input shape is now retrieved from models

deepface/basemodels/ArcFace.py

@@ -53,6 +53,8 @@ class ArcFaceClient(FacialRecognition):
     def __init__(self):
         self.model = load_model()
         self.model_name = "ArcFace"
+        self.input_shape = (112, 112)
+        self.output_shape = 512
 
     def find_embeddings(self, img: np.ndarray) -> List[float]:
         """

deepface/basemodels/DeepID.py

@@ -49,6 +49,8 @@ class DeepIdClient(FacialRecognition):
     def __init__(self):
         self.model = load_model()
         self.model_name = "DeepId"
+        self.input_shape = (47, 55)
+        self.output_shape = 160
 
     def find_embeddings(self, img: np.ndarray) -> List[float]:
         """

deepface/basemodels/Dlib.py

@@ -20,6 +20,8 @@ class DlibClient(FacialRecognition):
     def __init__(self):
         self.model = DlibResNet()
         self.model_name = "Dlib"
+        self.input_shape = (150, 150)
+        self.output_shape = 128
 
     def find_embeddings(self, img: np.ndarray) -> List[float]:
         """

deepface/basemodels/Facenet.py

@@ -53,6 +53,8 @@ class FaceNet128dClient(FacialRecognition):
     def __init__(self):
         self.model = load_facenet128d_model()
         self.model_name = "FaceNet-128d"
+        self.input_shape = (160, 160)
+        self.output_shape = 128
 
     def find_embeddings(self, img: np.ndarray) -> List[float]:
         """
@@ -75,6 +77,8 @@ class FaceNet512dClient(FacialRecognition):
     def __init__(self):
         self.model = load_facenet512d_model()
         self.model_name = "FaceNet-512d"
+        self.input_shape = (160, 160)
+        self.output_shape = 512
 
     def find_embeddings(self, img: np.ndarray) -> List[float]:
         """

deepface/basemodels/FbDeepFace.py

@@ -46,6 +46,8 @@ class DeepFaceClient(FacialRecognition):
     def __init__(self):
         self.model = load_model()
         self.model_name = "DeepFace"
+        self.input_shape = (152, 152)
+        self.output_shape = 4096
 
     def find_embeddings(self, img: np.ndarray) -> List[float]:
         """

deepface/basemodels/OpenFace.py

@@ -36,6 +36,8 @@ class OpenFaceClient(FacialRecognition):
     def __init__(self):
         self.model = load_model()
         self.model_name = "OpenFace"
+        self.input_shape = (96, 96)
+        self.output_shape = 128
 
     def find_embeddings(self, img: np.ndarray) -> List[float]:
         """

deepface/basemodels/SFace.py

@@ -22,6 +22,8 @@ class SFaceClient(FacialRecognition):
     def __init__(self):
         self.model = load_model()
         self.model_name = "SFace"
+        self.input_shape = (112, 112)
+        self.output_shape = 128
 
     def find_embeddings(self, img: np.ndarray) -> List[float]:
         """

deepface/basemodels/VGGFace.py

@@ -43,6 +43,8 @@ class VggFaceClient(FacialRecognition):
     def __init__(self):
         self.model = load_model()
         self.model_name = "VGG-Face"
+        self.input_shape = (224, 224)
+        self.output_shape = 4096
 
     def find_embeddings(self, img: np.ndarray) -> List[float]:
         """

deepface/commons/functions.py

@@ -40,33 +40,3 @@ def get_deepface_home() -> str:
         str: the home directory.
     """
     return str(os.getenv("DEEPFACE_HOME", default=str(Path.home())))
-
-
-def find_target_size(model_name: str) -> tuple:
-    """Find the target size of the model.
-
-    Args:
-        model_name (str): the model name.
-
-    Returns:
-        tuple: the target size.
-    """
-
-    target_sizes = {
-        "VGG-Face": (224, 224),
-        "Facenet": (160, 160),
-        "Facenet512": (160, 160),
-        "OpenFace": (96, 96),
-        "DeepFace": (152, 152),
-        "DeepID": (47, 55),
-        "Dlib": (150, 150),
-        "ArcFace": (112, 112),
-        "SFace": (112, 112),
-    }
-
-    target_size = target_sizes.get(model_name)
-
-    if target_size == None:
-        raise ValueError(f"unimplemented model name - {model_name}")
-
-    return target_size

deepface/models/FacialRecognition.py

@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import Any, Union, List
+from typing import Any, Union, List, Tuple
 import numpy as np
 from deepface.commons import functions
 
@@ -15,6 +15,9 @@ else:
 class FacialRecognition(ABC):
     model: Union[Model, Any]
     model_name: str
+    input_shape: Tuple[int, int]
+    output_shape: int
+
 
