文章目录
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。
bash
import os
import random
from PIL import Image
def 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 result
def resize_image(image, target_size, resize_by='height'):
w, h = image.size
if resize_by == 'height':
if h != target_size:
ratio = target_size / h
new_width = int(w * ratio)
image = image.resize((new_width, target_size), Image.ANTIALIAS)
elif resize_by == 'width':
if w != target_size:
ratio = target_size / w
new_height = int(h * ratio)
image = image.resize((target_size, new_height), Image.ANTIALIAS)
return image
def 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, coords
def 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_size
new_image = Image.new('RGB', (total_width, max_height))
x_offset = 0
coords = []
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_size
new_image = Image.new('RGB', (max_width, total_height))
y_offset = 0
coords = []
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, coords
def generate_labels(coords, image_size):
labels = []
width, height = image_size
for coord in coords:
x1, y1, x2, y2, x3, y3, x4, y4 = coord
x1 /= width
y1 /= height
x2 /= width
y2 /= height
x3 /= width
y3 /= height
x4 /= width
y4 /= height
labels.append(f"0 {x1:.5f} {y1:.5f} {x2:.5f} {y2:.5f} {x3:.5f} {y3:.5f} {x4:.5f} {y4:.5f}")
return labels
def 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：
bash
docker run -it --gpus all --net host --shm-size=8g -v /ssd/xiedong/yolov8segdir:/ssd/xiedong/yolov8segdir ultralytics/ultralytics:8.2.62 bash
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：
bash
cd /ssd/xiedong/yolov8segdir
vim data.yaml
bash
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：
bash
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：
bash
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：
