mirror of
https://github.com/serengil/deepface.git
synced 2025-06-07 20:15:21 +00:00
290 lines
6.4 KiB
Python
290 lines
6.4 KiB
Python
import warnings
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warnings.filterwarnings("ignore")
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import os
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
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#------------------------------
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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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#------------------------------
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import tensorflow as tf
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tf_version = int(tf.__version__.split(".")[0])
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#------------------------------
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if tf_version == 2:
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import logging
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tf.get_logger().setLevel(logging.ERROR)
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#------------------------------
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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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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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detector_backend = 'opencv'
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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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if "detector_backend" in list(req.keys()):
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detector_backend = req["detector_backend"]
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#---------------------------
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try:
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resp_obj = DeepFace.analyze(instances, actions = actions)
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except Exception as err:
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print("Exception: ", str(err))
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return jsonify({'success': False, 'error': str(err)}), 205
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#---------------
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#print(resp_obj)
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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"; detector_backend = "opencv"
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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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if "detector_backend" in list(req.keys()):
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detector_backend = req["detector_backend"]
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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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try:
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resp_obj = DeepFace.verify(instances
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, model_name = model_name
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, distance_metric = distance_metric
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, detector_backend = detector_backend
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)
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if model_name == "Ensemble": #issue 198.
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for key in resp_obj: #issue 198.
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resp_obj[key]['verified'] = bool(resp_obj[key]['verified'])
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except Exception as err:
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resp_obj = jsonify({'success': False, 'error': str(err)}), 205
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return resp_obj
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@app.route('/represent', methods=['POST'])
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def represent():
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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 = representWrapper(req, trx_id)
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elif tf_version == 2:
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resp_obj = representWrapper(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 representWrapper(req, trx_id = 0):
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resp_obj = jsonify({'success': False})
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#-------------------------------------
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#find out model
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model_name = "VGG-Face"; distance_metric = "cosine"; detector_backend = 'opencv'
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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 "detector_backend" in list(req.keys()):
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detector_backend = req["detector_backend"]
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#-------------------------------------
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#retrieve images from request
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img = ""
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if "img" in list(req.keys()):
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img = req["img"] #list
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#print("img: ", img)
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validate_img = False
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if len(img) > 11 and img[0:11] == "data:image/":
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validate_img = True
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if validate_img != True:
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print("invalid image passed!")
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return jsonify({'success': False, 'error': 'you must pass img as base64 encoded string'}), 205
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#-------------------------------------
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#call represent function from the interface
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try:
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embedding = DeepFace.represent(img
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, model_name = model_name
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, detector_backend = detector_backend
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)
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except Exception as err:
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print("Exception: ",str(err))
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resp_obj = jsonify({'success': False, 'error': str(err)}), 205
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#-------------------------------------
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#print("embedding is ", len(embedding)," dimensional vector")
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resp_obj = {}
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resp_obj["embedding"] = embedding
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#-------------------------------------
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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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