false) {
try {
net = readNet(netPath);
}
catch (const std::exception&) {
return false;
}
//cuda
if (isCuda) {
net.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA);
net.setPreferableTarget(cv::dnn::DNN_TARGET_CUDA_FP16);
}
//cpu
else {
net.setPreferableBackend(cv::dnn::DNN_BACKEND_DEFAULT);
net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);
}
return true;
}
void YoloSeg::LetterBox(const cv::Mat& image, cv::Mat& outImage, cv::Vec4d& params, const cv::Size& newShape,
bool autoShape, bool scaleFill, bool scaleUp, int stride, const cv::Scalar& color)
{
if (false) {
int maxLen = MAX(image.rows, image.cols);
outImage = Mat::zeros(Size(maxLen, maxLen), CV_8UC3);
image.copyTo(outImage(Rect(0, 0, image.cols, image.rows)));
params[0] = 1;
params[1] = 1;
params[3] = 0;
params[2] = 0;
}
cv::Size shape = image.size();
float r = std::min((float)newShape.height / (float)shape.height,
(float)newShape.width / (float)shape.width);
if (!scaleUp)
r = std::min(r, 1.0f);
float ratio[2]{ r, r };
int newUnpad[2]{ (int)std::round((float)shape.width * r),
(int)std::round((float)shape.height * r) };
auto dw = (float)(newShape.width - newUnpad[0]);
auto dh = (float)(newShape.height - newUnpad[1]);
if (autoShape)
{
dw = (float)((int)dw % stride);
dh = (float)((int)dh % stride);
}
else if (scaleFill)
{
dw = 0.0f;
dh = 0.0f;
newUnpad[0] = newShape.width;
newUnpad[1] = newShape.height;
ratio[0] = (float)newShape.width / (float)shape.width;
ratio[1] = (float)newShape.height / (float)shape.height;
}
dw /= 2.0f;
dh /= 2.0f;
if (shape.width != newUnpad[0] && shape.height != newUnpad[1])
{
cv::resize(image, outImage, cv::Size(newUnpad[0], newUnpad[1]));
}
else {
outImage = image.clone();
}
int top = int(std::round(dh - 0.1f));
int bottom = int(std::round(dh + 0.1f));
int left = int(std::round(dw - 0.1f));
int right = int(std::round(dw + 0.1f));
params[0] = ratio[0];
params[1] = ratio[1];
params[2] = left;
params[3] = top;
cv::copyMakeBorder(outImage, outImage, top, bottom, left, right, cv::BORDER_CONSTANT, color);
}
void YoloSeg::GetMask(const Mat& maskProposals, const Mat& mask_protos, const cv::Vec4d& params, const cv::Size& srcImgShape, vector<OutputSeg>& output) {
Mat protos = mask_protos.reshape(0, { _segChannels,_segWidth * _segHeight });
Mat matmulRes = (maskProposals * protos).t();
Mat masks = matmulRes.reshape(output.size(), { _segWidth,_segHeight });
vector<Mat> maskChannels;
split(masks, maskChannels);
for (int i = 0; i < output.size(); ++i) {
Mat dest, mask;
//sigmoid
cv::exp(-maskChannels[i], dest);
dest = 1.0 / (1.0 + dest);
Rect roi(int(params[2] / _netWidth * _segWidth), int(params[3] / _netHeight * _segHeight), int(_segWidth - params[2] / 2), int(_segHeight - params[3] / 2));
dest = dest(roi);
resize(dest, mask, srcImgShape, INTER_LINEAR);
mask = mask > _maskThreshold;
//crop
Mat m = Mat::zeros(srcImgShape, CV_8UC1);
Rect temp_rect = output[i].box;
rectangle(m, temp_rect, Scalar(255), -1, 8);
mask &= m;
output[i].mask = mask;
}
}
bool YoloSeg::Detect(Mat& SrcImg, Net& net, vector<OutputSeg>& output) {
Mat blob;
int col = SrcImg.cols;
int row = SrcImg.rows;
int maxLen = MAX(col, row);
Mat netInputImg;
Vec4d params;
LetterBox(SrcImg, netInputImg, params, cv::Size(_netWidth, _netHeight));
blobFromImage(netInputImg, blob, 1 / 255.0, cv::Size(_netWidth, _netHeight), cv::Scalar(0, 0, 0), true, false);
//如果在其他设置没有问题的情况下但是结果偏差很大，可以尝试下用下面两句语句
