From f111cb002b9c6065fdf6bb274ce5857a9e875e8c Mon Sep 17 00:00:00 2001
From: chenshijun <csj_sky@126.com>
Date: 星期三, 05 六月 2019 15:38:49 +0800
Subject: [PATCH] face rectangle
---
code/datasets.py | 15 ++++-----------
1 files changed, 4 insertions(+), 11 deletions(-)
diff --git a/code/datasets.py b/code/datasets.py
index 5f1dfdc..e8ab9f4 100644
--- a/code/datasets.py
+++ b/code/datasets.py
@@ -96,7 +96,6 @@
img = Image.open(os.path.join(self.data_dir, self.X_train[index] + self.img_ext))
img = img.convert(self.image_mode)
mat_path = os.path.join(self.data_dir, self.y_train[index] + self.annot_ext)
- shape_path = os.path.join(self.data_dir, self.y_train[index] + '_shape.npy')
# Crop the face loosely
pt2d = utils.get_pt2d_from_mat(mat_path)
@@ -136,11 +135,8 @@
bins = np.array(range(-99, 102, 3))
binned_pose = np.digitize([yaw, pitch, roll], bins) - 1
- # Get shape
- shape = np.load(shape_path)
-
# Get target tensors
- labels = torch.LongTensor(np.concatenate((binned_pose, shape), axis = 0))
+ labels = binned_pose
cont_labels = torch.FloatTensor([yaw, pitch, roll])
if self.transform is not None:
@@ -171,7 +167,6 @@
img = Image.open(os.path.join(self.data_dir, self.X_train[index] + self.img_ext))
img = img.convert(self.image_mode)
mat_path = os.path.join(self.data_dir, self.y_train[index] + self.annot_ext)
- shape_path = os.path.join(self.data_dir, self.y_train[index] + '_shape.npy')
# Crop the face loosely
pt2d = utils.get_pt2d_from_mat(mat_path)
@@ -189,7 +184,8 @@
img = img.crop((int(x_min), int(y_min), int(x_max), int(y_max)))
# We get the pose in radians
- pose = utils.get_ypr_fro # Head pose from AFLW2000 datasetp.pi
+ pose = utils.get_ypr_from_mat(mat_path)
+ pitch = pose[0] * 180 / np.pi
yaw = pose[1] * 180 / np.pi
roll = pose[2] * 180 / np.pi
@@ -214,11 +210,8 @@
bins = np.array(range(-99, 102, 3))
binned_pose = np.digitize([yaw, pitch, roll], bins) - 1
- # Get shape
- shape = np.load(shape_path)
-
# Get target tensors
- labels = torch.LongTensor(np.concatenate((binned_pose, shape), axis = 0))
+ labels = binned_pose
cont_labels = torch.FloatTensor([yaw, pitch, roll])
if self.transform is not None:
--
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