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ensemble learning
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Şefik Serangil
parent
63046fef21
commit
cc5440c6d7
@ -195,6 +195,15 @@ def verify():
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resp_obj = DeepFace.verify(instances, model_name = model_name, distance_metric = distance_metric, model = openface_model)
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elif model_name == "DeepFace":
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resp_obj = DeepFace.verify(instances, model_name = model_name, distance_metric = distance_metric, model = deepface_model)
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elif model_name == "Ensemble":
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models = {}
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models["VGG-Face"] = vggface_model
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models["Facenet"] = facenet_model
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models["OpenFace"] = openface_model
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models["DeepFace"] = deepface_model
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resp_obj = DeepFace.verify(instances, model_name = model_name, model = models)
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else:
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return jsonify({'success': False, 'error': 'You must pass a valid model name. Available models are VGG-Face, Facenet, OpenFace, DeepFace but you passed %s' % (model_name)}), 205
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@ -14,10 +14,7 @@ import keras
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import tensorflow as tf
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import pickle
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#from basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
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#from extendedmodels import Age, Gender, Race, Emotion
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#from commons import functions, realtime, distance as dst
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from deepface import DeepFace
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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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from deepface.commons import functions, realtime, distance as dst
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@ -35,7 +32,152 @@ def verify(img1_path, img2_path=''
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img_list = [[img1_path, img2_path]]
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#------------------------------
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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"] = VGGFace.loadModel()
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elif index == 1:
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model_pbar.set_description("Loading Google FaceNet")
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model["Facenet"] = Facenet.loadModel()
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elif index == 2:
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model_pbar.set_description("Loading OpenFace")
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model["OpenFace"] = OpenFace.loadModel()
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elif index == 3:
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model_pbar.set_description("Loading Facebook DeepFace")
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model["DeepFace"] = FbDeepFace.loadModel()
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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]
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img1 = functions.detectFace(img1_path, input_shape, enforce_detection = enforce_detection)
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img2 = functions.detectFace(img2_path, input_shape, enforce_detection = enforce_detection)
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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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#find deepface path
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deepface_path = DeepFace.__file__
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deepface_path = deepface_path.replace("\\", "/").replace("/deepface/DeepFace.py", "")
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ensemble_model_path = deepface_path+"/models/face-recognition-ensemble-model.txt"
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#print(ensemble_model_path)
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deepface_ensemble = lgb.Booster(model_file = ensemble_model_path)
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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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if verified: identified = "true"
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else: identified = "false"
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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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resp_obj += "\"verified\": "+identified
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resp_obj += ", \"score\": "+str(score)
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resp_obj += ", \"distance\": "+ensemble_features_string
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resp_obj += ", \"model\": [\"VGG-Face\", \"Facenet\", \"OpenFace\", \"DeepFace\"]"
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resp_obj += ", \"similarity_metric\": [\"cosine\", \"euclidean\", \"euclidean_l2\"]"
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resp_obj += "}"
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#print(resp_obj)
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resp_obj = json.loads(resp_obj) #string to json
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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 = json.dumps(resp_objects[i])
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if i > 0:
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resp_obj += ", "
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resp_obj += "\"pair_"+str(i+1)+"\": "+resp_item
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resp_obj += "}"
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resp_obj = json.loads(resp_obj)
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return resp_obj
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return None
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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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if model_name == 'VGG-Face':
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print("Using VGG-Face model backend and", distance_metric,"distance.")
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@ -70,8 +212,6 @@ def verify(img1_path, img2_path=''
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#------------------------------
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pbar = tqdm(range(0,len(img_list)), desc='Verification')
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resp_objects = []
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#for instance in img_list:
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for index in pbar:
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@ -22,6 +22,17 @@ print(resp_obj["pair_2"]["verified"] == True)
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print("-----------------------------------------")
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print("Ensemble learning bulk")
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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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print("-----------------------------------------")
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print("Bulk facial analysis tests")
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dataset = [
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