From c11fc7717295d8f8e9295f1e7f623d5c19d26b32 Mon Sep 17 00:00:00 2001
From: ageorge <anjith.george@idiap.ch>
Date: Thu, 29 Oct 2020 08:57:46 +0100
Subject: [PATCH] For the SCalability experiments
---
bob/pad/face/config/grid.py | 2 +-
bob/pad/face/database/hqwmca_warp.py | 2 +
bob/pad/face/preprocessor/FaceCropAlign.py | 63 +++++++++++++++++++++-
3 files changed, 65 insertions(+), 2 deletions(-)
diff --git a/bob/pad/face/config/grid.py b/bob/pad/face/config/grid.py
index 7c5713d4..29851085 100644
--- a/bob/pad/face/config/grid.py
+++ b/bob/pad/face/config/grid.py
@@ -1,7 +1,7 @@
#!/usr/bin/env python
# vim: set fileencoding=utf-8 :
-from bob.bio.base.grid import Grid
+from bob.bio.base_legacy.grid import Grid
# Configuration to run on computation cluster:
idiap = Grid(
diff --git a/bob/pad/face/database/hqwmca_warp.py b/bob/pad/face/database/hqwmca_warp.py
index 1a7d20f9..eac223fe 100644
--- a/bob/pad/face/database/hqwmca_warp.py
+++ b/bob/pad/face/database/hqwmca_warp.py
@@ -239,6 +239,8 @@ class HQWMCAPadDatabase_warp(PadDatabase):
file_path = os.path.join(self.annotations_dir, ff.path + ".json")
+ print('Annotation path',file_path)
+
if not os.path.isfile(file_path): # no file with annotations
# original values of the arguments of f:
diff --git a/bob/pad/face/preprocessor/FaceCropAlign.py b/bob/pad/face/preprocessor/FaceCropAlign.py
index aab0e6a8..a101900a 100644
--- a/bob/pad/face/preprocessor/FaceCropAlign.py
+++ b/bob/pad/face/preprocessor/FaceCropAlign.py
@@ -28,6 +28,67 @@ logger = logging.getLogger("bob.pad.face")
# ==============================================================================
+
+
+def depth_auto_norm_image(data, annotations, n_sigma=3.0, norm_method='MAD', FD=500):
+ """
+ Normalizes a single channel image to range 0-255, using the data distribution
+ Expects single channel images
+
+ **method: Gaussian , MAD, MINMAX
+ **n_sigma: The range which is normalized
+
+ """
+
+ # print('annotation',annotations)
+
+ face = data[annotations['topleft'][0]:annotations['bottomright'][0],
+ annotations['topleft'][1]:annotations['bottomright'][1]]
+
+ face = face.astype('float')
+ data = data.astype('float')
+
+ assert(len(data.shape)==2)
+
+ face_c = np.ma.array(face).compressed()
+ # Avoiding zeros from flat field in thermal and holes in depth
+
+ face_c=face_c[face_c>1.0]
+
+ if norm_method=='STD':
+
+ mu = np.mean(face_c)
+ std = np.std(face_c)
+
+ data_n=((data-mu+n_sigma*std)/(2.0*n_sigma*std))*255.0
+
+
+ if norm_method=='MAD':
+
+ med = np.median(face_c)
+ mad = np.median(np.abs(face_c - med))
+
+ data_n = ((data-med+n_sigma*mad)/(2.0*n_sigma*mad))*255.0
+
+
+ if norm_method=='MINMAX':
+
+ t_min = np.min(face_c)
+ t_max = np.max(face_c)
+
+ data_n = ((data-t_min)/(t_max-t_min))*255.0
+
+
+ # Clamping to 0-255
+ data_n=np.maximum(data_n,0)
+ data_n=np.minimum(data_n,255)
+
+ data_n = data_n.astype('uint8')
+
+ return data_n
+
+
+
def auto_norm_image(data, annotations, n_sigma=3.0, norm_method='MAD'):
"""
Normalizes a single channel image to range 0-255, using the data distribution
@@ -581,7 +642,7 @@ class FaceCropAlign(Preprocessor):
- print('INSIDE FACECROP ALIGN',annotations.keys(), annotations)
+ #print('INSIDE FACECROP ALIGN',annotations.keys(), annotations)
if self.normalization_function is not None:
image = self.normalization_function(image, annotations, **self.normalization_function_kwargs)
--
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