opencv

api/app.py

@@ -0,0 +1,121 @@
+from flask import Flask, jsonify, request, make_response
+
+import uuid
+import json
+import time
+
+from deepface import DeepFace
+
+#------------------------------
+
+app = Flask(__name__)
+
+#------------------------------
+#Service API Interface
+
+@app.route('/')
+def index():
+	return '<h1>Hello, world!</h1>'
+
+@app.route('/analyze', methods=['POST'])
+def analyze():
+
+	req = request.get_json()
+	trx_id = uuid.uuid4()
+
+	#---------------------------
+
+	tic = time.time()
+
+	instances = []
+	if "img" in list(req.keys()):
+		raw_content = req["img"] #list
+
+		for item in raw_content: #item is in type of dict
+			instances.append(item)
+	
+	if len(instances) == 0:
+		return jsonify({'success': False, 'error': 'you must pass at least one img object in your request'}), 205
+	
+	print("Analyzing ", len(instances)," instances")
+
+	#---------------------------
+
+	actions= ['emotion', 'age', 'gender', 'race']
+	if "actions" in list(req.keys()):
+		actions = req["actions"]
+	
+	#---------------------------
+
+	resp_obj = DeepFace.analyze(instances, actions=actions)
+	
+	#---------------------------
+
+	toc = time.time()
+
+	resp_obj["trx_id"] = trx_id
+	resp_obj["seconds"] = toc-tic
+
+	return resp_obj
+
+@app.route('/verify', methods=['POST'])
+
+def verify():
+
+	req = request.get_json()
+	trx_id = uuid.uuid4()
+
+	tic = time.time()
+	
+	#-------------------------
+	
+	model_name = "VGG-Face"; distance_metric = "cosine"
+	if "model_name" in list(req.keys()):
+		model_name = req["model_name"]
+	if "distance_metric" in list(req.keys()):
+		distance_metric = req["distance_metric"]
+	
+	instances = []
+	if "img" in list(req.keys()):
+		raw_content = req["img"] #list
+
+		for item in raw_content: #item is in type of dict
+			instance = []
+			img1 = item["img1"]; img2 = item["img2"]
+
+			validate_img1 = False
+			if len(img1) > 11 and img1[0:11] == "data:image/":
+				validate_img1 = True
+			
+			validate_img2 = False
+			if len(img2) > 11 and img2[0:11] == "data:image/":
+				validate_img2 = True
+
+			if validate_img1 != True or validate_img2 != True:
+				return jsonify({'success': False, 'error': 'you must pass both img1 and img2 as base64 encoded string'}), 205
+
+			instance.append(img1); instance.append(img2)
+			instances.append(instance)
+		
+	#--------------------------
+
+	if len(instances) == 0:
+		return jsonify({'success': False, 'error': 'you must pass at least one img object in your request'}), 205
+	
+	print("Input request of ", trx_id, " has ",len(instances)," pairs to verify")
+	
+	#--------------------------
+	resp_obj = DeepFace.verify(instances, model_name = model_name, distance_metric = distance_metric)
+
+	toc =  time.time()
+
+	resp_obj["trx_id"] = trx_id
+	resp_obj["seconds"] = toc-tic
+
+	#--------------------------
+	
+	return resp_obj, 200
+
+if __name__ == '__main__':
+	
+	app.run()

setup.py

@@ -5,7 +5,7 @@ with open("README.md", "r", encoding="utf-8") as fh:
 
 setuptools.setup(
     name="deepface",  
-    version="0.0.15",
+    version="0.0.16",
     author="Sefik Ilkin Serengil",
     author_email="serengil@gmail.com",
     description="Deep Face Analysis Framework for Face Recognition and Demography",
@@ -19,5 +19,5 @@ setuptools.setup(
         "Operating System :: OS Independent",
     ],
     python_requires='>=3.5.5',
-    install_requires=["numpy>=1.14.0", "pandas>=0.23.4", "tqdm>=4.30.0", "gdown>=3.10.1", "opencv-python>=3.4.4", "Pillow>=5.2.0", "tensorflow>=1.9.0", "keras>=2.2.0"]
+    install_requires=["numpy>=1.14.0", "pandas>=0.23.4", "tqdm>=4.30.0", "gdown>=3.10.1", "Pillow>=5.2.0", "tensorflow>=1.9.0", "keras>=2.2.0"]
 )