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unit tests
This commit is contained in:
Sefik Ilkin Serengil
parent
ef5fa518e5
commit
eeaf1253da
1
.gitignore
vendored
1
.gitignore
vendored
@ -1,6 +1,7 @@
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__pycache__/*
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basemodels/__pycache__/*
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configurations/__pycache__/*
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tests/__pycache__/*
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build/
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dist/
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Pipfile
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@ -1,183 +1,42 @@
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import warnings
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warnings.filterwarnings("ignore")
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import os
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#os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
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import tensorflow as tf
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import cv2
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from deepface import DeepFace
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print("-----------------------------------------")
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warnings.filterwarnings("ignore")
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
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from deepface import DeepFace
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from deepface.commons import functions
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import json
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import time
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tf_major_version = int(tf.__version__.split(".")[0])
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#-----------------------------------------
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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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if tf_major_version == 2:
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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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from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
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from deepface.extendedmodels import Age, Gender, Race, Emotion
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print("-----------------------------------------")
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#-----------------------------------------
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print("DeepFace.detectFace test")
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#detectors = ['opencv', 'ssd', 'dlib', 'mtcnn', 'retinaface']
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detectors = ['opencv', 'ssd', 'mtcnn', 'retinaface']
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for detector in detectors:
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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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#import matplotlib.pyplot as plt
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#plt.imshow(img)
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#plt.show()
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#-----------------------------------------
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print("-----------------------------------------")
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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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test_threshold = 97
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num_cases = 0
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succeed_cases = 0
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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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embedding = DeepFace.represent(img_path, model = model)
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print("Represent function returned ", len(embedding), "dimensional vector")
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def evaluate(condition):
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#-----------------------------------------
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global num_cases, succeed_cases
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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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if condition is True:
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succeed_cases += 1
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num_cases += 1
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print("-----------------------------------------")
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# ------------------------------------------------
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print("Face detectors test")
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print("retinaface detector")
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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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res = DeepFace.verify(dataset, detector_backend = 'ssd')
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print(res)
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print("opencv detector")
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res = DeepFace.verify(dataset, detector_backend = 'opencv')
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print(res)
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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("mtcnn detector")
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res = DeepFace.verify(dataset, detector_backend = 'mtcnn')
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print(res)
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print("-----------------------------------------")
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print("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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print("-----------------------------------------")
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print("Pre-built model for single find function test")
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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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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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print("-----------------------------------------")
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print("Bulk find function 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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print("-----------------------------------------")
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print("Bulk verification 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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print("-----------------------------------------")
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print("Bulk facial analysis tests")
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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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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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print("-----------------------------------------")
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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("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("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("-----------------------------------------")
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print("Face recognition tests")
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detectors = ['opencv', 'mtcnn', 'retinaface']
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models = ['VGG-Face', 'Facenet', 'Facenet512', 'ArcFace', 'SFace']
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metrics = ['cosine', 'euclidean', 'euclidean_l2']
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dataset = [
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['dataset/img1.jpg', 'dataset/img2.jpg', True],
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@ -193,152 +52,190 @@ dataset = [
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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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models = ['VGG-Face', 'Facenet', 'Facenet512', 'ArcFace', 'SFace'] #those are robust models
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metrics = ['cosine', 'euclidean', 'euclidean_l2']
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print("-----------------------------------------")
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passed_tests = 0; test_cases = 0
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def test_cases():
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for model in models:
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#prebuilt_model = DeepFace.build_model(model)
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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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print("DeepFace.detectFace test")
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resp_obj = DeepFace.verify(img1, img2
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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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for detector in detectors:
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img = DeepFace.detectFace("dataset/img11.jpg", detector_backend = detector)
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evaluate( img.shape[0] > 0 and img.shape[1] > 0 )
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print(detector," test is done")
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prediction = resp_obj["verified"]
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distance = round(resp_obj["distance"], 2)
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threshold = resp_obj["threshold"]
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print("-----------------------------------------")
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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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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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evaluate( len(embedding) > 0 )
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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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print("Face detectors test")
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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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for detector in detectors:
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print(detector + " detector")
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res = DeepFace.verify(dataset[0][0], dataset[0][1], detector_backend = detector)
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print(res)
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assert res["verified"] == dataset[0][2]
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print("--------------------------")
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print("-----------------------------------------")
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#-----------------------------------------
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print("Single find function test")
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print("Passed unit tests: ",passed_tests," / ",test_cases)
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min_score = 70
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accuracy = 100 * passed_tests / test_cases
