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import pytest
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import cv2
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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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models = ["VGG-Face", "Facenet", "Facenet512", "ArcFace", "GhostFaceNet"]
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metrics = ["cosine", "euclidean", "euclidean_l2"]
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detectors = ["opencv", "mtcnn"]
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def test_different_facial_recognition_models():
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dataset = [
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["dataset/img1.jpg", "dataset/img2.jpg", True],
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["dataset/img5.jpg", "dataset/img6.jpg", True],
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["dataset/img6.jpg", "dataset/img7.jpg", True],
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["dataset/img8.jpg", "dataset/img9.jpg", True],
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["dataset/img1.jpg", "dataset/img11.jpg", True],
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["dataset/img2.jpg", "dataset/img11.jpg", True],
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["dataset/img1.jpg", "dataset/img3.jpg", False],
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["dataset/img2.jpg", "dataset/img3.jpg", False],
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["dataset/img6.jpg", "dataset/img8.jpg", False],
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["dataset/img6.jpg", "dataset/img9.jpg", False],
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]
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expected_coverage = 97.53
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successful_tests = 0
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unsuccessful_tests = 0
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for model in models:
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for metric in metrics:
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for instance in dataset:
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img1 = instance[0]
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img2 = instance[1]
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result = instance[2]
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resp_obj = DeepFace.verify(img1, img2, model_name=model, distance_metric=metric)
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prediction = resp_obj["verified"]
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distance = round(resp_obj["distance"], 2)
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threshold = resp_obj["threshold"]
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if prediction is result:
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test_result_label = "✅"
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successful_tests += 1
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else:
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test_result_label = "❌"
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unsuccessful_tests += 1
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if prediction is True:
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classified_label = "same person"
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else:
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classified_label = "different persons"
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img1_alias = img1.split("/", maxsplit=1)[-1]
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img2_alias = img2.split("/", maxsplit=1)[-1]
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logger.debug(
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f"{test_result_label} Pair {img1_alias}-{img2_alias}"
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f" is {classified_label} based on {model}-{metric}"
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f" (Distance: {distance}, Threshold: {threshold})",
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)
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coverage_score = (100 * successful_tests) / (successful_tests + unsuccessful_tests)
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assert (
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coverage_score > expected_coverage
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), f"⛔ facial recognition models test failed with {coverage_score} score"
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logger.info(f"✅ facial recognition models test passed with {coverage_score}")
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def test_different_face_detectors():
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for detector in detectors:
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res = DeepFace.verify("dataset/img1.jpg", "dataset/img2.jpg", detector_backend=detector)
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assert isinstance(res, dict)
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assert "verified" in res.keys()
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assert res["verified"] in [True, False]
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assert "distance" in res.keys()
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assert "threshold" in res.keys()
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assert "model" in res.keys()
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assert "detector_backend" in res.keys()
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assert "similarity_metric" in res.keys()
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assert "facial_areas" in res.keys()
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assert "img1" in res["facial_areas"].keys()
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assert "img2" in res["facial_areas"].keys()
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assert "x" in res["facial_areas"]["img1"].keys()
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assert "y" in res["facial_areas"]["img1"].keys()
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assert "w" in res["facial_areas"]["img1"].keys()
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assert "h" in res["facial_areas"]["img1"].keys()
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assert "x" in res["facial_areas"]["img2"].keys()
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assert "y" in res["facial_areas"]["img2"].keys()
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assert "w" in res["facial_areas"]["img2"].keys()
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assert "h" in res["facial_areas"]["img2"].keys()
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logger.info(f"✅ test verify for {detector} backend done")
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def test_verify_for_preloaded_image():
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img1 = cv2.imread("dataset/img1.jpg")
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img2 = cv2.imread("dataset/img2.jpg")
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res = DeepFace.verify(img1, img2)
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assert res["verified"] is True
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logger.info("✅ test verify for pre-loaded image done")
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def test_verify_for_precalculated_embeddings():
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model_name = "Facenet"
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img1_path = "dataset/img1.jpg"
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img2_path = "dataset/img2.jpg"
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img1_embedding = DeepFace.represent(img_path=img1_path, model_name=model_name)[0]["embedding"]
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img2_embedding = DeepFace.represent(img_path=img2_path, model_name=model_name)[0]["embedding"]
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result = DeepFace.verify(
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img1_path=img1_embedding, img2_path=img2_embedding, model_name=model_name, silent=True
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)
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assert result["verified"] is True
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assert result["distance"] < result["threshold"]
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assert result["model"] == model_name
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assert result["facial_areas"]["img1"] is not None
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assert result["facial_areas"]["img2"] is not None
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assert isinstance(result["facial_areas"]["img1"], dict)
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assert isinstance(result["facial_areas"]["img2"], dict)
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assert "x" in result["facial_areas"]["img1"].keys()
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assert "y" in result["facial_areas"]["img1"].keys()
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assert "w" in result["facial_areas"]["img1"].keys()
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assert "h" in result["facial_areas"]["img1"].keys()
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assert "left_eye" in result["facial_areas"]["img1"].keys()
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assert "right_eye" in result["facial_areas"]["img1"].keys()
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assert "x" in result["facial_areas"]["img2"].keys()
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assert "y" in result["facial_areas"]["img2"].keys()
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assert "w" in result["facial_areas"]["img2"].keys()
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assert "h" in result["facial_areas"]["img2"].keys()
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assert "left_eye" in result["facial_areas"]["img2"].keys()
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assert "right_eye" in result["facial_areas"]["img2"].keys()
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logger.info("✅ test verify for pre-calculated embeddings done")
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def test_verify_with_precalculated_embeddings_for_incorrect_model():
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img1_path = "dataset/img1.jpg"
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img2_path = "dataset/img2.jpg"
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img1_embedding = DeepFace.represent(img_path=img1_path)[0]["embedding"]
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img2_embedding = DeepFace.represent(img_path=img2_path)[0]["embedding"]
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with pytest.raises(
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ValueError,
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match="embeddings of Facenet should have 128 dimensions, but 1-th image has 4096 dimensions input",
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):
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_ = DeepFace.verify(
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img1_path=img1_embedding, img2_path=img2_embedding, model_name="Facenet", silent=True
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)
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logger.info("✅ test verify with pre-calculated embeddings for incorrect model done")
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def test_verify_for_broken_embeddings():
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img1_embeddings = ["a", "b", "c"]
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img2_embeddings = [1, 2, 3]
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with pytest.raises(
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ValueError,
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match="When passing img1_path as a list, ensure that all its items are of type float.",
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):
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_ = DeepFace.verify(img1_path=img1_embeddings, img2_path=img2_embeddings)
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logger.info("✅ test verify for broken embeddings content is done")
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def test_verify_for_nested_embeddings():
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"""
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batch embeddings not supported
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"""
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img1_embeddings = [[1, 2, 3], [4, 5, 6]]
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img2_path = "dataset/img1.jpg"
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with pytest.raises(
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ValueError,
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match="When passing img1_path as a list, ensure that all its items are of type float",
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):
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_ = DeepFace.verify(img1_path=img1_embeddings, img2_path=img2_path)
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logger.info("✅ test verify for nested embeddings is done")
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