getting rid of target_size everywhere

Author: serengil

README.md

@@ -237,7 +237,6 @@ demographies = DeepFace.analyze(img_path = "img4.jpg",
 
 #face detection and alignment
 face_objs = DeepFace.extract_faces(img_path = "img.jpg", 
-        target_size = (224, 224), 
         detector_backend = backends[4]
 )
 ```

deepface/DeepFace.py

@@ -10,6 +10,7 @@
 # pylint: disable=wrong-import-position
 
 # 3rd party dependencies
+import cv2
 import numpy as np
 import pandas as pd
 import tensorflow as tf
@@ -532,7 +533,6 @@ def detectFace(
     logger.warn("Function detectFace is deprecated. Use extract_faces instead.")
     face_objs = extract_faces(
         img_path=img_path,
-        target_size=target_size,
         detector_backend=detector_backend,
         enforce_detection=enforce_detection,
         align=align,
@@ -541,4 +541,5 @@ def detectFace(
     extracted_face = None
     if len(face_objs) > 0:
         extracted_face = face_objs[0]["face"]
+        extracted_face = cv2.resize(extracted_face, target_size)
     return extracted_face

deepface/modules/recognition.py

@@ -321,8 +321,6 @@ def __find_bulk_embeddings(
         model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
             OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
 
-        target_size (tuple): expected input shape of facial recognition model
-
         detector_backend (str): face detector model name
 
         enforce_detection (bool): set this to False if you

deepface/modules/streaming.py

@@ -10,7 +10,6 @@
 
 # project dependencies
 from deepface import DeepFace
-from deepface.models.FacialRecognition import FacialRecognition
 from deepface.commons.logger import Logger
 
 logger = Logger(module="commons.realtime")

tests/face-recognition-how.py

@@ -1,5 +1,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
+import cv2
+
 from deepface import DeepFace
 from deepface.modules import verification
 from deepface.models.FacialRecognition import FacialRecognition
@@ -21,11 +23,13 @@
 # ----------------------------------------------
 # load images and find embeddings
 
-img1 = DeepFace.extract_faces(img_path="dataset/img1.jpg", target_size=target_size)[0]["face"]
+img1 = DeepFace.extract_faces(img_path="dataset/img1.jpg")[0]["face"]
+img1 = cv2.resize(img1, target_size)
 img1 = np.expand_dims(img1, axis=0)  # to (1, 224, 224, 3)
 img1_representation = model.forward(img1)
 
-img2 = DeepFace.extract_faces(img_path="dataset/img3.jpg", target_size=target_size)[0]["face"]
+img2 = DeepFace.extract_faces(img_path="dataset/img3.jpg")[0]["face"]
+img2 = cv2.resize(img2, target_size)
 img2 = np.expand_dims(img2, axis=0)
 img2_representation = model.forward(img2)
 

tests/overlay.py

@@ -7,9 +7,11 @@
 img = cv2.imread(img_path)
 
 overlay_img_path = "dataset/img6.jpg"
-face_objs = DeepFace.extract_faces(overlay_img_path, target_size=(112, 112))
+face_objs = DeepFace.extract_faces(overlay_img_path)
 overlay_img = face_objs[0]["face"][:, :, ::-1] * 255
 
+overlay_img = cv2.resize(overlay_img, (112, 112))
+
 raw_img = img.copy()
 
 demographies = DeepFace.analyze(img_path=img_path, actions=("age", "gender", "emotion"))