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https://github.com/serengil/deepface.git
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api support init
This commit is contained in:
Şefik Serangil
parent
54c1c28b35
commit
8427be05c2
120
api/DeepFaceApi.py
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120
api/DeepFaceApi.py
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@ -0,0 +1,120 @@
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from flask import Flask, jsonify, request, make_response
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import uuid
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import json
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import time
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from deepface import DeepFace
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from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
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#------------------------------
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app = Flask(__name__)
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#------------------------------
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#Service API Interface
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@app.route('/analyze', methods=['POST'])
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def analyze():
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req = request.get_json()
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trx_id = uuid.uuid4()
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#---------------------------
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tic = time.time()
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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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#---------------------------
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#resp_obj = json.loads("{\"success\": true}")
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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
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@app.route('/verify', methods=['POST'])
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def verify():
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req = request.get_json()
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trx_id = uuid.uuid4()
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tic = time.time()
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#-------------------------
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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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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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resp_obj = DeepFace.verify(instances, model_name = model_name, distance_metric = distance_metric)
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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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#--------------------------
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return resp_obj, 200
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if __name__ == '__main__':
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app.run()
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137
api/deepface.postman_collection.json
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137
api/deepface.postman_collection.json
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File diff suppressed because one or more lines are too long
@ -9,6 +9,7 @@ import pandas as pd
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from tqdm import tqdm
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import json
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import cv2
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from keras import backend as K
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#from basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
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#from extendedmodels import Age, Gender, Race, Emotion
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@ -18,10 +19,14 @@ from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
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from deepface.extendedmodels import Age, Gender, Race, Emotion
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from deepface.commons import functions, realtime, distance as dst
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#TO-DO: pass built model optionally as input. I will get complex models up in rest api once and call these functions directly
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def verify(img1_path, img2_path=''
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, model_name ='VGG-Face', distance_metric = 'cosine', plot = False):
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tic = time.time()
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K.clear_session()
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if type(img1_path) == list:
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bulkProcess = True
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@ -54,7 +59,7 @@ def verify(img1_path, img2_path=''
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else:
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raise ValueError("Invalid model_name passed - ", model_name)
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#------------------------------
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#tuned thresholds for model and metric pair
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@ -67,14 +72,6 @@ def verify(img1_path, img2_path=''
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img1_path = instance[0]
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img2_path = instance[1]
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#----------------------
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if os.path.isfile(img1_path) != True:
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raise ValueError("Confirm that ",img1_path," exists")
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if os.path.isfile(img2_path) != True:
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raise ValueError("Confirm that ",img2_path," exists")
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#----------------------
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#crop and align faces
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@ -83,7 +80,7 @@ def verify(img1_path, img2_path=''
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#----------------------
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#find embeddings
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img1_representation = model.predict(img1)[0,:]
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img2_representation = model.predict(img2)[0,:]
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@ -140,6 +137,7 @@ def verify(img1_path, img2_path=''
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if bulkProcess == True:
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resp_objects.append(resp_obj)
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else:
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K.clear_session()
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return resp_obj
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#----------------------
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@ -153,9 +151,25 @@ def verify(img1_path, img2_path=''
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#print("identification lasts ",toc-tic," seconds")
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if bulkProcess == True:
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return resp_objects
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K.clear_session()
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resp_obj = "{"
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for i in range(0, len(resp_objects)):
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resp_item = json.dumps(resp_objects[i])
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if i > 0:
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resp_obj += ", "
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resp_obj += "\"pair_"+str(i+1)+"\": "+resp_item
