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ensemble in find function
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Şefik Serangil
parent
cc5440c6d7
commit
2f9815a1fc
@ -498,13 +498,51 @@ def find(img_path, db_path
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elif model_name == 'DeepFace':
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print("Using FB DeepFace model backend", distance_metric,"distance.")
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model = FbDeepFace.loadModel()
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elif 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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model_names = ['VGG-Face', 'Facenet', 'OpenFace', 'DeepFace']
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metric_names = ['cosine', 'euclidean', 'euclidean_l2']
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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'] = VGGFace.loadModel()
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elif index == 1:
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pbar.set_description("Loading FaceNet")
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models['Facenet'] = Facenet.loadModel()
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elif index == 2:
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pbar.set_description("Loading OpenFace")
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models['OpenFace'] = OpenFace.loadModel()
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elif index == 3:
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pbar.set_description("Loading DeepFace")
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models['DeepFace'] = FbDeepFace.loadModel()
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else:
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raise ValueError("Invalid model_name passed - ", model_name)
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else: #model != None
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print("Already built model is passed")
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if model_name == 'Ensemble':
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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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input_shape = model.layers[0].input_shape[1:3]
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threshold = functions.findThreshold(model_name, distance_metric)
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#threshold = functions.findThreshold(model_name, distance_metric)
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#---------------------------------------
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@ -542,12 +580,30 @@ def find(img_path, db_path
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#for employee in employees:
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for index in pbar:
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employee = employees[index]
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img = functions.detectFace(employee, input_shape, enforce_detection = enforce_detection)
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representation = model.predict(img)[0,:]
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instance = []
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instance.append(employee)
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instance.append(representation)
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if model_name != 'Ensemble':
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input_shape = model.layers[0].input_shape[1:3]
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img = functions.detectFace(employee, input_shape, enforce_detection = enforce_detection)
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representation = model.predict(img)[0,:]
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instance = []
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instance.append(employee)
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instance.append(representation)
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else: #ensemble learning
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instance = []
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instance.append(employee)
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for j in model_names:
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model = models[j]
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input_shape = model.layers[0].input_shape[1:3]
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img = functions.detectFace(employee, input_shape, enforce_detection = enforce_detection)
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representation = model.predict(img)[0,:]
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instance.append(representation)
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#-------------------------------
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representations.append(instance)
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@ -559,7 +615,12 @@ def find(img_path, db_path
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#----------------------------
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#we got representations for database
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df = pd.DataFrame(representations, columns = ["identity", "representation"])
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if model_name != 'Ensemble':
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df = pd.DataFrame(representations, columns = ["identity", "representation"])
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else: #ensemble learning
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df = pd.DataFrame(representations, columns = ["identity", "VGG-Face_representation", "Facenet_representation", "OpenFace_representation", "DeepFace_representation"])
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df_base = df.copy()
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resp_obj = []
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@ -569,31 +630,106 @@ def find(img_path, db_path
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img_path = img_paths[j]
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#find representation for passed image
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img = functions.detectFace(img_path, input_shape, enforce_detection = enforce_detection)
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target_representation = model.predict(img)[0,:]
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distances = []
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for index, instance in df.iterrows():
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source_representation = instance["representation"]
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if model_name == 'Ensemble':
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for j in model_names:
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model = models[j]
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input_shape = model.layers[0].input_shape[1:3]
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img = functions.detectFace(img_path, input_shape, enforce_detection = enforce_detection)
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target_representation = model.predict(img)[0,:]
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for k in metric_names:
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distances = []
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for index, instance in df.iterrows():
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source_representation = instance["%s_representation" % (j)]
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if k == 'cosine':
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distance = dst.findCosineDistance(source_representation, target_representation)
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elif k == 'euclidean':
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distance = dst.findEuclideanDistance(source_representation, target_representation)
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elif k == 'euclidean_l2':
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distance = dst.findEuclideanDistance(dst.l2_normalize(source_representation), dst.l2_normalize(target_representation))
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distances.append(distance)
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if j == 'OpenFace' and k == 'euclidean':
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continue
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else:
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df["%s_%s" % (j, k)] = distances
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if distance_metric == 'cosine':
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distance = dst.findCosineDistance(source_representation, target_representation)
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elif distance_metric == 'euclidean':
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distance = dst.findEuclideanDistance(source_representation, target_representation)
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elif distance_metric == 'euclidean_l2':
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distance = dst.findEuclideanDistance(dst.l2_normalize(source_representation), dst.l2_normalize(target_representation))
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else:
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raise ValueError("Invalid distance_metric passed - ", distance_metric)
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#----------------------------------
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distances.append(distance)
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feature_names = []
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for j in model_names:
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for k in metric_names:
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if j == 'OpenFace' and k == 'euclidean':
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continue
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else:
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feature = '%s_%s' % (j, k)
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feature_names.append(feature)
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#print(df[feature_names].head())
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x = df[feature_names].values
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#----------------------------------
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#lightgbm model
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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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deepface_ensemble = lgb.Booster(model_file = ensemble_model_path)
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y = deepface_ensemble.predict(x)
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verified_labels = []; scores = []
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for i in y:
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verified = np.argmax(i) == 1
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score = i[np.argmax(i)]
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verified_labels.append(verified)
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scores.append(score)
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df['verified'] = verified_labels
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df['score'] = scores
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df = df[df.verified == True]
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df = df[df.score > 0.99] #confidence score
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df = df.sort_values(by = ["score"], ascending=False).reset_index(drop=True)
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resp_obj.append(df)
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df = df_base.copy() #restore df for the next iteration
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#----------------------------------
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if model_name != 'Ensemble':
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input_shape = model.layers[0].input_shape[1:3]
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img = functions.detectFace(img_path, input_shape, enforce_detection = enforce_detection)
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target_representation = model.predict(img)[0,:]
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df["distance"] = distances
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df = df.drop(columns = ["representation"])
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df = df[df.distance <= threshold]
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df = df.sort_values(by = ["distance"], ascending=True).reset_index(drop=True)
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resp_obj.append(df)
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df = df_base.copy() #restore df for the next iteration
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distances = []
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for index, instance in df.iterrows():
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source_representation = instance["representation"]
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if distance_metric == 'cosine':
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distance = dst.findCosineDistance(source_representation, target_representation)
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elif distance_metric == 'euclidean':
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distance = dst.findEuclideanDistance(source_representation, target_representation)
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elif distance_metric == 'euclidean_l2':
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distance = dst.findEuclideanDistance(dst.l2_normalize(source_representation), dst.l2_normalize(target_representation))
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else:
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raise ValueError("Invalid distance_metric passed - ", distance_metric)
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distances.append(distance)
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threshold = functions.findThreshold(model_name, distance_metric)
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df["distance"] = distances
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df = df.drop(columns = ["representation"])
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df = df[df.distance <= threshold]
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df = df.sort_values(by = ["distance"], ascending=True).reset_index(drop=True)
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resp_obj.append(df)
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df = df_base.copy() #restore df for the next iteration
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toc = time.time()
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@ -5,6 +5,8 @@ import os
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
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#-----------------------------------------
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#TODO: add find function in unit tests
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print("Bulk tests")
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print("-----------------------------------------")
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