mirror of
https://github.com/serengil/deepface.git
synced 2025-06-06 11:35:21 +00:00
251 lines
6.8 KiB
Python
251 lines
6.8 KiB
Python
from flask import Flask, jsonify, request, make_response
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import argparse
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import uuid
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import json
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import time
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from tqdm import tqdm
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import tensorflow as tf
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tf_version = int(tf.__version__.split(".")[0])
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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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tic = time.time()
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print("Loading Face Recognition Models...")
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pbar = tqdm(range(0, 6), desc='Loading 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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vggface_model = DeepFace.build_model("VGG-Face")
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elif index == 1:
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pbar.set_description("Loading OpenFace")
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openface_model = DeepFace.build_model("OpenFace")
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elif index == 2:
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pbar.set_description("Loading Google FaceNet")
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facenet_model = DeepFace.build_model("Facenet")
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elif index == 3:
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pbar.set_description("Loading Facebook DeepFace")
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deepface_model = DeepFace.build_model("DeepFace")
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elif index == 4:
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pbar.set_description("Loading DeepID DeepFace")
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deepid_model = DeepFace.build_model("DeepID")
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elif index == 5:
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pbar.set_description("Loading ArcFace DeepFace")
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arcface_model = DeepFace.build_model("ArcFace")
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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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#------------------------------
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tic = time.time()
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print("Loading Facial Attribute Analysis Models...")
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pbar = tqdm(range(0,4), desc='Loading Facial Attribute Analysis Models...')
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for index in pbar:
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if index == 0:
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pbar.set_description("Loading emotion analysis model")
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emotion_model = DeepFace.build_model('Emotion')
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elif index == 1:
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pbar.set_description("Loading age prediction model")
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age_model = DeepFace.build_model('Age')
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elif index == 2:
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pbar.set_description("Loading gender prediction model")
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gender_model = DeepFace.build_model('Gender')
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elif index == 3:
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pbar.set_description("Loading race prediction model")
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race_model = DeepFace.build_model('Race')
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toc = time.time()
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facial_attribute_models = {}
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facial_attribute_models["emotion"] = emotion_model
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facial_attribute_models["age"] = age_model
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facial_attribute_models["gender"] = gender_model
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facial_attribute_models["race"] = race_model
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print("Facial attribute analysis models are built in ", toc-tic," seconds")
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#------------------------------
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if tf_version == 1:
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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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if tf_version == 1:
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with graph.as_default():
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resp_obj = analyzeWrapper(req, trx_id)
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elif tf_version == 2:
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resp_obj = analyzeWrapper(req, trx_id)
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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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def analyzeWrapper(req, trx_id = 0):
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resp_obj = jsonify({'success': False})
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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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resp_obj = DeepFace.analyze(instances, actions=actions, models=facial_attribute_models)
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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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if tf_version == 1:
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with graph.as_default():
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resp_obj = verifyWrapper(req, trx_id)
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elif tf_version == 2:
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resp_obj = verifyWrapper(req, trx_id)
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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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def verifyWrapper(req, trx_id = 0):
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resp_obj = jsonify({'success': False})
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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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elif model_name == "DeepID":
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resp_obj = DeepFace.verify(instances, model_name = model_name, distance_metric = distance_metric, model = deepid_model)
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elif model_name == "ArcFace":
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resp_obj = DeepFace.verify(instances, model_name = model_name, distance_metric = distance_metric, model = arcface_model)
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elif model_name == "Ensemble":
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models = {}
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models["VGG-Face"] = vggface_model
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models["Facenet"] = facenet_model
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models["OpenFace"] = openface_model
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models["DeepFace"] = deepface_model
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resp_obj = DeepFace.verify(instances, model_name = model_name, model = models)
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else:
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resp_obj = jsonify({'success': False, 'error': 'You must pass a valid model name. You passed %s' % (model_name)}), 205
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return resp_obj
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument(
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'-p', '--port',
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type=int,
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default=5000,
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help='Port of serving api')
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args = parser.parse_args()
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app.run(host='0.0.0.0', port=args.port)
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