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| # built-in dependencies | |
| import os | |
| # 3rd party dependencies | |
| import cv2 | |
| import pandas as pd | |
| # project dependencies | |
| from deepface import DeepFace | |
| from deepface.modules import verification | |
| from deepface.commons import image_utils | |
| from deepface.commons import logger as log | |
| logger = log.get_singletonish_logger() | |
| threshold = verification.find_threshold(model_name="VGG-Face", distance_metric="cosine") | |
| def test_find_with_exact_path(): | |
| img_path = os.path.join("dataset", "img1.jpg") | |
| dfs = DeepFace.find(img_path=img_path, db_path="dataset", silent=True) | |
| assert len(dfs) > 0 | |
| for df in dfs: | |
| assert isinstance(df, pd.DataFrame) | |
| # one is img1.jpg itself | |
| identity_df = df[df["identity"] == img_path] | |
| assert identity_df.shape[0] > 0 | |
| # validate reproducability | |
| assert identity_df["distance"].values[0] < threshold | |
| df = df[df["identity"] != img_path] | |
| logger.debug(df.head()) | |
| assert df.shape[0] > 0 | |
| logger.info("✅ test find for exact path done") | |
| def test_find_with_array_input(): | |
| img_path = os.path.join("dataset", "img1.jpg") | |
| img1 = cv2.imread(img_path) | |
| dfs = DeepFace.find(img1, db_path="dataset", silent=True) | |
| assert len(dfs) > 0 | |
| for df in dfs: | |
| assert isinstance(df, pd.DataFrame) | |
| # one is img1.jpg itself | |
| identity_df = df[df["identity"] == img_path] | |
| assert identity_df.shape[0] > 0 | |
| # validate reproducability | |
| assert identity_df["distance"].values[0] < threshold | |
| df = df[df["identity"] != img_path] | |
| logger.debug(df.head()) | |
| assert df.shape[0] > 0 | |
| logger.info("✅ test find for array input done") | |
| def test_find_with_extracted_faces(): | |
| img_path = os.path.join("dataset", "img1.jpg") | |
| face_objs = DeepFace.extract_faces(img_path) | |
| img = face_objs[0]["face"] | |
| dfs = DeepFace.find(img, db_path="dataset", detector_backend="skip", silent=True) | |
| assert len(dfs) > 0 | |
| for df in dfs: | |
| assert isinstance(df, pd.DataFrame) | |
| # one is img1.jpg itself | |
| identity_df = df[df["identity"] == img_path] | |
| assert identity_df.shape[0] > 0 | |
| # validate reproducability | |
| assert identity_df["distance"].values[0] < threshold | |
| df = df[df["identity"] != img_path] | |
| logger.debug(df.head()) | |
| assert df.shape[0] > 0 | |
| logger.info("✅ test find for extracted face input done") | |
| def test_filetype_for_find(): | |
| """ | |
| only images as jpg and png can be loaded into database | |
| """ | |
| img_path = os.path.join("dataset", "img1.jpg") | |
| dfs = DeepFace.find(img_path=img_path, db_path="dataset", silent=True) | |
| df = dfs[0] | |
| # img47 is webp even though its extension is jpg | |
| assert df[df["identity"] == "dataset/img47.jpg"].shape[0] == 0 | |
| def test_filetype_for_find_bulk_embeddings(): | |
| imgs = image_utils.list_images("dataset") | |
| assert len(imgs) > 0 | |
| # img47 is webp even though its extension is jpg | |
| assert "dataset/img47.jpg" not in imgs | |