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https://github.com/serengil/deepface.git
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Merge pull request #135 from ma7555/master
Loads MTCNN once only + convert to RGB before inferenence + add better readibility
This commit is contained in:
Sefik Ilkin Serengil
GitHub
commit
ba480a323b
@ -10,18 +10,20 @@ 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 = '', model_name ='VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'opencv'):
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def DlibResNet_():
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from deepface.basemodels.DlibResNet import DlibResNet
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return DlibResNet()
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def verify(img1_path, img2_path = '', model_name='VGG-Face', distance_metric='cosine',
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model=None, enforce_detection=True, detector_backend = 'opencv'):
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# this is not a must because it is very huge.
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tic = time.time()
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@ -34,6 +36,9 @@ def verify(img1_path, img2_path = '', model_name ='VGG-Face', distance_metric =
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#------------------------------
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if detector_backend == 'mtcnn':
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functions.load_mtcnn()
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resp_objects = []
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if model_name == 'Ensemble':
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@ -199,33 +204,23 @@ def verify(img1_path, img2_path = '', model_name ='VGG-Face', distance_metric =
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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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elif model_name == 'Dlib':
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print("Using Dlib ResNet model backend", distance_metric,"distance.")
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from deepface.basemodels.DlibResNet import DlibResNet #this is not a must because it is very huge.
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model = DlibResNet()
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models = {
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'VGG-Face': VGGFace.loadModel,
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'OpenFace': OpenFace.loadModel,
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'Facenet': Facenet.loadModel,
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'DeepFace': FbDeepFace.loadModel,
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'DeepID': DeepID.loadModel,
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'Dlib': DlibResNet_
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}
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model = models.get(model_name)
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if model:
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model = model()
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print('Using {} model backend and {} distance'.format(model_name, distance_metric))
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else:
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raise ValueError("Invalid model_name passed - ", model_name)
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raise ValueError('Invalid model_name passed - {}'.format(model_name))
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else: #model != None
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print("Already built model is passed")
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@ -344,7 +339,6 @@ def verify(img1_path, img2_path = '', model_name ='VGG-Face', distance_metric =
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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, detector_backend = 'opencv'):
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if type(img_path) == list:
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@ -513,7 +507,6 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True, detec
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return resp_obj
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#return resp_objects
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def detectFace(img_path, detector_backend = 'opencv'):
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img = functions.preprocess_face(img = img_path, detector_backend = detector_backend)[0] #preprocess_face returns (1, 224, 224, 3)
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return img[:, :, ::-1] #bgr to rgb
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@ -20,6 +20,10 @@ import bz2
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from deepface.commons import distance
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from mtcnn import MTCNN #0.1.0
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def load_mtcnn():
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global mtcnn_detector
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mtcnn_detector = MTCNN()
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def loadBase64Img(uri):
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encoded_data = uri.split(',')[1]
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nparr = np.fromstring(base64.b64decode(encoded_data), np.uint8)
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@ -40,55 +44,18 @@ def initializeFolder():
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def findThreshold(model_name, distance_metric):
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threshold = 0.40
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base_threshold = {'cosine': 0.40, 'euclidean': 0.55, 'euclidean_l2': 0.75}
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if model_name == 'VGG-Face':
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if distance_metric == 'cosine':
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threshold = 0.40
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elif distance_metric == 'euclidean':
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threshold = 0.55
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elif distance_metric == 'euclidean_l2':
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threshold = 0.75
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elif model_name == 'OpenFace':
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if distance_metric == 'cosine':
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threshold = 0.10
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elif distance_metric == 'euclidean':
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threshold = 0.55
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elif distance_metric == 'euclidean_l2':
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threshold = 0.55
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elif model_name == 'Facenet':
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if distance_metric == 'cosine':
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threshold = 0.40
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elif distance_metric == 'euclidean':
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threshold = 10
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elif distance_metric == 'euclidean_l2':
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threshold = 0.80
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elif model_name == 'DeepFace':
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if distance_metric == 'cosine':
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threshold = 0.23
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elif distance_metric == 'euclidean':
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threshold = 64
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elif distance_metric == 'euclidean_l2':
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threshold = 0.64
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elif model_name == 'DeepID':
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if distance_metric == 'cosine':
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threshold = 0.015
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elif distance_metric == 'euclidean':
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threshold = 45
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elif distance_metric == 'euclidean_l2':
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threshold = 0.17
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elif model_name == 'Dlib':
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if distance_metric == 'cosine':
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threshold = 0.07
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elif distance_metric == 'euclidean':
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threshold = 0.60
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elif distance_metric == 'euclidean_l2':
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threshold = 0.60
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thresholds = {
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'VGG-Face': {'cosine': 0.40, 'euclidean': 0.55, 'euclidean_l2': 0.75},
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'OpenFace': {'cosine': 0.10, 'euclidean': 0.55, 'euclidean_l2': 0.55},
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'Facenet': {'cosine': 0.40, 'euclidean': 10, 'euclidean_l2': 0.80},
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'DeepFace': {'cosine': 0.23, 'euclidean': 64, 'euclidean_l2': 0.64},
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'DeepID': {'cosine': 0.015, 'euclidean': 45, 'euclidean_l2': 0.17},
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'Dlib': {'cosine': 0.07, 'euclidean': 0.6, 'euclidean_l2': 0.6}
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}
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threshold = thresholds.get(model_name, base_threshold).get(distance_metric, 0.4)
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return threshold
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@ -277,9 +244,9 @@ def detect_face(img, detector_backend = 'opencv', grayscale = False, enforce_det
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elif detector_backend == 'mtcnn':
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mtcnn_detector = MTCNN()
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detections = mtcnn_detector.detect_faces(img)
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# mtcnn_detector = MTCNN()
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img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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detections = mtcnn_detector.detect_faces(img_rgb)
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if len(detections) > 0:
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detection = detections[0]
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@ -288,7 +255,7 @@ def detect_face(img, detector_backend = 'opencv', grayscale = False, enforce_det
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return detected_face
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else: #if no face detected
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if enforce_detection != True:
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if not enforce_detection:
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return img
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else:
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@ -432,8 +399,9 @@ def align_face(img, detector_backend = 'opencv'):
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elif detector_backend == 'mtcnn':
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mtcnn_detector = MTCNN()
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detections = mtcnn_detector.detect_faces(img)
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# mtcnn_detector = MTCNN()
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img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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detections = mtcnn_detector.detect_faces(img_rgb)
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if len(detections) > 0:
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detection = detections[0]
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