weekmodel

1. KeypointDetection

1.1. CharPointDetection

识别字符中的俩个关键点。

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1.2. Facial-keypoints-detection

用于检测人脸的68个关键点示例。

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1.3. Hourglass-facekeypoints

使用基于论文Hourglass 的模型实现人体关键点检测。

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1.4. Realtime-Action-Recognition

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containing: - boxes (Tensor[N, 4]): the ground-truth boxes in [x0, y0, x1, y1] format, with values between 0 and H and 0 and W - labels (Tensor[N]): the class label for each ground-truth box - keypoints (Tensor[N, K, 3]): the K keypoints location for each of the N instances, in the format [x, y, visibility], where visibility=0 means that the keypoint is not visible.

The model returns a Dict[Tensor] during training, containing the classification and regression losses for both the RPN and the R-CNN, and the keypoint loss.

During inference, the model requires only the input tensors, and returns the post-processed

predictions as a List[Dict[Tensor]], one for each input image. The fields of the Dict are as

follows:

​ - boxes (Tensor[N, 4]): the predicted boxes in [x0, y0, x1, y1] format, with values between

0 and H and 0 and W

​ - labels (Tensor[N]): the predicted labels for each image

​ - scores (Tensor[N]): the scores or each prediction

​ - keypoints (Tensor[N, K, 3]): the locations of the predicted keypoints, in [x, y, v] format.

2. Classification

2.1 image_classification

使用pretain resnet系列模型进行五种花卉识别。结果比较清晰。

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2.2. yolo5_helmetDetect

基于Yolo5 进行的安全帽检测;

学习了yolo5 模型的使用,学习了使用yolo5进行自定义数据集使用;学习yolo5数据集标注形式,并使用yolo5进行数据集标注生成;

2.3. pytorch_classification

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2.4. 驾驶员状态检测

c0: safe driving
c1: texting - right
c2: talking on the phone - right
c3: texting - left
c4: talking on the phone - left
c5: operating the radio
c6: drinking
c7: reaching behind
c8: hair and makeup
c9: talking to passenger

2.5. Pytorch GoodStructure

2.5.1. CNN_LSTM

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下次进行代码开发,可以采用这种方式。

2.6. 人脸识别

2.6.1. Face_recognition 库

  • G:\weakmodel\weeklystudy\faceRecognition

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2.6.2. SeetaFace6

  • G:\weakmodel\weeklystudy\faceRecognition\seetaFace6Python

2.6.3 眼部跟踪

  • G:\weakmodel\weeklystudy\faceRecognition\eyetracking 该目录记录眼部追踪处理相关demo。

2. NLP relative

2.1. textclassify

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  • 学习了 通过gensin库训练 word2Vec模型;
  • 学习了通过sklearn.linear_model进行ML相关操作;
  • movierecommand 使用pyspark进行ALS 推荐;
  • movieComment 使用nltk库进行分词,使用sklearn.feature_extraction.text库进行文本特征处理,并使用相关模型;
  • hospitalEmotion 使用sklearn.linear_model进行 pos,neg 分类;
  • crimeanalyse 使用pyspark 进行犯罪类别的分类,使用了各种学习模型;
  • jobrequ.ipynb: 自己学习编写的 pyspark ML 使用,以及自定义函数UDF使用;

3. 前端

3.1. Book-Management-System

  • 基本代码都有了,学习只关注了flask那登录到主页那部分,其他的处理逻辑没有具体细看。如果不考虑css中 layui 如何使用;
  • 以后自己写python的管理系统,基于这个代码修改;
  • 登录,数据库连接,请求啥的都齐全了;
  • 后续自己编写相关系统的时候可以具体在学习 block 块使用规则。
  • 项目效果; 如果做物联网可以添加卡片式的那种显示效果

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3.2. BookManageSystem

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3.3 LibrayManageSystem

  • LibrarySystem
  • 前端用bootstrap框架搭建ui+ajax异步请求,后台用SSH+Quartz框架搭建的图书管理系统。

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3.4. 电子相册

1. 3Dalbum-master

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2. 3DRotatePhote

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3.5. python qt 教程

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1. qt UI 主题

  • G:\weakmodel\weeklystudy\QDarkStyleSheet

2. TCP&UDP通信

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3. 基于PyQt 电影天堂 爬虫

基本代码思路可以参考,原始代码爬虫出现问题;

4. Raspberry

  • 目录结构功能如下:

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4.2. fruitnanny

  • 基于树莓派婴儿监控系统

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  • 通过解析print数据实现python和js通信;

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4.3. [cortex-license-plate-reader-client](D:\work_OneNote\OneDrive - tju.edu.cn\work_project\hardware\cortex-license-plate-reader-client)

  • python 上传api请求推理结果
  • 图片进行编码上传
  • gps模块处理函数,使用线程的方式进行处理
  • 使用若干worker并行处理,多线程方式的使用

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5. GAN

5.1. GAN_Minist_keras-master

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