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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
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