From ec44ac453f794a5368e702315addfedcea3a4299 Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期二, 19 九月 2017 06:01:47 +0800
Subject: [PATCH] Added continuous labels
---
code/datasets.py | 108 ++++++++++++++++++++++++++++++++++++++++++++---------
1 files changed, 89 insertions(+), 19 deletions(-)
diff --git a/code/datasets.py b/code/datasets.py
index 589da5c..6f1f1ff 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
@@ -77,11 +77,12 @@
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, elf.X_train[index]
def __len__(self):
# 122,450
@@ -120,13 +121,6 @@
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)
# And convert to degrees.
@@ -136,14 +130,83 @@
# 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 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):
@@ -178,11 +241,12 @@
# 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
@@ -217,22 +281,27 @@
yaw, pitch, roll = [float(line[1]), float(line[2]), float(line[3])]
# Crop the face
- 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)
+ k = 0.40
+ 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
@@ -311,11 +380,12 @@
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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