218 lines
7.6 KiB
Python
218 lines
7.6 KiB
Python
import torch
|
||
import random
|
||
import numpy as np
|
||
import cv2
|
||
|
||
from torchvision.transforms import v2
|
||
from typing import List
|
||
from PIL import Image
|
||
|
||
from ...globals import (
|
||
IMG_CHANNELS,
|
||
FIXED_IMG_SIZE,
|
||
IMAGE_MEAN, IMAGE_STD,
|
||
MAX_RESIZE_RATIO, MIN_RESIZE_RATIO
|
||
)
|
||
from .ocr_aug import ocr_augmentation_pipeline
|
||
|
||
# train_pipeline = default_augraphy_pipeline(scan_only=True)
|
||
train_pipeline = ocr_augmentation_pipeline()
|
||
|
||
general_transform_pipeline = v2.Compose([
|
||
v2.ToImage(), # Convert to tensor, only needed if you had a PIL image
|
||
#+返回一个List of torchvision.Image，list的长度就是batch_size
|
||
#+因此在整个Compose pipeline的最后，输出的也是一个List of torchvision.Image
|
||
#+注意：不是返回一整个torchvision.Image，batch_size的维度是拿出来的
|
||
v2.ToDtype(torch.uint8, scale=True), # optional, most input are already uint8 at this point
|
||
v2.Grayscale(), # 转灰度图（视具体任务而定）
|
||
|
||
v2.Resize( # 固定resize到一个正方形上
|
||
size=FIXED_IMG_SIZE - 1, # size必须小于max_size
|
||
interpolation=v2.InterpolationMode.BICUBIC,
|
||
max_size=FIXED_IMG_SIZE,
|
||
antialias=True
|
||
),
|
||
|
||
v2.ToDtype(torch.float32, scale=True), # Normalize expects float input
|
||
v2.Normalize(mean=[IMAGE_MEAN], std=[IMAGE_STD]),
|
||
|
||
# v2.ToPILImage() # 用于观察转换后的结果是否正确（debug用）
|
||
])
|
||
|
||
|
||
def trim_white_border(image: np.ndarray):
|
||
# 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）
|
||
|
||
# # 检查images中的第一个元素是否是嵌套的列表结构
|
||
# if isinstance(image, list):
|
||
# image = np.array(image, dtype=np.uint8)
|
||
|
||
# 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
|
||
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")
|
||
|
||
# 检查图片是否使用 uint8 类型
|
||
if image.dtype != np.uint8:
|
||
raise ValueError(f"Image should stored in uint8")
|
||
|
||
# 创建与原图像同样大小的纯白背景图像
|
||
h, w = image.shape[:2]
|
||
bg = np.full((h, w, 3), 255, dtype=np.uint8)
|
||
|
||
# 计算差异
|
||
diff = cv2.absdiff(image, bg)
|
||
|
||
# 只要差值大于1，就全部转化为255
|
||
_, diff = cv2.threshold(diff, 1, 255, cv2.THRESH_BINARY)
|
||
|
||
# 把差值转灰度图
|
||
gray_diff = cv2.cvtColor(diff, cv2.COLOR_RGB2GRAY)
|
||
# 计算图像中非零像素点的最小外接矩阵
|
||
x, y, w, h = cv2.boundingRect(gray_diff)
|
||
|
||
# 裁剪图像
|
||
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]:
|
||
# 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）
|
||
|
||
# # 检查images中的第一个元素是否是嵌套的列表结构
|
||
# if isinstance(images[0], list):
|
||
# # 将嵌套的列表结构转换为np.ndarray
|
||
# images = [np.array(img, dtype=np.uint8) for img in images]
|
||
|
||
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 [
|
||
cv2.resize(img, (int(img.shape[1] * r), int(img.shape[0] * r)), interpolation=cv2.INTER_LANCZOS4) # 抗锯齿
|
||
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:
|
||
# 20%的概率进行随机旋转
|
||
if random.random() < 0.2:
|
||
image = rotate(image, -5, 5)
|
||
# 增加白边
|
||
image = add_white_border(image, max_size=25).permute(1, 2, 0).numpy()
|
||
# 数据增强
|
||
image = train_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)
|
||
# 裁剪掉白边
|
||
images = [trim_white_border(image) for image in images]
|
||
|
||
# OCR augmentation
|
||
images = [ocr_aug(image) for image in images]
|
||
|
||
# general transform pipeline
|
||
images = [general_transform_pipeline(image) for image in images]
|
||
# padding to fixed size
|
||
images = padding(images, FIXED_IMG_SIZE)
|
||
return images
|
||
|
||
|
||
def inference_transform(images: List[np.ndarray]) -> List[torch.Tensor]:
|
||
assert IMG_CHANNELS == 1 , "Only support grayscale images for now"
|
||
# 裁剪掉白边
|
||
images = [trim_white_border(image) for image in images]
|
||
# general transform pipeline
|
||
images = [general_transform_pipeline(image) for image in images] # imgs: List[PIL.Image.Image]
|
||
# padding to fixed size
|
||
images = padding(images, FIXED_IMG_SIZE)
|
||
|
||
return images
|
||
|
||
|
||
if __name__ == '__main__':
|
||
from pathlib import Path
|
||
from .helpers import convert2rgb
|
||
base_dir = Path('/home/lhy/code/TeXify/src/models/ocr_model/model')
|
||
imgs_path = [
|
||
base_dir / '1.jpg',
|
||
base_dir / '2.jpg',
|
||
base_dir / '3.jpg',
|
||
base_dir / '4.jpg',
|
||
base_dir / '5.jpg',
|
||
base_dir / '6.jpg',
|
||
base_dir / '7.jpg',
|
||
]
|
||
imgs_path = [str(img_path) for img_path in imgs_path]
|
||
imgs = convert2rgb(imgs_path)
|
||
res = random_resize(imgs, 0.5, 1.5)
|
||
pause = 1
|
||
|