api support init

api/DeepFaceApi.py

@@ -0,0 +1,120 @@
+from flask import Flask, jsonify, request, make_response
+
+import uuid
+import json
+import time
+
+from deepface import DeepFace
+from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
+
+#------------------------------
+
+app = Flask(__name__)
+
+#------------------------------
+#Service API Interface
+
+@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)
+	
+	#---------------------------
+
+	#resp_obj = json.loads("{\"success\": true}")
+
+	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()

deepface/DeepFace.py

@@ -9,6 +9,7 @@ import pandas as pd
 from tqdm import tqdm
 import json
 import cv2
+from keras import backend as K
 
 #from basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
 #from extendedmodels import Age, Gender, Race, Emotion
@@ -18,10 +19,14 @@ from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
 from deepface.extendedmodels import Age, Gender, Race, Emotion
 from deepface.commons import functions, realtime, distance as dst
 
+#TO-DO: pass built model optionally as input. I will get complex models up in rest api once and call these functions directly
+
 def verify(img1_path, img2_path=''
 	, model_name ='VGG-Face', distance_metric = 'cosine', plot = False):
 	
 	tic = time.time()
+
+	K.clear_session()
 	
 	if type(img1_path) == list:
 		bulkProcess = True
@@ -54,7 +59,7 @@ def verify(img1_path, img2_path=''
 	
 	else:
 		raise ValueError("Invalid model_name passed - ", model_name)
-	
+
 	#------------------------------
 	
 	#tuned thresholds for model and metric pair
@@ -67,14 +72,6 @@ def verify(img1_path, img2_path=''
 			img1_path = instance[0]
 			img2_path = instance[1]
 			
-			#----------------------
-			
-			if os.path.isfile(img1_path) != True:
-				raise ValueError("Confirm that ",img1_path," exists")
-			
-			if os.path.isfile(img2_path) != True:
-				raise ValueError("Confirm that ",img2_path," exists")
-			
 			#----------------------
 			#crop and align faces
 			
@@ -83,7 +80,7 @@ def verify(img1_path, img2_path=''
 			
 			#----------------------
 			#find embeddings
-	
+			
 			img1_representation = model.predict(img1)[0,:]
 			img2_representation = model.predict(img2)[0,:]
 			
@@ -140,6 +137,7 @@ def verify(img1_path, img2_path=''
 			if bulkProcess == True:
 				resp_objects.append(resp_obj)
 			else:
+				K.clear_session()
 				return resp_obj
 			#----------------------
 			
@@ -153,9 +151,25 @@ def verify(img1_path, img2_path=''
 	#print("identification lasts ",toc-tic," seconds")
 	
 	if bulkProcess == True:
-		return resp_objects
+		K.clear_session()
+
+		resp_obj = "{"
+		
+		for i in range(0, len(resp_objects)):
+			resp_item = json.dumps(resp_objects[i]) 
+
+			if i > 0:
+				resp_obj += ", "
+
+			resp_obj += "\"pair_"+str(i+1)+"\": "+resp_item
+		resp_obj += "}"
+		resp_obj = json.loads(resp_obj)
+		return resp_obj
+		#return resp_objects
 
 def analyze(img_path, actions= []):
+
+	K.clear_session()
 	
 	if type(img_path) == list:
 		img_paths = img_path.copy()
@@ -190,12 +204,6 @@ def analyze(img_path, actions= []):
 	resp_objects = []
 	for img_path in img_paths:
 		
-		if type(img_path) != str:
-			raise ValueError("You should pass string data type for image paths but you passed ", type(img_path))
-		
-		if os.path.isfile(img_path) != True:
-			raise ValueError("Confirm that ",img_path," exists")
-		
 		resp_obj = "{"
 		
 		#TO-DO: do this in parallel
@@ -285,10 +293,25 @@ def analyze(img_path, actions= []):
 		if bulkProcess == True:
 			resp_objects.append(resp_obj)
 		else:
+			K.clear_session()
 			return resp_obj
 	
 	if bulkProcess == True:
-		return resp_objects
+		K.clear_session()
+
+		resp_obj = "{"
+		
+		for i in range(0, len(resp_objects)):
+			resp_item = json.dumps(resp_objects[i]) 
+
+			if i > 0:
+				resp_obj += ", "
+
+			resp_obj += "\"instance_"+str(i+1)+"\": "+resp_item
+		resp_obj += "}"
+		resp_obj = json.loads(resp_obj)
+		return resp_obj
+		#return resp_objects
 
 def detectFace(img_path):
 	img = functions.detectFace(img_path)[0] #detectFace returns (1, 224, 224, 3)

deepface/commons/functions.py

@@ -12,6 +12,13 @@ import hashlib
 import math
 from PIL import Image
 import copy
+import base64
+
+def loadBase64Img(uri):
+   encoded_data = uri.split(',')[1]
+   nparr = np.fromstring(base64.b64decode(encoded_data), np.uint8)
+   img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+   return img
 
 def distance(a, b):
 	x1 = a[0]; y1 = a[1]
@@ -133,6 +140,10 @@ def detectFace(img, target_size=(224, 224), grayscale = False):
 	if type(img).__module__ == np.__name__:
 		exact_image = True
 	
+	base64_img = False
+	if len(img) > 11 and img[0:11] == "data:image/":
+		base64_img = True
+
 	#-----------------------
 	
 	opencv_path = get_opencv_path()
@@ -147,9 +158,16 @@ def detectFace(img, target_size=(224, 224), grayscale = False):
 	face_detector = cv2.CascadeClassifier(face_detector_path)
 	eye_detector = cv2.CascadeClassifier(eye_detector_path)
 	
-	if exact_image != True: #image path passed as input
+	if base64_img == True:
+		img = loadBase64Img(img)
+		
+	elif exact_image != True: #image path passed as input
+		
+		if os.path.isfile(img) != True:
+			raise ValueError("Confirm that ",img," exists")
+		
 		img = cv2.imread(img)
-
+	
 	img_raw = img.copy()
 	
 	#--------------------------------

tests/unit_tests.py

@@ -17,8 +17,8 @@ dataset = [
 ]
 
 resp_obj = DeepFace.verify(dataset)
-print(resp_obj[0]["verified"] == True)
+print(resp_obj["pair_1"]["verified"] == True)
-print(resp_obj[1]["verified"] == True)
+print(resp_obj["pair_2"]["verified"] == True)
 
 print("-----------------------------------------")
 
@@ -32,10 +32,10 @@ dataset = [
 ]
 
 resp_obj = DeepFace.analyze(dataset)
-print(resp_obj[0]["age"]," years old ", resp_obj[0]["dominant_emotion"], " ",resp_obj[0]["gender"])
+print(resp_obj["instance_1"]["age"]," years old ", resp_obj["instance_1"]["dominant_emotion"], " ",resp_obj["instance_1"]["gender"])
-print(resp_obj[1]["age"]," years old ", resp_obj[1]["dominant_emotion"], " ",resp_obj[1]["gender"])
+print(resp_obj["instance_2"]["age"]," years old ", resp_obj["instance_2"]["dominant_emotion"], " ",resp_obj["instance_2"]["gender"])
-print(resp_obj[2]["age"]," years old ", resp_obj[2]["dominant_emotion"], " ",resp_obj[2]["gender"])
+print(resp_obj["instance_3"]["age"]," years old ", resp_obj["instance_3"]["dominant_emotion"], " ",resp_obj["instance_3"]["gender"])
-print(resp_obj[3]["age"]," years old ", resp_obj[3]["dominant_emotion"], " ",resp_obj[3]["gender"])
+print(resp_obj["instance_4"]["age"]," years old ", resp_obj["instance_4"]["dominant_emotion"], " ",resp_obj["instance_4"]["gender"])
 
 
 print("-----------------------------------------")