import numpy as np
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import torch
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import cv2
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from torch.utils.data.dataset import Dataset
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import os
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from PIL import Image
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import utils
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def stack_grayscale_tensor(tensor):
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tensor = torch.cat([tensor, tensor, tensor], 0)
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return tensor
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class Pose_300W_LP(Dataset):
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def __init__(self, data_dir, filename_path, transform, img_ext='.jpg', annot_ext='.mat'):
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self.data_dir = data_dir
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self.transform = transform
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self.img_ext = img_ext
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self.annot_ext = annot_ext
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filename_list = get_list_from_filenames(filename_path)
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self.X_train = filename_list
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self.y_train = filename_list
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self.length = len(filename_list)
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def __getitem__(self, index):
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img = Image.open(os.path.join(self.data_dir, self.X_train[index] + self.img_ext))
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img = img.convert('RGB')
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pose = utils.get_ypr_from_mat(os.path.join(self.data_dir, self.y_train[index] + self.annot_ext))
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label = torch.FloatTensor(pose)
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if self.transform is not None:
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img = self.transform(img)
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return img, label, self.X_train[index]
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def __len__(self):
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# 122,450
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return self.length
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class AFLW2000(Dataset):
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def __init__(self, data_dir, filename_path, transform, img_ext='.jpg', annot_ext='.mat'):
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self.data_dir = data_dir
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self.transform = transform
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self.img_ext = img_ext
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self.annot_ext = annot_ext
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filename_list = get_list_from_filenames(filename_path)
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self.X_train = filename_list
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self.y_train = filename_list
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self.length = len(filename_list)
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def __getitem__(self, index):
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img = Image.open(os.path.join(self.data_dir, self.X_train[index] + self.img_ext))
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img = img.convert('RGB')
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pose = utils.get_ypr_from_mat(os.path.join(self.data_dir, self.y_train[index] + self.annot_ext))
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label = torch.FloatTensor(pose)
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if self.transform is not None:
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img = self.transform(img)
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return img, label, self.X_train[index]
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def __len__(self):
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# 2,000
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return self.length
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class Pose_300W_LP_binned(Dataset):
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def __init__(self, data_dir, filename_path, transform, img_ext='.jpg', annot_ext='.mat', image_mode='RGB'):
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self.data_dir = data_dir
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self.transform = transform
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self.img_ext = img_ext
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self.annot_ext = annot_ext
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filename_list = get_list_from_filenames(filename_path)
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self.X_train = filename_list
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self.y_train = filename_list
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self.image_mode = image_mode
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self.length = len(filename_list)
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def __getitem__(self, index):
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img = Image.open(os.path.join(self.data_dir, self.X_train[index] + self.img_ext))
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img = img.convert(self.image_mode)
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mat_path = os.path.join(self.data_dir, self.y_train[index] + self.annot_ext)
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shape_path = os.path.join(self.data_dir, self.y_train[index] + '_shape.npy')
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# Crop the face
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pt2d = utils.get_pt2d_from_mat(mat_path)
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x_min = min(pt2d[0,:])
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y_min = min(pt2d[1,:])
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x_max = max(pt2d[0,:])
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y_max = max(pt2d[1,:])
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k = 0.15
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x_min -= k * abs(x_max - x_min)
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y_min -= 4 * k * abs(y_max - y_min)
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x_max += k * abs(x_max - x_min)
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y_max += 0.4 * k * abs(y_max - y_min)
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img = img.crop((int(x_min), int(y_min), int(x_max), int(y_max)))
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# We get the pose in radians
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pose = utils.get_ypr_from_mat(mat_path)
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# And convert to degrees.
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pitch = pose[0] * 180 / np.pi
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yaw = pose[1] * 180 / np.pi
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roll = pose[2] * 180 / np.pi
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# Bin values
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bins = np.array(range(-99, 102, 3))
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binned_pose = np.digitize([yaw, pitch, roll], bins) - 1
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# Get shape
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shape = np.load(shape_path)
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labels = torch.LongTensor(np.concatenate((binned_pose, shape), axis = 0))
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if self.transform is not None:
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img = self.transform(img)
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return img, labels, self.X_train[index]
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def __len__(self):
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# 122,450
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return self.length
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class AFLW2000_binned(Dataset):
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def __init__(self, data_dir, filename_path, transform, img_ext='.jpg', annot_ext='.mat', image_mode='RGB'):
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self.data_dir = data_dir
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self.transform = transform
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self.img_ext = img_ext
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self.annot_ext = annot_ext
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filename_list = get_list_from_filenames(filename_path)
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self.X_train = filename_list
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self.y_train = filename_list
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self.image_mode = image_mode
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self.length = len(filename_list)
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def __getitem__(self, index):
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img = Image.open(os.path.join(self.data_dir, self.X_train[index] + self.img_ext))
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img = img.convert(self.image_mode)
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mat_path = os.path.join(self.data_dir, self.y_train[index] + self.annot_ext)
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# Crop the face
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pt2d = utils.get_pt2d_from_mat(mat_path)
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x_min = min(pt2d[0,:])
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y_min = min(pt2d[1,:])
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x_max = max(pt2d[0,:])
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y_max = max(pt2d[1,:])
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k = 0.15
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x_min -= k * abs(x_max - x_min)
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y_min -= 4 * k * abs(y_max - y_min)
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x_max += k * abs(x_max - x_min)
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y_max += 0.4 * k * abs(y_max - y_min)
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img = img.crop((int(x_min), int(y_min), int(x_max), int(y_max)))
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# We get the pose in radians
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pose = utils.get_ypr_from_mat(mat_path)
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# And convert to degrees.
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pitch = pose[0] * 180 / np.pi
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yaw = pose[1] * 180 / np.pi
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roll = pose[2] * 180 / np.pi
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# Bin values
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bins = np.array(range(-99, 102, 3))
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labels = torch.LongTensor(np.digitize([yaw, pitch, roll], bins) - 1)
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if self.transform is not None:
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img = self.transform(img)
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return img, labels, self.X_train[index]
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def __len__(self):
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# 2,000
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return self.length
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class AFLW(Dataset):
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def __init__(self, data_dir, filename_path, transform, img_ext='.jpg', annot_ext='.txt', image_mode='RGB'):
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self.data_dir = data_dir
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self.transform = transform
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self.img_ext = img_ext
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self.annot_ext = annot_ext
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filename_list = get_list_from_filenames(filename_path)
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self.X_train = filename_list
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self.y_train = filename_list
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self.image_mode = image_mode
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self.length = len(filename_list)
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def __getitem__(self, index):
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img = Image.open(os.path.join(self.data_dir, self.X_train[index] + self.img_ext))
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img = img.convert(self.image_mode)
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txt_path = os.path.join(self.data_dir, self.y_train[index] + self.annot_ext)
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# We get the pose in radians
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annot = open(txt_path, 'r')
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line = annot.readline().split(' ')
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pose = [float(line[1]), float(line[2]), float(line[3])]
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# And convert to degrees.
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yaw = pose[0] * 180 / np.pi
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pitch = pose[1] * 180 / np.pi
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roll = pose[2] * 180 / np.pi
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# Bin values
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bins = np.array(range(-99, 102, 3))
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labels = torch.LongTensor(np.digitize([yaw, pitch, roll], bins) - 1)
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if self.transform is not None:
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img = self.transform(img)
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return img, labels, self.X_train[index]
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def __len__(self):
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# Check how many
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return self.length
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def get_list_from_filenames(file_path):
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# input: relative path to .txt file with file names
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# output: list of relative path names
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with open(file_path) as f:
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lines = f.read().splitlines()
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return lines
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