version 0.0.52

api/api.py

@@ -261,6 +261,86 @@ def verifyWrapper(req, trx_id = 0):
 
 	return resp_obj
 
+@app.route('/represent', methods=['POST'])
+def represent():
+
+	global graph
+
+	tic = time.time()
+	req = request.get_json()
+	trx_id = uuid.uuid4()
+
+	resp_obj = jsonify({'success': False})
+
+	if tf_version == 1:
+		with graph.as_default():
+			resp_obj = representWrapper(req, trx_id)
+	elif tf_version == 2:
+		resp_obj = representWrapper(req, trx_id)
+
+	#--------------------------
+
+	toc =  time.time()
+	
+	resp_obj["trx_id"] = trx_id
+	resp_obj["seconds"] = toc-tic
+
+	return resp_obj, 200
+
+def representWrapper(req, trx_id = 0):
+
+	resp_obj = jsonify({'success': False})
+
+	#-------------------------------------
+	#find out model
+
+	model_name = "VGG-Face"; distance_metric = "cosine"
+
+	if "model_name" in list(req.keys()):
+		model_name = req["model_name"]
+
+	#-------------------------------------
+	#retrieve images from request
+
+	img = ""
+	if "img" in list(req.keys()):
+		img = req["img"] #list
+		#print("img: ", img)
+
+	validate_img = False
+	if len(img) > 11 and img[0:11] == "data:image/":
+		validate_img = True
+
+	if validate_img != True:
+		print("invalid image passed!")
+		return jsonify({'success': False, 'error': 'you must pass img as base64 encoded string'}), 205
+
+	#-------------------------------------
+	#cal represent function from the interface
+
+	embedding = []
+	if model_name == "VGG-Face":
+		embedding = DeepFace.represent(img, model_name = model_name, model = vggface_model)
+	elif model_name == "Facenet":
+		embedding = DeepFace.represent(img, model_name = model_name, model = facenet_model)
+	elif model_name == "OpenFace":
+		embedding = DeepFace.represent(img, model_name = model_name, model = openface_model)
+	elif model_name == "DeepFace":
+		embedding = DeepFace.represent(img, model_name = model_name, model = deepface_model)
+	elif model_name == "DeepID":
+		embedding = DeepFace.represent(img, model_name = model_name, model = deepid_model)
+	elif model_name == "ArcFace":
+		embedding = DeepFace.represent(img, model_name = model_name, model = arcface_model)
+	else:
+		resp_obj = jsonify({'success': False, 'error': 'You must pass a valid model name. You passed %s' % (model_name)}), 205
+
+	#print("embedding is ", len(embedding)," dimensional vector")
+	resp_obj = {}
+	resp_obj["embedding"] = embedding
+	#-------------------------------------
+
+	return resp_obj
+
 if __name__ == '__main__':
 	parser = argparse.ArgumentParser()
 	parser.add_argument(

deepface/extendedmodels/Age.py

@@ -16,7 +16,7 @@ elif tf_version == 2:
 	from tensorflow.keras.models import Model, Sequential
 	from tensorflow.keras.layers import Convolution2D, Flatten, Activation
 
-def loadModel():
+def loadModel(url = 'https://drive.google.com/uc?id=1YCox_4kJ-BYeXq27uUbasu--yz28zUMV'):
 
 	model = VGGFace.baseModel()
 
@@ -41,7 +41,6 @@ def loadModel():
 	if os.path.isfile(home+'/.deepface/weights/age_model_weights.h5') != True:
 		print("age_model_weights.h5 will be downloaded...")
 
-		url = 'https://drive.google.com/uc?id=1YCox_4kJ-BYeXq27uUbasu--yz28zUMV'
 		output = home+'/.deepface/weights/age_model_weights.h5'
 		gdown.download(url, output, quiet=False)
 

deepface/extendedmodels/Emotion.py

@@ -15,7 +15,7 @@ elif tf_version == 2:
 	from tensorflow.keras.models import Model, Sequential
 	from tensorflow.keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, Flatten, Dense, Dropout
 
-def loadModel():
+def loadModel(url = 'https://drive.google.com/uc?id=13iUHHP3SlNg53qSuQZDdHDSDNdBP9nwy'):
 
 	num_classes = 7
 
@@ -55,7 +55,6 @@ def loadModel():
 		#TO-DO: upload weights to google drive
 
 		#zip
-		url = 'https://drive.google.com/uc?id=13iUHHP3SlNg53qSuQZDdHDSDNdBP9nwy'
 		output = home+'/.deepface/weights/facial_expression_model_weights.zip'
 		gdown.download(url, output, quiet=False)
 
@@ -66,7 +65,3 @@ def loadModel():
 	model.load_weights(home+'/.deepface/weights/facial_expression_model_weights.h5')
 
 	return model
-	
-	#----------------------------
-	
-	return 0

deepface/extendedmodels/Gender.py

@@ -14,7 +14,7 @@ elif tf_version == 2:
 	from tensorflow.keras.models import Model, Sequential
 	from tensorflow.keras.layers import Convolution2D, Flatten, Activation
 
-def loadModel():
+def loadModel(url = 'https://drive.google.com/uc?id=1wUXRVlbsni2FN9-jkS_f4UTUrm1bRLyk'):
 
 	model = VGGFace.baseModel()
 
@@ -39,7 +39,6 @@ def loadModel():
 	if os.path.isfile(home+'/.deepface/weights/gender_model_weights.h5') != True:
 		print("gender_model_weights.h5 will be downloaded...")
 
-		url = 'https://drive.google.com/uc?id=1wUXRVlbsni2FN9-jkS_f4UTUrm1bRLyk'
 		output = home+'/.deepface/weights/gender_model_weights.h5'
 		gdown.download(url, output, quiet=False)
 

deepface/extendedmodels/Race.py

@@ -16,7 +16,7 @@ elif tf_version == 2:
 	from tensorflow.keras.models import Model, Sequential
 	from tensorflow.keras.layers import Convolution2D, Flatten, Activation
 
-def loadModel():
+def loadModel(url = 'https://drive.google.com/uc?id=1nz-WDhghGQBC4biwShQ9kYjvQMpO6smj'):
 
 	model = VGGFace.baseModel()
 
@@ -42,7 +42,6 @@ def loadModel():
 		print("race_model_single_batch.h5 will be downloaded...")
 
 		#zip
-		url = 'https://drive.google.com/uc?id=1nz-WDhghGQBC4biwShQ9kYjvQMpO6smj'
 		output = home+'/.deepface/weights/race_model_single_batch.zip'
 		gdown.download(url, output, quiet=False)