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resp_obj += "}"
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resp_obj = json.loads(resp_obj)
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return resp_obj
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#return resp_objects
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def analyze(img_path, actions= []):
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K.clear_session()
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if type(img_path) == list:
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img_paths = img_path.copy()
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@ -190,12 +204,6 @@ def analyze(img_path, actions= []):
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resp_objects = []
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for img_path in img_paths:
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if type(img_path) != str:
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raise ValueError("You should pass string data type for image paths but you passed ", type(img_path))
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if os.path.isfile(img_path) != True:
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raise ValueError("Confirm that ",img_path," exists")
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resp_obj = "{"
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#TO-DO: do this in parallel
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@ -285,10 +293,25 @@ def analyze(img_path, actions= []):
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if bulkProcess == True:
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resp_objects.append(resp_obj)
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else:
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K.clear_session()
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return resp_obj
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if bulkProcess == True:
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return resp_objects
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K.clear_session()
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resp_obj = "{"
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for i in range(0, len(resp_objects)):
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resp_item = json.dumps(resp_objects[i])
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if i > 0:
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resp_obj += ", "
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resp_obj += "\"instance_"+str(i+1)+"\": "+resp_item
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resp_obj += "}"
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resp_obj = json.loads(resp_obj)
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return resp_obj
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#return resp_objects
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def detectFace(img_path):
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img = functions.detectFace(img_path)[0] #detectFace returns (1, 224, 224, 3)
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@ -12,6 +12,13 @@ import hashlib
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import math
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from PIL import Image
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import copy
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import base64
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def loadBase64Img(uri):
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encoded_data = uri.split(',')[1]
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nparr = np.fromstring(base64.b64decode(encoded_data), np.uint8)
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img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
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return img
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def distance(a, b):
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x1 = a[0]; y1 = a[1]
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@ -133,6 +140,10 @@ def detectFace(img, target_size=(224, 224), grayscale = False):
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if type(img).__module__ == np.__name__:
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exact_image = True
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base64_img = False
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if len(img) > 11 and img[0:11] == "data:image/":
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base64_img = True
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#-----------------------
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opencv_path = get_opencv_path()
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@ -147,9 +158,16 @@ def detectFace(img, target_size=(224, 224), grayscale = False):
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face_detector = cv2.CascadeClassifier(face_detector_path)
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eye_detector = cv2.CascadeClassifier(eye_detector_path)
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if exact_image != True: #image path passed as input
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if base64_img == True:
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img = loadBase64Img(img)
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elif exact_image != True: #image path passed as input
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if os.path.isfile(img) != True:
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raise ValueError("Confirm that ",img," exists")
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img = cv2.imread(img)
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img_raw = img.copy()
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#--------------------------------
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@ -17,8 +17,8 @@ dataset = [
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]
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resp_obj = DeepFace.verify(dataset)
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print(resp_obj[0]["verified"] == True)
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print(resp_obj[1]["verified"] == True)
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print(resp_obj["pair_1"]["verified"] == True)
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print(resp_obj["pair_2"]["verified"] == True)
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print("-----------------------------------------")
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@ -32,10 +32,10 @@ dataset = [
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]
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resp_obj = DeepFace.analyze(dataset)
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print(resp_obj[0]["age"]," years old ", resp_obj[0]["dominant_emotion"], " ",resp_obj[0]["gender"])
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print(resp_obj[1]["age"]," years old ", resp_obj[1]["dominant_emotion"], " ",resp_obj[1]["gender"])
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print(resp_obj[2]["age"]," years old ", resp_obj[2]["dominant_emotion"], " ",resp_obj[2]["gender"])
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print(resp_obj[3]["age"]," years old ", resp_obj[3]["dominant_emotion"], " ",resp_obj[3]["gender"])
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print(resp_obj["instance_1"]["age"]," years old ", resp_obj["instance_1"]["dominant_emotion"], " ",resp_obj["instance_1"]["gender"])
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print(resp_obj["instance_2"]["age"]," years old ", resp_obj["instance_2"]["dominant_emotion"], " ",resp_obj["instance_2"]["gender"])
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print(resp_obj["instance_3"]["age"]," years old ", resp_obj["instance_3"]["dominant_emotion"], " ",resp_obj["instance_3"]["gender"])
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print(resp_obj["instance_4"]["age"]," years old ", resp_obj["instance_4"]["dominant_emotion"], " ",resp_obj["instance_4"]["gender"])
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print("-----------------------------------------")
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