First Commit

Author: natthasath

.coveragerc

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+[run]
+omit = test/*

.gitignore

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+
+# Weight files
+bin/
+
+# Input files
+inputs/input.mp4
+
+# Output giles
+outputs/production.avi
+
+# Built cython files
+darkflow/cython_utils/*.pyd
+darkflow/cython_utils/*.c
+
+#egg-info
+darkflow.egg-info/
+
+#Other build stuff
+build/

.ionide/symbolCache.db

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+dist: trusty
+sudo: required
+
+language: python
+python:
+  - "3.6"
+
+cache:
+    directories:
+    - bin #cache .weights files
+    
+# command to install dependencies
+install: 
+    - pip install -r test/requirements-testing.txt
+    - pip install -e .
+
+# command to run tests
+script: pytest -x --cov=./
+
+#Upload code coverage statistics
+after_success:
+  - codecov

.travis.yml

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+{
+    "python.pythonPath": "C:\\Users\\natthasath.sak\\envs\\graduate-gpu\\Scripts\\python.exe"
+}

.vscode/settings.json

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+# YOLO-Object-Counting-API
+Real time Object Counting api. Implemented with the [YOLO](https://arxiv.org/pdf/1612.08242.pdf) algorithm and with the [SORT](https://arxiv.org/pdf/1703.07402.pdf) algorithm
+
+The implementation is using model in same format as darkflow and darknet. Weight files, as well as cfg files can be found [here](http://pjreddie.com/darknet/yolo/). Darklow supports only YOLOv1 and YOLOv2. Support for YOLOv3 has not yet been implemented.
+
+In order to achieve the best performance, you should have Cuda and tensorflow-gpu installed on Your device. 
+
+## Demo
+### Count objects of a specified class crossing a virtual line
+
+#### Graduate pedestrian
+
+[![Watch the video](https://i9.ytimg.com/vi/CF_UPJvWr-s/mq1.jpg?sqp=COjT7pcG&rs=AOn4CLBR2yVAPbF6F7rqkYxeudTkc0KwOA)](https://www.youtube.com/watch?v=CF_UPJvWr-s&ab_channel=NatthasathSaksupanara)
+
+#### Counting pedestrains 
+
+<p align="center"> <img src="inputs/pedestrians_output.gif"/> </p>
+
+#### Highway traffic counting
+
+<p align="center"> <img src="inputs/highway_traffic_output.gif"/> </p>
+
+### Count objects on a video
+
+<p align="center"> <img src="inputs/count_objects_on_video.gif"/> </p>
+
+### Count objects on a single frame
+
+<p align="center"> <img src="inputs/count_people_output.jpg"/> </p>
+
+
+# Set up
+## Dependencies
+
+```
+-tensorflow 1.0
+-numpy
+-opencv 3
+```
+
+## Getting started
+
+You can choose _one_ of the following three ways to get started with darkflow.
+
+1. Just build the Cython extensions in place. NOTE: If installing this way you will have to use `./flow` in the cloned darkflow directory instead of `flow` as darkflow is not installed globally.
+    ```
+    python3 setup.py build_ext --inplace
+    ```
+
+2. Let pip install darkflow globally in dev mode (still globally accessible, but changes to the code immediately take effect)
+    ```
+    pip3 install -e .
+    ```
+
+3. Install with pip globally
+    ```
+    pip3 install .
+    ```
+
+## Required files
+
+The YOLO algoritym impementation used in this project requires 3 files. Configuration of network (.cfg), trained weights (.weights) and labels.txt. 
+
+YOLO implementation used in this project enables usage of YOLOv1 and YOLOv2, and its tiny versions. Support for YOLOv3 has not yet been implemented.
+
+
+### .cfg files
+Configuration file determines a network architecture. Configurations can be found [here](http://pjreddie.com/darknet/yolo/). In example scripts we assume that the configuration is placed in cfg/ folder. Location of used .cfg file is specyfied in the options object used in the code. 
+
+The .cfg file can be downloaded using the following command: 
+```bash
+wget https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov2.cfg -O cfg/yolov2.cfg
+```
+### .weights files
+The .weights files contain trained parameters of a network. In example scripts we assume the weights are placed in bin/ folder. Location of used .weights file is specyfied in the options object used in the code.
+
+The .weights file can be downloaded using the following command:
+```bash
+wget https://pjreddie.com/media/files/yolov2.weights -O bin/yolov2.weights
+```
+### labels.txt files
+
+This file is list of classes detected by a YOLO netowork. It shoud contain as many classes as it is specyfied in a .cfg file. 
+
+## Run counting
+
+Once You have all dependencies instaled and all required files You can start counting objects. Object counting is carried out by an ObjectCuntingAPI object.
+
+Examples of counting below
+
+### Count cars on crosing a virtual line 
+```bash
+python3 count_cars_crosing_virtual_line.py
+```
+
+### Count objects on video from Video Camera 
+```bash
+python3 count_objects_from_camera.py
+```
+
+### Count people on image 
+```bash
+python3 count_people_on_image.py
+```
+
+## Credits
+The following open source projects were used in the implementation
+
+### Darkflow
+
+The YOLO algorithm impementation  - [Darkflow](https://github.com/thtrieu/darkflow)
+
+### Python Traffic Counter
+
+[Object counting with YOLO and SORT](https://github.com/bharath5673/python-traffic-counter-with-yolo-and-sort). Similar project, but instead of using the darklow YOLO implementation, it uses the opencv YOLO implementation, so there is no GPU acceleration.
+
+### Deep Sort 
+Object tracking and counting - [SORT](https://github.com/abewley/sort)
+
+## Images and Videos sources
+Highway surveillance [video](https://www.youtube.com/watch?v=PJ5xXXcfuTc)
+
+Pedestrian surveillance [video](https://www.youtube.com/watch?v=aUdKzb4LGJI)
+
+## Authors
+* [tugot17](https://github.com/tugot17)
+
+## License
+
+This project is licensed under the GNU General Public License v3.0 - see the [LICENSE](LICENSE) file for details
+
+
+That's all.