From 1c49a056ea7a847216b816b359cfc313e268001f Mon Sep 17 00:00:00 2001
From: Tiago Freitas Pereira <tiagofrepereira@gmail.com>
Date: Tue, 1 Jun 2021 10:46:46 +0200
Subject: [PATCH] Harmonized opencv interface
---
bob/bio/face/embeddings/opencv.py | 46 +++++++++++++++++++++++++------
1 file changed, 37 insertions(+), 9 deletions(-)
diff --git a/bob/bio/face/embeddings/opencv.py b/bob/bio/face/embeddings/opencv.py
index 0535b6d4..bcf1c7d0 100644
--- a/bob/bio/face/embeddings/opencv.py
+++ b/bob/bio/face/embeddings/opencv.py
@@ -13,7 +13,7 @@ from bob.extension.download import get_file
class OpenCVTransformer(TransformerMixin, BaseEstimator):
"""
- Base Transformer using the OpenCV interface.
+ Base Transformer using the OpenCV DNN interface (https://docs.opencv.org/master/d2/d58/tutorial_table_of_content_dnn.html).
.. note::
@@ -28,13 +28,24 @@ class OpenCVTransformer(TransformerMixin, BaseEstimator):
config:
Path containing some configuration file (e.g. .json, .prototxt)
+
+ preprocessor:
+ A function that will transform the data right before forward. The default transformation is `X/255`
+
"""
- def __init__(self, checkpoint_path=None, config=None, **kwargs):
+ def __init__(
+ self,
+ checkpoint_path=None,
+ config=None,
+ preprocessor=lambda x: x / 255,
+ **kwargs,
+ ):
super().__init__(**kwargs)
self.checkpoint_path = checkpoint_path
self.config = config
self.model = None
+ self.preprocessor = preprocessor
def _load_model(self):
import cv2
@@ -63,14 +74,11 @@ class OpenCVTransformer(TransformerMixin, BaseEstimator):
if self.model is None:
self._load_model()
- import ipdb
-
- ipdb.set_trace()
+ X = check_array(X, allow_nd=True)
- img = np.array(X)
- img = img / 255
+ X = self.preprocessor(X)
- self.model.setInput(img)
+ self.model.setInput(X)
return self.model.forward()
@@ -108,10 +116,30 @@ class VGG16_Oxford(OpenCVTransformer):
config = os.path.join(path, "vgg_face_caffe", "VGG_FACE_deploy.prototxt")
checkpoint_path = os.path.join(path, "vgg_face_caffe", "VGG_FACE.caffemodel")
- super(VGG16_Oxford, self).__init__(checkpoint_path, config)
+ caffe_average_img = [129.1863, 104.7624, 93.5940]
+
+ def preprocessor(X):
+ """
+ Normalize using data from caffe
+
+ Caffe has the shape `C x H x W` and the chanel is BGR and
+
+ """
+ # To BGR
+ X = X[:, ::-1, :, :].astype("float32")
+
+ # Subtracting
+ X[:, :, :, 0] -= caffe_average_img[0]
+ X[:, :, :, 1] -= caffe_average_img[1]
+ X[:, :, :, 2] -= caffe_average_img[2]
+
+ return X
+
+ super(VGG16_Oxford, self).__init__(checkpoint_path, config, preprocessor)
def _load_model(self):
import cv2
net = cv2.dnn.readNet(self.checkpoint_path, self.config)
self.model = net
+
--
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