     @abstractmethod
     def find_embeddings(self, img: np.ndarray) -> List[float]:

deepface/modules/realtime.py

@@ -4,7 +4,7 @@ import numpy as np
 import pandas as pd
 import cv2
 from deepface import DeepFace
-from deepface.commons import functions
+from deepface.models.FacialRecognition import FacialRecognition
 from deepface.commons.logger import Logger
 
 logger = Logger(module="commons.realtime")
@@ -32,12 +32,13 @@ def analysis(
     enable_emotion = True
     enable_age_gender = True
     # ------------------------
-    # find custom values for this input set
-    target_size = functions.find_target_size(model_name=model_name)
-    # ------------------------
     # build models once to store them in the memory
     # otherwise, they will be built after cam started and this will cause delays
-    DeepFace.build_model(model_name=model_name)
+    model: FacialRecognition = DeepFace.build_model(model_name=model_name)
+
+    # find custom values for this input set
+    target_size = model.input_shape
+
     logger.info(f"facial recognition model {model_name} is just built")
 
     if enable_face_analysis:

deepface/modules/recognition.py

@@ -10,9 +10,10 @@ import pandas as pd
 from tqdm import tqdm
 
 # project dependencies
-from deepface.commons import functions, distance as dst
+from deepface.commons import distance as dst
 from deepface.commons.logger import Logger
-from deepface.modules import representation, detection
+from deepface.modules import representation, detection, modeling
+from deepface.models.FacialRecognition import FacialRecognition
 
 logger = Logger(module="deepface/modules/recognition.py")
 
@@ -89,7 +90,8 @@ def find(
     if os.path.isdir(db_path) is not True:
         raise ValueError("Passed db_path does not exist!")
 
-    target_size = functions.find_target_size(model_name=model_name)
+    model: FacialRecognition = modeling.build_model(model_name)
+    target_size = model.input_shape
 
     # ---------------------------------------
 

deepface/modules/representation.py

@@ -7,7 +7,6 @@ import cv2
 
 # project dependencies
 from deepface.modules import modeling, detection, preprocessing
-from deepface.commons import functions
 from deepface.models.FacialRecognition import FacialRecognition
 
 
@@ -61,7 +60,7 @@ def represent(
 
     # ---------------------------------
     # we have run pre-process in verification. so, this can be skipped if it is coming from verify.
-    target_size = functions.find_target_size(model_name=model_name)
+    target_size = model.input_shape
     if detector_backend != "skip":
         img_objs = detection.extract_faces(
             img_path=img_path,

deepface/modules/verification.py

@@ -6,8 +6,9 @@ from typing import Any, Dict, Union
 import numpy as np
 
 # project dependencies
-from deepface.commons import functions, distance as dst
-from deepface.modules import representation, detection
+from deepface.commons import distance as dst
+from deepface.modules import representation, detection, modeling
+from deepface.models.FacialRecognition import FacialRecognition
 
 
 def verify(
@@ -79,7 +80,8 @@ def verify(
     tic = time.time()
 
     # --------------------------------
-    target_size = functions.find_target_size(model_name=model_name)
+    model: FacialRecognition = modeling.build_model(model_name)
+    target_size = model.input_shape
 
     # img pairs might have many faces
     img1_objs = detection.extract_faces(

tests/face-recognition-how.py

@@ -1,7 +1,8 @@
 import matplotlib.pyplot as plt
 import numpy as np
 from deepface import DeepFace
-from deepface.commons import functions
+from deepface.commons import distance
+from deepface.models.FacialRecognition import FacialRecognition
 from deepface.commons.logger import Logger
 
 logger = Logger()
@@ -11,9 +12,9 @@ logger = Logger()
 
 model_name = "VGG-Face"
 
-model = DeepFace.build_model(model_name=model_name)
+model: FacialRecognition = DeepFace.build_model(model_name=model_name)
 
-target_size = functions.find_target_size(model_name)
+target_size = model.input_shape
 
 logger.info(f"target_size: {target_size}")
 
@@ -22,21 +23,34 @@ logger.info(f"target_size: {target_size}")
 
 img1 = DeepFace.extract_faces(img_path="dataset/img1.jpg", target_size=target_size)[0]["face"]
 img1 = np.expand_dims(img1, axis=0)  # to (1, 224, 224, 3)
-img1_representation = model.predict(img1)[0, :]
+img1_representation = model.find_embeddings(img1)
 
 img2 = DeepFace.extract_faces(img_path="dataset/img3.jpg", target_size=target_size)[0]["face"]
 img2 = np.expand_dims(img2, axis=0)
-img2_representation = model.predict(img2)[0, :]
+img2_representation = model.find_embeddings(img2)
+
+img1_representation = np.array(img1_representation)
+img2_representation = np.array(img2_representation)
 
 # ----------------------------------------------
-# distance between two images
-
+# distance between two images - euclidean distance formula
 distance_vector = np.square(img1_representation - img2_representation)
-logger.debug(distance_vector)
+current_distance = np.sqrt(distance_vector.sum())
+logger.info(f"Euclidean distance: {current_distance}")
 
-distance = np.sqrt(distance_vector.sum())
-logger.info(f"Euclidean distance: {distance}")
+threshold = distance.findThreshold(model_name=model_name, distance_metric="euclidean")
+logger.info(f"Threshold for {model_name}-euclidean pair is {threshold}")
 
+if current_distance < threshold:
+    logger.info(
+        f"This pair is same person because its distance {current_distance}"
+        f" is less than threshold {threshold}"
+    )
+else:
+    logger.info(
+        f"This pair is different persons because its distance {current_distance}"
+        f" is greater than threshold {threshold}"
+    )
 # ----------------------------------------------
 # expand vectors to be shown better in graph
 
@@ -75,7 +89,7 @@ im = plt.imshow(img2_graph, interpolation="nearest", cmap=plt.cm.ocean)
 plt.colorbar()
 
 ax5 = fig.add_subplot(3, 2, 5)
-plt.text(0.35, 0, f"Distance: {distance}")
+plt.text(0.35, 0, f"Distance: {current_distance}")
 plt.axis("off")
 
 ax6 = fig.add_subplot(3, 2, 6)