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https://github.com/serengil/deepface.git
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781 lines
24 KiB
Python
781 lines
24 KiB
Python
from keras.preprocessing import image
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import warnings
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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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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 json
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import cv2
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from keras import backend as K
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import keras
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import tensorflow as tf
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import pickle
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from deepface import DeepFace
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from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace, DeepID
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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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def verify(img1_path, img2_path=''
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, model_name ='VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True):
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tic = time.time()
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if type(img1_path) == list:
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bulkProcess = True
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img_list = img1_path.copy()
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else:
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bulkProcess = False
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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.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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model = VGGFace.loadModel()
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elif model_name == 'OpenFace':
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print("Using OpenFace model backend", distance_metric,"distance.")
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model = OpenFace.loadModel()
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elif model_name == 'Facenet':
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print("Using Facenet model backend", distance_metric,"distance.")
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model = Facenet.loadModel()
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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 == 'DeepID':
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print("Using DeepID2 model backend", distance_metric,"distance.")
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model = DeepID.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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#------------------------------
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#face recognition models have different size of inputs
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input_shape = model.layers[0].input_shape
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if type(input_shape) is list:
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input_shape = input_shape[0][1:3]
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input_shape_x = input_shape[0]
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input_shape_y = input_shape[1]
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#------------------------------
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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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#------------------------------
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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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#----------------------
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#crop and align faces
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img1 = functions.detectFace(img1_path, (input_shape_y, input_shape_x), enforce_detection = enforce_detection)
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img2 = functions.detectFace(img2_path, (input_shape_y, input_shape_x), enforce_detection = enforce_detection)
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#----------------------
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#find embeddings
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img1_representation = model.predict(img1)[0,:]
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img2_representation = model.predict(img2)[0,:]
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#----------------------
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#find distances between embeddings
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if distance_metric == 'cosine':
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distance = dst.findCosineDistance(img1_representation, img2_representation)
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elif distance_metric == 'euclidean':
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distance = dst.findEuclideanDistance(img1_representation, img2_representation)
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elif distance_metric == 'euclidean_l2':
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distance = dst.findEuclideanDistance(dst.l2_normalize(img1_representation), dst.l2_normalize(img2_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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#decision
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if distance <= threshold:
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identified = "true"
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else:
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identified = "false"
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#----------------------
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#response object
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resp_obj = "{"
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resp_obj += "\"verified\": "+identified
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resp_obj += ", \"distance\": "+str(distance)
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resp_obj += ", \"max_threshold_to_verify\": "+str(threshold)
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resp_obj += ", \"model\": \""+model_name+"\""
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resp_obj += ", \"similarity_metric\": \""+distance_metric+"\""
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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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#K.clear_session()
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return resp_obj
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#----------------------
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else:
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raise ValueError("Invalid arguments passed to verify function: ", instance)
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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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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 resp_objects
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def analyze(img_path, actions = [], models = {}, enforce_detection = True):
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if type(img_path) == list:
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img_paths = img_path.copy()
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bulkProcess = True
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else:
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img_paths = [img_path]
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bulkProcess = False
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#---------------------------------
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#if a specific target is not passed, then find them all
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if len(actions) == 0:
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actions= ['emotion', 'age', 'gender', 'race']
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print("Actions to do: ", actions)
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#---------------------------------
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if 'emotion' in actions:
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if 'emotion' in models:
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print("already built emotion model is passed")
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emotion_model = models['emotion']
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else:
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emotion_model = Emotion.loadModel()
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if 'age' in actions:
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if 'age' in models:
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print("already built age model is passed")
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age_model = models['age']
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else:
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age_model = Age.loadModel()
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if 'gender' in actions:
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if 'gender' in models:
