diff --git a/tests/unit_tests.py b/tests/unit_tests.py index 09d7501..57a71de 100644 --- a/tests/unit_tests.py +++ b/tests/unit_tests.py @@ -1,353 +1,223 @@ import warnings -warnings.filterwarnings("ignore") - import os -#os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' +import tensorflow as tf +import cv2 +from deepface import DeepFace + +print("-----------------------------------------") + +warnings.filterwarnings("ignore") os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' -import json -import time -import unittest +tf_major_version = int(tf.__version__.split(".")[0]) -#----------------------------------------- +if tf_major_version == 2: + import logging + tf.get_logger().setLevel(logging.ERROR) -import tensorflow as tf +print("Running unit tests for TF ", tf.__version__) -class deepface_unit_tests(unittest.TestCase): +print("-----------------------------------------") - def test_deepface(self): - tf_version = int(tf.__version__.split(".")[0]) +expected_coverage = 97 +num_cases = 0; succeed_cases = 0 - if tf_version == 2: - import logging - tf.get_logger().setLevel(logging.ERROR) +def evaluate(condition): - print("Running unit tests for TF ", tf.__version__) + global num_cases, succeed_cases - from deepface import DeepFace - from deepface.commons import functions - from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace - from deepface.extendedmodels import Age, Gender, Race, Emotion + if condition is True: + succeed_cases += 1 + + num_cases += 1 - print("-----------------------------------------") - #----------------------------------------- +# ------------------------------------------------ - print("DeepFace.detectFace test") - #detectors = ['opencv', 'ssd', 'dlib', 'mtcnn', 'retinaface'] - detectors = ['opencv', 'ssd', 'mtcnn', 'retinaface'] +detectors = ['opencv', 'mtcnn', 'retinaface'] +models = ['VGG-Face', 'Facenet', 'Facenet512', 'ArcFace', 'SFace'] +metrics = ['cosine', 'euclidean', 'euclidean_l2'] - for detector in detectors: - img = DeepFace.detectFace("dataset/img11.jpg", detector_backend = detector) - print(detector," test is done") +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], - #import matplotlib.pyplot as plt - #plt.imshow(img) - #plt.show() + ['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("-----------------------------------------") +print("-----------------------------------------") - img_path = "dataset/img1.jpg" - embedding = DeepFace.represent(img_path) - print("Function returned ", len(embedding), "dimensional vector") +def test_cases(): - model_name = "VGG-Face" - model = DeepFace.build_model(model_name) - print(model_name," is built") - embedding = DeepFace.represent(img_path, model = model) - print("Represent function returned ", len(embedding), "dimensional vector") + 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") - dataset = [ - ['dataset/img1.jpg', 'dataset/img2.jpg', True], - ['dataset/img1.jpg', 'dataset/img6.jpg', True] - ] + print("-----------------------------------------") - print("-----------------------------------------") + img_path = "dataset/img1.jpg" + embedding = DeepFace.represent(img_path) + print("Function returned ", len(embedding), "dimensional vector") + evaluate(len(embedding) > 0) - print("Face detectors test") + print("-----------------------------------------") - print("retinaface detector") - res = DeepFace.verify(dataset, detector_backend = 'retinaface') - print(res) + print("Face detectors test") - print("ssd detector") - res = DeepFace.verify(dataset, detector_backend = 'ssd') - print(res) + 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("opencv detector") - res = DeepFace.verify(dataset, detector_backend = 'opencv') - print(res) + print("-----------------------------------------") - if False: - print("dlib detector") - res = DeepFace.verify(dataset, detector_backend = 'dlib') - print(res) + print("Find function test") - print("mtcnn detector") - res = DeepFace.verify(dataset, detector_backend = 'mtcnn') - print(res) + df = DeepFace.find(img_path = "dataset/img1.jpg", db_path = "dataset") + print(df.head()) + evaluate(df.shape[0] > 0) - print("-----------------------------------------") + print("-----------------------------------------") - print("Single find function test") + print("Facial analysis test. Passing nothing as an action") - df = DeepFace.find(img_path = "dataset/img1.jpg", db_path = "dataset" - #, model_name = 'Dlib' - ) - print(df.head()) + img = "dataset/img4.jpg" + demography = DeepFace.analyze(img) + print(demography) - print("-----------------------------------------") + evaluate(demography["age"] > 20 and demography["age"] < 40) + evaluate(demography["gender"] == "Woman") - print("Pre-built model for single find function test") + print("-----------------------------------------") - #model_name = "VGG-Face" - #model = DeepFace.build_model(model_name) - #print(model_name," is built") + print("Facial analysis test. Passing all to the action") + demography = DeepFace.analyze(img, ['age', 'gender', 'race', 'emotion']) - df = DeepFace.find(img_path = "dataset/img1.jpg", db_path = "dataset" - , model_name = model_name, model = model - ) - print(df.head()) + print("Demography:") + print(demography) - print("-----------------------------------------") + #check response is a valid json + print("Age: ", demography["age"]) + print("Gender: ", demography["gender"]) + print("Race: ", demography["dominant_race"]) + print("Emotion: ", demography["dominant_emotion"]) - print("Bulk find function tests") + 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) - dfs = DeepFace.find(img_path = ["dataset/img1.jpg", "dataset/img2.jpg"], db_path = "dataset" - #, model_name = 'Dlib' - ) - print(dfs[0].head()) - print(dfs[1].head()) + print("-----------------------------------------") - print("-----------------------------------------") + print("Facial analysis test 2. Remove some actions and check they are not computed") + demography = DeepFace.analyze(img, ['age', 'gender']) - print("Bulk verification tests") + print("Age: ", demography.get("age")) + print("Gender: ", demography.get("gender")) + print("Race: ", demography.get("dominant_race")) + print("Emotion: ", demography.get("dominant_emotion")) - resp_obj = DeepFace.verify(dataset) - print(resp_obj) - print(resp_obj["pair_1"]["verified"] == True) - print(resp_obj["pair_2"]["verified"] == True) + 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("-----------------------------------------") - print("Bulk facial analysis tests") + print("Facial recognition tests") - dataset = [ - 'dataset/img1.jpg', - 'dataset/img2.jpg', - 'dataset/img5.jpg', - 'dataset/img6.jpg' - ] + for model in models: + for metric in metrics: + for instance in dataset: + img1 = instance[0] + img2 = instance[1] + result = instance[2] - resp_obj = DeepFace.analyze(dataset) - print(resp_obj["instance_1"]["age"]," years old ", resp_obj["instance_1"]["dominant_emotion"], " ",resp_obj["instance_1"]["gender"]) - print(resp_obj["instance_2"]["age"]," years old ", resp_obj["instance_2"]["dominant_emotion"], " ",resp_obj["instance_2"]["gender"]) - print(resp_obj["instance_3"]["age"]," years old ", resp_obj["instance_3"]["dominant_emotion"], " ",resp_obj["instance_3"]["gender"]) - print(resp_obj["instance_4"]["age"]," years old ", resp_obj["instance_4"]["dominant_emotion"], " ",resp_obj["instance_4"]["gender"]) + resp_obj = DeepFace.verify(img1, img2 + , model_name = model + , distance_metric = metric) - print("-----------------------------------------") + prediction = resp_obj["verified"] + distance = round(resp_obj["distance"], 2) + threshold = resp_obj["threshold"] - print("Facial analysis test. Passing nothing as an action") + passed = prediction == result - img = "dataset/img4.jpg" - demography = DeepFace.analyze(img) - print(demography) + evaluate(passed) - print("-----------------------------------------") + if passed: + test_result_label = "passed" + else: + test_result_label = "failed" - print("Facial analysis test. Passing all to the action") - demography = DeepFace.analyze(img, ['age', 'gender', 'race', 'emotion']) + if prediction == True: + classified_label = "verified" + else: + classified_label = "unverified" - print("Demography:") - print(demography) + print(img1.split("/")[-1], "-", img2.split("/")[-1], classified_label, "as same person based on", model,"and",metric,". Distance:",distance,", Threshold:", threshold,"(",test_result_label,")") - #check response is a valid json - print("Age: ", demography["age"]) - print("Gender: ", demography["gender"]) - print("Race: ", demography["dominant_race"]) - print("Emotion: ", demography["dominant_emotion"]) + print("--------------------------") - print("-----------------------------------------") + # ----------------------------------------- + + print("Passing numpy array to analyze function") - print("Facial analysis test 2. Remove some actions and check they are not computed") - demography = DeepFace.analyze(img, ['age', 'gender']) + img = cv2.imread("dataset/img1.jpg") + resp_obj = DeepFace.analyze(img) + print(resp_obj) - print("Age: ", demography.get("age")) - print("Gender: ", demography.get("gender")) - print("Race: ", demography.get("dominant_race")) - print("Emotion: ", demography.get("dominant_emotion")) + evaluate(resp_obj["age"] > 20 and resp_obj["age"] < 40) + evaluate(resp_obj["gender"] == "Woman") + print("--------------------------") - print("-----------------------------------------") + print("Passing numpy array to verify function") - print("Face recognition tests") + img1 = cv2.imread("dataset/img1.jpg") + img2 = cv2.imread("dataset/img2.jpg") - 