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 | 266 ++++++++++++++++++++++++++++++++---------------------
1 files changed, 161 insertions(+), 105 deletions(-)
diff --git a/code/datasets.py b/code/datasets.py
index f24f063..17f1899 100644
--- a/code/datasets.py
+++ b/code/datasets.py
@@ -3,7 +3,7 @@
import cv2
from torch.utils.data.dataset import Dataset
import os
-from PIL import Image
+from PIL import Image, ImageFilter
import utils
@@ -38,7 +38,9 @@
x_max = max(pt2d[0,:])
y_max = max(pt2d[1,:])
- k = 0.35
+ # k = 0.35 was being used beforehand
+ # k = 0.2 to 0.40
+ k = np.random.random_sample() * 0.2 + 0.2
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)
@@ -59,15 +61,10 @@
roll = -roll
img = img.transpose(Image.FLIP_LEFT_RIGHT)
- # Rotate?
- # 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
+ # Blur?
+ rnd = np.random.random_sample()
+ if rnd < 0.05:
+ img = img.filter(ImageFilter.BLUR)
# Bin values
bins = np.array(range(-99, 102, 3))
@@ -77,11 +74,12 @@
shape = np.load(shape_path)
labels = torch.LongTensor(np.concatenate((binned_pose, shape), axis = 0))
+ cont_labels = torch.FloatTensor([yaw, pitch, roll])
if self.transform is not None:
img = self.transform(img)
- return img, labels, self.X_train[index]
+ return img, labels, cont_labels, self.X_train[index]
def __len__(self):
# 122,450
@@ -108,23 +106,17 @@
# 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 = 0.35
- # 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)
+ 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)))
# We get the pose in radians
@@ -136,14 +128,141 @@
# 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, self.X_train[index]
+ return img, labels, cont_labels, self.X_train[index]
def __len__(self):
# 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
+ 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
+ # Something weird with the roll in AFLW
+ roll *= -1
+
+ # Augment
+ # Flip?
+ rnd = np.random.random_sample()
+ if rnd < 0.5:
+ yaw = -yaw
+ roll = -roll
+ img = img.transpose(Image.FLIP_LEFT_RIGHT)
+
+ # Blur?
+ # rnd = np.random.random_sample()
+ # if rnd < 0.05:
+ # img = img.filter(ImageFilter.BLUR)
+ # if rnd < 0.025:
+ # img = img.filter(ImageFilter.BLUR)
+ #
+ # rnd = np.random.random_sample()
+ # if rnd < 0.05:
+ # nb = np.random.randint(1,5)
+ # img = img.rotate(-nb)
+ # elif rnd > 0.95:
+ # nb = np.random.randint(1,5)
+ # img = img.rotate(nb)
+
+ # 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):
+ # train: 18,863
+ # test: 1,966
return self.length
class AFLW(Dataset):
@@ -174,16 +293,16 @@
pitch = pose[1] * 180 / np.pi
roll = pose[2] * 180 / np.pi
# Something weird with the roll in AFLW
- # if yaw < 0:
roll *= -1
# 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, self.X_train[index]
+ return img, labels, cont_labels, self.X_train[index]
def __len__(self):
# train: 18,863
@@ -218,87 +337,30 @@
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 = 0.32
+ x1 = float(line[4])
+ y1 = float(line[5])
+ x2 = float(line[6])
+ y2 = float(line[7])
+ x1 -= 0.8 * k * abs(x2 - x1)
+ y1 -= 2 * k * abs(y2 - y1)
+ x2 += 0.8 * k * abs(x2 - x1)
+ y2 += 1 * k * abs(y2 - y1)
- img = img.crop((int(x_min), int(y_min), int(x_max), int(y_max)))
+ img = img.crop((int(x1), int(y1), int(x2), int(y2)))
# 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, self.X_train[index]
+ return img, labels, cont_labels, self.X_train[index]
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):
@@ -350,7 +412,7 @@
R = R[:3,:]
pose_annot.close()
- roll = np.arctan2(R[1][0], R[0][0]) * 180 / np.pi
+ 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
@@ -362,23 +424,17 @@
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)))
- # Flip?
- # rnd = np.random.random_sample()
- # if rnd < 0.5:
- # yaw = -yaw
- # roll = -roll
- # img = img.transpose(Image.FLIP_LEFT_RIGHT)
-
# Bin values
bins = np.array(range(-99, 102, 3))
binned_pose = np.digitize([yaw, pitch, roll], bins) - 1
labels = torch.LongTensor(binned_pose)
+ cont_labels = torch.FloatTensor([yaw, pitch, roll])
if self.transform is not None:
img = self.transform(img)
- return img, labels, self.X_train[index]
+ return img, labels, cont_labels, self.X_train[index]
def __len__(self):
# 15,667
--
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