【深度学习】yolov8-seg分割训练，拼接图的分割复原

项目背景

在日常开发中，经常会遇到一些图片是由多个图片拼接来的，如下图就是三个图片横向拼接来的。是否可以利用yolov8-seg模型来识别出这张图片的三张子图区域呢，这是文本要做的事情。

造数据

假设拼接方式有：横向拼接2张图为新图（最短边是高reisze到768，另一边等比resize）、横向拼接3张图为新图（最短边是高reisze到768，另一边等比resize）、纵向拼接2张图为新图（最短边是高reisze到768，另一边等比resize）、纵向拼接3张图为新图（最短边是高reisze到768，另一边等比resize）、拼接一个22的图（每张图大小resize到一样，总大小12901280）。

这个代码会造分割数据。

import os
import random
from PIL import Imagedef list_path_all_files(dirname):result = []for maindir, subdir, file_name_list in os.walk(dirname):for filename in file_name_list:if filename.lower().endswith('.jpg'):apath = os.path.join(maindir, filename)result.append(apath)return resultdef resize_image(image, target_size, resize_by='height'):w, h = image.sizeif resize_by == 'height':if h != target_size:ratio = target_size / hnew_width = int(w * ratio)image = image.resize((new_width, target_size), Image.ANTIALIAS)elif resize_by == 'width':if w != target_size:ratio = target_size / wnew_height = int(h * ratio)image = image.resize((target_size, new_height), Image.ANTIALIAS)return imagedef create_2x2_image(images):target_size = (640, 640)new_image = Image.new('RGB', (1280, 1280))coords = []for i, img in enumerate(images):img = img.resize(target_size, Image.ANTIALIAS)if i == 0:new_image.paste(img, (0, 0))coords.append((0, 0, 640, 0, 640, 640, 0, 640))elif i == 1:new_image.paste(img, (640, 0))coords.append((640, 0, 1280, 0, 1280, 640, 640, 640))elif i == 2:new_image.paste(img, (0, 640))coords.append((0, 640, 640, 640, 640, 1280, 0, 1280))elif i == 3:new_image.paste(img, (640, 640))coords.append((640, 640, 1280, 640, 1280, 1280, 640, 1280))return new_image, coordsdef concatenate_images(image_list, mode='horizontal', target_size=768):if mode == 'horizontal':resized_images = [resize_image(image, target_size, 'height') for image in image_list]total_width = sum(image.size[0] for image in resized_images)max_height = target_sizenew_image = Image.new('RGB', (total_width, max_height))x_offset = 0coords = []for image in resized_images:new_image.paste(image, (x_offset, 0))coords.append((x_offset, 0, x_offset + image.size[0], 0, x_offset + image.size[0], max_height, x_offset, max_height))x_offset += image.size[0]elif mode == 'vertical':resized_images = [resize_image(image, target_size, 'width') for image in image_list]total_height = sum(image.size[1] for image in resized_images)max_width = target_sizenew_image = Image.new('RGB', (max_width, total_height))y_offset = 0coords = []for image in resized_images:new_image.paste(image, (0, y_offset))coords.append((0, y_offset, max_width, y_offset, max_width, y_offset + image.size[1], 0, y_offset + image.size[1]))y_offset += image.size[1]return new_image, coordsdef generate_labels(coords, image_size):labels = []width, height = image_sizefor coord in coords:x1, y1, x2, y2, x3, y3, x4, y4 = coordx1 /= widthy1 /= heightx2 /= widthy2 /= heightx3 /= widthy3 /= heightx4 /= widthy4 /= heightlabels.append(f"0 {x1:.5f} {y1:.5f} {x2:.5f} {y2:.5f} {x3:.5f} {y3:.5f} {x4:.5f} {y4:.5f}")return labelsdef generate_dataset(image_folder, output_folder, label_folder, num_images):image_paths = list_path_all_files(image_folder)if not os.path.exists(output_folder):os.makedirs(output_folder)if not os.path.exists(label_folder):os.makedirs(label_folder)for i in range(num_images):random_choice = random.randint(1, 5)if random_choice == 1:selected_images = [Image.open(random.choice(image_paths)) for _ in range(2)]new_image, coords = concatenate_images(selected_images, mode='horizontal')elif random_choice == 2:selected_images = [Image.open(random.choice(image_paths)) for _ in range(3)]new_image, coords = concatenate_images(selected_images, mode='horizontal')elif random_choice == 3:selected_images = [Image.open(random.choice(image_paths)) for _ in range(2)]new_image, coords = concatenate_images(selected_images, mode='vertical')elif random_choice == 4:selected_images = [Image.open(random.choice(image_paths)) for _ in range(3)]new_image, coords = concatenate_images(selected_images, mode='vertical')elif random_choice == 5:selected_images = [Image.open(random.choice(image_paths)) for _ in range(4)]new_image, coords = create_2x2_image(selected_images)output_image_path = os.path.join(output_folder, f'composite_image_paper_{i + 1:06d}.jpg')new_image.save(output_image_path, 'JPEG')label_path = os.path.join(label_folder, f'composite_image_paper_{i + 1:06d}.txt')labels = generate_labels(coords, new_image.size)with open(label_path, 'w') as label_file:for label in labels:label_file.write(label + '\n')# 示例用法
image_folder = '/ssd/xiedong/datasets/multilabelsTask/multilabels_new/10025doc_textPaperShot/'
# image_folder = '/ssd/xiedong/datasets/multilabelsTask/multilabels_new/'
output_folder = '/ssd/xiedong/datasets/composite_images_yolov8seg/images'
label_folder = '/ssd/xiedong/datasets/composite_images_yolov8seg/labels'
num_images = 10000
generate_dataset(image_folder, output_folder, label_folder, num_images)

有的图片还是很有难度的，比如这张图，分界不明显，模型是否能搞定是个未知数。当然，我会认为模型可以在一定程度上识别语义或者排版，还是有几率可以识别对的。

训练

我想得到一个后续可以直接用的环境，我直接用docker搞个环境。搞的过程：

docker run -it --gpus all --net host  --shm-size=8g -v /ssd/xiedong/yolov8segdir:/ssd/xiedong/yolov8segdir ultralytics/ultralytics:8.2.62  bash

docker tag ultralytics/ultralytics:8.2.62 kevinchina/deeplearning:ultralytics-8.2.62
docker push kevinchina/deeplearning:ultralytics-8.2.62

写一个数据集data.yaml：

cd /ssd/xiedong/yolov8segdir
vim data.yaml

path: /ssd/xiedong/yolov8segdir/composite_images_yolov8seg
train: images # train images (relative to 'path') 128 images
val: images # val images (relative to 'path') 128 images
test: # test images (optional)# Classes
names:0: paper

执行这个代码开始训练模型：

from ultralytics import YOLO# Load a model
model = YOLO("yolov8m-seg.pt")  # load a pretrained model (recommended for training)# Train the model with 2 GPUs
results = model.train(data="data.yaml", epochs=50, imgsz=640, device=[1, 2, 3], batch=180)

代码会自动下载这个模型到本地，网络问题，也可能需要自己用wget下载到当前训练代码的执行目录。

https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-seg.pt

开始训练：

python -m torch.distributed.run --nproc_per_node 3 x03train.py

这样训练就可以了：

看起来任务是简单的：