Glasses Detector

[](https://colab.research.google.com/github/mantasu/glasses-detector/blob/main/notebooks/demo.ipynb) [](https://mantasu.github.io/glasses-detector/) [](https://pypi.org/project/glasses-detector/) [](https://docs.python.org/3/) [](https://developer.nvidia.com/cuda-toolkit) [](https://zenodo.org/badge/latestdoi/610509640) [](https://opensource.org/licenses/MIT) 

Classification	👓 transparent 🕶️ opaque 🥽 any ➿shadows
Detection	🤓 worn 👓 standalone 👀 eye-area
Segmentation	😎 full 🖼️ frames 🦿 legs 🔍 lenses 👥 shadows

$\color{gray}{\textit{Note: }\text{refer to}}$ [Glasses Detector Features](https://mantasu.github.io/glasses-detector/docs/features.html) $\color{gray}{\text{for visual examples.}}$

pip install glasses-detector

git clone https://github.com/mantasu/glasses-detector
cd glasses-detector && pip install .

git clone https://github.com/mantasu/glasses-detector
cd glasses-detector && pip install -r requirements.txt

conda create -n glasses-detector python=3.12
conda activate glasses-detector 

glasses-detector -i path/to/img.jpg -t classification -d cuda -f int # Prints 1 or 0
glasses-detector -i path/to/img_dir -t segmentation -f mask -e .jpg  # Generates masks

from glasses_detector import GlassesClassifier

# Generates a CSV with each line "<img_name.jpg>,<True|False>"
classifier = GlassesClassifier(size="small", kind="sunglasses")
classifier.process_dir("path/to/dir", "path/to/preds.csv", format="bool")

from glasses_detector import GlassesDetector

# Generates dir_preds with bboxes as .txt for each img
detector = GlassesDetector(kind="eyes", device="cuda")
detector.process_dir("path/to/dir", ext=".txt", batch_size=64)

git clone https://github.com/mantasu/glasses-detector && cd glasses-detector/data
glasses-detector -i demo -o demo_labels.csv --task classification:eyeglasses

sudo apt-get install unrar

chmod +x scripts/*

python scripts/preprocess.py --root data -f -d

└── data                    # The data directory (root) under project
    ├── classification
    │   ├── anyglasses      # Datasets with any glasses as positives
    │   ├── eyeglasses      # Datasets with transparent glasses as positives
    │   ├── shadows         # Datasets with visible glasses frames shadows as positives
    │   └── sunglasses      # Datasets with semi-transparent/opaque glasses as positives 
    │
    ├── detection
    │   ├── eyes            # Datasets with bounding boxes for eye area 
    │   ├── solo            # Datasets with bounding boxes for standalone glasses
    │   └── worn            # Datasets with bounding boxes for worn glasses
    │
    └── segmentation
        ├── frames          # Datasets with masks for glasses frames
        ├── full            # Datasets with masks for full glasses (frames + lenses)
        ├── legs            # Datasets with masks for glasses legs (part of frames)
        ├── lenses          # Datasets with masks for glasses lenses
        ├── shadows         # Datasets with masks for eyeglasses frames cast shadows
        └── smart           # Datasets with masks for glasses frames and lenses if opaque

python scripts/run.py fit --task classification:anyglasses --size medium 

python scripts/run.py fit --batch-size 64 --trainer.max_epochs 300 --checkpoint.dirname ckpt

python scripts/run.py fit --optimizer Adam --optimizer.lr 1e-3 --lr_scheduler CosineAnnealingLR

python scripts/run.py test -t classification:anyglasses -s small --ckpt_path path/to/model.ckpt

python scripts/run.py test -t classification:anyglasses -s small -w path/to/weights.pth