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https://github.com/serengil/deepface.git
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91 lines
2.7 KiB
Python
91 lines
2.7 KiB
Python
from deepface import DeepFace
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import json
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import os
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
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#-----------------------------------------
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print("Facial analysis test. Passing nothing as an action")
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img = "dataset/img4.jpg"
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demography = DeepFace.analyze(img)
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print(demography)
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print("-----------------------------------------")
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print("Facial analysis test. Passing all to the action")
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demography = DeepFace.analyze(img, ['age', 'gender', 'race', 'emotion'])
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print("Demography:")
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print(demography)
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#check response is a valid json
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print("Age: ", demography["age"])
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print("Gender: ", demography["gender"])
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print("Race: ", demography["dominant_race"])
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print("Emotion: ", demography["dominant_emotion"])
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print("-----------------------------------------")
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print("Face recognition tests")
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dataset = [
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['dataset/img1.jpg', 'dataset/img2.jpg', True],
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['dataset/img5.jpg', 'dataset/img6.jpg', True],
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['dataset/img6.jpg', 'dataset/img7.jpg', True],
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['dataset/img8.jpg', 'dataset/img9.jpg', True],
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['dataset/img1.jpg', 'dataset/img11.jpg', True],
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['dataset/img2.jpg', 'dataset/img11.jpg', True],
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['dataset/img1.jpg', 'dataset/img3.jpg', False],
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['dataset/img2.jpg', 'dataset/img3.jpg', False],
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['dataset/img6.jpg', 'dataset/img8.jpg', False],
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['dataset/img6.jpg', 'dataset/img9.jpg', False],
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]
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models = ['VGG-Face', 'Facenet', 'OpenFace', 'DeepFace']
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metrics = ['cosine', 'euclidean', 'euclidean_l2']
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passed_tests = 0; test_cases = 0
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for model in models:
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for metric in metrics:
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for instance in dataset:
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img1 = instance[0]
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img2 = instance[1]
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result = instance[2]
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resp_obj = DeepFace.verify(img1, img2, model_name = model, distance_metric = metric)
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prediction = resp_obj["verified"]
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distance = round(resp_obj["distance"], 2)
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required_threshold = resp_obj["max_threshold_to_verify"]
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test_result_label = "failed"
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if prediction == result:
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passed_tests = passed_tests + 1
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test_result_label = "passed"
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if prediction == True:
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classified_label = "verified"
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else:
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classified_label = "unverified"
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test_cases = test_cases + 1
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print(img1, " and ", img2," are ", classified_label, " as same person based on ", model," model and ",metric," distance metric. Distance: ",distance,", Required Threshold: ", required_threshold," (",test_result_label,")")
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print("--------------------------")
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#-----------------------------------------
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print("Passed unit tests: ",passed_tests," / ",test_cases)
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accuracy = 100 * passed_tests / test_cases
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accuracy = round(accuracy, 2)
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if accuracy > 75:
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print("Unit tests are completed successfully. Score: ",accuracy,"%")
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else:
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raise ValueError("Unit test score does not satisfy the minimum required accuracy. Minimum expected score is 80% but this got ",accuracy,"%")
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