new interface

README.md

@@ -46,7 +46,7 @@ A modern [**face recognition pipeline**](https://sefiks.com/2020/05/01/a-gentle-
 
 **Face Verification** - [`Demo`](https://youtu.be/KRCvkNCOphE)
 
-This function verifies face pairs as same person or different persons. It expects exact image paths as inputs. Passing numpy or base64 encoded images is also welcome. Then, it is going to return a dictionary and you should check just its verified key. Verification function can also handle many faces in the face pairs. In this case, the most similar faces will be compared.
+This function verifies face pairs as same person or different persons. It expects exact image paths as inputs. Passing numpy or base64 encoded images is also welcome. Then, it is going to return a dictionary and you should check just its verified key.
 
 ```python
 result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg")
@@ -54,9 +54,11 @@ result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg")
 
 <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/stock-1.jpg" width="95%" height="95%"></p>
 
+Verification function can also handle many faces in the face pairs. In this case, the most similar faces will be compared.
+
 **Face recognition** - [`Demo`](https://youtu.be/Hrjp-EStM_s)
 
-[Face recognition](https://sefiks.com/2020/05/25/large-scale-face-recognition-for-deep-learning/) requires applying face verification many times. Herein, deepface has an out-of-the-box find function to handle this action. It's going to look for the identity of input image in the database path and it will return list of pandas data frame as output. Result is going to be the size of faces appearing in the image path.
+[Face recognition](https://sefiks.com/2020/05/25/large-scale-face-recognition-for-deep-learning/) requires applying face verification many times. Herein, deepface has an out-of-the-box find function to handle this action. It's going to look for the identity of input image in the database path and it will return list of pandas data frame as output. Result is going to be the size of faces appearing in the source image.
 
 
 ```python

api/api.py

@@ -66,21 +66,13 @@ def analyze():
 
 	toc = time.time()
 
-	resp = {}
+	resp_obj["trx_id"] = trx_id
+	resp_obj["seconds"] = toc-tic
 
-	resp["trx_id"] = trx_id
-	resp["seconds"] = toc-tic
-
-	if isinstance(resp_obj, list):
-		for idx, instance in enumerate(resp_obj):
-			resp[f"instance_{idx+1}"] = instance
-	elif isinstance(resp_obj, dict):
-		resp["instance_1"] = resp_obj
-
-	return resp, 200
+	return resp_obj, 200
 
 def analyzeWrapper(req, trx_id = 0):
-	resp_obj = jsonify({'success': False})
+	resp_obj = {}
 
 	instances = []
 	if "img" in list(req.keys()):
@@ -90,32 +82,46 @@ def analyzeWrapper(req, trx_id = 0):
 			instances.append(item)
 
 	if len(instances) == 0:
-		return jsonify({'success': False, 'error': 'you must pass at least one img object in your request'}), 205
+		return {'success': False, 'error': 'you must pass at least one img object in your request'}
 
 	print("Analyzing ", len(instances)," instances")
 
 	#---------------------------
 
 	detector_backend = 'opencv'
-
 	actions= ['emotion', 'age', 'gender', 'race']
+	align = True
+	enforce_detection = True
 
 	if "actions" in list(req.keys()):
 		actions = req["actions"]
 
 	if "detector_backend" in list(req.keys()):
 		detector_backend = req["detector_backend"]
+	
+	if "align" in list(req.keys()):
+		align = req["align"]
+	
+	if "enforce_detection" in list(req.keys()):
+		enforce_detection = req["enforce_detection"]
 
 	#---------------------------
 
 	try:
-		resp_obj = DeepFace.analyze(instances, actions = actions)
+		resp_obj["demographies"] = {}
+		for idx, instance in enumerate(instances):
+
+			demographies = DeepFace.analyze(img_path = instance, 
+										detector_backend = detector_backend, 
+										actions = actions, align = align, 
+										enforce_detection = enforce_detection)
+			resp_obj["demographies"][f"img_{idx+1}"] = demographies
+
 	except Exception as err:
 		print("Exception: ", str(err))
 		return jsonify({'success': False, 'error': str(err)}), 205
 
 	#---------------
-	#print(resp_obj)
 	return resp_obj
 
 @app.route('/verify', methods=['POST'])
@@ -146,62 +152,58 @@ def verify():
 
 def verifyWrapper(req, trx_id = 0):
 
-	resp_obj = jsonify({'success': False})
+	resp_obj = {}
 
 	model_name = "VGG-Face"; distance_metric = "cosine"; detector_backend = "opencv"
+	align = True; enforce_detection = True
 	if "model_name" in list(req.keys()):
 		model_name = req["model_name"]
 	if "distance_metric" in list(req.keys()):
 		distance_metric = req["distance_metric"]
 	if "detector_backend" in list(req.keys()):
 		detector_backend = req["detector_backend"]
-
+	if "align" in list(req.keys()):
+		align = req["align"]
+	if "enforce_detection" in list(req.keys()):
+		enforce_detection = req["enforce_detection"]
 	#----------------------
-
-	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")
-
-	#--------------------------
-
 	try:
-		resp_obj = DeepFace.verify(instances
-			, model_name = model_name
-			, distance_metric = distance_metric
-			, detector_backend = detector_backend
-		)
+		if "img" in list(req.keys()):
+			raw_content = req["img"] #list
 
