Detection masks on faces.
MIT License
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
WebApp with face detector and mask classifier
pip install -U facemask_detection
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())
Train dataset was composed from the data:
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>