Commit 0db3dc36 authored by Dmytro Nikolaiev's avatar Dmytro Nikolaiev 🐵
Browse files

Update README with more detailed explanation of dataset folder structure

parent 0991ba1b
# Real-time 'me-not_me' Face Detector
Real-time face detector with Python, TensorFlow/Keras and OpenCV. This is program, that do real-time face detection on webcam image and also can distinguish me from other people.
Real-time face detector built using Python, TensorFlow/Keras and OpenCV.
This is a program, that does real-time face detection on webcam image and also can distinguish me from other people.
| ![preview.jpg](article/img/preview.jpg) |
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......@@ -20,7 +22,7 @@ If for some reason you can't see the video above here is the link - [Real-time '
To run this code, you must have *tensorflow* and *opencv* libraries installed.
You should create virtual environment, activate it and run `pip install -r requirements.txt`. You can also do it with conda - create virtual environment, activate it and run following commands (they are listed in `requirements.txt` file too):
You should create a virtual environment, activate it and run `pip install -r requirements.txt`. You can also do it with conda - create virtual environment, activate it and run the following commands (they are listed in the `requirements.txt` file too):
```
conda install -c conda-forge numpy
......@@ -36,6 +38,15 @@ The project has the following structure:
me_not_me_detector
├───article
├───datasets
│ ├───face_dataset_test_images
│ │ ├───me # this folder contains TEST images for ME class
│ │ └───not_me # this folder contains TEST images for NOT_ME class
│ ├───face_dataset_train_aug_images
│ │ ├───me # this folder contains augmented TRAIN images for ME class (optional)
│ │ └───not_me # this folder contains augmented TRAIN images for NOT_ME class (optional)
│ └───face_dataset_train_images
│ ├───me # this folder contains TRAIN images for ME class
│ └───not_me # this folder contains TRAIN images for NOT_ME class
├───models
│ .gitignore
│ data_augmentation.ipynb
......@@ -48,8 +59,8 @@ me_not_me_detector
Let's talk about folders.
- The `article` folder contains the data for the tutorial.
- The `datasets` folder contains datasets, each of them has two classes - *me* and *not_me*. To know more about the dataset see `data_augmentation.ipynb`.
- The `models` folder contains the trained models for their test and further use.
- The `models` folder contains trained models for their test and further use.
- The `datasets` folder contains three folders - for a train set, test set and augmented train set (optional). Each of them contains two subfolders for two classes - *me* and *not_me*. In the general case, it contains N subfolders for N classes.
Now let's talk about the code files - jupyter notebooks.
- `data_augmentation.ipynb` file creates an augmented dataset from an initial one and provides some information about the dataset.
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