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getting rid of target_size everywhere

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Sefik Ilkin Serengil 2024-04-07 18:23:28 +01:00
parent 1078be9f12
commit ae5d5b967a
6 changed files with 11 additions and 8 deletions
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1
README.md
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@ -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]
)
```

3
deepface/DeepFace.py
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@ -10,6 +10,7 @@ os.environ["TF_USE_LEGACY_KERAS"] = "1"
# 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

2
deepface/modules/recognition.py
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@ -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

1
deepface/modules/streaming.py
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@ -10,7 +10,6 @@ import cv2
# project dependencies
from deepface import DeepFace
from deepface.models.FacialRecognition import FacialRecognition
from deepface.commons.logger import Logger
logger = Logger(module="commons.realtime")

8
tests/face-recognition-how.py
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@ -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 @@ logger.info(f"target_size: {target_size}")
# ----------------------------------------------
# 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)

4
tests/overlay.py
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@ -7,9 +7,11 @@ img_path = "dataset/img1.jpg"
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"))