mirror of
https://github.com/serengil/deepface.git
synced 2025-06-07 03:55:21 +00:00
344 lines
9.9 KiB
Python
344 lines
9.9 KiB
Python
import warnings
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import os
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import tensorflow as tf
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import numpy as np
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import pandas as pd
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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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tf_major_version = int(tf.__version__.split(".")[0])
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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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print("-----------------------------------------")
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expected_coverage = 97
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num_cases = 0; succeed_cases = 0
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def evaluate(condition):
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global num_cases, succeed_cases
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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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# ------------------------------------------------
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detectors = ['opencv', 'mtcnn']
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models = ['VGG-Face', 'Facenet', 'ArcFace']
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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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['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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print("-----------------------------------------")
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def test_cases():
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print("Enforce detection test")
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black_img = np.zeros([224, 224, 3])
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# enforce detection on for represent
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try:
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DeepFace.represent(img_path=black_img)
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exception_thrown = False
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except:
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exception_thrown = True
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assert exception_thrown is True
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# -------------------------------------------
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# enforce detection off for represent
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try:
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objs = DeepFace.represent(img_path=black_img, enforce_detection=False)
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exception_thrown = False
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# validate response of represent function
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assert isinstance(objs, list)
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assert len(objs) > 0
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assert isinstance(objs[0], dict)
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assert "embedding" in objs[0].keys()
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assert "facial_area" in objs[0].keys()
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assert isinstance(objs[0]["facial_area"], dict)
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assert "x" in objs[0]["facial_area"].keys()
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assert "y" in objs[0]["facial_area"].keys()
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assert "w" in objs[0]["facial_area"].keys()
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assert "h" in objs[0]["facial_area"].keys()
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assert isinstance(objs[0]["embedding"], list)
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assert len(objs[0]["embedding"]) == 2622 #embedding of VGG-Face
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except:
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exception_thrown = True
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assert exception_thrown is False
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# -------------------------------------------
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# enforce detection on for verify
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try:
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obj = DeepFace.verify(img1_path=black_img, img2_path=black_img)
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exception_thrown = False
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except:
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exception_thrown = True
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assert exception_thrown is True
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# -------------------------------------------
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# enforce detection off for verify
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try:
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obj = DeepFace.verify(img1_path=black_img, img2_path=black_img, enforce_detection=False)
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assert isinstance(obj, dict)
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exception_thrown = False
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except:
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exception_thrown = True
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assert exception_thrown is False
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# -------------------------------------------
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print("-----------------------------------------")
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print("Extract faces test")
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for detector in detectors:
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img_objs = DeepFace.extract_faces(img_path="dataset/img11.jpg", detector_backend = detector)
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for img_obj in img_objs:
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assert "face" in img_obj.keys()
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assert "facial_area" in img_obj.keys()
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assert isinstance(img_obj["facial_area"], dict)
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assert "x" in img_obj["facial_area"].keys()
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assert "y" in img_obj["facial_area"].keys()
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assert "w" in img_obj["facial_area"].keys()
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assert "h" in img_obj["facial_area"].keys()
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assert "confidence" in img_obj.keys()
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img = img_obj["face"]
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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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print("-----------------------------------------")
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img_path = "dataset/img1.jpg"
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embedding_objs = DeepFace.represent(img_path)
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for embedding_obj in embedding_objs:
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embedding = embedding_obj["embedding"]
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print("Function returned ", len(embedding), "dimensional vector")
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evaluate(len(embedding) == 2622)
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print("-----------------------------------------")
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print("Different face detectors on verification test")
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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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assert isinstance(res, dict)
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assert "verified" in res.keys()
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assert res["verified"] in [True, False]
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assert "distance" in res.keys()
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assert "threshold" in res.keys()
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assert "model" in res.keys()
