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