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bug fix after pr

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Sefik Ilkin Serengil 2022-01-08 09:41:56 +03:00
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2 changed files with 12 additions and 13 deletions
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README.md
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@ -8,7 +8,7 @@
<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/deepface-icon-labeled.png" width="200" height="240"></p>
Deepface is a lightweight [face recognition](https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/) and facial attribute analysis ([age](https://sefiks.com/2019/02/13/apparent-age-and-gender-prediction-in-keras/), [gender](https://sefiks.com/2019/02/13/apparent-age-and-gender-prediction-in-keras/), [emotion](https://sefiks.com/2018/01/01/facial-expression-recognition-with-keras/) and [race](https://sefiks.com/2019/11/11/race-and-ethnicity-prediction-in-keras/)) framework for python. It is a hybrid face recognition framework wrapping **state-of-the-art** models: [`VGG-Face`](https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/), [`Google FaceNet`](https://sefiks.com/2018/09/03/face-recognition-with-facenet-in-keras/), [`OpenFace`](https://sefiks.com/2019/07/21/face-recognition-with-openface-in-keras/), [`Facebook DeepFace`](https://sefiks.com/2020/02/17/face-recognition-with-facebook-deepface-in-keras/), [`DeepID`](https://sefiks.com/2020/06/16/face-recognition-with-deepid-in-keras/), [`ArcFace`](https://sefiks.com/2020/12/14/deep-face-recognition-with-arcface-in-keras-and-python/) and [`Dlib`](https://sefiks.com/2020/07/11/face-recognition-with-dlib-in-python/).
Deepface is a lightweight [face recognition](https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/) and facial attribute analysis ([age](https://sefiks.com/2019/02/13/apparent-age-and-gender-prediction-in-keras/), [gender](https://sefiks.com/2019/02/13/apparent-age-and-gender-prediction-in-keras/), [emotion](https://sefiks.com/2018/01/01/facial-expression-recognition-with-keras/) and [race](https://sefiks.com/2019/11/11/race-and-ethnicity-prediction-in-keras/)) framework for python. It is a hybrid face recognition framework wrapping **state-of-the-art** models: [`VGG-Face`](https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/), [`Google FaceNet`](https://sefiks.com/2018/09/03/face-recognition-with-facenet-in-keras/), [`OpenFace`](https://sefiks.com/2019/07/21/face-recognition-with-openface-in-keras/), [`Facebook DeepFace`](https://sefiks.com/2020/02/17/face-recognition-with-facebook-deepface-in-keras/), [`DeepID`](https://sefiks.com/2020/06/16/face-recognition-with-deepid-in-keras/), [`ArcFace`](https://sefiks.com/2020/12/14/deep-face-recognition-with-arcface-in-keras-and-python/) and [`Dlib`](https://sefiks.com/2020/07/11/face-recognition-with-dlib-in-python/).
Experiments show that human beings have 97.53% accuracy on facial recognition tasks whereas those models already reached and passed that accuracy level.
@ -195,7 +195,7 @@ Pull requests are welcome. You should run the unit tests locally by running [`te
## Support
There are many ways to support a project - starring⭐️ the GitHub repo is just one.
There are many ways to support a project - starring⭐️ the GitHub repo is just one 🙏.
## Citation

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deepface/commons/functions.py
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@ -66,23 +66,22 @@ def loadBase64Img(uri):
return img
def load_image(img):
exact_image = False
exact_image = False; base64_img = False; url_img = False
if type(img).__module__ == np.__name__:
exact_image = True
base64_img = False
if len(img) > 11 and img[0:11] == "data:image/":
elif len(img) > 11 and img[0:11] == "data:image/":
base64_img = True
url_img = False
if len(img) > 11 and img.startswith("http"):
elif len(img) > 11 and img.startswith("http"):
url_img = True
#---------------------------
if base64_img == True:
img = loadBase64Img(img)
elif url_img:
img = np.array(Image.open(requests.get(img, stream=True).raw))
@ -196,15 +195,15 @@ def preprocess_face(img, target_size=(224, 224), grayscale = False, enforce_dete
#resize image to expected shape
# img = cv2.resize(img, target_size) #resize causes transformation on base image, adding black pixels to resize will not deform the base image
if img.shape[0] > 0 and img.shape[1] > 0:
factor_0 = target_size[0] / img.shape[0]
factor_1 = target_size[1] / img.shape[1]
factor = min(factor_0, factor_1)
dsize = (int(img.shape[1] * factor), int(img.shape[0] * factor))
img = cv2.resize(img, dsize)
# Then pad the other side to the target size by adding black pixels
diff_0 = target_size[0] - img.shape[0]
diff_1 = target_size[1] - img.shape[1]
@ -213,9 +212,9 @@ def preprocess_face(img, target_size=(224, 224), grayscale = False, enforce_dete
img = np.pad(img, ((diff_0 // 2, diff_0 - diff_0 // 2), (diff_1 // 2, diff_1 - diff_1 // 2), (0, 0)), 'constant')
else:
img = np.pad(img, ((diff_0 // 2, diff_0 - diff_0 // 2), (diff_1 // 2, diff_1 - diff_1 // 2)), 'constant')
#------------------------------------------
#double check: if target image is not still the same size with target.
if img.shape[0:2] != target_size:
img = cv2.resize(img, target_size)