mirror of
https://github.com/serengil/deepface.git
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310 lines
8.4 KiB
Python
310 lines
8.4 KiB
Python
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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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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#-----------------------------------------
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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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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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#-----------------------------------------
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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("-----------------------------------------")
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print("Face detectors test")
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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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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("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', 'DeepID', 'Dlib', 'ArcFace']
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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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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, 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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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.split("/")[-1], "-", img2.split("/")[-1], classified_label, "as same person based on", model,"and",metric,". Distance:",distance,", 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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threshold = 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 >= threshold:
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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 ",threshold,"% but this got ",accuracy,"%")
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#-----------------------------------
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# api tests - already built models will be passed to the functions
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from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
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#-----------------------------------
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print("--------------------------")
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print("Verify function with passing pre-trained model")
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vggface_model = VGGFace.loadModel()
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resp_obj = DeepFace.verify("dataset/img1.jpg", "dataset/img2.jpg", model_name = "VGG-Face", model = vggface_model)
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print(resp_obj)
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#-----------------------------------
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print("--------------------------")
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print("Analyze function with passing pre-trained model")
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from deepface.extendedmodels import Age, Gender, Race, Emotion
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emotion_model = Emotion.loadModel()
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age_model = Age.loadModel()
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gender_model = Gender.loadModel()
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race_model = Race.loadModel()
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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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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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print(df.head())
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#-----------------------------------
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print("--------------------------")
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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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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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#-----------------------------------
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print("--------------------------")
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print("Pre-trained ensemble method")
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from deepface import DeepFace
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from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
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model = {}
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model["VGG-Face"] = VGGFace.loadModel()
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print("VGG loaded")
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model["Facenet"] = Facenet.loadModel()
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print("Facenet loaded")
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model["OpenFace"] = OpenFace.loadModel()
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print("OpenFace loaded")
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model["DeepFace"] = FbDeepFace.loadModel()
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print("DeepFace loaded")
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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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print(df)
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print("--------------------------")
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import cv2
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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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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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print("--------------------------")
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print("Passing numpy array to find function")
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img1 = cv2.imread("dataset/img1.jpg")
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df = DeepFace.find(img1, db_path = "dataset")
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print(df.head())
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print("--------------------------")
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