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clean codes
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README.md
13
README.md
@ -175,12 +175,6 @@ models["race"] = DeepFace.build_model('Race')
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DeepFace.analyze("img1.jpg", models=models)
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```
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## Disclaimer
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Reference face recognition models have different type of licenses. This framework is just a wrapper for those models. That's why, licence types are inherited as well. You should check the licenses for the face recognition models before use.
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Herein, [OpenFace](https://github.com/cmusatyalab/openface/blob/master/LICENSE) is licensed under Apache License 2.0. [FB DeepFace](https://github.com/swghosh/DeepFace) and [Facenet](https://github.com/davidsandberg/facenet/blob/master/LICENSE.md) is licensed under MIT License. [Dlib](https://github.com/davisking/dlib/blob/master/dlib/LICENSE.txt) is licensed under Boost Software License. They all allow you to use for personal and commercial purpose free. On the other hand, [VGG-Face](http://www.robots.ox.ac.uk/~vgg/software/vgg_face/) is licensed under Creative Commons Attribution License. That's why, it is restricted to adopt VGG-Face for commercial use.
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## Support
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There are many ways to support a project - starring⭐️ the GitHub repos is just one.
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@ -189,11 +183,12 @@ There are many ways to support a project - starring⭐️ the GitHub repos is ju
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Please cite [deepface](https://ieeexplore.ieee.org/document/9259802) in your publications if it helps your research. Here is an example BibTeX entry:
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```
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@inproceedings{9259802,
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```BibTeX
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@inproceedings{serengil2020lightface,
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title={LightFace: A Hybrid Deep Face Recognition Framework},
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author={Serengil, Sefik Ilkin and Ozpinar, Alper},
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booktitle={2020 Innovations in Intelligent Systems and Applications Conference (ASYU)},
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pages={23--27},
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year={2020},
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doi={10.1109/ASYU50717.2020.9259802},
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organization={IEEE}
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@ -202,6 +197,6 @@ Please cite [deepface](https://ieeexplore.ieee.org/document/9259802) in your pub
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## Licence
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Deepface is licensed under the MIT License - see [`LICENSE`](https://github.com/serengil/deepface/blob/master/LICENSE) for more details.
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Deepface is licensed under the MIT License - see [`LICENSE`](https://github.com/serengil/deepface/blob/master/LICENSE) for more details. However, the library wraps some face recognition models: [VGG-Face](http://www.robots.ox.ac.uk/~vgg/software/vgg_face/), [Facenet](https://github.com/davidsandberg/facenet/blob/master/LICENSE.md), [OpenFace](https://github.com/cmusatyalab/openface/blob/master/LICENSE), [DeepFace](https://github.com/swghosh/DeepFace) and [Dlib](https://github.com/davisking/dlib/blob/master/dlib/LICENSE.txt). Licence types will be inherited if you are going to use those models.
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[Logo](https://thenounproject.com/term/face-recognition/2965879/) is created by [Adrien Coquet](https://thenounproject.com/coquet_adrien/). Licensed under [Creative Commons: By Attribution 3.0 License](https://creativecommons.org/licenses/by/3.0/).
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@ -58,7 +58,6 @@ def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric =
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metrics.append(distance_metric)
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#--------------------------------
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#ensemble learning disabled
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if model == None:
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if model_name == 'Ensemble':
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@ -135,7 +134,7 @@ def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric =
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if model_name != 'Ensemble':
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threshold = functions.findThreshold(i, j)
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threshold = dst.findThreshold(i, j)
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if distance <= threshold:
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identified = True
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@ -196,8 +195,6 @@ def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric =
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#-------------------------
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toc = time.time()
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#print("identification lasts ",toc-tic," seconds")
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if bulkProcess == True:
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@ -520,7 +517,7 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
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df["%s_%s" % (j, k)] = distances
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if model_name != 'Ensemble':
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threshold = functions.findThreshold(j, k)
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threshold = dst.findThreshold(j, k)
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df = df.drop(columns = ["%s_representation" % (j)])
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df = df[df["%s_%s" % (j, k)] <= threshold]
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@ -9,24 +9,19 @@ import lightgbm as lgb #lightgbm==2.3.1
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from deepface.commons import functions, distance as dst
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def loadModel():
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model_names = ['VGG-Face', 'Facenet', 'OpenFace', 'DeepFace']
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model = {}
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model_pbar = tqdm(range(0, 4), desc='Face recognition models')
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for index in model_pbar:
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if index == 0:
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model_pbar.set_description("Loading VGG-Face")
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model["VGG-Face"] = DeepFace.build_model('VGG-Face')
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elif index == 1:
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model_pbar.set_description("Loading Google FaceNet")
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model["Facenet"] = DeepFace.build_model('Facenet')
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elif index == 2:
