From dd62d6fa4a85f18a29de009a972f5599b19ec946 Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期四, 14 九月 2017 00:51:53 +0800
Subject: [PATCH] Fixing hopenet
---
code/datasets.py | 110 ++++++++++++-------------------------------------------
1 files changed, 24 insertions(+), 86 deletions(-)
diff --git a/code/datasets.py b/code/datasets.py
index 9a4aeb5..6ed209b 100644
--- a/code/datasets.py
+++ b/code/datasets.py
@@ -60,14 +60,14 @@
img = img.transpose(Image.FLIP_LEFT_RIGHT)
# Rotate?
- rnd = np.random.random_sample()
- if rnd < 0.5:
- if roll >= 0:
- img = img.rotate(60)
- roll -= 60
- else:
- img = img.rotate(-60)
- roll += 60
+ # rnd = np.random.random_sample()
+ # if rnd < 0.5:
+ # if roll >= 0:
+ # img = img.rotate(30)
+ # roll -= 30
+ # else:
+ # img = img.rotate(-30)
+ # roll += 30
# Bin values
bins = np.array(range(-99, 102, 3))
@@ -113,19 +113,19 @@
x_max = max(pt2d[0,:])
y_max = max(pt2d[1,:])
- # k = 0.35
- # x_min -= 0.6 * 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)
- # 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)))
-
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)
+ x_min -= 0.6 * 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)
+ 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)))
+
+ # 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)
@@ -218,11 +218,11 @@
yaw, pitch, roll = [float(line[1]), float(line[2]), float(line[3])]
# Crop the face
- margin = 40
- x_min = float(line[4]) - margin
- y_min = float(line[5]) - margin
- x_max = float(line[6]) + margin
- y_max = float(line[7]) + margin
+ k = 0.25
+ x1 -= 0.6 * k * abs(x2 - x1)
+ y1 -= 3 * k * abs(y2 - y1)
+ x2 += 0.6 * k * abs(x2 - x1)
+ y2 += 0.6 * k * abs(y2 - y1)
img = img.crop((int(x_min), int(y_min), int(x_max), int(y_max)))
@@ -237,68 +237,6 @@
def __len__(self):
# 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 = 0.35
- x_min -= 0.6 * 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)
- 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)))
-
- # 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
class BIWI(Dataset):
--
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