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api becomes compatible /w tf2
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serengil
parent
5b24de8de2
commit
3e93c47d09
201
api/api.py
201
api/api.py
@ -7,6 +7,7 @@ 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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from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace, DeepID
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@ -87,7 +88,8 @@ print("Facial attribute analysis models are built in ", toc-tic," seconds")
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#------------------------------
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graph = tf.get_default_graph()
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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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@ -107,32 +109,13 @@ def analyze():
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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 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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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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#---------------------------
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#---------------------------
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toc = time.time()
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@ -141,8 +124,35 @@ def analyze():
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return resp_obj, 200
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@app.route('/verify', methods=['POST'])
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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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@ -153,70 +163,11 @@ def verify():
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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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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 == "Dlib":
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resp_obj = DeepFace.verify(instances, model_name = model_name, distance_metric = distance_metric, model = dlib_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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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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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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@ -227,6 +178,74 @@ def verify():
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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 == "Dlib":
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resp_obj = DeepFace.verify(instances, model_name = model_name, distance_metric = distance_metric, model = dlib_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. Available models are VGG-Face, Facenet, OpenFace, DeepFace but 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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