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| # 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 | |