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print("already built gender model is passed")
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gender_model = models['gender']
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else:
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gender_model = Gender.loadModel()
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if 'race' in actions:
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if 'race' in models:
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print("already built race model is passed")
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race_model = models['race']
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else:
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race_model = Race.loadModel()
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#---------------------------------
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resp_objects = []
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global_pbar = tqdm(range(0,len(img_paths)), desc='Analyzing')
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#for img_path in img_paths:
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for j in global_pbar:
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img_path = img_paths[j]
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resp_obj = "{"
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#TO-DO: do this in parallel
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pbar = tqdm(range(0,len(actions)), desc='Finding actions')
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action_idx = 0
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img_224 = None # Set to prevent re-detection
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#for action in actions:
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for index in pbar:
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action = actions[index]
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pbar.set_description("Action: %s" % (action))
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if action_idx > 0:
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resp_obj += ", "
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if action == 'emotion':
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emotion_labels = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']
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img = functions.detectFace(img_path, target_size = (48, 48), grayscale = True, enforce_detection = enforce_detection)
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emotion_predictions = emotion_model.predict(img)[0,:]
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sum_of_predictions = emotion_predictions.sum()
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emotion_obj = "\"emotion\": {"
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for i in range(0, len(emotion_labels)):
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emotion_label = emotion_labels[i]
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emotion_prediction = 100 * emotion_predictions[i] / sum_of_predictions
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if i > 0: emotion_obj += ", "
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emotion_obj += "\"%s\": %s" % (emotion_label, emotion_prediction)
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emotion_obj += "}"
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emotion_obj += ", \"dominant_emotion\": \"%s\"" % (emotion_labels[np.argmax(emotion_predictions)])
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resp_obj += emotion_obj
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elif action == 'age':
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if img_224 is None:
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img_224 = functions.detectFace(img_path, target_size = (224, 224), grayscale = False, enforce_detection = enforce_detection) #just emotion model expects grayscale images
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#print("age prediction")
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age_predictions = age_model.predict(img_224)[0,:]
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apparent_age = Age.findApparentAge(age_predictions)
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resp_obj += "\"age\": %s" % (apparent_age)
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elif action == 'gender':
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if img_224 is None:
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img_224 = functions.detectFace(img_path, target_size = (224, 224), grayscale = False, enforce_detection = enforce_detection) #just emotion model expects grayscale images
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#print("gender prediction")
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gender_prediction = gender_model.predict(img_224)[0,:]
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if np.argmax(gender_prediction) == 0:
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gender = "Woman"
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elif np.argmax(gender_prediction) == 1:
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gender = "Man"
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resp_obj += "\"gender\": \"%s\"" % (gender)
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elif action == 'race':
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if img_224 is None:
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img_224 = functions.detectFace(img_path, target_size = (224, 224), grayscale = False, enforce_detection = enforce_detection) #just emotion model expects grayscale images
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race_predictions = race_model.predict(img_224)[0,:]
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race_labels = ['asian', 'indian', 'black', 'white', 'middle eastern', 'latino hispanic']
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sum_of_predictions = race_predictions.sum()
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race_obj = "\"race\": {"
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for i in range(0, len(race_labels)):
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race_label = race_labels[i]
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race_prediction = 100 * race_predictions[i] / sum_of_predictions
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if i > 0: race_obj += ", "
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race_obj += "\"%s\": %s" % (race_label, race_prediction)
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race_obj += "}"
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race_obj += ", \"dominant_race\": \"%s\"" % (race_labels[np.argmax(race_predictions)])
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resp_obj += race_obj
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action_idx = action_idx + 1
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resp_obj += "}"
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resp_obj = json.loads(resp_obj)
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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 = json.dumps(resp_objects[i])
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if i > 0:
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resp_obj += ", "
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resp_obj += "\"instance_"+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 resp_objects
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def detectFace(img_path):
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img = functions.detectFace(img_path)[0] #detectFace returns (1, 224, 224, 3)
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return img[:, :, ::-1] #bgr to rgb
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def find(img_path, db_path
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, model_name ='VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True):
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tic = time.time()
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if type(img_path) == list:
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bulkProcess = True
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img_paths = img_path.copy()
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else:
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bulkProcess = False
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img_paths = [img_path]
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if os.path.isdir(db_path) == True:
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#---------------------------------------
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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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model = VGGFace.loadModel()
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elif model_name == 'OpenFace':
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print("Using OpenFace model backend", distance_metric,"distance.")
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model = OpenFace.loadModel()
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elif model_name == 'Facenet':
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print("Using Facenet model backend", distance_metric,"distance.")
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model = Facenet.loadModel()
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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 == 'DeepID':
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print("Using DeepID model backend", distance_metric,"distance.")