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], + res = DeepFace.verify(img1, img2) + print(res) - ['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], - ] + evaluate(res["verified"] == True) - #models = ['VGG-Face', 'Facenet', 'OpenFace', 'DeepFace', 'DeepID', 'Dlib', 'ArcFace'] - models = ['VGG-Face', 'Facenet', 'Facenet512', 'ArcFace', 'SFace'] #those are robust models - metrics = ['cosine', 'euclidean', 'euclidean_l2'] + print("--------------------------") - passed_tests = 0; test_cases = 0 + print("Passing numpy array to find function") - 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] + img1 = cv2.imread("dataset/img1.jpg") - resp_obj = DeepFace.verify(img1, img2 - , model_name = model - #, model = prebuilt_model - , distance_metric = metric) + df = DeepFace.find(img1, db_path = "dataset") - prediction = resp_obj["verified"] - distance = round(resp_obj["distance"], 2) - threshold = resp_obj["threshold"] + print(df.head()) - test_result_label = "failed" - if prediction == result: - passed_tests = passed_tests + 1 - test_result_label = "passed" + evaluate(df.shape[0] > 0) - if prediction == True: - classified_label = "verified" - else: - classified_label = "unverified" + print("--------------------------") - test_cases = test_cases + 1 +test_cases() - 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("num of test cases run: " + str(num_cases)) +print("succeeded test cases: " + str(succeed_cases)) - print("--------------------------") +test_score = (100 * succeed_cases) / num_cases - #----------------------------------------- +print("test coverage: " + str(test_score)) - print("Passed unit tests: ",passed_tests," / ",test_cases) +if test_score > expected_coverage: + print("well done! min required test coverage is satisfied") +else: + print("min required test coverage is NOT satisfied") - min_score = 70 - - accuracy = 100 * passed_tests / test_cases - accuracy = round(accuracy, 2) - - if accuracy >= min_score: - print("Unit tests are completed successfully. Score: ",accuracy,"%") - else: - raise ValueError("Unit test score does not satisfy the minimum required accuracy. Minimum expected score is ", min_score,"% but this got ",accuracy,"%") - - #----------------------------------- - #----------------------------------- - - print("Analyze function with passing pre-trained model") - - emotion_model = DeepFace.build_model("Emotion") - age_model = DeepFace.build_model("Age") - gender_model = DeepFace.build_model("Gender") - race_model = DeepFace.build_model("Race") - - facial_attribute_models = {} - facial_attribute_models["emotion"] = emotion_model - facial_attribute_models["age"] = age_model - facial_attribute_models["gender"] = gender_model - facial_attribute_models["race"] = race_model - - resp_obj = DeepFace.analyze("dataset/img1.jpg", models=facial_attribute_models) - print(resp_obj) - - #----------------------------------- - print("--------------------------") - - if False: - print("Ensemble for find function") - df = DeepFace.find(img_path = "dataset/img1.jpg", db_path = "dataset", model_name = "Ensemble") - print(df.head()) - - #----------------------------------- - print("--------------------------") - - if False: - print("Ensemble for verify function") - resp_obj = DeepFace.verify(dataset, model_name = "Ensemble") - - for i in range(0, len(dataset)): - item = resp_obj['pair_%s' % (i+1)] - verified = item["verified"] - score = item["score"] - print(verified) - - #----------------------------------- - print("--------------------------") - - if False: - - print("Pre-trained ensemble method - find") - - from deepface import DeepFace - from deepface.basemodels import Boosting - - model = Boosting.loadModel() - df = DeepFace.find("dataset/img1.jpg", db_path = "dataset", model_name = 'Ensemble', model = model, enforce_detection=False) - - print(df) - - #----------------------------------- - print("--------------------------") - - if False: - print("Pre-trained ensemble method - verify") - res = DeepFace.verify(dataset, model_name = "Ensemble", model = model) - print(res) - - #----------------------------------- - print("--------------------------") - - import cv2 - - print("Passing numpy array to analyze function") - - img = cv2.imread("dataset/img1.jpg") - resp_obj = DeepFace.analyze(img) - print(resp_obj) - - 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) - - print("--------------------------") - - print("Passing numpy array to find function") - - img1 = cv2.imread("dataset/img1.jpg") - - df = DeepFace.find(img1, db_path = "dataset") - - print(df.head()) - - print("--------------------------") - - self.assertEqual(accuracy >= min_score, True, "A problem on the deepface installation.") - -unittest.main(exit=False) \ No newline at end of file +assert test_score > expected_coverage \ No newline at end of file