tf-pose-estimation | Deep Pose Estimation implemented using Tensorflow | Machine Learning library

by ildoonet Python Version: Current License: Apache-2.0

X-Ray Key Features Code Snippets(7)Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | tf-pose-estimation Summary

tf-pose-estimation is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. tf-pose-estimation has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. However tf-pose-estimation has 1 bugs. You can install using 'pip install tf-pose-estimation' or download it from GitHub, PyPI.

Deep Pose Estimation implemented using Tensorflow with Custom Architectures for fast inference.

Support

Quality

Security

License

Reuse

Support

tf-pose-estimation has a medium active ecosystem.

It has 3752 star(s) with 1529 fork(s). There are 158 watchers for this library.

It had no major release in the last 6 months.

There are 337 open issues and 285 have been closed. On average issues are closed in 52 days. There are 12 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of tf-pose-estimation is current.

Quality

tf-pose-estimation has 1 bugs (1 blocker, 0 critical, 0 major, 0 minor) and 187 code smells.

Security

tf-pose-estimation has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

tf-pose-estimation code analysis shows 0 unresolved vulnerabilities.

There are 5 security hotspots that need review.

License

tf-pose-estimation is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

tf-pose-estimation releases are not available. You will need to build from source code and install.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

tf-pose-estimation saves you 1853 person hours of effort in developing the same functionality from scratch.

It has 4089 lines of code, 227 functions and 46 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi's functional review helps you automatically verify the functionalities of the libraries and avoid rework.
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of tf-pose-estimation

Get all kandi verified functions for this library.

tf-pose-estimation Key Features

No Key Features are available at this moment for tf-pose-estimation.

tf-pose-estimation Examples and Code Snippets

Install Dependencies

Python

Lines of Code : 16

License : Permissive (MIT)

Copy

$ cd tf-pose-estimation/models/graph/cmu  
$ bash download.sh  

conda create -n tf tensorflow-gpu
conda activate tf

cd $MyRoot
pip install -r requirements.txt
pip install jupyter tqdm
pip install tensorflow-gpu==1.13.1
sudo apt install swig
pip ins

tf-pose-estimation,Python Usage

Python

Lines of Code : 5

License : Permissive (Apache-2.0)

Copy

e = TfPoseEstimator(get_graph_path(args.model), target_size=(w, h))
humans = e.inference(image)
image = TfPoseEstimator.draw_humans(image, humans, imgcopy=False)

import tf_pose
coco_style = tf_pose.infer(image_path)

Install

Python

Lines of Code : 5

License : Permissive (Apache-2.0)

Copy

$ git clone https://www.github.com/ildoonet/tf-pose-estimation
$ cd tf-pose-estimation
$ pip3 install -r requirements.txt

$ cd tf_pose/pafprocess
$ swig -python -c++ pafprocess.i && python3 setup.py build_ext --inplace

NCHW and NHWC network format conversion Tensorflow model

Python

Lines of Code : 7

License : Strong Copyleft (CC BY-SA 4.0)

Copy

input_ = tf.convert_to_tensor(np.random.rand(1, 3, 24, 32))
a1 = tf.transpose(input_, perm=[0, 2, 3, 1])
print(a1.shape)  # 1, 24, 32, 3

a2 = tf.reshape(input_, (-1, input_.shape[2], input_.shape[3], input_.shape[1

How to install numba library on Jetson tx2?

Python

Lines of Code : 16

License : Strong Copyleft (CC BY-SA 4.0)

Copy

$ wget http://releases.llvm.org/7.0.1/llvm-7.0.1.src.tar.xz
$ tar -xvf llvm-7.0.1.src.tar.xz
$ cd llvm-7.0.1.src.tar.xz
$ mkdir llvm_build_dir
$ cd llvm_build_dir/
$ cmake ../ -DCMAKE_BUILD_TYPE=Release -DLLVM_TARGETS_TO_BUILD="ARM;X86;AAr

Reading Multiple Images One at a Time

Python

Lines of Code : 11

License : Strong Copyleft (CC BY-SA 4.0)

Copy

import os
s1 = os.listdir('.')
for  s in s1:
    if ".jpg" not in s:
        continue
    if len(s)==8: # handle pic1.jpg pic2.jpg
        #print(s[:-5] + '00' + s[3] + '.jpg')
        os.rename(s, s[:-5] + '00' + s[3] + '.jpg')
    elif l

How to restore two graphs in Tensorflow?

Python

Lines of Code : 61

License : Strong Copyleft (CC BY-SA 4.0)

Copy

class ActivityRecognition:
     #Utility functions for training:
     def LSTM_RNN(self,_X, _weights, _biases):
      # model architecture based on "guillaume-chevalier" and "aymericdamien" under the MIT license.
          _X = tf.transpos

Community Discussions

Trending Discussions on tf-pose-estimation

NCHW and NHWC network format conversion Tensorflow model

How to change a saved model input shape in Tensorflow?

How to restore two graphs in Tensorflow?

QUESTION

NCHW and NHWC network format conversion Tensorflow model

Asked 2019-Dec-06 at 17:29

For this Humanpose Tensorflow network, network_cmu and base, it accepts only NHWC input format. If I construct the network in NCHW format, there is error as

...

ANSWER

Answered 2019-Aug-30 at 05:45

You can make use of tf.transpose to shift your axis from NHWC to NCHW

Source https://stackoverflow.com/questions/57719499

QUESTION

How to change a saved model input shape in Tensorflow?

Asked 2019-Jun-02 at 09:20

I want to make this repo https://github.com/ildoonet/tf-pose-estimation run with Intel Movidius, so I tried convert the pb model using mvNCCompile.

The problem is mvNCCompile require a fixed input shape but the model I have is a dynamic one.

I tried this

...

ANSWER

Answered 2019-Jun-02 at 09:20

I manage to solve this problem using this.

Source https://stackoverflow.com/questions/55841854

QUESTION

How to restore two graphs in Tensorflow?

Asked 2019-Feb-22 at 05:44

I have LSTM training in tensorflow and the whole sess is saved with saver = tf.train.Saver(). The whole code is shown below.

...

ANSWER

Answered 2019-Feb-22 at 05:44

I made a separate class for LSTM and LSTM graph is loaded with a sess in the class. So main python code has another sess with default graph. The default graph at main python load postestimator graph.

My LSTM class is defined as

Source https://stackoverflow.com/questions/54779529

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install tf-pose-estimation

Clone the repo and install 3rd-party libraries. Build c++ library for post processing. See : https://github.com/ildoonet/tf-pose-estimation/tree/master/tf_pose/pafprocess.
Alternatively, you can install this repo as a shared package using pip.
Before running demo, you should download graph files. You can deploy this graph on your mobile or other platforms. CMU's model graphs are too large for git, so I uploaded them on an external cloud. You should download them if you want to use cmu's original model. Download scripts are provided in the model folder.
cmu (trained in 656x368)
mobilenet_thin (trained in 432x368)
mobilenet_v2_large (trained in 432x368)
mobilenet_v2_small (trained in 432x368)

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

Find more information at: