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Converted to unittest
This commit is contained in:
Onur Atakan ULUSOY
parent
6622d84a91
commit
34c2dbef17
2
.github/workflows/tests.yml
vendored
2
.github/workflows/tests.yml
vendored
@ -67,4 +67,4 @@ jobs:
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- name: Test with pytest
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- name: Test with pytest
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run: |
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run: |
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cd tests
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cd tests
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pytest /home/runner/work/deepface/tests/unit_tests.py
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pytest unit_tests.py
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@ -9,336 +9,345 @@ from deepface import DeepFace
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from deepface.commons import functions
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from deepface.commons import functions
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import json
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import json
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import time
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import time
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import unittest
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#-----------------------------------------
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#-----------------------------------------
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import tensorflow as tf
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import tensorflow as tf
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tf_version = int(tf.__version__.split(".")[0])
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if tf_version == 2:
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class deepface_unit_tests(unittest.TestCase):
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import logging
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tf.get_logger().setLevel(logging.ERROR)
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print("Running unit tests for TF ", tf.__version__)
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def test_deepface(self):
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tf_version = int(tf.__version__.split(".")[0])
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from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
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if tf_version == 2:
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from deepface.extendedmodels import Age, Gender, Race, Emotion
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import logging
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tf.get_logger().setLevel(logging.ERROR)
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print("-----------------------------------------")
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print("Running unit tests for TF ", tf.__version__)
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#-----------------------------------------
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print("DeepFace.detectFace test")
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from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
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#detectors = ['opencv', 'ssd', 'dlib', 'mtcnn', 'retinaface']
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from deepface.extendedmodels import Age, Gender, Race, Emotion
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detectors = ['opencv', 'ssd', 'mtcnn', 'retinaface']
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for detector in detectors:
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print("-----------------------------------------")
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img = DeepFace.detectFace("dataset/img11.jpg", detector_backend = detector)
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#-----------------------------------------
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print(detector," test is done")
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#import matplotlib.pyplot as plt
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print("DeepFace.detectFace test")
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#plt.imshow(img)
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#detectors = ['opencv', 'ssd', 'dlib', 'mtcnn', 'retinaface']
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#plt.show()
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detectors = ['opencv', 'ssd', 'mtcnn', 'retinaface']
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#-----------------------------------------
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for detector in detectors:
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print("-----------------------------------------")
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img = DeepFace.detectFace("dataset/img11.jpg", detector_backend = detector)
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print(detector," test is done")
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img_path = "dataset/img1.jpg"
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#import matplotlib.pyplot as plt
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embedding = DeepFace.represent(img_path)
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#plt.imshow(img)
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print("Function returned ", len(embedding), "dimensional vector")
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#plt.show()
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model_name = "VGG-Face"
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#-----------------------------------------
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model = DeepFace.build_model(model_name)
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print("-----------------------------------------")
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print(model_name," is built")
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embedding = DeepFace.represent(img_path, model = model)
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print("Represent function returned ", len(embedding), "dimensional vector")
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#-----------------------------------------
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img_path = "dataset/img1.jpg"
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embedding = DeepFace.represent(img_path)
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print("Function returned ", len(embedding), "dimensional vector")
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dataset = [
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model_name = "VGG-Face"
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['dataset/img1.jpg', 'dataset/img2.jpg', True],
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model = DeepFace.build_model(model_name)
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['dataset/img1.jpg', 'dataset/img6.jpg', True]
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print(model_name," is built")
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]
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embedding = DeepFace.represent(img_path, model = model)
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print("Represent function returned ", len(embedding), "dimensional vector")
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print("-----------------------------------------")
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#-----------------------------------------
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print("Face detectors test")
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dataset = [
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['dataset/img1.jpg', 'dataset/img2.jpg', True],
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['dataset/img1.jpg', 'dataset/img6.jpg', True]
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]
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print("retinaface detector")
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print("-----------------------------------------")
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res = DeepFace.verify(dataset, detector_backend = 'retinaface')
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print(res)
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print("ssd detector")
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print("Face detectors test")
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res = DeepFace.verify(dataset, detector_backend = 'ssd')
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print(res)
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print("opencv detector")
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print("retinaface detector")
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res = DeepFace.verify(dataset, detector_backend = 'opencv')
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res = DeepFace.verify(dataset, detector_backend = 'retinaface')
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print(res)
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print(res)
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if False:
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print("ssd detector")
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print("dlib detector")
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res = DeepFace.verify(dataset, detector_backend = 'ssd')
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res = DeepFace.verify(dataset, detector_backend = 'dlib')
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print(res)
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print(res)
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print("mtcnn detector")
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print("opencv detector")
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res = DeepFace.verify(dataset, detector_backend = 'mtcnn')
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res = DeepFace.verify(dataset, detector_backend = 'opencv')
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print(res)
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print(res)
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print("-----------------------------------------")
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if False:
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print("dlib detector")
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res = DeepFace.verify(dataset, detector_backend = 'dlib')
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print(res)
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print("Single find function test")
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print("mtcnn detector")
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res = DeepFace.verify(dataset, detector_backend = 'mtcnn')
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print(res)
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df = DeepFace.find(img_path = "dataset/img1.jpg", db_path = "dataset"
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print("-----------------------------------------")
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#, model_name = 'Dlib'
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)
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print(df.head())
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print("-----------------------------------------")
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print("Single find function test")
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print("Pre-built model for single find function test")
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df = DeepFace.find(img_path = "dataset/img1.jpg", db_path = "dataset"
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#, model_name = 'Dlib'
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)
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print(df.head())
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#model_name = "VGG-Face"
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print("-----------------------------------------")
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#model = DeepFace.build_model(model_name)
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#print(model_name," is built")
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df = DeepFace.find(img_path = "dataset/img1.jpg", db_path = "dataset"
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print("Pre-built model for single find function test")
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, model_name = model_name, model = model
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)
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print(df.head())
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print("-----------------------------------------")
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#model_name = "VGG-Face"
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#model = DeepFace.build_model(model_name)
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#print(model_name," is built")
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print("Bulk find function tests")
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df = DeepFace.find(img_path = "dataset/img1.jpg", db_path = "dataset"
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, model_name = model_name, model = model
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)
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print(df.head())
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dfs = DeepFace.find(img_path = ["dataset/img1.jpg", "dataset/img2.jpg"], db_path = "dataset"
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print("-----------------------------------------")
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#, model_name = 'Dlib'
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)
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print(dfs[0].head())
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print(dfs[1].head())
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print("-----------------------------------------")
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print("Bulk find function tests")
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print("Bulk verification tests")
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dfs = DeepFace.find(img_path = ["dataset/img1.jpg", "dataset/img2.jpg"], db_path = "dataset"
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#, model_name = 'Dlib'
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)
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print(dfs[0].head())
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print(dfs[1].head())
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resp_obj = DeepFace.verify(dataset)
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print("-----------------------------------------")
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print(resp_obj)
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print(resp_obj["pair_1"]["verified"] == True)
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print(resp_obj["pair_2"]["verified"] == True)
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print("-----------------------------------------")
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print("Bulk verification tests")
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print("Bulk facial analysis tests")
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resp_obj = DeepFace.verify(dataset)
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print(resp_obj)
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print(resp_obj["pair_1"]["verified"] == True)
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print(resp_obj["pair_2"]["verified"] == True)
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dataset = [
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print("-----------------------------------------")
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'dataset/img1.jpg',
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'dataset/img2.jpg',
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'dataset/img5.jpg',
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'dataset/img6.jpg'
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]
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resp_obj = DeepFace.analyze(dataset)
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print("Bulk facial analysis tests")
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print(resp_obj["instance_1"]["age"]," years old ", resp_obj["instance_1"]["dominant_emotion"], " ",resp_obj["instance_1"]["gender"])
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print(resp_obj["instance_2"]["age"]," years old ", resp_obj["instance_2"]["dominant_emotion"], " ",resp_obj["instance_2"]["gender"])
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print(resp_obj["instance_3"]["age"]," years old ", resp_obj["instance_3"]["dominant_emotion"], " ",resp_obj["instance_3"]["gender"])
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print(resp_obj["instance_4"]["age"]," years old ", resp_obj["instance_4"]["dominant_emotion"], " ",resp_obj["instance_4"]["gender"])
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print("-----------------------------------------")
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dataset = [
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'dataset/img1.jpg',
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'dataset/img2.jpg',
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'dataset/img5.jpg',
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'dataset/img6.jpg'
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]
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print("Facial analysis test. Passing nothing as an action")
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resp_obj = DeepFace.analyze(dataset)
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print(resp_obj["instance_1"]["age"]," years old ", resp_obj["instance_1"]["dominant_emotion"], " ",resp_obj["instance_1"]["gender"])
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print(resp_obj["instance_2"]["age"]," years old ", resp_obj["instance_2"]["dominant_emotion"], " ",resp_obj["instance_2"]["gender"])
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print(resp_obj["instance_3"]["age"]," years old ", resp_obj["instance_3"]["dominant_emotion"], " ",resp_obj["instance_3"]["gender"])
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print(resp_obj["instance_4"]["age"]," years old ", resp_obj["instance_4"]["dominant_emotion"], " ",resp_obj["instance_4"]["gender"])
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img = "dataset/img4.jpg"
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print("-----------------------------------------")
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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 nothing as an action")
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print("Facial analysis test. Passing all to the action")
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img = "dataset/img4.jpg"
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demography = DeepFace.analyze(img, ['age', 'gender', 'race', 'emotion'])
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demography = DeepFace.analyze(img)
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print(demography)
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print("Demography:")
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print("-----------------------------------------")
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print(demography)
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#check response is a valid json
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print("Facial analysis test. Passing all to the action")
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print("Age: ", demography["age"])
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demography = DeepFace.analyze(img, ['age', 'gender', 'race', 'emotion'])
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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("Demography:")
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print(demography)
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print("Facial analysis test 2. Remove some actions and check they are not computed")
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#check response is a valid json
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demography = DeepFace.analyze(img, ['age', 'gender'])
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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("Age: ", demography.get("age"))
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print("-----------------------------------------")
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print("Gender: ", demography.get("gender"))
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print("Race: ", demography.get("dominant_race"))
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print("Emotion: ", demography.get("dominant_emotion"))
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print("Facial analysis test 2. Remove some actions and check they are not computed")
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demography = DeepFace.analyze(img, ['age', 'gender'])
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print("-----------------------------------------")
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print("Age: ", demography.get("age"))
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print("Gender: ", demography.get("gender"))
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print("Race: ", demography.get("dominant_race"))
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print("Emotion: ", demography.get("dominant_emotion"))
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print("Face recognition tests")
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dataset = [
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print("-----------------------------------------")
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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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print("Face recognition tests")
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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', 'DeepID', 'Dlib', 'ArcFace']
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dataset = [
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models = ['VGG-Face', 'Facenet', 'Facenet512', 'ArcFace', 'SFace'] #those are robust models
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['dataset/img1.jpg', 'dataset/img2.jpg', True],
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metrics = ['cosine', 'euclidean', 'euclidean_l2']
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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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passed_tests = 0; test_cases = 0
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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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for model in models:
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#models = ['VGG-Face', 'Facenet', 'OpenFace', 'DeepFace', 'DeepID', 'Dlib', 'ArcFace']
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#prebuilt_model = DeepFace.build_model(model)
