From 7e2652eace4f443b6a01f006b420e91128f0bb7a Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期一, 04 九月 2017 03:25:59 +0800
Subject: [PATCH] Before modifying dataset loading for data augmentation.
---
code/datasets.py | 62 +++++++++++++++++++++++++++++++
1 files changed, 62 insertions(+), 0 deletions(-)
diff --git a/code/datasets.py b/code/datasets.py
index 63ee287..4594cbc 100644
--- a/code/datasets.py
+++ b/code/datasets.py
@@ -214,6 +214,68 @@
# Around 200
return self.length
+class LP_300W_LP(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)
+ shape_path = os.path.join(self.data_dir, self.y_train[index] + '_shape.npy')
+
+ # Crop the face
+ # TODO: Change bounding box.
+ 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 -= k * abs(x_max - x_min)
+ y_min -= 4 * k * abs(y_max - y_min)
+ x_max += k * abs(x_max - x_min)
+ y_max += 0.4 * k * abs(y_max - y_min)
+ 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_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))
+ binned_pose = np.digitize([yaw, pitch, roll], bins) - 1
+
+ # Get shape binned shape
+ shape = np.load(shape_path)
+
+ # Convert pt2d to maps of image size
+ # that have
+
+ labels = torch.LongTensor(np.concatenate((binned_pose, shape), axis = 0))
+
+ if self.transform is not None:
+ img = self.transform(img)
+
+ return img, labels, self.X_train[index]
+
+ def __len__(self):
+ # 122,450
+ return self.length
+
def get_list_from_filenames(file_path):
# input: relative path to .txt file with file names
--
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