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51 lines
2.1 KiB
Python
51 lines
2.1 KiB
Python
import numpy as np
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import pytest
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from deepface import DeepFace
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from deepface.commons.logger import Logger
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logger = Logger("tests/test_extract_faces.py")
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detectors = ["opencv", "mtcnn"]
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def test_different_detectors():
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for detector in detectors:
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img_objs = DeepFace.extract_faces(img_path="dataset/img11.jpg", detector_backend=detector)
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for img_obj in img_objs:
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assert "face" in img_obj.keys()
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assert "facial_area" in img_obj.keys()
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assert isinstance(img_obj["facial_area"], dict)
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assert "x" in img_obj["facial_area"].keys()
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assert "y" in img_obj["facial_area"].keys()
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assert "w" in img_obj["facial_area"].keys()
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assert "h" in img_obj["facial_area"].keys()
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# is left eye set with respect to the person instead of observer
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assert "left_eye" in img_obj["facial_area"].keys()
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assert "right_eye" in img_obj["facial_area"].keys()
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right_eye = img_obj["facial_area"]["right_eye"]
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left_eye = img_obj["facial_area"]["left_eye"]
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assert left_eye[0] > right_eye[0]
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assert "confidence" in img_obj.keys()
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img = img_obj["face"]
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assert img.shape[0] > 0 and img.shape[1] > 0
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logger.info(f"✅ extract_faces for {detector} backend test is done")
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def test_backends_for_enforced_detection_with_non_facial_inputs():
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black_img = np.zeros([224, 224, 3])
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for detector in detectors:
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with pytest.raises(ValueError):
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_ = DeepFace.extract_faces(img_path=black_img, detector_backend=detector)
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logger.info("✅ extract_faces for enforced detection and non-facial image test is done")
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def test_backends_for_not_enforced_detection_with_non_facial_inputs():
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black_img = np.zeros([224, 224, 3])
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for detector in detectors:
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objs = DeepFace.extract_faces(
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img_path=black_img, detector_backend=detector, enforce_detection=False
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)
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assert objs[0]["face"].shape == (224, 224, 3)
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logger.info("✅ extract_faces for not enforced detection and non-facial image test is done")
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