from flask import Flask, jsonify, request, make_response import argparse import uuid import json import time from tqdm import tqdm import tensorflow as tf from deepface import DeepFace from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace from deepface.extendedmodels import Age, Gender, Race, Emotion #import DeepFace #from basemodels import VGGFace, OpenFace, Facenet, FbDeepFace #from extendedmodels import Age, Gender, Race, Emotion #------------------------------ app = Flask(__name__) #------------------------------ tic = time.time() print("Loading Face Recognition Models...") pbar = tqdm(range(0,4), desc='Loading Face Recognition Models...') for index in pbar: if index == 0: pbar.set_description("Loading VGG-Face") vggface_model = VGGFace.loadModel() elif index == 1: pbar.set_description("Loading OpenFace") openface_model = OpenFace.loadModel() elif index == 2: pbar.set_description("Loading Google FaceNet") facenet_model = Facenet.loadModel() elif index == 3: pbar.set_description("Loading Facebook DeepFace") deepface_model = FbDeepFace.loadModel() toc = time.time() print("Face recognition models are built in ", toc-tic," seconds") #------------------------------ tic = time.time() print("Loading Facial Attribute Analysis Models...") pbar = tqdm(range(0,4), desc='Loading Facial Attribute Analysis Models...') for index in pbar: if index == 0: pbar.set_description("Loading emotion analysis model") emotion_model = Emotion.loadModel() elif index == 1: pbar.set_description("Loading age prediction model") age_model = Age.loadModel() elif index == 2: pbar.set_description("Loading gender prediction model") gender_model = Gender.loadModel() elif index == 3: pbar.set_description("Loading race prediction model") race_model = Race.loadModel() toc = time.time() facial_attribute_models = {} facial_attribute_models["emotion"] = emotion_model facial_attribute_models["age"] = age_model facial_attribute_models["gender"] = gender_model facial_attribute_models["race"] = race_model print("Facial attribute analysis models are built in ", toc-tic," seconds") #------------------------------ graph = tf.get_default_graph() #------------------------------ #Service API Interface @app.route('/') def index(): return '

Hello, world!

' @app.route('/analyze', methods=['POST']) def analyze(): global graph tic = time.time() req = request.get_json() trx_id = uuid.uuid4() #--------------------------- resp_obj = jsonify({'success': False}) with graph.as_default(): instances = [] if "img" in list(req.keys()): raw_content = req["img"] #list for item in raw_content: #item is in type of dict instances.append(item) if len(instances) == 0: return jsonify({'success': False, 'error': 'you must pass at least one img object in your request'}), 205 print("Analyzing ", len(instances)," instances") #--------------------------- actions= ['emotion', 'age', 'gender', 'race'] if "actions" in list(req.keys()): actions = req["actions"] #--------------------------- #resp_obj = DeepFace.analyze(instances, actions=actions) resp_obj = DeepFace.analyze(instances, actions=actions, models=facial_attribute_models) #--------------------------- toc = time.time() resp_obj["trx_id"] = trx_id resp_obj["seconds"] = toc-tic return resp_obj, 200 @app.route('/verify', methods=['POST']) def verify(): global graph tic = time.time() req = request.get_json() trx_id = uuid.uuid4() resp_obj = jsonify({'success': False}) with graph.as_default(): model_name = "VGG-Face"; distance_metric = "cosine" if "model_name" in list(req.keys()): model_name = req["model_name"] if "distance_metric" in list(req.keys()): distance_metric = req["distance_metric"] #---------------------- instances = [] if "img" in list(req.keys()): raw_content = req["img"] #list for item in raw_content: #item is in type of dict instance = [] img1 = item["img1"]; img2 = item["img2"] validate_img1 = False if len(img1) > 11 and img1[0:11] == "data:image/": validate_img1 = True validate_img2 = False if len(img2) > 11 and img2[0:11] == "data:image/": validate_img2 = True if validate_img1 != True or validate_img2 != True: return jsonify({'success': False, 'error': 'you must pass both img1 and img2 as base64 encoded string'}), 205 instance.append(img1); instance.append(img2) instances.append(instance) #-------------------------- if len(instances) == 0: return jsonify({'success': False, 'error': 'you must pass at least one img object in your request'}), 205 print("Input request of ", trx_id, " has ",len(instances)," pairs to verify") #-------------------------- if model_name == "VGG-Face": resp_obj = DeepFace.verify(instances, model_name = model_name, distance_metric = distance_metric, model = vggface_model) elif model_name == "Facenet": resp_obj = DeepFace.verify(instances, model_name = model_name, distance_metric = distance_metric, model = facenet_model) elif model_name == "OpenFace": resp_obj = DeepFace.verify(instances, model_name = model_name, distance_metric = distance_metric, model = openface_model) elif model_name == "DeepFace": resp_obj = DeepFace.verify(instances, model_name = model_name, distance_metric = distance_metric, model = deepface_model) elif model_name == "Ensemble": models = {} models["VGG-Face"] = vggface_model models["Facenet"] = facenet_model models["OpenFace"] = openface_model models["DeepFace"] = deepface_model resp_obj = DeepFace.verify(instances, model_name = model_name, model = models) else: 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 #-------------------------- toc = time.time() resp_obj["trx_id"] = trx_id resp_obj["seconds"] = toc-tic return resp_obj, 200 if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument( '-p', '--port', type=int, default=5000, help='Port of serving api') args = parser.parse_args() app.run(host='0.0.0.0', port=args.port)