mirror of
https://github.com/serengil/deepface.git
synced 2025-06-07 12:05:22 +00:00
241 lines
6.4 KiB
Python
241 lines
6.4 KiB
Python
import warnings
|
|
import os
|
|
import tensorflow as tf
|
|
import cv2
|
|
from deepface import DeepFace
|
|
|
|
print("-----------------------------------------")
|
|
|
|
warnings.filterwarnings("ignore")
|
|
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
|
|
|
|
tf_major_version = int(tf.__version__.split(".")[0])
|
|
|
|
if tf_major_version == 2:
|
|
import logging
|
|
tf.get_logger().setLevel(logging.ERROR)
|
|
|
|
print("Running unit tests for TF ", tf.__version__)
|
|
|
|
print("-----------------------------------------")
|
|
|
|
test_threshold = 97
|
|
num_cases = 0
|
|
succeed_cases = 0
|
|
|
|
def evaluate(condition):
|
|
|
|
global num_cases, succeed_cases
|
|
|
|
if condition is True:
|
|
succeed_cases += 1
|
|
|
|
num_cases += 1
|
|
|
|
# ------------------------------------------------
|
|
|
|
detectors = ['opencv', 'mtcnn', 'retinaface']
|
|
models = ['VGG-Face', 'Facenet', 'Facenet512', 'ArcFace', 'SFace']
|
|
metrics = ['cosine', 'euclidean', 'euclidean_l2']
|
|
|
|
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],
|
|
]
|
|
|
|
print("-----------------------------------------")
|
|
|
|
def test_cases():
|
|
|
|
print("DeepFace.detectFace test")
|
|
|
|
for detector in detectors:
|
|
img = DeepFace.detectFace("dataset/img11.jpg", detector_backend = detector)
|
|
evaluate( img.shape[0] > 0 and img.shape[1] > 0 )
|
|
print(detector," test is done")
|
|
|
|
print("-----------------------------------------")
|
|
|
|
img_path = "dataset/img1.jpg"
|
|
embedding = DeepFace.represent(img_path)
|
|
print("Function returned ", len(embedding), "dimensional vector")
|
|
evaluate( len(embedding) > 0 )
|
|
|
|
print("-----------------------------------------")
|
|
|
|
print("Face detectors test")
|
|
|
|
for detector in detectors:
|
|
print(detector + " detector")
|
|
res = DeepFace.verify(dataset[0][0], dataset[0][1], detector_backend = detector)
|
|
print(res)
|
|
assert res["verified"] == dataset[0][2]
|
|
|
|
print("-----------------------------------------")
|
|
|
|
print("Single find function test")
|
|
|
|
df = DeepFace.find(img_path = "dataset/img1.jpg", db_path = "dataset")
|
|
print(df.head())
|
|
evaluate( df.shape[0] > 0 )
|
|
|
|
print("-----------------------------------------")
|
|
|
|
print("Facial analysis test. Passing nothing as an action")
|
|
|
|
img = "dataset/img4.jpg"
|
|
demography = DeepFace.analyze(img)
|
|
print(demography)
|
|
|
|
evaluate( demography["age"] > 20 and demography["age"] < 40 )
|
|
evaluate( demography["gender"] == "Woman" )
|
|
|
|
print("-----------------------------------------")
|
|
|
|
print("Facial analysis test. Passing all to the action")
|
|
demography = DeepFace.analyze(img, ['age', 'gender', 'race', 'emotion'])
|
|
|
|
print("Demography:")
|
|
print(demography)
|
|
|
|
#check response is a valid json
|
|
print("Age: ", demography["age"])
|
|
print("Gender: ", demography["gender"])
|
|
print("Race: ", demography["dominant_race"])
|
|
print("Emotion: ", demography["dominant_emotion"])
|
|
|
|
evaluate( demography.get("age") is not None )
|
|
evaluate( demography.get("gender") is not None )
|
|
evaluate( demography.get("dominant_race") is not None )
|
|
evaluate( demography.get("dominant_emotion") is not None )
