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111 lines
4.0 KiB
Python
111 lines
4.0 KiB
Python
# built-in dependencies
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import io
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import cv2
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import pytest
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# project dependencies
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from deepface import DeepFace
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from deepface.commons.logger import Logger
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logger = Logger()
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def test_standard_represent():
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img_path = "dataset/img1.jpg"
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embedding_objs = DeepFace.represent(img_path)
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for embedding_obj in embedding_objs:
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embedding = embedding_obj["embedding"]
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logger.debug(f"Function returned {len(embedding)} dimensional vector")
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assert len(embedding) == 4096
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logger.info("✅ test standard represent function done")
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def test_standard_represent_with_io_object():
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img_path = "dataset/img1.jpg"
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default_embedding_objs = DeepFace.represent(img_path)
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io_embedding_objs = DeepFace.represent(open(img_path, 'rb'))
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assert default_embedding_objs == io_embedding_objs
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# Confirm non-seekable io objects are handled properly
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io_obj = io.BytesIO(open(img_path, 'rb').read())
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io_obj.seek = None
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no_seek_io_embedding_objs = DeepFace.represent(io_obj)
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assert default_embedding_objs == no_seek_io_embedding_objs
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# Confirm non-image io objects raise exceptions
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with pytest.raises(ValueError, match='Failed to decode image'):
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DeepFace.represent(io.BytesIO(open(r'../requirements.txt', 'rb').read()))
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logger.info("✅ test standard represent with io object function done")
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def test_represent_for_skipped_detector_backend_with_image_path():
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face_img = "dataset/img5.jpg"
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img_objs = DeepFace.represent(img_path=face_img, detector_backend="skip")
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assert len(img_objs) >= 1
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img_obj = img_objs[0]
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assert "embedding" 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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assert "face_confidence" in img_obj.keys()
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logger.info("✅ test represent function for skipped detector and image path input backend done")
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def test_represent_for_skipped_detector_backend_with_preloaded_image():
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face_img = "dataset/img5.jpg"
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img = cv2.imread(face_img)
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img_objs = DeepFace.represent(img_path=img, detector_backend="skip")
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assert len(img_objs) >= 1
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img_obj = img_objs[0]
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assert "embedding" 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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assert "face_confidence" in img_obj.keys()
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logger.info("✅ test represent function for skipped detector and preloaded image done")
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def test_max_faces():
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# confirm that input image has more than one face
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results = DeepFace.represent(img_path="dataset/couple.jpg")
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assert len(results) > 1
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# test it with max faces arg
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max_faces = 1
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results = DeepFace.represent(img_path="dataset/couple.jpg", max_faces=max_faces)
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assert len(results) == max_faces
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def test_represent_detector_backend():
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"""
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There shouldn't be a difference between:
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- Using a detector backend provided by `represent`
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- Manually calling a detector backend, then calling `represent`.
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"""
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# Results using a detection backend.
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results_1 = DeepFace.represent(img_path="dataset/img1.jpg")
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assert len(results_1) == 1
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# Results performing face extraction first.
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faces = DeepFace.extract_faces(img_path="dataset/img1.jpg", color_face='bgr')
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assert len(faces) == 1
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# Images sent into represent need to be in BGR format.
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img = faces[0]['face']
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results_2 = DeepFace.represent(img_path=img, detector_backend="skip")
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assert len(results_2) == 1
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# The embeddings should be the exact same for both cases.
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embedding_1 = results_1[0]['embedding']
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embedding_2 = results_2[0]['embedding']
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assert embedding_1 == embedding_2
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logger.info("✅ test represent function for consistent output.")
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