222 lines
7.8 KiB
Python
222 lines
7.8 KiB
Python
import torch
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import random
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import numpy as np
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import cv2
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from torchvision.transforms import v2
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from typing import List
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from PIL import Image
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from ...globals import (
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OCR_IMG_CHANNELS,
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OCR_IMG_SIZE,
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OCR_FIX_SIZE,
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IMAGE_MEAN, IMAGE_STD,
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MAX_RESIZE_RATIO, MIN_RESIZE_RATIO
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)
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from .ocr_aug import ocr_augmentation_pipeline
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# train_pipeline = default_augraphy_pipeline(scan_only=True)
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train_pipeline = ocr_augmentation_pipeline()
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general_transform_pipeline = v2.Compose([
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v2.ToImage(), # Convert to tensor, only needed if you had a PIL image
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#+返回一个List of torchvision.Image，list的长度就是batch_size
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#+因此在整个Compose pipeline的最后，输出的也是一个List of torchvision.Image
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#+注意：不是返回一整个torchvision.Image，batch_size的维度是拿出来的
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v2.ToDtype(torch.uint8, scale=True), # optional, most input are already uint8 at this point
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v2.Grayscale(), # 转灰度图（视具体任务而定）
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v2.Resize( # 固定resize到一个正方形上
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size=OCR_IMG_SIZE - 1, # size必须小于max_size
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interpolation=v2.InterpolationMode.BICUBIC,
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max_size=OCR_IMG_SIZE,
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antialias=True
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),
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v2.ToDtype(torch.float32, scale=True), # Normalize expects float input
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v2.Normalize(mean=[IMAGE_MEAN], std=[IMAGE_STD]),
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# v2.ToPILImage() # 用于观察转换后的结果是否正确（debug用）
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])
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def trim_white_border(image: np.ndarray):
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# image是一个3维的ndarray，RGB格式，维度分布为[H, W, C]（通道维在第三维上）
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# # 检查images中的第一个元素是否是嵌套的列表结构
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# if isinstance(image, list):
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# image = np.array(image, dtype=np.uint8)
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# 检查图像是否为RGB格式，同时检查通道维是不是在第三维上
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if len(image.shape) != 3 or image.shape[2] != 3:
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raise ValueError("Image is not in RGB format or channel is not in third dimension")
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# 检查图片是否使用 uint8 类型
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if image.dtype != np.uint8:
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raise ValueError(f"Image should stored in uint8")
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# 创建与原图像同样大小的纯白背景图像
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h, w = image.shape[:2]
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bg = np.full((h, w, 3), 255, dtype=np.uint8)
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# 计算差异
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diff = cv2.absdiff(image, bg)
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# 只要差值大于1，就全部转化为255
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_, diff = cv2.threshold(diff, 1, 255, cv2.THRESH_BINARY)
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# 把差值转灰度图
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gray_diff = cv2.cvtColor(diff, cv2.COLOR_RGB2GRAY)
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# 计算图像中非零像素点的最小外接矩阵
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x, y, w, h = cv2.boundingRect(gray_diff)
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# 裁剪图像
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trimmed_image = image[y:y+h, x:x+w]
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return trimmed_image
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def add_white_border(image: np.ndarray, max_size: int) -> np.ndarray:
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randi = [random.randint(0, max_size) for _ in range(4)]
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pad_height_size = randi[1] + randi[3]
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pad_width_size = randi[0] + randi[2]
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if (pad_height_size + image.shape[0] < 30):
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compensate_height = int((30 - (pad_height_size + image.shape[0])) * 0.5) + 1
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randi[1] += compensate_height
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randi[3] += compensate_height
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if (pad_width_size + image.shape[1] < 30):
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compensate_width = int((30 - (pad_width_size + image.shape[1])) * 0.5) + 1
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randi[0] += compensate_width
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randi[2] += compensate_width
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return v2.functional.pad(
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torch.from_numpy(image).permute(2, 0, 1),
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padding=randi,
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padding_mode='constant',
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fill=(255, 255, 255)
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)
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def padding(images: List[torch.Tensor], required_size: int) -> List[torch.Tensor]:
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images = [
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v2.functional.pad(
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img,
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padding=[0, 0, required_size - img.shape[2], required_size - img.shape[1]]
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)
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for img in images
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]
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return images
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def random_resize(
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images: List[np.ndarray],
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minr: float,
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maxr: float
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) -> List[np.ndarray]:
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# np.ndarray的格式：3维，RGB格式，维度分布为[H, W, C]（通道维在第三维上）
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# # 检查images中的第一个元素是否是嵌套的列表结构
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# if isinstance(images[0], list):
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# # 将嵌套的列表结构转换为np.ndarray
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# images = [np.array(img, dtype=np.uint8) for img in images]
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if len(images[0].shape) != 3 or images[0].shape[2] != 3:
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raise ValueError("Image is not in RGB format or channel is not in third dimension")
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ratios = [random.uniform(minr, maxr) for _ in range(len(images))]
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return [
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cv2.resize(img, (int(img.shape[1] * r), int(img.shape[0] * r)), interpolation=cv2.INTER_LANCZOS4) # 抗锯齿
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for img, r in zip(images, ratios)
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]
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def rotate(image: np.ndarray, min_angle: int, max_angle: int) -> np.ndarray:
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# Get the center of the image to define the point of rotation
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image_center = tuple(np.array(image.shape[1::-1]) / 2)
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# Generate a random angle within the specified range
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angle = random.randint(min_angle, max_angle)
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# Get the rotation matrix for rotating the image around its center
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rotation_mat = cv2.getRotationMatrix2D(image_center, angle, 1.0)
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# Determine the size of the rotated image
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cos = np.abs(rotation_mat[0, 0])
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sin = np.abs(rotation_mat[0, 1])
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new_width = int((image.shape[0] * sin) + (image.shape[1] * cos))
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new_height = int((image.shape[0] * cos) + (image.shape[1] * sin))
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# Adjust the rotation matrix to take into account translation
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rotation_mat[0, 2] += (new_width / 2) - image_center[0]
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rotation_mat[1, 2] += (new_height / 2) - image_center[1]
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# Rotate the image with the specified border color (white in this case)
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rotated_image = cv2.warpAffine(image, rotation_mat, (new_width, new_height), borderValue=(255, 255, 255))
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return rotated_image
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def ocr_aug(image: np.ndarray) -> np.ndarray:
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# 20%的概率进行随机旋转
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if random.random() < 0.2:
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image = rotate(image, -5, 5)
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# 增加白边
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image = add_white_border(image, max_size=25).permute(1, 2, 0).numpy()
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# 数据增强
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image = train_pipeline(image)
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return image
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def train_transform(images: List[Image.Image]) -> List[torch.Tensor]:
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assert OCR_IMG_CHANNELS == 1 , "Only support grayscale images for now"
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assert OCR_FIX_SIZE == True, "Only support fixed size images for now"
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images = [np.array(img.convert('RGB')) for img in images]
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# random resize first
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images = random_resize(images, MIN_RESIZE_RATIO, MAX_RESIZE_RATIO)
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# 裁剪掉白边
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images = [trim_white_border(image) for image in images]
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# OCR augmentation
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images = [ocr_aug(image) for image in images]
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# general transform pipeline
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images = [general_transform_pipeline(image) for image in images]
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# padding to fixed size
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images = padding(images, OCR_IMG_SIZE)
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return images
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def inference_transform(images: List[np.ndarray]) -> List[torch.Tensor]:
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assert OCR_IMG_CHANNELS == 1 , "Only support grayscale images for now"
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assert OCR_FIX_SIZE == True, "Only support fixed size images for now"
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images = [np.array(img.convert('RGB')) for img in images]
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# 裁剪掉白边
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images = [trim_white_border(image) for image in images]
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# general transform pipeline
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images = [general_transform_pipeline(image) for image in images] # imgs: List[PIL.Image.Image]
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# padding to fixed size
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images = padding(images, OCR_IMG_SIZE)
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return images
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if __name__ == '__main__':
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from pathlib import Path
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from .helpers import convert2rgb
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base_dir = Path('/home/lhy/code/TeXify/src/models/ocr_model/model')
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imgs_path = [
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base_dir / '1.jpg',
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base_dir / '2.jpg',
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base_dir / '3.jpg',
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base_dir / '4.jpg',
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base_dir / '5.jpg',
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base_dir / '6.jpg',
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base_dir / '7.jpg',
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]
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imgs_path = [str(img_path) for img_path in imgs_path]
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imgs = convert2rgb(imgs_path)
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res = random_resize(imgs, 0.5, 1.5)
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pause = 1
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