-		if model_name == "Ensemble": #issue 198.
-			for key in resp_obj: #issue 198.
-				resp_obj[key]['verified'] = bool(resp_obj[key]['verified'])
+			if len(raw_content) == 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(raw_content)," pairs to verify")
 
+			results = []
+			for idx, item in enumerate(raw_content): #item is in type of dict
+				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
+				
+				result = DeepFace.verify(img1_path=img1, 
+								img2_path=img2, 
+								model_name=model_name, 
+								detector_backend=detector_backend, 
+								distance_metric=distance_metric,
+								align=align,
+								enforce_detection=enforce_detection,
+								)
+				results.append(result)
+				
+			resp_obj[f"pairs"] = results
 	except Exception as err:
-		resp_obj = jsonify({'success': False, 'error': str(err)}), 205
+		resp_obj = jsonify({'success': False, 'error': str(err)}), 205		
 
 	return resp_obj
 
@@ -238,7 +240,7 @@ def representWrapper(req, trx_id = 0):
 	#-------------------------------------
 	#find out model
 
-	model_name = "VGG-Face"; distance_metric = "cosine"; detector_backend = 'opencv'
+	model_name = "VGG-Face"; detector_backend = 'opencv'
 
 	if "model_name" in list(req.keys()):
 		model_name = req["model_name"]
@@ -267,7 +269,7 @@ def representWrapper(req, trx_id = 0):
 
 	try:
 
-		embedding = DeepFace.represent(img
+		embedding_objs = DeepFace.represent(img
 			, model_name = model_name
 			, detector_backend = detector_backend
 		)
@@ -280,7 +282,16 @@ def representWrapper(req, trx_id = 0):
 
 	#print("embedding is ", len(embedding)," dimensional vector")
 	resp_obj = {}
-	resp_obj["embedding"] = embedding
+	faces = []
+	for embedding_obj in embedding_objs:
+		face = {}
+		face["embedding"] = embedding_obj["embedding"]
+		face["facial_area"] = embedding_obj["facial_area"]
+		face["model_name"] = model_name
+		face["detector_backend"] = detector_backend
+		faces.append(face)
+
+	resp_obj["embeddings"] = faces
 
 	#-------------------------------------
 

deepface/DeepFace.py

@@ -473,7 +473,10 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
 			target_representation = target_embedding_obj[0]["embedding"]
 
 			result_df = df.copy() #df will be filtered in each img
-			#TODO: add facial area in df
+			result_df["source_x"] = target_region["x"]
+			result_df["source_y"] = target_region["y"]
+			result_df["source_w"] = target_region["w"]
+			result_df["source_h"] = target_region["h"]
 
 			distances = []
 			for index, instance in df.iterrows():

deepface/commons/distance.py

@@ -31,10 +31,7 @@ def findThreshold(model_name, distance_metric):
         'Facenet512':  {'cosine': 0.30, 'euclidean': 23.56, 'euclidean_l2': 1.04},
         'ArcFace':  {'cosine': 0.68, 'euclidean': 4.15, 'euclidean_l2': 1.13},
         'Dlib': 	{'cosine': 0.07, 'euclidean': 0.6, 'euclidean_l2': 0.4},
-
-        #TODO: find the best threshold values
         'SFace': 	{'cosine': 0.5932763306134152, 'euclidean': 10.734038121282206, 'euclidean_l2': 1.055836701022614},
-
 		'OpenFace': {'cosine': 0.10, 'euclidean': 0.55, 'euclidean_l2': 0.55},
 		'DeepFace': {'cosine': 0.23, 'euclidean': 64, 'euclidean_l2': 0.64},
 		'DeepID': 	{'cosine': 0.015, 'euclidean': 45, 'euclidean_l2': 0.17}

deepface/commons/functions.py

@@ -197,7 +197,7 @@ def find_target_size(model_name):
 		"Facenet512": (160, 160),
 		"OpenFace": (96, 96),
 		"DeepFace": (152, 152),
-		"DeepID": (55, 47), #TODO: might be opposite
+		"DeepID": (47, 55),
 		"Dlib": (150, 150),
 		"ArcFace": (112, 112),
 		"SFace": (112, 112)

deepface/commons/realtime.py

@@ -85,9 +85,7 @@ def analysis(db_path, model_name = 'VGG-Face', detector_backend = 'opencv', dist
 	#-----------------------
 
 	pbar = tqdm(range(0, len(employees)), desc='Finding embeddings')
-
-	#TODO: why don't you store those embeddings in a pickle file similar to find function?
-
+	
 	embeddings = []
 	#for employee in employees:
 	for index in pbar: