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boost verify
This commit is contained in:
serengil
parent
4535e37c52
commit
45f17417af
@ -4,13 +4,12 @@ warnings.filterwarnings("ignore")
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import time
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import os
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from os import path
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from pathlib import Path
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import numpy as np
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import pandas as pd
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from tqdm import tqdm
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import pickle
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from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace, DeepID, DlibWrapper
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from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace, DeepID, DlibWrapper, Boosting
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from deepface.extendedmodels import Age, Gender, Race, Emotion
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from deepface.commons import functions, realtime, distance as dst
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@ -48,150 +47,19 @@ def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric =
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resp_objects = []
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if model_name == 'Ensemble':
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print("Ensemble learning enabled")
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import lightgbm as lgb #lightgbm==2.3.1
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if model == None:
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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"] = 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"] = 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"] = 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"] = build_model('DeepFace')
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#--------------------------
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#validate model dictionary because it might be passed from input as pre-trained
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found_models = []
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for key, value in model.items():
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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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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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#--------------------------
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model_names = ["VGG-Face", "Facenet", "OpenFace", "DeepFace"]
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metrics = ["cosine", "euclidean", "euclidean_l2"]
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pbar = tqdm(range(0,len(img_list)), desc='Verification')
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#for instance in img_list:
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for index in pbar:
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instance = img_list[index]
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if type(instance) == list and len(instance) >= 2:
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img1_path = instance[0]
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img2_path = instance[1]
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ensemble_features = []; ensemble_features_string = "["
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for i in model_names:
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custom_model = model[i]
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#input_shape = custom_model.layers[0].input_shape[1:3] #my environment returns (None, 224, 224, 3) but some people mentioned that they got [(None, 224, 224, 3)]. I think this is because of version issue.
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#--------------------------------
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input_shape = custom_model.layers[0].input_shape
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if type(input_shape) == list:
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input_shape = input_shape[0][1:3]
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else:
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input_shape = input_shape[1:3]
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img1 = functions.preprocess_face(img = img1_path, target_size = input_shape, enforce_detection = enforce_detection, detector_backend = detector_backend)
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img2 = functions.preprocess_face(img = img2_path, target_size = input_shape, enforce_detection = enforce_detection, detector_backend = detector_backend)
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img1_representation = custom_model.predict(img1)[0,:]
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img2_representation = custom_model.predict(img2)[0,:]
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for j in metrics:
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if j == 'cosine':
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distance = dst.findCosineDistance(img1_representation, img2_representation)
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elif j == 'euclidean':
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distance = dst.findEuclideanDistance(img1_representation, img2_representation)
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elif j == 'euclidean_l2':
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distance = dst.findEuclideanDistance(dst.l2_normalize(img1_representation), dst.l2_normalize(img2_representation))
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if i == 'OpenFace' and j == 'euclidean': #this returns same with OpenFace - euclidean_l2
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continue
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else:
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ensemble_features.append(distance)
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if len(ensemble_features) > 1:
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ensemble_features_string += ", "
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ensemble_features_string += str(distance)
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#print("ensemble_features: ", ensemble_features)
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ensemble_features_string += "]"
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#-------------------------------
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deepface_ensemble = functions.boosting_method()
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#---------------------------
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prediction = deepface_ensemble.predict(np.expand_dims(np.array(ensemble_features), axis=0))[0]
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verified = np.argmax(prediction) == 1
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score = prediction[np.argmax(prediction)]
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#print("verified: ", verified,", score: ", score)
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resp_obj = {
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"verified": verified
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, "score": score
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, "distance": ensemble_features_string
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, "model": ["VGG-Face", "Facenet", "OpenFace", "DeepFace"]
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, "similarity_metric": ["cosine", "euclidean", "euclidean_l2"]
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}
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if bulkProcess == True:
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resp_objects.append(resp_obj)
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else:
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return resp_obj
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#-------------------------------
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if bulkProcess == True:
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resp_obj = {}
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for i in range(0, len(resp_objects)):
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resp_item = resp_objects[i]
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resp_obj["pair_%d" % (i+1)] = resp_item
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return resp_obj
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return None
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if model_name == 'Ensemble':
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return Boosting.verify(model = model, img_list = img_list, bulkProcess = bulkProcess, enforce_detection = enforce_detection, detector_backend = detector_backend)
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#ensemble learning block end
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#--------------------------------
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#ensemble learning disabled
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if model == None:
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model = build_model(model_name)
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"""else: #model != None
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print("Already built model is passed")"""
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#------------------------------
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#face recognition models have different size of inputs
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#my environment returns (None, 224, 224, 3) but some people mentioned that they got [(None, 224, 224, 3)]. I think this is because of version issue.
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@ -458,28 +326,8 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
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if model == None:
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if model_name == 'Ensemble':
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print("Ensemble learning enabled")
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#TODO: include DeepID in ensemble method
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import lightgbm as lgb #lightgbm==2.3.1
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models = {}
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pbar = tqdm(range(0, len(model_names)), desc='Face recognition models')
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for index in pbar:
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if index == 0:
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pbar.set_description("Loading VGG-Face")
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models['VGG-Face'] = build_model('VGG-Face')
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elif index == 1:
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pbar.set_description("Loading FaceNet")
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models['Facenet'] = build_model('Facenet')
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elif index == 2:
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pbar.set_description("Loading OpenFace")
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models['OpenFace'] = build_model('OpenFace')
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elif index == 3:
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pbar.set_description("Loading DeepFace")
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models['DeepFace'] = build_model('DeepFace')
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print("Ensemble learning enabled")
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models = Boosting.loadModel()
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else: #model is not ensemble
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model = build_model(model_name)
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@ -488,24 +336,10 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
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print("Already built model is passed")
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if model_name == 'Ensemble':
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import lightgbm as lgb #lightgbm==2.3.1
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#validate model dictionary because it might be passed from input as pre-trained
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found_models = []
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for key, value in model.items():
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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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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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Boosting.validate_model(model)
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models = model.copy()
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#threshold = functions.findThreshold(model_name, distance_metric)
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#---------------------------------------
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file_name = "representations_%s.pkl" % (model_name)
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@ -666,7 +500,7 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
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#----------------------------------
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#lightgbm model
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deepface_ensemble = functions.boosting_method()
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deepface_ensemble = Boosting.build_gbm()
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y = deepface_ensemble.predict(x)
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@ -747,7 +581,7 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
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raise ValueError("Passed db_path does not exist!")
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return None
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def stream(db_path = '', model_name ='VGG-Face', distance_metric = 'cosine', enable_face_analysis = True):
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functions.initialize_detector(detector_backend = 'opencv')
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@ -790,4 +624,3 @@ def initialize_input(img1_path, img2_path = None):
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#main
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functions.initializeFolder()
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179
deepface/basemodels/Boosting.py
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179
deepface/basemodels/Boosting.py
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@ -0,0 +1,179 @@
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from deepface import DeepFace
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from tqdm import tqdm
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import os
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from os import path
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from pathlib import Path
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import numpy as np
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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 = {}
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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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return model
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def validate_model(model):
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#validate model dictionary because it might be passed from input as pre-trained
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found_models = []
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for key, value in model.items():
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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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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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def build_gbm():
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home = str(Path.home())
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if os.path.isfile(home+'/.deepface/weights/face-recognition-ensemble-model.txt') != True:
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print("face-recognition-ensemble-model.txt will be downloaded...")
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url = 'https://raw.githubusercontent.com/serengil/deepface/master/deepface/models/face-recognition-ensemble-model.txt'
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output = home+'/.deepface/weights/face-recognition-ensemble-model.txt'
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gdown.download(url, output, quiet=False)
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ensemble_model_path = home+'/.deepface/weights/face-recognition-ensemble-model.txt'
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deepface_ensemble = lgb.Booster(model_file = ensemble_model_path)
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return deepface_ensemble
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def verify(model, img_list, bulkProcess, enforce_detection, detector_backend):
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print("Ensemble learning enabled")
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if model == None:
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model = loadModel()
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validate_model(model)
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#--------------------------
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model_names = ["VGG-Face", "Facenet", "OpenFace", "DeepFace"]
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metrics = ["cosine", "euclidean", "euclidean_l2"]
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resp_objects = []
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#--------------------------
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if model == None:
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model = loadModel()
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#--------------------------
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validate_model(model)
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#--------------------------
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pbar = tqdm(range(0,len(img_list)), desc='Verification')
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for index in pbar:
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instance = img_list[index]
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if type(instance) == list and len(instance) >= 2:
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img1_path = instance[0]
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img2_path = instance[1]
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ensemble_features = []; ensemble_features_string = "["
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for i in model_names:
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custom_model = model[i]
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input_shape = custom_model.layers[0].input_shape
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if type(input_shape) == list:
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input_shape = input_shape[0][1:3]
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else:
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input_shape = input_shape[1:3]
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#----------------------------------
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img1 = functions.preprocess_face(img = img1_path, target_size = input_shape
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, enforce_detection = enforce_detection
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, detector_backend = detector_backend)
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img2 = functions.preprocess_face(img = img2_path, target_size = input_shape
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, enforce_detection = enforce_detection
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, detector_backend = detector_backend)
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img1_representation = custom_model.predict(img1)[0,:]
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img2_representation = custom_model.predict(img2)[0,:]
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for j in metrics:
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if j == 'cosine':
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distance = dst.findCosineDistance(img1_representation, img2_representation)
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elif j == 'euclidean':
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distance = dst.findEuclideanDistance(img1_representation, img2_representation)
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elif j == 'euclidean_l2':
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distance = dst.findEuclideanDistance(dst.l2_normalize(img1_representation), dst.l2_normalize(img2_representation))
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#------------------------
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#this returns same with OpenFace - euclidean_l2
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if i == 'OpenFace' and j == 'euclidean':
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continue
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else:
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ensemble_features.append(distance)
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if len(ensemble_features) > 1:
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ensemble_features_string += ", "
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ensemble_features_string += str(distance)
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ensemble_features_string += "]"
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#-------------------------------
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deepface_ensemble = build_gbm()
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#-------------------------------
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prediction = deepface_ensemble.predict(np.expand_dims(np.array(ensemble_features), axis=0))[0]
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verified = np.argmax(prediction) == 1
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score = prediction[np.argmax(prediction)]
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#print("verified: ", verified,", score: ", score)
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resp_obj = {
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"verified": verified
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, "score": score
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, "distance": ensemble_features_string
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, "model": ["VGG-Face", "Facenet", "OpenFace", "DeepFace"]
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, "similarity_metric": ["cosine", "euclidean", "euclidean_l2"]
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}
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if bulkProcess == True:
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resp_objects.append(resp_obj)
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else:
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return resp_obj
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if bulkProcess == True:
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resp_obj = {}
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for i in range(0, len(resp_objects)):
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resp_item = resp_objects[i]
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resp_obj["pair_%d" % (i+1)] = resp_item
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return resp_obj
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@ -447,21 +447,3 @@ def preprocess_face(img, target_size=(224, 224), grayscale = False, enforce_dete
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img_pixels /= 255 #normalize input in [0, 1]
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return img_pixels
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def boosting_method():
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import lightgbm as lgb #lightgbm==2.3.1
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home = str(Path.home())
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if os.path.isfile(home+'/.deepface/weights/face-recognition-ensemble-model.txt') != True:
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print("face-recognition-ensemble-model.txt will be downloaded...")
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url = 'https://raw.githubusercontent.com/serengil/deepface/master/deepface/models/face-recognition-ensemble-model.txt'
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output = home+'/.deepface/weights/face-recognition-ensemble-model.txt'
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gdown.download(url, output, quiet=False)
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ensemble_model_path = home+'/.deepface/weights/face-recognition-ensemble-model.txt'
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deepface_ensemble = lgb.Booster(model_file = ensemble_model_path)
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return deepface_ensemble
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