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assert "detector_backend" in res.keys()
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assert "similarity_metric" in res.keys()
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assert "facial_areas" in res.keys()
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assert "img1" in res["facial_areas"].keys()
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assert "img2" in res["facial_areas"].keys()
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assert "x" in res["facial_areas"]["img1"].keys()
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assert "y" in res["facial_areas"]["img1"].keys()
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assert "w" in res["facial_areas"]["img1"].keys()
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assert "h" in res["facial_areas"]["img1"].keys()
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assert "x" in res["facial_areas"]["img2"].keys()
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assert "y" in res["facial_areas"]["img2"].keys()
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assert "w" in res["facial_areas"]["img2"].keys()
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assert "h" in res["facial_areas"]["img2"].keys()
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print(res)
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evaluate(res["verified"] == dataset[0][2])
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print("-----------------------------------------")
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print("Find function test")
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dfs = DeepFace.find(img_path = "dataset/img1.jpg", db_path = "dataset")
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for df in dfs:
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assert isinstance(df, pd.DataFrame)
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print(df.head())
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evaluate(df.shape[0] > 0)
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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_objs = DeepFace.analyze(img)
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for demography in demography_objs:
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print(demography)
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evaluate(demography["age"] > 20 and demography["age"] < 40)
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evaluate(demography["dominant_gender"] == "Woman")
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print("-----------------------------------------")
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print("Facial analysis test. Passing all to the action")
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demography_objs = DeepFace.analyze(img, ['age', 'gender', 'race', 'emotion'])
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for demography in demography_objs:
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#print(f"Demography: {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["dominant_gender"])
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print("Race: ", demography["dominant_race"])
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print("Emotion: ", demography["dominant_emotion"])
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evaluate(demography.get("age") is not None)
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evaluate(demography.get("dominant_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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print("-----------------------------------------")
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print("Facial analysis test 2. Remove some actions and check they are not computed")
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demography_objs = DeepFace.analyze(img, ['age', 'gender'])
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for demography in demography_objs:
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print("Age: ", demography.get("age"))
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print("Gender: ", demography.get("dominant_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("dominant_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("Facial recognition tests")
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for model in models:
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for metric in metrics:
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for instance in dataset:
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img1 = instance[0]
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img2 = instance[1]
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result = instance[2]
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resp_obj = DeepFace.verify(img1, img2
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, model_name = 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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passed = prediction == result
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evaluate(passed)
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if passed:
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test_result_label = "passed"
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else:
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test_result_label = "failed"
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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(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("Passing numpy array to analyze function")
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img = cv2.imread("dataset/img1.jpg")
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resp_objs = DeepFace.analyze(img)
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for resp_obj in resp_objs:
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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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evaluate(res["verified"] == True)
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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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dfs = DeepFace.find(img1, db_path = "dataset")
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for df in dfs:
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print(df.head())
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evaluate(df.shape[0] > 0)
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print("--------------------------")
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print("non-binary gender tests")
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#interface validation - no need to call evaluate here
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for img1_path, img2_path, verified in dataset:
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for detector in detectors:
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results = DeepFace.analyze(img1_path, actions=('gender',), detector_backend=detector, enforce_detection=False)
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for result in results:
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print(result)
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assert 'gender' in result.keys()
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assert 'dominant_gender' in result.keys() and result["dominant_gender"] in ["Man", "Woman"]
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if result["dominant_gender"] == "Man":
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assert result["gender"]["Man"] > result["gender"]["Woman"]
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else:
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assert result["gender"]["Man"] < result["gender"]["Woman"]
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# ---------------------------------------------
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test_cases()
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print("num of test cases run: " + str(num_cases))
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print("succeeded test cases: " + str(succeed_cases))
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test_score = (100 * succeed_cases) / num_cases
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print("test coverage: " + str(test_score))
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if test_score > expected_coverage:
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print("well done! min required test coverage is satisfied")
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else:
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print("min required test coverage is NOT satisfied")
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assert test_score > expected_coverage
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