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model = DeepID.loadModel()
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elif 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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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
|
|
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():
|
|
found_models.append(key)
|
|
|
|
if ('VGG-Face' in found_models) and ('Facenet' in found_models) and ('OpenFace' in found_models) and ('DeepFace' in found_models):
|
|
print("Ensemble learning will be applied for ", found_models," models")
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|
else:
|
|
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)
|
|
|
|
#threshold = functions.findThreshold(model_name, distance_metric)
|
|
|
|
#---------------------------------------
|
|
|
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file_name = "representations_%s.pkl" % (model_name)
|
|
file_name = file_name.replace("-", "_").lower()
|
|
|
|
if path.exists(db_path+"/"+file_name):
|
|
|
|
print("WARNING: Representations for images in ",db_path," folder were previously stored in ", file_name, ". If you added new instances after this file creation, then please delete this file and call find function again. It will create it again.")
|
|
|
|
f = open(db_path+'/'+file_name, 'rb')
|
|
representations = pickle.load(f)
|
|
|
|
print("There are ", len(representations)," representations found in ",file_name)
|
|
|
|
else:
|
|
employees = []
|
|
|
|
for r, d, f in os.walk(db_path): # r=root, d=directories, f = files
|
|
for file in f:
|
|
if ('.jpg' in file):
|
|
exact_path = r + "/" + file
|
|
employees.append(exact_path)
|
|
|
|
if len(employees) == 0:
|
|
raise ValueError("There is no image in ", db_path," folder!")
|
|
|
|
#------------------------
|
|
#find representations for db images
|
|
|
|
representations = []
|
|
|
|
pbar = tqdm(range(0,len(employees)), desc='Finding representations')
|
|
|
|
#for employee in employees:
|
|
for index in pbar:
|
|
employee = employees[index]
|
|
|
|
if model_name != 'Ensemble':
|
|
|
|
input_shape = model.layers[0].input_shape[1:3]
|
|
input_shape_x = input_shape[0]; input_shape_y = input_shape[1]
|
|
|
|
img = functions.detectFace(employee, (input_shape_y, input_shape_x), enforce_detection = enforce_detection)
|
|
representation = model.predict(img)[0,:]
|
|
|
|
instance = []
|
|
instance.append(employee)
|
|
instance.append(representation)
|
|
|
|
else: #ensemble learning
|
|
|
|
instance = []
|
|
instance.append(employee)
|
|
|
|
for j in model_names:
|
|
model = models[j]
|
|
input_shape = model.layers[0].input_shape[1:3]
|
|
input_shape_x = input_shape[0]; input_shape_y = input_shape[1]
|
|
|
|
img = functions.detectFace(employee, (input_shape_y, input_shape_x), enforce_detection = enforce_detection)
|
|
representation = model.predict(img)[0,:]
|
|
instance.append(representation)
|
|
|
|
#-------------------------------
|
|
|
|
representations.append(instance)
|
|
|
|
f = open(db_path+'/'+file_name, "wb")
|
|
pickle.dump(representations, f)
|
|
f.close()
|
|
|
|
print("Representations stored in ",db_path,"/",file_name," file. Please delete this file when you add new identities in your database.")
|
|
|
|
#----------------------------
|
|
#we got representations for database
|
|
|
|
if model_name != 'Ensemble':
|
|
df = pd.DataFrame(representations, columns = ["identity", "representation"])
|
|
else: #ensemble learning
|
|
df = pd.DataFrame(representations, columns = ["identity", "VGG-Face_representation", "Facenet_representation", "OpenFace_representation", "DeepFace_representation"])
|
|
|
|
df_base = df.copy()
|
|
|
|
resp_obj = []
|
|
|
|
global_pbar = tqdm(range(0,len(img_paths)), desc='Analyzing')
|
|
for j in global_pbar:
|
|
img_path = img_paths[j]
|
|
|
|
#find representation for passed image
|
|
|
|
if model_name == 'Ensemble':
|
|
for j in model_names:
|
|
model = models[j]
|
|
input_shape = model.layers[0].input_shape[1:3]
|
|