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models = ['VGG-Face', 'Facenet', 'Facenet512', 'ArcFace', 'SFace'] #those are robust models
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#print(model," is built")
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metrics = ['cosine', 'euclidean', 'euclidean_l2']
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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
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passed_tests = 0; test_cases = 0
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, model_name = model
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#, model = prebuilt_model
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, distance_metric = metric)
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prediction = resp_obj["verified"]
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for model in models:
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distance = round(resp_obj["distance"], 2)
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#prebuilt_model = DeepFace.build_model(model)
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threshold = resp_obj["threshold"]
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#print(model," is built")
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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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test_result_label = "failed"
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resp_obj = DeepFace.verify(img1, img2
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if prediction == result:
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, model_name = model
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passed_tests = passed_tests + 1
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#, model = prebuilt_model
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test_result_label = "passed"
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, distance_metric = metric)
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if prediction == True:
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prediction = resp_obj["verified"]
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classified_label = "verified"
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distance = round(resp_obj["distance"], 2)
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else:
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threshold = resp_obj["threshold"]
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classified_label = "unverified"
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test_cases = test_cases + 1
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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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print(img1.split("/")[-1], "-", img2.split("/")[-1], classified_label, "as same person based on", model,"and",metric,". Distance:",distance,", Threshold:", threshold,"(",test_result_label,")")
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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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print("--------------------------")
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test_cases = test_cases + 1
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#-----------------------------------------
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print(img1.split("/")[-1], "-", img2.split("/")[-1], classified_label, "as same person based on", model,"and",metric,". Distance:",distance,", Threshold:", threshold,"(",test_result_label,")")
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print("Passed unit tests: ",passed_tests," / ",test_cases)
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print("--------------------------")
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min_score = 70
|
#-----------------------------------------
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|
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accuracy = 100 * passed_tests / test_cases
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print("Passed unit tests: ",passed_tests," / ",test_cases)
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accuracy = round(accuracy, 2)
|
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|
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if accuracy >= min_score:
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min_score = 70
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print("Unit tests are completed successfully. Score: ",accuracy,"%")
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else:
|
|
||||||
raise ValueError("Unit test score does not satisfy the minimum required accuracy. Minimum expected score is ", min_score,"% but this got ",accuracy,"%")
|
|
||||||
|
|
||||||
#-----------------------------------
|
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 = {}
|
print("Analyze function with passing pre-trained model")
|
||||||
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)
|
emotion_model = DeepFace.build_model("Emotion")
|
||||||
print(resp_obj)
|
age_model = DeepFace.build_model("Age")
|
||||||
|
gender_model = DeepFace.build_model("Gender")
|
||||||
|
race_model = DeepFace.build_model("Race")
|
||||||
|
|
||||||
#-----------------------------------
|
facial_attribute_models = {}
|
||||||
print("--------------------------")
|
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:
|
resp_obj = DeepFace.analyze("dataset/img1.jpg", models=facial_attribute_models)
|
||||||
print("Ensemble for find function")
|
print(resp_obj)
|
||||||
df = DeepFace.find(img_path = "dataset/img1.jpg", db_path = "dataset", model_name = "Ensemble")
|
|
||||||
print(df.head())
|
|
||||||
|
|
||||||
#-----------------------------------
|
#-----------------------------------
|
||||||
print("--------------------------")
|
print("--------------------------")
|
||||||
|
|
||||||
if False:
|
if False:
|
||||||
print("Ensemble for verify function")
|
print("Ensemble for find function")
|
||||||
resp_obj = DeepFace.verify(dataset, model_name = "Ensemble")
|
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)]
|
print("--------------------------")
|
||||||
verified = item["verified"]
|
|
||||||
score = item["score"]
|
|
||||||
print(verified)
|
|
||||||
|
|
||||||
#-----------------------------------
|
if False:
|
||||||
print("--------------------------")
|
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
|
if False:
|
||||||
from deepface.basemodels import Boosting
|
|
||||||
|
|
||||||
model = Boosting.loadModel()
|
print("Pre-trained ensemble method - find")
|
||||||
df = DeepFace.find("dataset/img1.jpg", db_path = "dataset", model_name = 'Ensemble', model = model, enforce_detection=False)
|
|
||||||
|
|
||||||
print(df)
|
from deepface import DeepFace
|
||||||
|
from deepface.basemodels import Boosting
|
||||||
|
|
||||||
#-----------------------------------
|
model = Boosting.loadModel()
|
||||||
print("--------------------------")
|
df = DeepFace.find("dataset/img1.jpg", db_path = "dataset", model_name = 'Ensemble', model = model, enforce_detection=False)
|
||||||
|
|
||||||
if False:
|
print(df)
|
||||||
print("Pre-trained ensemble method - verify")
|
|
||||||
res = DeepFace.verify(dataset, model_name = "Ensemble", model = model)
|
|
||||||
print(res)
|
|
||||||
|
|
||||||
#-----------------------------------
|
#-----------------------------------
|
||||||
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")
|
import cv2
|
||||||
resp_obj = DeepFace.analyze(img)
|
|
||||||
print(resp_obj)
|
|
||||||
|
|
||||||
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")
|
print("--------------------------")
|
||||||
img2 = cv2.imread("dataset/img2.jpg")
|
|
||||||
|
|
||||||
res = DeepFace.verify(img1, img2)
|
print("Passing numpy array to verify function")
|
||||||
print(res)
|
|
||||||
|
|
||||||
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)
|
||||||
Loading…
x
Reference in New Issue
Block a user