facemask-detection 0.0.2

Unstructured set of the helper functions.

Facemask detection

Detection of the masks on the faces.

Use

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)>

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:

Train set

Train dataset was composed from the data:

No mask:

• VGGFace2

Mask:

• https://www.kaggle.com/andrewmvd/face-mask-detection
• https://www.kaggle.com/alexandralorenzo/maskdetection
• https://github.com/X-zhangyang/Real-World-Masked-Face-Dataset
• https://humansintheloop.org/medical-mask-dataset

Trainining

Define config, similar to facemask_detection_configs/2020-07-29.yaml.

Run

python facemask_detection/train.py -c <config>

Inference

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>