https://github.com/ternaus/facemask_detection

Detection masks on faces.
https://github.com/ternaus/facemask_detection

classification image-processing mask-detection python pytorch

Last synced: 4 days ago
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Detection masks on faces.

• Host: GitHub
• URL: https://github.com/ternaus/facemask_detection
• Owner: ternaus
• License: mit
• Created: 2020-07-29T03:26:45.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-08-14T00:51:03.000Z (over 4 years ago)
• Last Synced: 2024-12-30T08:21:47.637Z (11 days ago)
• Topics: classification, image-processing, mask-detection, python, pytorch
• Language: Python
• Homepage:
• Size: 31.3 KB
• Stars: 29
• Watchers: 4
• Forks: 5
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE

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README

        
# Facemask detection

It could be confusing, but the model in this library perform classifications of the images.

It takes image as an input and outputs probability of person in the image wearing a mask.

Hence in order to get expected results the model should be combined with face detector, for example from

https://github.com/ternaus/retinaface.

[Example on how to combine face detector with mask detector](https://colab.research.google.com/drive/13Ktsrx164eQHfDmYLyMCoI-Kq0gC5Kg1?usp=sharing)

[WebApp with face detector and mask classifier](https://facemaskd.herokuapp.com/)

![https://habrastorage.org/webt/b_/ja/ww/b_jawwxndpkdl2pjlxlcxvars6m.png](https://habrastorage.org/webt/b_/ja/ww/b_jawwxndpkdl2pjlxlcxvars6m.png)

# Installation

`pip install -U facemask_detection`

# Use

```python

import albumentations as A

import torch

from facemask_detection.pre_trained_models import get_model

model = get_model("tf_efficientnet_b0_ns_2020-07-29")

model.eval()

transform = A.Compose([A.SmallestMaxSize(max_size=256, p=1),

                       A.CenterCrop(height=224, width=224, p=1),

                       A.Normalize(p=1)])

```

`image = `

```python

transformed_image = transform(image=image)['image']

input = torch.from_numpy(np.transpose(transformed_image, (2, 0, 1))).unsqueeze(0)

print("Probability of the mask on the face = ", model(input)[0].item())

```

* Jupyter notebook with the example: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1VkSK5MKIuGPIA31KJpGiFe_FafYC4xfD?usp=sharing)

* Jupyter notebook with the example on how to combine face detector with mask detector: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/13Ktsrx164eQHfDmYLyMCoI-Kq0gC5Kg1?usp=sharing)

## Train set

Train dataset was composed from the data:

### No mask:

*  [VGGFace2](http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/)

### Mask:

*  [https://www.kaggle.com/andrewmvd/face-mask-detection](https://www.kaggle.com/andrewmvd/face-mask-detection)

*  [https://www.kaggle.com/alexandralorenzo/maskdetection](https://www.kaggle.com/alexandralorenzo/maskdetection)

*  [https://github.com/X-zhangyang/Real-World-Masked-Face-Dataset](https://github.com/X-zhangyang/Real-World-Masked-Face-Dataset)

*  [https://humansintheloop.org/medical-mask-dataset](https://humansintheloop.org/medical-mask-dataset)

# Trainining

Define config, similar to [facemask_detection_configs/2020-07-29.yaml](facemask_detection_configs/2020-07-29.yaml).

Run

```bash

python facemask_detection/train.py -c 

```

Inference

```bash

python -m torch.distributed.launch --nproc_per_node=1 facemask_detection/inference.py -h

usage: inference.py [-h] -i INPUT_PATH -c CONFIG_PATH -o OUTPUT_PATH

                    [-b BATCH_SIZE] [-j NUM_WORKERS] -w WEIGHT_PATH

                    [--world_size WORLD_SIZE] [--local_rank LOCAL_RANK]

                    [--fp16]

optional arguments:

  -h, --help            show this help message and exit

  -i INPUT_PATH, --input_path INPUT_PATH

                        Path with images.

  -c CONFIG_PATH, --config_path CONFIG_PATH

                        Path to config.

  -o OUTPUT_PATH, --output_path OUTPUT_PATH

                        Path to save jsons.

  -b BATCH_SIZE, --batch_size BATCH_SIZE

                        batch_size

  -j NUM_WORKERS, --num_workers NUM_WORKERS

                        num_workers

  -w WEIGHT_PATH, --weight_path WEIGHT_PATH

                        Path to weights.

  --world_size WORLD_SIZE

                        number of nodes for distributed training

  --local_rank LOCAL_RANK

                        node rank for distributed training

  --fp16                Use fp6

```

Example:

```

python -m torch.distributed.launch --nproc_per_node= facemask_detection/inference.py \

                                   -i  \

                                   -w  \

                                   -o  \

                                   -c 

                                   -b 

```

Web https://facemaskd.herokuapp.com/