diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index d7f4e9d..f1b3836 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -67,4 +67,4 @@ jobs: - name: Test with pytest run: | cd tests - pytest /home/runner/work/deepface/tests/unit_tests.py \ No newline at end of file + pytest unit_tests.py \ No newline at end of file diff --git a/tests/unit_tests.py b/tests/unit_tests.py index c83e065..c2f3223 100644 --- a/tests/unit_tests.py +++ b/tests/unit_tests.py @@ -9,336 +9,345 @@ from deepface import DeepFace from deepface.commons import functions import json import time +import unittest #----------------------------------------- import tensorflow as tf -tf_version = int(tf.__version__.split(".")[0]) -if tf_version == 2: - import logging - tf.get_logger().setLevel(logging.ERROR) +class deepface_unit_tests(unittest.TestCase): -print("Running unit tests for TF ", tf.__version__) + def test_deepface(self): + tf_version = int(tf.__version__.split(".")[0]) -from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace -from deepface.extendedmodels import Age, Gender, Race, Emotion + if tf_version == 2: + import logging + tf.get_logger().setLevel(logging.ERROR) -print("-----------------------------------------") -#----------------------------------------- + print("Running unit tests for TF ", tf.__version__) -print("DeepFace.detectFace test") -#detectors = ['opencv', 'ssd', 'dlib', 'mtcnn', 'retinaface'] -detectors = ['opencv', 'ssd', 'mtcnn', 'retinaface'] + from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace + from deepface.extendedmodels import Age, Gender, Race, Emotion -for detector in detectors: - img = DeepFace.detectFace("dataset/img11.jpg", detector_backend = detector) - print(detector," test is done") + print("-----------------------------------------") + #----------------------------------------- -#import matplotlib.pyplot as plt -#plt.imshow(img) -#plt.show() + print("DeepFace.detectFace test") + #detectors = ['opencv', 'ssd', 'dlib', 'mtcnn', 'retinaface'] + detectors = ['opencv', 'ssd', 'mtcnn', 'retinaface'] -#----------------------------------------- -print("-----------------------------------------") + for detector in detectors: + img = DeepFace.detectFace("dataset/img11.jpg", detector_backend = detector) + print(detector," test is done") -img_path = "dataset/img1.jpg" -embedding = DeepFace.represent(img_path) -print("Function returned ", len(embedding), "dimensional vector") + #import matplotlib.pyplot as plt + #plt.imshow(img) + #plt.show() -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("-----------------------------------------") -#----------------------------------------- + img_path = "dataset/img1.jpg" + embedding = DeepFace.represent(img_path) + print("Function returned ", len(embedding), "dimensional vector") -dataset = [ - ['dataset/img1.jpg', 'dataset/img2.jpg', True], - ['dataset/img1.jpg', 'dataset/img6.jpg', True] -] + 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("-----------------------------------------") + #----------------------------------------- -print("Face detectors test") + dataset = [ + ['dataset/img1.jpg', 'dataset/img2.jpg', True], + ['dataset/img1.jpg', 'dataset/img6.jpg', True] + ] -print("retinaface detector") -res = DeepFace.verify(dataset, detector_backend = 'retinaface') -print(res) + print("-----------------------------------------") -print("ssd detector") -res = DeepFace.verify(dataset, detector_backend = 'ssd') -print(res) + print("Face detectors test") -print("opencv detector") -res = DeepFace.verify(dataset, detector_backend = 'opencv') -print(res) + print("retinaface detector") + res = DeepFace.verify(dataset, detector_backend = 'retinaface') + print(res) -if False: - print("dlib detector") - res = DeepFace.verify(dataset, detector_backend = 'dlib') - print(res) + print("ssd detector") + res = DeepFace.verify(dataset, detector_backend = 'ssd') + print(res) -print("mtcnn detector") -res = DeepFace.verify(dataset, detector_backend = 'mtcnn') -print(res) + 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("Single 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" - #, model_name = 'Dlib' -) -print(df.head()) + print("-----------------------------------------") -print("-----------------------------------------") + print("Single find function test") -print("Pre-built model for single find function test") + df = DeepFace.find(img_path = "dataset/img1.jpg", db_path = "dataset" + #, model_name = 'Dlib' + ) + print(df.head()) -#model_name = "VGG-Face" -#model = DeepFace.build_model(model_name) -#print(model_name," is built") + print("-----------------------------------------") -df = DeepFace.find(img_path = "dataset/img1.jpg", db_path = "dataset" - , model_name = model_name, model = model -) -print(df.head()) + 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("Bulk find function tests") + df = DeepFace.find(img_path = "dataset/img1.jpg", db_path = "dataset" + , model_name = model_name, model = model + ) + print(df.head()) -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("Bulk find function tests") -print("Bulk verification tests") + 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()) -resp_obj = DeepFace.verify(dataset) -print(resp_obj) -print(resp_obj["pair_1"]["verified"] == True) -print(resp_obj["pair_2"]["verified"] == True) + print("-----------------------------------------") -print("-----------------------------------------") + print("Bulk verification tests") -print("Bulk facial analysis tests") + resp_obj = DeepFace.verify(dataset) + print(resp_obj) + print(resp_obj["pair_1"]["verified"] == True) + print(resp_obj["pair_2"]["verified"] == True) -dataset = [ - 'dataset/img1.jpg', - 'dataset/img2.jpg', - 'dataset/img5.jpg', - 'dataset/img6.jpg' -] + print("-----------------------------------------") -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"]) + print("Bulk facial analysis tests") -print("-----------------------------------------") + dataset = [ + 'dataset/img1.jpg', + 'dataset/img2.jpg', + 'dataset/img5.jpg', + 'dataset/img6.jpg' + ] -print("Facial analysis test. Passing nothing as an action") + 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"]) -img = "dataset/img4.jpg" -demography = DeepFace.analyze(img) -print(demography) + print("-----------------------------------------") -print("-----------------------------------------") + print("Facial analysis test. Passing nothing as an action") -print("Facial analysis test. Passing all to the action") -demography = DeepFace.analyze(img, ['age', 'gender', 'race', 'emotion']) + img = "dataset/img4.jpg" + demography = DeepFace.analyze(img) + print(demography) -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("Facial analysis test. Passing all to the action") + demography = DeepFace.analyze(img, ['age', 'gender', 'race', 'emotion']) -print("-----------------------------------------") + print("Demography:") + print(demography) -print("Facial analysis test 2. Remove some actions and check they are not computed") -demography = DeepFace.analyze(img, ['age', 'gender']) + #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("Age: ", demography.get("age")) -print("Gender: ", demography.get("gender")) -print("Race: ", demography.get("dominant_race")) -print("Emotion: ", demography.get("dominant_emotion")) + print("-----------------------------------------") + print("Facial analysis test 2. Remove some actions and check they are not computed") + demography = DeepFace.analyze(img, ['age', 'gender']) -print("-----------------------------------------") + print("Age: ", demography.get("age")) + print("Gender: ", demography.get("gender")) + print("Race: ", demography.get("dominant_race")) + print("Emotion: ", demography.get("dominant_emotion")) -print("Face recognition tests") -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], + print("-----------------------------------------") - ['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("Face recognition tests") -#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'] + 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], -passed_tests = 0; test_cases = 0 + ['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], + ] -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] + #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'] - resp_obj = DeepFace.verify(img1, img2 - , model_name = model - #, model = prebuilt_model - , distance_metric = metric) + passed_tests = 0; test_cases = 0 - prediction = resp_obj["verified"] - distance = round(resp_obj["distance"], 2) - threshold = resp_obj["threshold"] + 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] - test_result_label = "failed" - if prediction == result: - passed_tests = passed_tests + 1 - test_result_label = "passed" + resp_obj = DeepFace.verify(img1, img2 + , model_name = model + #, model = prebuilt_model + , distance_metric = metric) - if prediction == True: - classified_label = "verified" - else: - classified_label = "unverified" + prediction = resp_obj["verified"] + distance = round(resp_obj["distance"], 2) + threshold = resp_obj["threshold"] - test_cases = test_cases + 1 + test_result_label = "failed" + if prediction == result: + passed_tests = passed_tests + 1 + test_result_label = "passed" - print(img1.split("/")[-1], "-", img2.split("/")[-1], classified_label, "as same person based on", model,"and",metric,". Distance:",distance,", Threshold:", threshold,"(",test_result_label,")") + if prediction == True: + classified_label = "verified" + else: + classified_label = "unverified" - print("--------------------------") + 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("Passed unit tests: ",passed_tests," / ",test_cases) + print("--------------------------") -min_score = 70 + #----------------------------------------- -accuracy = 100 * passed_tests / test_cases -accuracy = round(accuracy, 2) + print("Passed unit tests: ",passed_tests," / ",test_cases) -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,"%") + min_score = 70 -#----------------------------------- -#----------------------------------- + accuracy = 100 * passed_tests / test_cases + accuracy = round(accuracy, 2) -print("Analyze function with passing pre-trained model") + 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,"%") -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 + print("Analyze function with passing pre-trained model") -resp_obj = DeepFace.analyze("dataset/img1.jpg", models=facial_attribute_models) -print(resp_obj) + emotion_model = DeepFace.build_model("Emotion") + age_model = DeepFace.build_model("Age") + gender_model = DeepFace.build_model("Gender") + race_model = DeepFace.build_model("Race") -#----------------------------------- -print("--------------------------") + 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 -if False: - print("Ensemble for find function") - df = DeepFace.find(img_path = "dataset/img1.jpg", db_path = "dataset", model_name = "Ensemble") - print(df.head()) + resp_obj = DeepFace.analyze("dataset/img1.jpg", models=facial_attribute_models) + print(resp_obj) -#----------------------------------- -print("--------------------------") + #----------------------------------- + print("--------------------------") -if False: - print("Ensemble for verify function") - resp_obj = DeepFace.verify(dataset, model_name = "Ensemble") + if False: + print("Ensemble for find function") + df = DeepFace.find(img_path = "dataset/img1.jpg", db_path = "dataset", model_name = "Ensemble") + print(df.head()) - for i in range(0, len(dataset)): - item = resp_obj['pair_%s' % (i+1)] - verified = item["verified"] - score = item["score"] - print(verified) + #----------------------------------- + print("--------------------------") -#----------------------------------- -print("--------------------------") + if False: + print("Ensemble for verify function") + resp_obj = DeepFace.verify(dataset, model_name = "Ensemble") -if False: + for i in range(0, len(dataset)): + item = resp_obj['pair_%s' % (i+1)] + verified = item["verified"] + score = item["score"] + print(verified) - print("Pre-trained ensemble method - find") + #----------------------------------- + print("--------------------------") - from deepface import DeepFace - from deepface.basemodels import Boosting + if False: - model = Boosting.loadModel() - df = DeepFace.find("dataset/img1.jpg", db_path = "dataset", model_name = 'Ensemble', model = model, enforce_detection=False) + print("Pre-trained ensemble method - find") - print(df) + from deepface import DeepFace + from deepface.basemodels import Boosting -#----------------------------------- -print("--------------------------") + model = Boosting.loadModel() + df = DeepFace.find("dataset/img1.jpg", db_path = "dataset", model_name = 'Ensemble', model = model, enforce_detection=False) -if False: - print("Pre-trained ensemble method - verify") - res = DeepFace.verify(dataset, model_name = "Ensemble", model = model) - print(res) + print(df) -#----------------------------------- -print("--------------------------") + #----------------------------------- + print("--------------------------") -import cv2 + if False: + print("Pre-trained ensemble method - verify") + res = DeepFace.verify(dataset, model_name = "Ensemble", model = model) + print(res) -print("Passing numpy array to analyze function") + #----------------------------------- + print("--------------------------") -img = cv2.imread("dataset/img1.jpg") -resp_obj = DeepFace.analyze(img) -print(resp_obj) + import cv2 -print("--------------------------") + print("Passing numpy array to analyze function") -print("Passing numpy array to verify function") + img = cv2.imread("dataset/img1.jpg") + resp_obj = DeepFace.analyze(img) + print(resp_obj) -img1 = cv2.imread("dataset/img1.jpg") -img2 = cv2.imread("dataset/img2.jpg") + print("--------------------------") -res = DeepFace.verify(img1, img2) -print(res) + print("Passing numpy array to verify function") -print("--------------------------") + img1 = cv2.imread("dataset/img1.jpg") + img2 = cv2.imread("dataset/img2.jpg") -print("Passing numpy array to find function") + res = DeepFace.verify(img1, img2) + print(res) -img1 = cv2.imread("dataset/img1.jpg") + print("--------------------------") -df = DeepFace.find(img1, db_path = "dataset") + print("Passing numpy array to find function") -print(df.head()) + img1 = cv2.imread("dataset/img1.jpg") -print("--------------------------") + df = DeepFace.find(img1, db_path = "dataset") + + print(df.head()) + + print("--------------------------") + + self.assertEqual(accuracy >= min_score, False, "A problem on the deepface installation.") + +unittest.main(exit=False) \ No newline at end of file