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https://github.com/serengil/deepface.git
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lightweight api
This commit is contained in:
Sefik Ilkin Serengil
parent
dcd883f223
commit
0fc1672c89
155
api/api.py
155
api/api.py
@ -35,71 +35,6 @@ 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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@ -153,15 +88,26 @@ def analyzeWrapper(req, trx_id = 0):
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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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#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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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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@ -194,11 +140,13 @@ 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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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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@ -233,31 +181,19 @@ def verifyWrapper(req, trx_id = 0):
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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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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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for key in resp_obj: #issue 198.
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resp_obj[key]['verified'] = bool(resp_obj[key]['verified'])
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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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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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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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@ -281,7 +217,7 @@ def represent():
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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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@ -294,11 +230,14 @@ def representWrapper(req, trx_id = 0):
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#-------------------------------------
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#find out model
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model_name = "VGG-Face"; distance_metric = "cosine"
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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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@ -316,27 +255,25 @@ def representWrapper(req, trx_id = 0):
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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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#cal represent function from the interface
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#call represent function from the interface
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embedding = []
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if model_name == "VGG-Face":
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embedding = DeepFace.represent(img, model_name = model_name, model = vggface_model)
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elif model_name == "Facenet":
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embedding = DeepFace.represent(img, model_name = model_name, model = facenet_model)
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elif model_name == "OpenFace":
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embedding = DeepFace.represent(img, model_name = model_name, model = openface_model)
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elif model_name == "DeepFace":
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embedding = DeepFace.represent(img, model_name = model_name, model = deepface_model)
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elif model_name == "DeepID":
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embedding = DeepFace.represent(img, model_name = model_name, model = deepid_model)
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elif model_name == "ArcFace":
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embedding = DeepFace.represent(img, model_name = model_name, model = arcface_model)
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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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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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@ -360,13 +360,15 @@ def analyze(img_path, actions = ['emotion', 'age', 'gender', 'race'] , models =
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disable_option = False if len(actions) > 1 else True
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pbar = tqdm(range(0,len(actions)), desc='Finding actions', disable = disable_option)
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pbar = tqdm(range(0, len(actions)), desc='Finding actions', disable = disable_option)
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img_224 = None # Set to prevent re-detection
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region = [] # x, y, w, h of the detected face region
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region_labels = ['x', 'y', 'w', 'h']
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is_region_set = False
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#facial attribute analysis
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for index in pbar:
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action = actions[index]
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@ -376,10 +378,11 @@ def analyze(img_path, actions = ['emotion', 'age', 'gender', 'race'] , models =
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emotion_labels = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']
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img, region = functions.preprocess_face(img = img_path, target_size = (48, 48), grayscale = True, enforce_detection = enforce_detection, detector_backend = detector_backend, return_region = True)
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resp_obj["region"] = {}
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for i, parameter in enumerate(region_labels):
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resp_obj["region"][parameter] = region[i]
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if is_region_set != True:
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resp_obj["region"] = {}
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is_region_set = True
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for i, parameter in enumerate(region_labels):
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resp_obj["region"][parameter] = int(region[i]) #int cast is for the exception - object of type 'float32' is not JSON serializable
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emotion_predictions = models['emotion'].predict(img)[0,:]
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@ -398,24 +401,27 @@ def analyze(img_path, actions = ['emotion', 'age', 'gender', 'race'] , models =
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if img_224 is None:
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img_224, region = functions.preprocess_face(img = img_path, target_size = (224, 224), grayscale = False, enforce_detection = enforce_detection, detector_backend = detector_backend, return_region = True)
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resp_obj["region"] = {}
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for i, parameter in enumerate(region_labels):
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resp_obj["region"][parameter] = region[i]
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if is_region_set != True:
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resp_obj["region"] = {}
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is_region_set = True
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for i, parameter in enumerate(region_labels):
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resp_obj["region"][parameter] = int(region[i]) # #int cast is for the exception - object of type 'float32' is not JSON serializable
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age_predictions = models['age'].predict(img_224)[0,:]
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apparent_age = Age.findApparentAge(age_predictions)
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resp_obj["age"] = int(apparent_age)
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#int cast is for the exception - object of type 'float32' is not JSON serializable
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elif action == 'gender':
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if img_224 is None:
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img_224, region = functions.preprocess_face(img = img_path, target_size = (224, 224), grayscale = False, enforce_detection = enforce_detection, detector_backend = detector_backend, return_region = True)
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resp_obj["region"] = {}
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for i, parameter in enumerate(region_labels):
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resp_obj["region"][parameter] = region[i]
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if is_region_set != True:
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resp_obj["region"] = {}
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is_region_set = True
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for i, parameter in enumerate(region_labels):
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resp_obj["region"][parameter] = int(region[i]) ##int cast is for the exception - object of type 'float32' is not JSON serializable
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gender_prediction = models['gender'].predict(img_224)[0,:]
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@ -432,10 +438,11 @@ def analyze(img_path, actions = ['emotion', 'age', 'gender', 'race'] , models =
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race_predictions = models['race'].predict(img_224)[0,:]
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race_labels = ['asian', 'indian', 'black', 'white', 'middle eastern', 'latino hispanic']
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resp_obj["region"] = {}
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for i, parameter in enumerate(region_labels):
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resp_obj["region"][parameter] = region[i]
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if is_region_set != True:
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resp_obj["region"] = {}
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is_region_set = True
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for i, parameter in enumerate(region_labels):
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resp_obj["region"][parameter] = int(region[i]) ##int cast is for the exception - object of type 'float32' is not JSON serializable
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sum_of_predictions = race_predictions.sum()
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@ -794,7 +801,7 @@ def detectFace(img_path, detector_backend = 'opencv', enforce_detection = True):
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Returns:
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deteced and aligned face in numpy format
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"""
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img = functions.preprocess_face(img = img_path, detector_backend = detector_backend
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, enforce_detection = enforce_detection)[0] #preprocess_face returns (1, 224, 224, 3)
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return img[:, :, ::-1] #bgr to rgb
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