Metadata-Version: 2.1
Name: vhh-cmc
Version: 1.1.0
Summary: Camera Movements Classification Package
Home-page: https://github.com/dahe-cvl/vhh_cmc
Author: Daniel Helm
Author-email: daniel.helm@tuwien.ac.at
License: UNKNOWN
Description: # Plugin package: Camera Movements Classification
        
        This package includes all methods to classify a given shot/or image sequence in one of the categories Pan, Tilt or NA.
        
        ## Package Description
        
        PDF format: [vhh_cmc_pdf](https://github.com/dahe-cvl/vhh_cmc/blob/master/ApiSphinxDocumentation/build/latex/vhhpluginpackagecameramovementsclassificationvhh_cmc.pdf)
            
        HTML format (only usable if repository is available in local storage): [vhh_cmc_html](https://github.com/dahe-cvl/vhh_cmc/blob/master/ApiSphinxDocumentation/build/html/index.html)
            
            
        ## Quick Setup
        
        **Requirements:**
        
           * Ubuntu 18.04 LTS
           * python version 3.6.x
           
        ### 0 Environment Setup (optional)
        
        **Create a virtual environment:**
        
           * create a folder to a specified path (e.g. /xxx/vhh_cmc/)
           * python3 -m venv /xxx/vhh_cmc/
        
        **Activate the environment:**
        
           * source /xxx/vhh_cmc/bin/activate
           
        ### 1A Install using Pip
        
        The VHH Shot Boundary Detection package is available on [PyPI](https://pypi.org/project/vhh-cmc/) and can be installed via ```pip```.
        
        * Update pip and setuptools (tested using pip\==20.2.3 and setuptools==50.3.0)
        * ```pip install vhh-cmc```
        
        Alternatively, you can also build the package from source.
        
        ### 1B Install by building from Source
        
        **Checkout vhh_cmc repository to a specified folder:**
        
           * git clone https://github.com/dahe-cvl/vhh_cmc
        
        **Install the cmc package and all dependencies:**
        
           * Update ```pip``` and ```setuptools``` (tested using pip\==20.2.3 and setuptools==50.3.0)
           * Install the ```wheel``` package: ```pip install wheel```
           * change to the root directory of the repository (includes setup.py)
           * ```python setup.py bdist_wheel```
           * The aforementioned command should create a /dist directory containing a wheel. Install the package using ```python -m pip install dist/xxx.whl```
           
        > **_NOTE:_**
        You can check the success of the installation by using the commend *pip list*. This command should give you a list
        with all installed python packages and it should include *vhh-cmc*.
        
        ### 2 Setup environment variables (optional)
        
           * source /data/dhelm/python_virtenv/vhh_sbd_env/bin/activate
           * export CUDA_VISIBLE_DEVICES=0
           * export PYTHONPATH=$PYTHONPATH:/XXX/vhh_cmc/:/XXX/vhh_cmc/Develop/:/XXX/vhh_cmc/Demo/
        
        ### 3 Run demo script (optional)
        
           * change to root directory of the repository
           * python Demo/vhh_cmc_run_on_single_video.py
           
        ## Release Generation
        
        * Create and checkout release branch: (e.g. v1.1.0): ```git checkout -b v1.1.0```
        * Update version number in setup.py
        * Update Sphinx documentation and release version
        * Make sure that ```pip``` and ```setuptools``` are up to date
        * Install ```wheel``` and ```twine```
        * Build Source Archive and Built Distribution using ```python setup.py sdist bdist_wheel```
        * Upload package to PyPI using ```twine upload dist/*```
        
        ## Evaluation & Results
        
        Experiment 1:
        Most Common Angle + Random Features + LK Optical Flow (pescoller)
        
        |      | precision  | recall  | f1-score  | support  |
        |------|------------|---------|-----------|----------|
        | na   |    0.00    |   0.00  |    0.00   |     0    |
        | pan  |    0.91    |   0.51  |    0.65   |   182    |
        | tilt |    0.50    |   0.79  |    0.61   |    78    |
        
        
        |     accuracy   |      |      | 0.60   |    260 |
        |----------------|------|------|--------|--------|
        |    macro avg   | 0.47 | 0.44 | 0.42   |    260 |
        | weighted avg   | 0.79 | 0.60 | 0.64   |    260 |
        
        
        Experiment 2:
        Most Common Angle + GoodFeatures(Shi Tomasi Corner) + LK Optical Flow (pescoller)
        
        |      | precision  | recall  | f1-score  | support  |
        |------|------------|---------|-----------|----------|
        | na   |    0.00    |   0.00  |    0.00   |     0    |
        | pan  |    0.93    |   0.64  |    0.76   |   182    |
        | tilt |    0.69    |   0.77  |    0.73   |    78    |
        
        
        |     accuracy   |      |      | 0.68   |    260 |
        |----------------|------|------|--------|--------|
        |    macro avg   | 0.54 | 0.47 | 0.50   |    260 |
        | weighted avg   | 0.86 | 0.68 | 0.75   |    260 |
        
        
        
