tests/test_extract_faces.py

# built-in dependencies
import base64

# 3rd party dependencies
import numpy as np
import pytest

# project dependencies
from deepface import DeepFace
from deepface.commons import image_utils
from deepface.commons import logger as log

logger = log.get_singletonish_logger()

detectors = ["opencv", "mtcnn"]


def test_different_detectors():
    for detector in detectors:
        img_objs = DeepFace.extract_faces(img_path="dataset/img11.jpg", detector_backend=detector)
        for img_obj in img_objs:
            assert "face" in img_obj.keys()
            assert "facial_area" in img_obj.keys()
            assert isinstance(img_obj["facial_area"], dict)
            assert "x" in img_obj["facial_area"].keys()
            assert "y" in img_obj["facial_area"].keys()
            assert "w" in img_obj["facial_area"].keys()
            assert "h" in img_obj["facial_area"].keys()
            # is left eye set with respect to the person instead of observer
            assert "left_eye" in img_obj["facial_area"].keys()
            assert "right_eye" in img_obj["facial_area"].keys()
            right_eye = img_obj["facial_area"]["right_eye"]
            left_eye = img_obj["facial_area"]["left_eye"]
            assert left_eye[0] > right_eye[0]
            assert "confidence" in img_obj.keys()

            img = img_obj["face"]
            assert img.shape[0] > 0 and img.shape[1] > 0
        logger.info(f"✅ extract_faces for {detector} backend test is done")


def test_backends_for_enforced_detection_with_non_facial_inputs():
    black_img = np.zeros([224, 224, 3])
    for detector in detectors:
        with pytest.raises(ValueError):
            _ = DeepFace.extract_faces(img_path=black_img, detector_backend=detector)
    logger.info("✅ extract_faces for enforced detection and non-facial image test is done")


def test_backends_for_not_enforced_detection_with_non_facial_inputs():
    black_img = np.zeros([224, 224, 3])
    for detector in detectors:
        objs = DeepFace.extract_faces(
            img_path=black_img, detector_backend=detector, enforce_detection=False
        )
        assert objs[0]["face"].shape == (224, 224, 3)
    logger.info("✅ extract_faces for not enforced detection and non-facial image test is done")


def test_file_types_while_loading_base64():
    img1_path = "dataset/img47.jpg"
    img1_base64 = image_to_base64(image_path=img1_path)

    with pytest.raises(ValueError, match="input image can be jpg or png, but it is"):
        _ = image_utils.load_image_from_base64(uri=img1_base64)

    img2_path = "dataset/img1.jpg"
    img2_base64 = image_to_base64(image_path=img2_path)

    img2 = image_utils.load_image_from_base64(uri=img2_base64)
    # 3 dimensional image should be loaded
    assert len(img2.shape) == 3


def image_to_base64(image_path):
    with open(image_path, "rb") as image_file:
        encoded_string = base64.b64encode(image_file.read()).decode("utf-8")
    return "data:image/jpeg," + encoded_string