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model_pbar.set_description("Loading OpenFace")
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model["OpenFace"] = DeepFace.build_model('OpenFace')
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elif index == 3:
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model_pbar.set_description("Loading Facebook DeepFace")
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model["DeepFace"] = DeepFace.build_model('DeepFace')
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model_name = model_names[index]
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model_pbar.set_description("Loading %s" % (model_name))
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model[model_name] = DeepFace.build_model(model_name)
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return model
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@ -37,7 +32,8 @@ def validate_model(model):
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found_models.append(key)
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if ('VGG-Face' in found_models) and ('Facenet' in found_models) and ('OpenFace' in found_models) and ('DeepFace' in found_models):
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print("Ensemble learning will be applied for ", found_models," models")
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#print("Ensemble learning will be applied for ", found_models," models")
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valid = True
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else:
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raise ValueError("You would like to apply ensemble learning and pass pre-built models but models must contain [VGG-Face, Facenet, OpenFace, DeepFace] but you passed "+found_models)
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@ -14,3 +14,20 @@ def findEuclideanDistance(source_representation, test_representation):
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def l2_normalize(x):
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return x / np.sqrt(np.sum(np.multiply(x, x)))
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def findThreshold(model_name, distance_metric):
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base_threshold = {'cosine': 0.40, 'euclidean': 0.55, 'euclidean_l2': 0.75}
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thresholds = {
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'VGG-Face': {'cosine': 0.40, 'euclidean': 0.55, 'euclidean_l2': 0.75},
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'OpenFace': {'cosine': 0.10, 'euclidean': 0.55, 'euclidean_l2': 0.55},
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'Facenet': {'cosine': 0.40, 'euclidean': 10, 'euclidean_l2': 0.80},
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'DeepFace': {'cosine': 0.23, 'euclidean': 64, 'euclidean_l2': 0.64},
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'DeepID': {'cosine': 0.015, 'euclidean': 45, 'euclidean_l2': 0.17},
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'Dlib': {'cosine': 0.07, 'euclidean': 0.6, 'euclidean_l2': 0.6}
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}
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threshold = thresholds.get(model_name, base_threshold).get(distance_metric, 0.4)
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return threshold
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@ -97,12 +97,6 @@ def initialize_detector(detector_backend):
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elif detector_backend == 'mtcnn':
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face_detector = MTCNN()
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def loadBase64Img(uri):
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encoded_data = uri.split(',')[1]
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nparr = np.fromstring(base64.b64decode(encoded_data), np.uint8)
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img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
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return img
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def initializeFolder():
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@ -115,23 +109,12 @@ def initializeFolder():
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if not os.path.exists(home+"/.deepface/weights"):
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os.mkdir(home+"/.deepface/weights")
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print("Directory ",home,"/.deepface/weights created")
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def findThreshold(model_name, distance_metric):
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base_threshold = {'cosine': 0.40, 'euclidean': 0.55, 'euclidean_l2': 0.75}
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thresholds = {
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'VGG-Face': {'cosine': 0.40, 'euclidean': 0.55, 'euclidean_l2': 0.75},
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'OpenFace': {'cosine': 0.10, 'euclidean': 0.55, 'euclidean_l2': 0.55},
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'Facenet': {'cosine': 0.40, 'euclidean': 10, 'euclidean_l2': 0.80},
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'DeepFace': {'cosine': 0.23, 'euclidean': 64, 'euclidean_l2': 0.64},
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'DeepID': {'cosine': 0.015, 'euclidean': 45, 'euclidean_l2': 0.17},
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'Dlib': {'cosine': 0.07, 'euclidean': 0.6, 'euclidean_l2': 0.6}
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}
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threshold = thresholds.get(model_name, base_threshold).get(distance_metric, 0.4)
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return threshold
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def loadBase64Img(uri):
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encoded_data = uri.split(',')[1]
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nparr = np.fromstring(base64.b64decode(encoded_data), np.uint8)
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img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
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return img
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def get_opencv_path():
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opencv_home = cv2.__file__
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@ -76,7 +76,7 @@ def analysis(db_path, model_name, distance_metric, enable_face_analysis = True):
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input_shape_y = input_shape[1]
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#tuned thresholds for model and metric pair
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threshold = functions.findThreshold(model_name, distance_metric)
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threshold = dst.findThreshold(model_name, distance_metric)
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#------------------------
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#facial attribute analysis models
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@ -177,13 +177,15 @@ for model in models:
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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 > 75:
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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 80% but this got ",accuracy,"%")
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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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