| | |
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| | | # Around 200 |
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| | | return self.length |
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| | | |
|---|
| | | class LP_300W_LP(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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| | | |
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| | | filename_list = get_list_from_filenames(filename_path) |
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| | | |
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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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| | | |
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| | | def __getitem__(self, index): |
|---|
| | | 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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| | | |
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| | | # Crop the face |
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| | | # TODO: Change bounding box. |
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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 |
|---|
| | | 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 binned shape |
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| | | shape = np.load(shape_path) |
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| | | |
|---|
| | | # Convert pt2d to maps of image size |
|---|
| | | # that have |
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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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| | | |
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| | | return img, labels, self.X_train[index] |
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| | | |
|---|
| | | def __len__(self): |
|---|
| | | # 122,450 |
|---|
| | | return self.length |
|---|
| | | |
|---|
| | | |
|---|
| | | def get_list_from_filenames(file_path): |
|---|
| | | # input: relative path to .txt file with file names |
|---|
