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132 lines
4.6 KiB
Python
132 lines
4.6 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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import numpy as np
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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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@pytest.mark.parametrize("model_name", [
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"VGG-Face",
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"Facenet",
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"Facenet512",
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"OpenFace",
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"DeepFace",
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"DeepID",
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"Dlib",
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"ArcFace",
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"SFace",
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"GhostFaceNet"
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])
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def test_batched_represent(model_name):
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img_paths = [
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"dataset/img1.jpg",
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"dataset/img2.jpg",
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"dataset/img3.jpg",
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"dataset/img4.jpg",
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"dataset/img5.jpg",
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]
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embedding_objs = DeepFace.represent(img_path=img_paths, model_name=model_name)
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assert len(embedding_objs) == len(img_paths), f"Expected {len(img_paths)} embeddings, got {len(embedding_objs)}"
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if model_name == "VGG-Face":
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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, f"Expected embedding of length 4096, got {len(embedding)}"
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embedding_objs_one_by_one = [
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embedding_obj
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for img_path in img_paths
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for embedding_obj in DeepFace.represent(img_path=img_path, model_name=model_name)
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]
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for embedding_obj_one_by_one, embedding_obj in zip(embedding_objs_one_by_one, embedding_objs):
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assert np.allclose(
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embedding_obj_one_by_one["embedding"],
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embedding_obj["embedding"],
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rtol=1e-2,
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atol=1e-2
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), "Embeddings do not match within tolerance"
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logger.info(f"✅ test batch represent function for model {model_name} done")
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