[feat] Add texteller training script

examples/train_texteller/utils/transforms.py

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+import torch
+import random
+import numpy as np
+import cv2
+
+from torchvision.transforms import v2
+from typing import Any
+from PIL import Image
+from collections import Counter
+
+from texteller.constants import (
+    IMG_CHANNELS,
+    MAX_RESIZE_RATIO,
+    MIN_RESIZE_RATIO,
+)
+from texteller.utils import transform as inference_transform
+from .augraphy_pipe import get_custom_augraphy
+
+augraphy_pipeline = get_custom_augraphy()
+
+
+def trim_white_border(image: np.ndarray):
+    if len(image.shape) != 3 or image.shape[2] != 3:
+        raise ValueError("Image is not in RGB format or channel is not in third dimension")
+
+    if image.dtype != np.uint8:
+        raise ValueError(f"Image should stored in uint8")
+
+    corners = [tuple(image[0, 0]), tuple(image[0, -1]), tuple(image[-1, 0]), tuple(image[-1, -1])]
+    bg_color = Counter(corners).most_common(1)[0][0]
+    bg_color_np = np.array(bg_color, dtype=np.uint8)
+
+    h, w = image.shape[:2]
+    bg = np.full((h, w, 3), bg_color_np, dtype=np.uint8)
+
+    diff = cv2.absdiff(image, bg)
+    mask = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY)
+
+    threshold = 15
+    _, diff = cv2.threshold(mask, threshold, 255, cv2.THRESH_BINARY)
+
+    x, y, w, h = cv2.boundingRect(diff)
+
+    trimmed_image = image[y : y + h, x : x + w]
+
+    return trimmed_image
+
+
+def add_white_border(image: np.ndarray, max_size: int) -> np.ndarray:
+    randi = [random.randint(0, max_size) for _ in range(4)]
+    pad_height_size = randi[1] + randi[3]
+    pad_width_size = randi[0] + randi[2]
+    if pad_height_size + image.shape[0] < 30:
+        compensate_height = int((30 - (pad_height_size + image.shape[0])) * 0.5) + 1
+        randi[1] += compensate_height
+        randi[3] += compensate_height
+    if pad_width_size + image.shape[1] < 30:
+        compensate_width = int((30 - (pad_width_size + image.shape[1])) * 0.5) + 1
+        randi[0] += compensate_width
+        randi[2] += compensate_width
+    return v2.functional.pad(
+        torch.from_numpy(image).permute(2, 0, 1),
+        padding=randi,
+        padding_mode="constant",
+        fill=(255, 255, 255),
+    )
+
+
+def padding(images: list[torch.Tensor], required_size: int) -> list[torch.Tensor]:
+    images = [
+        v2.functional.pad(
+            img, padding=[0, 0, required_size - img.shape[2], required_size - img.shape[1]]
+        )
+        for img in images
+    ]
+    return images
+
+
+def random_resize(images: list[np.ndarray], minr: float, maxr: float) -> list[np.ndarray]:
+    if len(images[0].shape) != 3 or images[0].shape[2] != 3:
+        raise ValueError("Image is not in RGB format or channel is not in third dimension")
+
+    ratios = [random.uniform(minr, maxr) for _ in range(len(images))]
+    return [
+        # Anti-aliasing
+        cv2.resize(
+            img, (int(img.shape[1] * r), int(img.shape[0] * r)), interpolation=cv2.INTER_LANCZOS4
+        )
+        for img, r in zip(images, ratios)
+    ]
+
+
+def rotate(image: np.ndarray, min_angle: int, max_angle: int) -> np.ndarray:
+    # Get the center of the image to define the point of rotation
+    image_center = tuple(np.array(image.shape[1::-1]) / 2)
+
+    # Generate a random angle within the specified range
+    angle = random.randint(min_angle, max_angle)
+
+    # Get the rotation matrix for rotating the image around its center
+    rotation_mat = cv2.getRotationMatrix2D(image_center, angle, 1.0)
+
+    # Determine the size of the rotated image
+    cos = np.abs(rotation_mat[0, 0])
+    sin = np.abs(rotation_mat[0, 1])
+    new_width = int((image.shape[0] * sin) + (image.shape[1] * cos))
+    new_height = int((image.shape[0] * cos) + (image.shape[1] * sin))
+
+    # Adjust the rotation matrix to take into account translation
+    rotation_mat[0, 2] += (new_width / 2) - image_center[0]
+    rotation_mat[1, 2] += (new_height / 2) - image_center[1]
+
+    # Rotate the image with the specified border color (white in this case)
+    rotated_image = cv2.warpAffine(
+        image, rotation_mat, (new_width, new_height), borderValue=(255, 255, 255)
+    )
+
+    return rotated_image
+
+
+def ocr_aug(image: np.ndarray) -> np.ndarray:
+    if random.random() < 0.2:
+        image = rotate(image, -5, 5)
+    image = add_white_border(image, max_size=25).permute(1, 2, 0).numpy()
+    image = augraphy_pipeline(image)
+    return image
+
+
+def train_transform(images: list[Image.Image]) -> list[torch.Tensor]:
+    assert IMG_CHANNELS == 1, "Only support grayscale images for now"
+
+    images = [np.array(img.convert("RGB")) for img in images]
+    # random resize first
+    images = random_resize(images, MIN_RESIZE_RATIO, MAX_RESIZE_RATIO)
+    images = [trim_white_border(image) for image in images]
+
+    # OCR augmentation
+    images = [ocr_aug(image) for image in images]
+
+    # general transform pipeline
+    images = inference_transform(images)
+    return images
+
+
+def img_train_transform(samples: dict[str, list[Any]]) -> dict[str, list[Any]]:
+    processed_img = train_transform(samples["pixel_values"])
+    samples["pixel_values"] = processed_img
+    return samples
+
+
+def img_inf_transform(samples: dict[str, list[Any]]) -> dict[str, list[Any]]:
+    processed_img = inference_transform(samples["pixel_values"])
+    samples["pixel_values"] = processed_img
+    return samples