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model building once for api
This commit is contained in:
Şefik Serangil
parent
fbbc931541
commit
6bb308a7e1
121
api/app.py
121
api/app.py
@ -1,121 +0,0 @@
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from flask import Flask, jsonify, request, make_response
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import uuid
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import json
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import time
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from deepface import DeepFace
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#------------------------------
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app = Flask(__name__)
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#------------------------------
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#Service API Interface
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@app.route('/')
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def index():
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return '<h1>Hello, world!</h1>'
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@app.route('/analyze', methods=['POST'])
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def analyze():
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req = request.get_json()
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trx_id = uuid.uuid4()
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#---------------------------
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tic = time.time()
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instances = []
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if "img" in list(req.keys()):
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raw_content = req["img"] #list
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for item in raw_content: #item is in type of dict
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instances.append(item)
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if len(instances) == 0:
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return jsonify({'success': False, 'error': 'you must pass at least one img object in your request'}), 205
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print("Analyzing ", len(instances)," instances")
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#---------------------------
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actions= ['emotion', 'age', 'gender', 'race']
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if "actions" in list(req.keys()):
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actions = req["actions"]
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#---------------------------
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resp_obj = DeepFace.analyze(instances, actions=actions)
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#---------------------------
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toc = time.time()
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resp_obj["trx_id"] = trx_id
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resp_obj["seconds"] = toc-tic
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return resp_obj
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@app.route('/verify', methods=['POST'])
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def verify():
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req = request.get_json()
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trx_id = uuid.uuid4()
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tic = time.time()
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#-------------------------
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model_name = "VGG-Face"; distance_metric = "cosine"
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if "model_name" in list(req.keys()):
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model_name = req["model_name"]
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if "distance_metric" in list(req.keys()):
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distance_metric = req["distance_metric"]
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instances = []
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if "img" in list(req.keys()):
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raw_content = req["img"] #list
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for item in raw_content: #item is in type of dict
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instance = []
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img1 = item["img1"]; img2 = item["img2"]
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validate_img1 = False
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if len(img1) > 11 and img1[0:11] == "data:image/":
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validate_img1 = True
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validate_img2 = False
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if len(img2) > 11 and img2[0:11] == "data:image/":
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validate_img2 = True
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if validate_img1 != True or validate_img2 != True:
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return jsonify({'success': False, 'error': 'you must pass both img1 and img2 as base64 encoded string'}), 205
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instance.append(img1); instance.append(img2)
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instances.append(instance)
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#--------------------------
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if len(instances) == 0:
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return jsonify({'success': False, 'error': 'you must pass at least one img object in your request'}), 205
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print("Input request of ", trx_id, " has ",len(instances)," pairs to verify")
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#--------------------------
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resp_obj = DeepFace.verify(instances, model_name = model_name, distance_metric = distance_metric)
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toc = time.time()
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resp_obj["trx_id"] = trx_id
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resp_obj["seconds"] = toc-tic
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#--------------------------
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return resp_obj, 200
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if __name__ == '__main__':
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app.run()
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@ -9,6 +9,8 @@ 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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#from basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
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#from extendedmodels import Age, Gender, Race, Emotion
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@ -18,15 +20,11 @@ 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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#TO-DO: pass built model optionally as input. I will get complex models up in rest api once and call these functions directly
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def verify(img1_path, img2_path=''
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, model_name ='VGG-Face', distance_metric = 'cosine', plot = False):
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, model_name ='VGG-Face', distance_metric = 'cosine', model = None):
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tic = time.time()
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K.clear_session()
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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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@ -36,28 +34,31 @@ def verify(img1_path, img2_path=''
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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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input_shape = (224, 224)
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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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input_shape = (96, 96)
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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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input_shape = (160, 160)
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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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input_shape = (152, 152)
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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[1:3]
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#------------------------------
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@ -133,8 +134,6 @@ def verify(img1_path, img2_path=''
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#print("identification lasts ",toc-tic," seconds")
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if bulkProcess == True:
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K.clear_session()
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resp_obj = "{"
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for i in range(0, len(resp_objects)):
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@ -151,8 +150,6 @@ def verify(img1_path, img2_path=''
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def analyze(img_path, actions= []):
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K.clear_session()
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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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@ -275,12 +272,9 @@ def analyze(img_path, actions= []):
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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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if bulkProcess == True:
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K.clear_session()
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resp_obj = "{"
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for i in range(0, len(resp_objects)):
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165
deepface/app.py
Normal file
165
deepface/app.py
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@ -0,0 +1,165 @@
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from flask import Flask, jsonify, request, make_response
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import uuid
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import json
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import time
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import tensorflow as tf
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from deepface import DeepFace
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from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
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#import DeepFace
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#from basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
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#------------------------------
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app = Flask(__name__)
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tic = time.time()
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vggface_model = VGGFace.loadModel()
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print("VGG-Face model is built.")
