From 1ddaaf685a3c5febca32305fc588f982b5e4cdaa Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期四, 28 九月 2017 06:21:15 +0800
Subject: [PATCH] Final code before submission
---
code/datasets.py | 70 +++--------------------------------
1 files changed, 6 insertions(+), 64 deletions(-)
diff --git a/code/datasets.py b/code/datasets.py
index aee5246..c7fc7f7 100644
--- a/code/datasets.py
+++ b/code/datasets.py
@@ -333,70 +333,10 @@
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 = 8
+ ds = 3
original_size = img.size
img = img.resize((img.size[0] / ds, img.size[1] / ds), resample=Image.NEAREST)
img = img.resize((original_size[0], original_size[1]), resample=Image.NEAREST)
-
- # 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 AFLW2000_random_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)))
-
- rnd = np.random.random_sample()
- if rnd < 0.5:
- ds = 10
- original_size = img.size
- img = img.resize((img.size[0] / ds, img.size[1] / ds), resample=Image.NEAREST)
- img = img.resize((original_size[0], original_size[1]), resample=Image.NEAREST)
# We get the pose in radians
pose = utils.get_ypr_from_mat(mat_path)
@@ -633,9 +573,11 @@
R = R[:3,:]
pose_annot.close()
- roll = np.arctan2(R[1][0], R[0][0]) * 180 / np.pi
- yaw = np.arctan2(-R[2][0], np.sqrt(R[2][1] ** 2 + R[2][2] ** 2)) * 180 / np.pi
- pitch = -np.arctan2(R[2][1], R[2][2]) * 180 / np.pi
+ R = np.transpose(R)
+
+ roll = -np.arctan2(R[1][0], R[0][0]) * 180 / np.pi
+ yaw = -np.arctan2(-R[2][0], np.sqrt(R[2][1] ** 2 + R[2][2] ** 2)) * 180 / np.pi
+ pitch = np.arctan2(R[2][1], R[2][2]) * 180 / np.pi
# Loosely crop face
k = 0.35
--
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