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77 lines
2.1 KiB
Python
77 lines
2.1 KiB
Python
from deepface import DeepFace
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import json
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#-----------------------------------------
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print("Facial analysis tests")
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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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#-----------------------------------------
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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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demography = json.loads(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/img1.jpg', 'dataset/img3.jpg', False],
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['dataset/img2.jpg', 'dataset/img3.jpg', False],
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]
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models = ['VGG-Face', 'Facenet', 'OpenFace']
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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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idx = DeepFace.verify(img1, img2, model_name = model, distance_metric = metric)
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test_result_label = "failed"
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if idx[0] == result:
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passed_tests = passed_tests + 1
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test_result_label = "passed"
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if idx[0] == 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: ",round(idx[1], 2),", Required Threshold: ", idx[2]," (",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 > 80:
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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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