diff options
Diffstat (limited to 'text_recognizer/data/stems')
| -rw-r--r-- | text_recognizer/data/stems/__init__.py | 0 | ||||
| -rw-r--r-- | text_recognizer/data/stems/image.py | 18 | ||||
| -rw-r--r-- | text_recognizer/data/stems/line.py | 93 | ||||
| -rw-r--r-- | text_recognizer/data/stems/paragraph.py | 66 |
4 files changed, 0 insertions, 177 deletions
diff --git a/text_recognizer/data/stems/__init__.py b/text_recognizer/data/stems/__init__.py deleted file mode 100644 index e69de29..0000000 --- a/text_recognizer/data/stems/__init__.py +++ /dev/null diff --git a/text_recognizer/data/stems/image.py b/text_recognizer/data/stems/image.py deleted file mode 100644 index f04b3a0..0000000 --- a/text_recognizer/data/stems/image.py +++ /dev/null @@ -1,18 +0,0 @@ -from PIL import Image -import torch -from torch import Tensor -import torchvision.transforms as T - - -class ImageStem: - def __init__(self) -> None: - self.pil_transform = T.Compose([]) - self.pil_to_tensor = T.ToTensor() - self.torch_transform = torch.nn.Sequential() - - def __call__(self, img: Image) -> Tensor: - img = self.pil_transform(img) - img = self.pil_to_tensor(img) - with torch.no_grad(): - img = self.torch_transform(img) - return img diff --git a/text_recognizer/data/stems/line.py b/text_recognizer/data/stems/line.py deleted file mode 100644 index 4f0ce05..0000000 --- a/text_recognizer/data/stems/line.py +++ /dev/null @@ -1,93 +0,0 @@ -import random -from typing import Any, Dict - -from PIL import Image -import torchvision.transforms as T - -import text_recognizer.metadata.iam_lines as metadata -from text_recognizer.data.stems.image import ImageStem - - -class LineStem(ImageStem): - """A stem for handling images containing a line of text.""" - - def __init__( - self, - augment: bool = False, - color_jitter_kwargs: Dict[str, Any] = None, - random_affine_kwargs: Dict[str, Any] = None, - ) -> None: - super().__init__() - if color_jitter_kwargs is None: - color_jitter_kwargs = {"brightness": (0.5, 1)} - if random_affine_kwargs is None: - random_affine_kwargs = { - "degrees": 3, - "translate": (0, 0.05), - "scale": (0.4, 1.1), - "shear": (-40, 50), - "interpolation": T.InterpolationMode.BILINEAR, - "fill": 0, - } - - if augment: - self.pil_transforms = T.Compose( - [ - T.ColorJitter(**color_jitter_kwargs), - T.RandomAffine(**random_affine_kwargs), - ] - ) - - -class IamLinesStem(ImageStem): - """A stem for handling images containing lines of text from the IAMLines dataset.""" - - def __init__( - self, - augment: bool = False, - color_jitter_kwargs: Dict[str, Any] = None, - random_affine_kwargs: Dict[str, Any] = None, - ) -> None: - super().__init__() - - def embed_crop(crop, augment=augment): - # crop is PIL.image of dtype="L" (so values range from 0 -> 255) - image = Image.new("L", (metadata.IMAGE_WIDTH, metadata.IMAGE_HEIGHT)) - - # Resize crop - crop_width, crop_height = crop.size - new_crop_height = metadata.IMAGE_HEIGHT - new_crop_width = int(new_crop_height * (crop_width / crop_height)) - if augment: - # Add random stretching - new_crop_width = int(new_crop_width * random.uniform(0.9, 1.1)) - new_crop_width = min(new_crop_width, metadata.IMAGE_WIDTH) - crop_resized = crop.resize( - (new_crop_width, new_crop_height), resample=Image.BILINEAR - ) - - # Embed in the image - x = min(metadata.CHAR_WIDTH, metadata.IMAGE_WIDTH - new_crop_width) - y = metadata.IMAGE_HEIGHT - new_crop_height - - image.paste(crop_resized, (x, y)) - - return image - - if color_jitter_kwargs is None: - color_jitter_kwargs = {"brightness": (0.8, 1.6)} - if random_affine_kwargs is None: - random_affine_kwargs = { - "degrees": 1, - "shear": (-30, 20), - "interpolation": T.InterpolationMode.BILINEAR, - "fill": 0, - } - - pil_transform_list = [T.Lambda(embed_crop)] - if augment: - pil_transform_list += [ - T.ColorJitter(**color_jitter_kwargs), - T.RandomAffine(**random_affine_kwargs), - ] - self.pil_transform = T.Compose(pil_transform_list) diff --git a/text_recognizer/data/stems/paragraph.py b/text_recognizer/data/stems/paragraph.py deleted file mode 100644 index 39e1e59..0000000 --- a/text_recognizer/data/stems/paragraph.py +++ /dev/null @@ -1,66 +0,0 @@ -"""Iam paragraph stem class.""" -import torchvision.transforms as T - -import text_recognizer.metadata.iam_paragraphs as metadata -from text_recognizer.data.stems.image import ImageStem - - -IMAGE_HEIGHT, IMAGE_WIDTH = metadata.IMAGE_HEIGHT, metadata.IMAGE_WIDTH -IMAGE_SHAPE = metadata.IMAGE_SHAPE - -MAX_LABEL_LENGTH = metadata.MAX_LABEL_LENGTH - - -class ParagraphStem(ImageStem): - """A stem for handling images that contain a paragraph of text.""" - - def __init__( - self, - augment=False, - color_jitter_kwargs=None, - random_affine_kwargs=None, - random_perspective_kwargs=None, - gaussian_blur_kwargs=None, - sharpness_kwargs=None, - ): - super().__init__() - - if not augment: - self.pil_transform = T.Compose([T.CenterCrop(IMAGE_SHAPE)]) - else: - if color_jitter_kwargs is None: - color_jitter_kwargs = {"brightness": 0.4, "contrast": 0.4} - if random_affine_kwargs is None: - random_affine_kwargs = { - "degrees": 3, - "shear": 6, - "scale": (0.95, 1), - "interpolation": T.InterpolationMode.BILINEAR, - } - if random_perspective_kwargs is None: - random_perspective_kwargs = { - "distortion_scale": 0.2, - "p": 0.5, - "interpolation": T.InterpolationMode.BILINEAR, - } - if gaussian_blur_kwargs is None: - gaussian_blur_kwargs = {"kernel_size": (3, 3), "sigma": (0.1, 1.0)} - if sharpness_kwargs is None: - sharpness_kwargs = {"sharpness_factor": 2, "p": 0.5} - - self.pil_transform = T.Compose( - [ - T.ColorJitter(**color_jitter_kwargs), - T.RandomCrop( - size=IMAGE_SHAPE, - padding=None, - pad_if_needed=True, - fill=0, - padding_mode="constant", - ), - T.RandomAffine(**random_affine_kwargs), - T.RandomPerspective(**random_perspective_kwargs), - T.GaussianBlur(**gaussian_blur_kwargs), - T.RandomAdjustSharpness(**sharpness_kwargs), - ] - ) |