//blobFromImage(netInputImg, blob, 1 / 255.0, cv::Size(_netWidth, _netHeight), cv::Scalar(104, 117, 123), true, false);
//blobFromImage(netInputImg, blob, 1 / 255.0, cv::Size(_netWidth, _netHeight), cv::Scalar(114, 114,114), true, false);
net.setInput(blob);
std::vector<cv::Mat> netOutputImg;
//net.forward(netOutputImg, net.getUnconnectedOutLayersNames());
//*********************************************************************************************************************************
//opencv4.5.x和4.6.x这里输出不一致，推荐使用下面的固定名称输出
// 如果使用net.forward(netOutputImg, net.getUnconnectedOutLayersNames())，需要确认下output0在前，output1在后，否者出错
//*********************************************************************************************************************************
vector<string> outputLayerName{ "output0","output1" };
net.forward(netOutputImg, outputLayerName); //获取output的输出
std::vector<int> classIds;//结果id数组
std::vector<float> confidences;//结果每个id对应置信度数组
std::vector<cv::Rect> boxes;//每个id矩形框
std::vector<vector<float>> picked_proposals; //存储output0[:,:, 5 + _className.size():net_width]用以后续计算mask
float ratio_h = (float)netInputImg.rows / _netHeight;
float ratio_w = (float)netInputImg.cols / _netWidth;
int net_width = _className.size() + 5 + _segChannels;
float* pdata = (float*)netOutputImg[0].data;
for (int stride = 0; stride < _strideSize; stride++) { //stride
int grid_x = (int)(_netWidth / _netStride[stride]);
int grid_y = (int)(_netHeight / _netStride[stride]);
for (int anchor = 0; anchor < 3; anchor++) { //anchors
const float anchor_w = _netAnchors[stride][anchor * 2];
const float anchor_h = _netAnchors[stride][anchor * 2 + 1];
for (int i = 0; i < grid_y; ++i) {
for (int j = 0; j < grid_x; ++j) {
float box_score = pdata[4]; ;//获取每一行的box框中含有某个物体的概率
if (box_score >= _boxThreshold) {
cv::Mat scores(1, _className.size(), CV_32FC1, pdata + 5);
Point classIdPoint;
double max_class_socre;
minMaxLoc(scores, 0, &max_class_socre, 0, &classIdPoint);
max_class_socre = (float)max_class_socre;
if (max_class_socre >= _classThreshold) {
vector<float> temp_proto(pdata + 5 + _className.size(), pdata + net_width);
picked_proposals.push_back(temp_proto);
//rect [x,y,w,h]
float x = (pdata[0] - params[2]) / params[0]; //x
float y = (pdata[1] - params[3]) / params[1]; //y
float w = pdata[2] / params[0]; //w
float h = pdata[3] / params[1]; //h
int left = (x - 0.5 * w) * ratio_w;
int top = (y - 0.5 * h) * ratio_h;
classIds.push_back(classIdPoint.x);
confidences.push_back(max_class_socre * box_score);
boxes.push_back(Rect(left, top, int(w * ratio_w), int(h * ratio_h)));
}
}
pdata += net_width;//下一行
}
}
}
}
//执行非最大抑制以消除具有较低置信度的冗余重叠框（NMS）
vector<int> nms_result;
NMSBoxes(boxes, confidences, _nmsScoreThreshold, _nmsThreshold, nms_result);
std::vector<vector<float>> temp_mask_proposals;
for (int i = 0; i < nms_result.size(); ++i) {
int idx = nms_result[i];
OutputSeg result;
result.id = classIds[idx];
result.confidence = confidences[idx];
result.box = boxes[idx];
temp_mask_proposals.push_back(picked_proposals[idx]);
output.push_back(result);
}
Mat mask_proposals;
for (int i = 0; i < temp_mask_proposals.size(); ++i)
mask_proposals.push_back(Mat(temp_mask_proposals[i]).t());
GetMask(mask_proposals, netOutputImg[1], params, SrcImg.size(), output);
if (output.size())
return true;
else
return false;
}
void YoloSeg::DrawPred(Mat& img, vector<OutputSeg> result, vector<Scalar> color) {
