From 0b8e19c1cc8ad03805d4ca68f32df6e4806a36e8 Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期五, 08 九月 2017 11:15:10 +0800
Subject: [PATCH] Finetune layer working
---
code/datasets.py | 325 +++++++++++++++++++++++++++++++++++++++++++++++++++---
1 files changed, 306 insertions(+), 19 deletions(-)
diff --git a/code/datasets.py b/code/datasets.py
index 030059f..f24f063 100644
--- a/code/datasets.py
+++ b/code/datasets.py
@@ -7,8 +7,12 @@
import utils
+def stack_grayscale_tensor(tensor):
+ tensor = torch.cat([tensor, tensor, tensor], 0)
+ return tensor
+
class Pose_300W_LP(Dataset):
- def __init__(self, data_dir, filename_path, transform, img_ext='.jpg', annot_ext='.mat'):
+ 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
@@ -18,26 +22,73 @@
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('RGB')
+ 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')
- pose = utils.get_ypr_from_mat(os.path.join(self.data_dir, self.y_train[index] + self.annot_ext))
- label = torch.FloatTensor(pose)
+ # 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)))
+
+ # 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
+
+ # Flip?
+ rnd = np.random.random_sample()
+ if rnd < 0.5:
+ yaw = -yaw
+ 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
+
+ # Bin values
+ bins = np.array(range(-99, 102, 3))
+ binned_pose = np.digitize([yaw, pitch, roll], bins) - 1
+
+ # Get shape
+ shape = np.load(shape_path)
+
+ labels = torch.LongTensor(np.concatenate((binned_pose, shape), axis = 0))
if self.transform is not None:
img = self.transform(img)
- return img, label, self.X_train[index]
+ return img, labels, self.X_train[index]
def __len__(self):
# 122,450
return self.length
class AFLW2000(Dataset):
- def __init__(self, data_dir, filename_path, transform, img_ext='.jpg', annot_ext='.mat'):
+ 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
@@ -47,26 +98,56 @@
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('RGB')
+ img = img.convert(self.image_mode)
+ mat_path = os.path.join(self.data_dir, self.y_train[index] + self.annot_ext)
- pose = utils.get_ypr_from_mat(os.path.join(self.data_dir, self.y_train[index] + self.annot_ext))
- label = torch.FloatTensor(pose)
+ # 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)
+ 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))
+ labels = torch.LongTensor(np.digitize([yaw, pitch, roll], bins) - 1)
if self.transform is not None:
img = self.transform(img)
- return img, label, self.X_train[index]
+ return img, labels, self.X_train[index]
def __len__(self):
# 2,000
return self.length
-class Pose_300W_LP_binned(Dataset):
- def __init__(self, data_dir, filename_path, transform, img_ext='.jpg', annot_ext='.mat'):
+class AFLW(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
@@ -76,28 +157,234 @@
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('RGB')
+ 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
- pose = utils.get_ypr_from_mat(os.path.join(self.data_dir, self.y_train[index] + self.annot_ext))
- # And convert to positive degrees.
- pose = pose * 180 / np.pi + 90
-
- label = torch.FloatTensor(pose)
+ 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
+ # if yaw < 0:
+ roll *= -1
+ # Bin values
+ bins = np.array(range(-99, 102, 3))
+ labels = torch.LongTensor(np.digitize([yaw, pitch, roll], bins) - 1)
if self.transform is not None:
img = self.transform(img)
- return img, label, self.X_train[index]
+ return img, labels, self.X_train[index]
+
+ def __len__(self):
+ # train: 18,863
+ # test: 1,966
+ return self.length
+
+class AFW(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):
+ txt_path = os.path.join(self.data_dir, self.y_train[index] + self.annot_ext)
+ img_name = self.X_train[index].split('_')[0]
+
+ img = Image.open(os.path.join(self.data_dir, img_name + 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 degrees
+ annot = open(txt_path, 'r')
+ line = annot.readline().split(' ')
+ 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
+
+ img = img.crop((int(x_min), int(y_min), int(x_max), int(y_max)))
+
+ # Bin values
+ bins = np.array(range(-99, 102, 3))
+ labels = torch.LongTensor(np.digitize([yaw, pitch, roll], bins) - 1)
+
+ if self.transform is not None:
+ img = self.transform(img)
+
+ return img, 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):
+ def __init__(self, data_dir, filename_path, transform, img_ext='.png', 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] + '_rgb' + self.img_ext))
+ img = img.convert(self.image_mode)
+ pose_path = os.path.join(self.data_dir, self.y_train[index] + '_pose' + self.annot_ext)
+
+ y_train_list = self.y_train[index].split('/')
+ bbox_path = os.path.join(self.data_dir, y_train_list[0] + '/dockerface-' + y_train_list[-1] + '_rgb' + self.annot_ext)
+
+ # Load bounding box
+ bbox = open(bbox_path, 'r')
+ line = bbox.readline().split(' ')
+ if len(line) < 4:
+ x_min, y_min, x_max, y_max = 0, 0, img.size[0], img.size[1]
+ else:
+ x_min, y_min, x_max, y_max = [float(line[1]), float(line[2]), float(line[3]), float(line[4])]
+ bbox.close()
+
+ # Load pose in degrees
+ pose_annot = open(pose_path, 'r')
+ R = []
+ for line in pose_annot:
+ line = line.strip('\n').split(' ')
+ l = []
+ if line[0] != '':
+ for nb in line:
+ if nb == '':
+ continue
+ l.append(float(nb))
+ R.append(l)
+
+ R = np.array(R)
+ T = R[3,:]
+ 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
+
+ # Loosely crop face
+ k = 0.35
+ x_min -= 0.6 * k * abs(x_max - x_min)
+ y_min -= 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)))
+
+ # 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)
+
+ if self.transform is not None:
+ img = self.transform(img)
+
+ return img, labels, self.X_train[index]
+
+ def __len__(self):
+ # 15,667
+ return self.length
+
+
def get_list_from_filenames(file_path):
# input: relative path to .txt file with file names
# output: list of relative path names
--
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