|
|
|
|
print("-----------------------------------------")
|
|
|
|
print("Facial analysis test 2. Remove some actions and check they are not computed")
|
|
demography = DeepFace.analyze(img, ['age', 'gender'])
|
|
|
|
print("Age: ", demography.get("age"))
|
|
print("Gender: ", demography.get("gender"))
|
|
print("Race: ", demography.get("dominant_race"))
|
|
print("Emotion: ", demography.get("dominant_emotion"))
|
|
|
|
evaluate( demography.get("age") is not None )
|
|
evaluate( demography.get("gender") is not None )
|
|
evaluate( demography.get("dominant_race") is None )
|
|
evaluate( demography.get("dominant_emotion") is None )
|
|
|
|
print("-----------------------------------------")
|
|
|
|
print("Face recognition tests")
|
|
|
|
passed_tests = 0; test_cases = 0
|
|
|
|
for model in models:
|
|
#prebuilt_model = DeepFace.build_model(model)
|
|
#print(model," is built")
|
|
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
|
|
#, model = prebuilt_model
|
|
, distance_metric = metric)
|
|
|
|
prediction = resp_obj["verified"]
|
|
distance = round(resp_obj["distance"], 2)
|
|
threshold = resp_obj["threshold"]
|
|
|
|
evaluate( prediction == result )
|
|
|
|
test_result_label = "failed"
|
|
if prediction == result:
|
|
passed_tests = passed_tests + 1
|
|
test_result_label = "passed"
|
|
|
|
if prediction == True:
|
|
classified_label = "verified"
|
|
else:
|
|
classified_label = "unverified"
|
|
|
|
test_cases = test_cases + 1
|
|
|
|
print(img1.split("/")[-1], "-", img2.split("/")[-1], classified_label, "as same person based on", model,"and",metric,". Distance:",distance,", Threshold:", threshold,"(",test_result_label,")")
|
|
|
|
print("--------------------------")
|
|
|
|
#-----------------------------------------
|
|
|
|
print("Passed unit tests: ",passed_tests," / ",test_cases)
|
|
|
|
min_score = 70
|
|
|
|
accuracy = 100 * passed_tests / test_cases
|
|
accuracy = round(accuracy, 2)
|
|
|
|
print("--------------------------")
|
|
|
|
#-----------------------------------
|
|
print("--------------------------")
|
|
|
|
print("Passing numpy array to analyze function")
|
|
|
|
img = cv2.imread("dataset/img1.jpg")
|
|
resp_obj = DeepFace.analyze(img)
|
|
print(resp_obj)
|
|
|
|
evaluate( resp_obj["age"] > 20 and resp_obj["age"] < 40 )
|
|
evaluate( resp_obj["gender"] == "Woman" )
|
|
|
|
print("--------------------------")
|
|
|
|
print("Passing numpy array to verify function")
|
|
|
|
img1 = cv2.imread("dataset/img1.jpg")
|
|
img2 = cv2.imread("dataset/img2.jpg")
|
|
|
|
res = DeepFace.verify(img1, img2)
|
|
print(res)
|
|
|
|
evaluate( res["verified"] == True )
|
|
|
|
print("--------------------------")
|
|
|
|
print("Passing numpy array to find function")
|
|
|
|
img1 = cv2.imread("dataset/img1.jpg")
|
|
|
|
df = DeepFace.find(img1, db_path = "dataset")
|
|
|
|
print(df.head())
|
|
|
|
evaluate( df.shape[0] > 0 )
|
|
|
|
print("--------------------------")
|
|
|
|
test_cases()
|
|
|
|
print("num of test cases run: " + str(num_cases))
|
|
print("succeeded test cases: " + str(succeed_cases))
|
|
|
|
test_score = (100 * succeed_cases) / num_cases
|
|
|
|
print("test coverage: " + str(test_score))
|
|
|
|
if test_score > test_threshold:
|
|
print("min required test coverage is satisfied")
|
|
else:
|
|
print("min required test coverage is NOT satisfied")
|
|
|
|
assert test_score > test_threshold |