Mat mask = img.clone();
for (int i = 0; i < result.size(); i++) {
int left, top;
left = result[i].box.x;
top = result[i].box.y;
int color_num = i;
rectangle(img, result[i].box, color[result[i].id], 2, 8);
mask.setTo(color[result[i].id], result[i].mask);
string label = _className[result[i].id] + ":" + to_string(result[i].confidence);
int baseLine;
Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
top = max(top, labelSize.height);
//rectangle(frame, Point(left, top - int(1.5 * labelSize.height)), Point(left + int(1.5 * labelSize.width), top + baseLine), Scalar(0, 255, 0), FILLED);
putText(img, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 1, color[result[i].id], 2);
}
addWeighted(img, 0.5, mask, 0.5, 0, img); //将mask加在原图上面
imshow("1", img);
//imwrite("out.bmp", img);
waitKey();
//destroyAllWindows();
}
yolo_seg.h 0 → 100644
浏览文件 @ c46410eb
#pragma once
#include<iostream>
#include<opencv2/opencv.hpp>
#define YOLO_P6 false //是否使用P6模型
struct OutputSeg {
int id; //结果类别id
float confidence; //结果置信度
cv::Rect box; //矩形框
cv::Mat mask;
};
class YoloSeg {
public:
YoloSeg() {
}
~YoloSeg() {}
bool ReadModel(cv::dnn::Net& net, std::string& netPath, bool isCuda);
bool Detect(cv::Mat& srcImg, cv::dnn::Net& net, std::vector<OutputSeg>& output);
void DrawPred(cv::Mat& img, std::vector<OutputSeg> result, std::vector<cv::Scalar> color);
void LetterBox(const cv::Mat& image, cv::Mat& outImage,
cv::Vec4d& params, //[ratio_x,ratio_y,dw,dh]
const cv::Size& newShape=cv::Size(640,640),
bool autoShape=false,
bool scaleFill=false,
bool scaleUp=true,
int stride=32,
const cv::Scalar& color=cv::Scalar(114,114,114));
private:
void GetMask(const cv::Mat & maskProposals, const cv::Mat & mask_protos, const cv::Vec4d& params, const cv::Size& srcImgShape, std::vector<OutputSeg>& output);
#if(defined YOLO_P6 && YOLO_P6==true)
const float _netAnchors[4][6] = { { 19,27, 44,40, 38,94 },{ 96,68, 86,152, 180,137 },{ 140,301, 303,264, 238,542 },{ 436,615, 739,380, 925,792 } };
const int _netWidth = 1280; //ONNX图片输入宽度
const int _netHeight = 1280; //ONNX图片输入高度
const int _segWidth = 160;
const int _segHeight = 160;
const int _segChannels = 32;
const int _strideSize = 4; //stride size
#else
const float _netAnchors[3][6] = { { 10,13, 16,30, 33,23 },{ 30,61, 62,45, 59,119 },{ 116,90, 156,198, 373,326 } };
const int _netWidth = 640; //ONNX图片输入宽度
const int _netHeight = 640; //ONNX图片输入高度
const int _segWidth = 160;
const int _segHeight = 160;
const int _segChannels = 32;
const int _strideSize = 3; //stride size
#endif // YOLO_P6
const float _netStride[4] = { 8, 16,32,64 };
float _boxThreshold = 0.25;
float _classThreshold = 0.25;
float _nmsThreshold = 0.45;
float _maskThreshold = 0.5;
float _nmsScoreThreshold = _boxThreshold * _classThreshold;
//类别名，自己的模型需要修改此项
std::vector<std::string> _className = { "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
"fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
"elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
"skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
"tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
"sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
"potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone",
"microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear",
"hair drier", "toothbrush" };
};
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或