img = functions.detectFace(img_path, input_shape, enforce_detection = enforce_detection)
|
|
target_representation = model.predict(img)[0,:]
|
|
|
|
for k in metric_names:
|
|
distances = []
|
|
for index, instance in df.iterrows():
|
|
source_representation = instance["%s_representation" % (j)]
|
|
|
|
if k == 'cosine':
|
|
distance = dst.findCosineDistance(source_representation, target_representation)
|
|
elif k == 'euclidean':
|
|
distance = dst.findEuclideanDistance(source_representation, target_representation)
|
|
elif k == 'euclidean_l2':
|
|
distance = dst.findEuclideanDistance(dst.l2_normalize(source_representation), dst.l2_normalize(target_representation))
|
|
|
|
distances.append(distance)
|
|
|
|
if j == 'OpenFace' and k == 'euclidean':
|
|
continue
|
|
else:
|
|
df["%s_%s" % (j, k)] = distances
|
|
|
|
#----------------------------------
|
|
|
|
feature_names = []
|
|
for j in model_names:
|
|
for k in metric_names:
|
|
if j == 'OpenFace' and k == 'euclidean':
|
|
continue
|
|
else:
|
|
feature = '%s_%s' % (j, k)
|
|
feature_names.append(feature)
|
|
|
|
#print(df[feature_names].head())
|
|
|
|
x = df[feature_names].values
|
|
|
|
#----------------------------------
|
|
#lightgbm model
|
|
deepface_path = DeepFace.__file__
|
|
deepface_path = deepface_path.replace("\\", "/").replace("/DeepFace.py", "")
|
|
ensemble_model_path = deepface_path+"/models/face-recognition-ensemble-model.txt"
|
|
deepface_ensemble = lgb.Booster(model_file = ensemble_model_path)
|
|
|
|
y = deepface_ensemble.predict(x)
|
|
|
|
verified_labels = []; scores = []
|
|
for i in y:
|
|
verified = np.argmax(i) == 1
|
|
score = i[np.argmax(i)]
|
|
|
|
verified_labels.append(verified)
|
|
scores.append(score)
|
|
|
|
df['verified'] = verified_labels
|
|
df['score'] = scores
|
|
|
|
df = df[df.verified == True]
|
|
#df = df[df.score > 0.99] #confidence score
|
|
df = df.sort_values(by = ["score"], ascending=False).reset_index(drop=True)
|
|
df = df[['identity', 'verified', 'score']]
|
|
|
|
resp_obj.append(df)
|
|
df = df_base.copy() #restore df for the next iteration
|
|
|
|
#----------------------------------
|
|
|
|
if model_name != 'Ensemble':
|
|
input_shape = model.layers[0].input_shape[1:3]
|
|
input_shape_x = input_shape[0]; input_shape_y = input_shape[1]
|
|
|
|
img = functions.detectFace(img_path, (input_shape_y, input_shape_x), enforce_detection = enforce_detection)
|
|
target_representation = model.predict(img)[0,:]
|
|
|
|
distances = []
|
|
for index, instance in df.iterrows():
|
|
source_representation = instance["representation"]
|
|
|
|
if distance_metric == 'cosine':
|
|
distance = dst.findCosineDistance(source_representation, target_representation)
|
|
elif distance_metric == 'euclidean':
|
|
distance = dst.findEuclideanDistance(source_representation, target_representation)
|
|
elif distance_metric == 'euclidean_l2':
|
|
distance = dst.findEuclideanDistance(dst.l2_normalize(source_representation), dst.l2_normalize(target_representation))
|
|
else:
|
|
raise ValueError("Invalid distance_metric passed - ", distance_metric)
|
|
|
|
distances.append(distance)
|
|
|
|
threshold = functions.findThreshold(model_name, distance_metric)
|
|
|
|
df["distance"] = distances
|
|
df = df.drop(columns = ["representation"])
|
|
df = df[df.distance <= threshold]
|
|
|
|
df = df.sort_values(by = ["distance"], ascending=True).reset_index(drop=True)
|
|
resp_obj.append(df)
|
|
df = df_base.copy() #restore df for the next iteration
|
|
|
|
toc = time.time()
|
|
|
|
print("find function lasts ",toc-tic," seconds")
|
|
|
|
if len(resp_obj) == 1:
|
|
return resp_obj[0]
|
|
|
|
return resp_obj
|
|
|
|
else:
|
|
raise ValueError("Passed db_path does not exist!")
|
|
|
|
return None
|
|
|
|
def stream(db_path = '', model_name ='VGG-Face', distance_metric = 'cosine', enable_face_analysis = True):
|
|
realtime.analysis(db_path, model_name, distance_metric, enable_face_analysis)
|
|
|
|
def allocateMemory():
|
|
print("Analyzing your system...")
|
|
functions.allocateMemory()
|
|
|
|
functions.initializeFolder()
|
|
|
|
#---------------------------
|
|
|