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openface_model = OpenFace.loadModel()
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print("OpenFace model is built")
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facenet_model = Facenet.loadModel()
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print("FaceNet model is built")
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deepface_model = FbDeepFace.loadModel()
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print("DeepFace model is built")
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toc = time.time()
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print("Face recognition models are built in ", toc-tic," seconds")
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graph = tf.get_default_graph()
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#------------------------------
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#Service API Interface
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@app.route('/')
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def index():
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return '<h1>Hello, world!</h1>'
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@app.route('/analyze', methods=['POST'])
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def analyze():
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global graph
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tic = time.time()
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req = request.get_json()
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trx_id = uuid.uuid4()
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#---------------------------
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resp_obj = jsonify({'success': False})
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with graph.as_default():
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instances = []
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if "img" in list(req.keys()):
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raw_content = req["img"] #list
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for item in raw_content: #item is in type of dict
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instances.append(item)
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if len(instances) == 0:
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return jsonify({'success': False, 'error': 'you must pass at least one img object in your request'}), 205
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print("Analyzing ", len(instances)," instances")
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#---------------------------
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actions= ['emotion', 'age', 'gender', 'race']
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if "actions" in list(req.keys()):
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actions = req["actions"]
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#---------------------------
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resp_obj = DeepFace.analyze(instances, actions=actions)
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#---------------------------
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toc = time.time()
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resp_obj["trx_id"] = trx_id
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resp_obj["seconds"] = toc-tic
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return resp_obj
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@app.route('/verify', methods=['POST'])
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def verify():
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global graph
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tic = time.time()
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req = request.get_json()
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trx_id = uuid.uuid4()
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resp_obj = jsonify({'success': False})
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with graph.as_default():
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model_name = "VGG-Face"; distance_metric = "cosine"
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if "model_name" in list(req.keys()):
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model_name = req["model_name"]
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if "distance_metric" in list(req.keys()):
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distance_metric = req["distance_metric"]
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#----------------------
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instances = []
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if "img" in list(req.keys()):
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raw_content = req["img"] #list
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for item in raw_content: #item is in type of dict
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instance = []
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img1 = item["img1"]; img2 = item["img2"]
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validate_img1 = False
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if len(img1) > 11 and img1[0:11] == "data:image/":
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validate_img1 = True
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validate_img2 = False
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if len(img2) > 11 and img2[0:11] == "data:image/":
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validate_img2 = True
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if validate_img1 != True or validate_img2 != True:
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return jsonify({'success': False, 'error': 'you must pass both img1 and img2 as base64 encoded string'}), 205
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instance.append(img1); instance.append(img2)
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instances.append(instance)
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#--------------------------
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if len(instances) == 0:
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return jsonify({'success': False, 'error': 'you must pass at least one img object in your request'}), 205
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print("Input request of ", trx_id, " has ",len(instances)," pairs to verify")
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#--------------------------
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if model_name == "VGG-Face":
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resp_obj = DeepFace.verify(instances, model_name = model_name, distance_metric = distance_metric, model = vggface_model)
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elif model_name == "Facenet":
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resp_obj = DeepFace.verify(instances, model_name = model_name, distance_metric = distance_metric, model = facenet_model)
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elif model_name == "OpenFace":
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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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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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#--------------------------
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toc = time.time()
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resp_obj["trx_id"] = trx_id
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resp_obj["seconds"] = toc-tic
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return resp_obj, 200
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if __name__ == '__main__':
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app.run()
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@ -32,7 +32,7 @@ def loadModel():
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x = Conv2D(192, (3, 3), name='conv3')(x)
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x = BatchNormalization(axis=3, epsilon=0.00001, name='bn3')(x)
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x = Activation('relu')(x)
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Lambda(lambda x: tf.nn.lrn(x, alpha=1e-4, beta=0.75), name='lrn_2')(x)
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x = Lambda(lambda x: tf.nn.lrn(x, alpha=1e-4, beta=0.75), name='lrn_2')(x) #x is equal added
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x = ZeroPadding2D(padding=(1, 1))(x)
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x = MaxPooling2D(pool_size=3, strides=2)(x)
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