| | |
|---|
| | | # 2,000 |
|---|
| | | return self.length |
|---|
| | | |
|---|
| | | 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 |
|---|
| | | 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) |
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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 |
|---|
| | | annot = open(txt_path, 'r') |
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| | | 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 |
|---|
| | | # Bin values |
|---|
| | | bins = np.array(range(-99, 102, 3)) |
|---|
| | | labels = torch.LongTensor(np.digitize([yaw, pitch, roll], bins) - 1) |
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| | | |
|---|
| | | if self.transform is not None: |
|---|
| | | img = self.transform(img) |
|---|
| | | |
|---|
| | | return img, labels, self.X_train[index] |
|---|
| | | |
|---|
| | | def __len__(self): |
|---|
| | | # Check how many |
|---|
| | | 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 |
|---|
