Metadata-Version: 2.1
Name: facemask-detection
Version: 0.0.4
Summary: Unstructured set of the helper functions.
Home-page: https://github.com/ternaus/facemask_detection
Author: Vladimir Iglovikov
License: MIT
Description: # 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)
        
        
        ![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 = <numpy array with the shape (height, width, 3)>`
        
        ```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 <config>
        ```
        
        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=<num_gpu> facemask_detection/inference.py \
                                           -i <input_path> \
                                           -w <path to weights> \
                                           -o <path to the output_csv> \
                                           -c <path to config>
                                           -b <batch size>
        ```
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
Provides-Extra: test
