From 31fc66b795c0a57b8009d7b03f49f6cd099ceb29 Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期六, 23 九月 2017 12:07:48 +0800
Subject: [PATCH] Trying superres
---
code/datasets.py | 65 +++++++++++++++++++++++++++++++-
1 files changed, 62 insertions(+), 3 deletions(-)
diff --git a/code/datasets.py b/code/datasets.py
index da0603f..17f1899 100644
--- a/code/datasets.py
+++ b/code/datasets.py
@@ -106,15 +106,16 @@
# Crop the face
pt2d = utils.get_pt2d_from_mat(mat_path)
+
x_min = min(pt2d[0,:])
y_min = min(pt2d[1,:])
x_max = max(pt2d[0,:])
y_max = max(pt2d[1,:])
- k = 0.15
- x_min -= 0.6 * k * abs(x_max - x_min)
+ k = 0.20
+ x_min -= 2 * k * abs(x_max - x_min)
y_min -= 2 * k * abs(y_max - y_min)
- x_max += 0.6 * k * abs(x_max - x_min)
+ x_max += 2 * k * abs(x_max - x_min)
y_max += 0.6 * k * abs(y_max - y_min)
img = img.crop((int(x_min), int(y_min), int(x_max), int(y_max)))
@@ -138,6 +139,64 @@
# 2,000
return self.length
+class AFLW2000_ds(Dataset):
+ 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
+ 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)
+ mat_path = os.path.join(self.data_dir, self.y_train[index] + self.annot_ext)
+
+ # Crop the face
+ pt2d = utils.get_pt2d_from_mat(mat_path)
+ x_min = min(pt2d[0,:])
+ y_min = min(pt2d[1,:])
+ x_max = max(pt2d[0,:])
+ y_max = max(pt2d[1,:])
+
+ k = 0.20
+ x_min -= 2 * k * abs(x_max - x_min)
+ y_min -= 2 * k * abs(y_max - y_min)
+ x_max += 2 * k * abs(x_max - x_min)
+ y_max += 0.6 * k * abs(y_max - y_min)
+ img = img.crop((int(x_min), int(y_min), int(x_max), int(y_max)))
+
+ ds = 5
+ original_size = img.size
+ img = img.resize((img.size[0] / ds, img.size[1] / ds), resample=0)
+ img = img.resize((original_size[0], original_size[1]), resample=0)
+
+ # We get the pose in radians
+ pose = utils.get_ypr_from_mat(mat_path)
+ # And convert to degrees.
+ pitch = pose[0] * 180 / np.pi
+ yaw = 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)
+ cont_labels = torch.FloatTensor([yaw, pitch, roll])
+
+ if self.transform is not None:
+ img = self.transform(img)
+
+ return img, labels, cont_labels, self.X_train[index]
+
+ def __len__(self):
+ # 2,000
+ return self.length
+
class AFLW_aug(Dataset):
def __init__(self, data_dir, filename_path, transform, img_ext='.jpg', annot_ext='.txt', image_mode='RGB'):
self.data_dir = data_dir
--
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