        Experiment 3: 
        ORB Features + BFmatcher 
        
        |      | precision  | recall  | f1-score  | support  |
        |------|------------|---------|-----------|----------|
        | na   |    0.00    |   0.00  |    0.00   |     0    |
        | pan  |    1.00    |   0.75  |    0.86   |   182    |
        | tilt |    0.73    |   0.99  |    0.84   |    78    |
        
        
        |     accuracy   |      |      | 0.82   |    260 |
        |----------------|------|------|--------|--------|
        |    macro avg   | 0.58 | 0.58 | 0.56   |    260 |
        | weighted avg   | 0.92 | 0.82 | 0.85   |    260 |
        
        
        Experiment 4:
        SIFT Features + knnMatcher
        
        |      | precision  | recall  | f1-score  | support  |
        |------|------------|---------|-----------|----------|
        | na   |    0.00    |   0.00  |    0.00   |     0    |
        | pan  |    1.00    |   0.75  |    0.86   |   182    |
        | tilt |    0.79    |   1.00  |    0.88   |    78    |
        
        
        |     accuracy   |      |      | 0.82   |    260 |
        |----------------|------|------|--------|--------|
        |    macro avg   | 0.60 | 0.59 | 0.58   |    260 |
        | weighted avg   | 0.94 | 0.83 | 0.87   |    260 |
        
        
        Experiment 5:
        SURF features + knn matcher
        
        |      | precision  | recall  | f1-score  | support  |
        |------|------------|---------|-----------|----------|
        | na   |    0.00    |   0.00  |    0.00   |     0    |
        | pan  |    1.00    |   0.74  |    0.85   |   182    |
        | tilt |    0.80    |   1.00  |    0.89   |    78    |
        
        
        |     accuracy   |      |      | 0.82   |    260 |
        |----------------|------|------|--------|--------|
        |    macro avg   | 0.60 | 0.58 | 0.58   |    260 |
        | weighted avg   | 0.94 | 0.82 | 0.86   |    260 |
        
        Experiment 5:
        BRIEF features + knn matcher
        
        |      | precision  | recall  | f1-score  | support  |
        |------|------------|---------|-----------|----------|
        | na   |    0.00    |   0.00  |    0.00   |     0    |
        | pan  |    0.97    |   0.73  |    0.83   |   182    |
        | tilt |    0.75    |   0.94  |    0.83   |    78    |
        
        
        |     accuracy   |      |      | 0.82   |    260 |
        |----------------|------|------|--------|--------|
        |    macro avg   | 0.57 | 0.55 | 0.55   |    260 |
        | weighted avg   | 0.91 | 0.79 | 0.83   |    260 |
        
        
        ### Experiments 10.7.2020
        
        all experiments with sift features + magnitude optimization
        
        #################
        TH = 5
        
        NOT DONE YET
        
        #################
        TH = 3
        
        |      | precision  | recall  | f1-score  | support  |
        |------|------------|---------|-----------|----------|
        | na   |    0.00    |   0.00  |    0.00   |     0    |
        | pan  |    0.99    |   0.85  |    0.91   |   182    |
        | tilt |    0.98    |   0.69  |    0.81   |    78    |
        
        
        |     accuracy   |      |      | 0.80   |    260 |
        |----------------|------|------|--------|--------|
        |    macro avg   | 0.66 | 0.51 | 0.58   |    260 |
        | weighted avg   | 0.99 | 0.80 | 0.88   |    260 |
        
        
        #################
        TH = 2
        
        |      | precision  | recall  | f1-score  | support  |
        |------|------------|---------|-----------|----------|
        | na   |    0.00    |   0.00  |    0.00   |     0    |
        | pan  |    0.98    |   0.97  |    0.98   |   182    |
        | tilt |    0.99    |   0.86  |    0.92   |    78    |
        
        
        |     accuracy   |      |      | 0.93   |    260 |
        |----------------|------|------|--------|--------|
        |    macro avg   | 0.66 | 0.61 | 0.63   |    260 |
        | weighted avg   | 0.98 | 0.93 | 0.96   |    260 |
        
        
        
        #################
        TH = 1
        
        |      | precision  | recall  | f1-score  | support  |
        |------|------------|---------|-----------|----------|
        | na   |    0.00    |   0.00  |    0.00   |     0    |
        | pan  |    0.98    |   0.99  |    0.99   |   182    |
        | tilt |    0.99    |   0.90  |    0.94   |    78    |
        
        
        |     accuracy   |      |      | 0.96   |    260 |
        |----------------|------|------|--------|--------|
        |    macro avg   | 0.66 | 0.63 | 0.64   |    260 |
        | weighted avg   | 0.98 | 0.96 | 0.97   |    260 |
        
        
        **complete dataset 1213 samples (pan and tilt)**
        
        ORB features and TH=2
        
        |      | precision  | recall  | f1-score  | support  |
        |------|------------|---------|-----------|----------|
        | na   |    0.00    |   0.00  |    0.00   |     0    |
        | pan  |    0.99    |   0.94  |    0.96   |   859    |
        | tilt |    0.87    |   0.96  |    0.91   |   354    |
        
        
        |     accuracy   |      |      | 0.94   |    1213 |
        |----------------|------|------|--------|--------|
        |    macro avg   | 0.62 | 0.63 | 0.63   |    1213 |
        | weighted avg   | 0.95 | 0.94 | 0.95   |    1213 |
Platform: UNKNOWN
Description-Content-Type: text/markdown