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accuracy = round(accuracy, 2)
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if accuracy >= min_score:
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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 ", min_score,"% but this got ",accuracy,"%")
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#-----------------------------------
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#-----------------------------------
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print("Analyze function with passing pre-trained model")
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emotion_model = DeepFace.build_model("Emotion")
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age_model = DeepFace.build_model("Age")
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gender_model = DeepFace.build_model("Gender")
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race_model = DeepFace.build_model("Race")
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facial_attribute_models = {}
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facial_attribute_models["emotion"] = emotion_model
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facial_attribute_models["age"] = age_model
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facial_attribute_models["gender"] = gender_model
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facial_attribute_models["race"] = race_model
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resp_obj = DeepFace.analyze("dataset/img1.jpg", models=facial_attribute_models)
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print(resp_obj)
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#-----------------------------------
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print("--------------------------")
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if False:
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print("Ensemble for find function")
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df = DeepFace.find(img_path = "dataset/img1.jpg", db_path = "dataset", model_name = "Ensemble")
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df = DeepFace.find(img_path = "dataset/img1.jpg", db_path = "dataset")
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print(df.head())
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evaluate( df.shape[0] > 0 )
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#-----------------------------------
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print("--------------------------")
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print("-----------------------------------------")
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if False:
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print("Ensemble for verify function")
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resp_obj = DeepFace.verify(dataset, model_name = "Ensemble")
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print("Facial analysis test. Passing nothing as an action")
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for i in range(0, len(dataset)):
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item = resp_obj['pair_%s' % (i+1)]
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verified = item["verified"]
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score = item["score"]
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print(verified)
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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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print("--------------------------")
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evaluate( demography["age"] > 20 and demography["age"] < 40 )
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evaluate( demography["gender"] == "Woman" )
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if False:
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print("-----------------------------------------")
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print("Pre-trained ensemble method - find")
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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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from deepface import DeepFace
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from deepface.basemodels import Boosting
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print("Demography:")
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print(demography)
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model = Boosting.loadModel()
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df = DeepFace.find("dataset/img1.jpg", db_path = "dataset", model_name = 'Ensemble', model = model, enforce_detection=False)
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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(df)
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evaluate( demography.get("age") is not None )
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evaluate( demography.get("gender") is not None )
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evaluate( demography.get("dominant_race") is not None )
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evaluate( demography.get("dominant_emotion") is not None )
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#-----------------------------------
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print("--------------------------")
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print("-----------------------------------------")
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if False:
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print("Pre-trained ensemble method - verify")
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res = DeepFace.verify(dataset, model_name = "Ensemble", model = model)
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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("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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evaluate( demography.get("age") is not None )
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evaluate( demography.get("gender") is not None )
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evaluate( demography.get("dominant_race") is None )
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evaluate( demography.get("dominant_emotion") is None )
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print("-----------------------------------------")
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print("Face recognition tests")
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passed_tests = 0; test_cases = 0
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for model in models:
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#prebuilt_model = DeepFace.build_model(model)
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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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resp_obj = DeepFace.verify(img1, img2
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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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distance = round(resp_obj["distance"], 2)
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threshold = resp_obj["threshold"]
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evaluate( prediction == result )
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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.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("--------------------------")
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#-----------------------------------------
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print("Passed unit tests: ",passed_tests," / ",test_cases)
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min_score = 70
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accuracy = 100 * passed_tests / test_cases
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accuracy = round(accuracy, 2)
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print("--------------------------")
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#-----------------------------------
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print("--------------------------")
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print("Passing numpy array to analyze function")
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img = cv2.imread("dataset/img1.jpg")
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resp_obj = DeepFace.analyze(img)
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print(resp_obj)
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evaluate( resp_obj["age"] > 20 and resp_obj["age"] < 40 )
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evaluate( resp_obj["gender"] == "Woman" )
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print("--------------------------")
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print("Passing numpy array to verify function")
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img1 = cv2.imread("dataset/img1.jpg")
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img2 = cv2.imread("dataset/img2.jpg")
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res = DeepFace.verify(img1, img2)
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print(res)
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#-----------------------------------
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print("--------------------------")
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evaluate( res["verified"] == True )
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import cv2
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print("--------------------------")
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print("Passing numpy array to analyze function")
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print("Passing numpy array to find function")
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img = cv2.imread("dataset/img1.jpg")
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resp_obj = DeepFace.analyze(img)
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print(resp_obj)
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img1 = cv2.imread("dataset/img1.jpg")
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print("--------------------------")
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df = DeepFace.find(img1, db_path = "dataset")
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print("Passing numpy array to verify function")
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print(df.head())
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img1 = cv2.imread("dataset/img1.jpg")
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img2 = cv2.imread("dataset/img2.jpg")
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evaluate( df.shape[0] > 0 )
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res = DeepFace.verify(img1, img2)
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print(res)
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print("--------------------------")
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print("--------------------------")
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test_cases()
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|
||||
print("Passing numpy array to find function")
|
||||
print("num of test cases run: " + str(num_cases))
|
||||
print("succeeded test cases: " + str(succeed_cases))
|
||||
|
||||
img1 = cv2.imread("dataset/img1.jpg")
|
||||
test_score = (100 * succeed_cases) / num_cases
|
||||
|
||||
df = DeepFace.find(img1, db_path = "dataset")
|
||||
print("test coverage: " + str(test_score))
|
||||
|
||||
print(df.head())
|
||||
if test_score > test_threshold:
|
||||
print("min required test coverage is satisfied")
|
||||
else:
|
||||
print("min required test coverage is NOT satisfied")
|
||||
|
||||
print("--------------------------")
|
||||
assert test_score > test_threshold
|
||||
Loading…
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Reference in New Issue
Block a user