From beb9f36419d0df03c3248757f54af032a633e05c Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期六, 12 八月 2017 11:49:10 +0800
Subject: [PATCH] AFLW training ready.
---
code/datasets.py | 62 ++++++++++++++++++++++++++++--
1 files changed, 57 insertions(+), 5 deletions(-)
diff --git a/code/datasets.py b/code/datasets.py
index 06cd433..f6fcc45 100644
--- a/code/datasets.py
+++ b/code/datasets.py
@@ -7,6 +7,10 @@
import utils
+def stack_grayscale_tensor(tensor):
+ tensor = torch.cat([tensor, tensor, tensor], 0)
+ return tensor
+
class Pose_300W_LP(Dataset):
def __init__(self, data_dir, filename_path, transform, img_ext='.jpg', annot_ext='.mat'):
self.data_dir = data_dir
@@ -66,7 +70,7 @@
return self.length
class Pose_300W_LP_binned(Dataset):
- def __init__(self, data_dir, filename_path, transform, img_ext='.jpg', annot_ext='.mat'):
+ def __init__(self, data_dir, filename_path, transform, img_ext='.jpg', annot_ext='.mat', image_mode='RGB'):
self.data_dir = data_dir
self.transform = transform
self.img_ext = img_ext
@@ -76,12 +80,14 @@
self.X_train = filename_list
self.y_train = filename_list
+ self.image_mode = image_mode
self.length = len(filename_list)
def __getitem__(self, index):
img = Image.open(os.path.join(self.data_dir, self.X_train[index] + self.img_ext))
- img = img.convert('RGB')
+ 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
pt2d = utils.get_pt2d_from_mat(mat_path)
@@ -105,7 +111,12 @@
roll = pose[2] * 180 / np.pi
# Bin values
bins = np.array(range(-99, 102, 3))
- labels = torch.LongTensor(np.digitize([yaw, pitch, roll], bins) - 1)
+ binned_pose = np.digitize([yaw, pitch, roll], bins) - 1
+
+ # Get shape
+ shape = np.load(shape_path)
+
+ labels = torch.LongTensor(np.concatenate((binned_pose, shape), axis = 0))
if self.transform is not None:
img = self.transform(img)
@@ -117,7 +128,7 @@
return self.length
class AFLW2000_binned(Dataset):
- def __init__(self, data_dir, filename_path, transform, img_ext='.jpg', annot_ext='.mat'):
+ def __init__(self, data_dir, filename_path, transform, img_ext='.jpg', annot_ext='.mat', image_mode='RGB'):
self.data_dir = data_dir
self.transform = transform
self.img_ext = img_ext
@@ -127,11 +138,12 @@
self.X_train = filename_list
self.y_train = filename_list
+ self.image_mode = image_mode
self.length = len(filename_list)
def __getitem__(self, index):
img = Image.open(os.path.join(self.data_dir, self.X_train[index] + self.img_ext))
- img = img.convert('RGB')
+ img = img.convert(self.image_mode)
mat_path = os.path.join(self.data_dir, self.y_train[index] + self.annot_ext)
# Crop the face
@@ -167,6 +179,46 @@
# 2,000
return self.length
+class AFLW(Dataset):
+ def __init__(self, data_dir, filename_path, transform, img_ext='.jpg', annot_ext='.txt', image_mode='RGB'):
+ self.data_dir = data_dir
+ self.transform = transform
+ self.img_ext = img_ext
+ self.annot_ext = annot_ext
+
+ filename_list = get_list_from_filenames(filename_path)
+
+ self.X_train = filename_list
+ self.y_train = filename_list
+ self.image_mode = image_mode
+ self.length = len(filename_list)
+
+ def __getitem__(self, index):
+ img = Image.open(os.path.join(self.data_dir, self.X_train[index] + self.img_ext))
+ img = img.convert(self.image_mode)
+ txt_path = os.path.join(self.data_dir, self.y_train[index] + self.annot_ext)
+
+ # We get the pose in radians
+ annot = open(txt_path, 'r')
+ line = annot.readline().split(' ')
+ pose = [float(line[1]), float(line[2]), float(line[3])]
+ # And convert to degrees.
+ yaw = pose[0] * 180 / np.pi
+ pitch = pose[1] * 180 / np.pi
+ roll = pose[2] * 180 / np.pi
+ # Bin values
+ bins = np.array(range(-99, 102, 3))
+ labels = torch.LongTensor(np.digitize([yaw, pitch, roll], bins) - 1)
+
+ if self.transform is not None:
+ img = self.transform(img)
+
+ return img, labels, self.X_train[index]
+
+ def __len__(self):
+ # Check how many
+ return self.length
+
def get_list_from_filenames(file_path):
# input: relative path to .txt file with file names
# output: list of relative path names
--
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