115 lines
4.4 KiB
Python
115 lines
4.4 KiB
Python
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# pip install ultralytics
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# 最快：YOLOv8-docLayNet（已训练好，直接下权重）
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"""
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# 直接浏览器或 wget 均可
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wget https://huggingface.co/hantian/yolo-doclaynet/resolve/main/yolov8n-doclaynet.pt
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下载后将文件放到你的工作目录或 ./weights/ 子目录即可 。
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"""
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import hashlib
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import logging
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import os
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import cv2
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from ultralytics import YOLO
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logger = logging.getLogger(__name__)
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# 定义需要扩展的像素值
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EXTEND_PIXELS = 10
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# 图片类型
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IMAGE_CLASS_ID = 6
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# 文本类型
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TEXT_CLASS_ID = 9
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# 利用YOLOv8-docLayNet进行图片裁剪
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def yoloCut(image_path):
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# 创建保存截取图片的目录
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md5 = hashlib.md5(image_path.encode()).hexdigest() # input_path的md5值
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output_dir = f'extracted/{md5}'
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processed_image_path = os.path.join(output_dir, 'processed_image.png')
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# 检查输出目录和处理后的图像是否存在
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if os.path.exists(output_dir) and os.path.exists(processed_image_path):
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# 尝试获取已存在的图像文件列表
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img_list = []
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for file_name in os.listdir(output_dir):
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if file_name.startswith('image_') and file_name.endswith('.png'):
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img_path = os.path.join(output_dir, file_name)
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img_path = img_path.replace('\\', '/')
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img_list.append(img_path)
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# 如果找到至少一个image_*.png文件，则认为文件集完整
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if img_list:
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logger.info(f"检测到已存在的输出文件，直接返回: {output_dir}")
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return output_dir, processed_image_path, img_list
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# 如果目录不存在或文件不完整，则创建目录并执行检测
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os.makedirs(output_dir, exist_ok=True)
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# 模型位置
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model = YOLO(r"D:\Model\yolov8n-doclaynet.pt")
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result = model(image_path)
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# 读取原始图像
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original_image = cv2.imread(image_path)
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if original_image is None:
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logger.error(f"无法读取图像文件: {image_path}")
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exit(1)
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# 创建原始图像的副本用于处理
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processed_image = original_image.copy()
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img_list = []
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# 初始化图像计数器
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image_counter = 0
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if result and len(result) > 0:
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detection_result = result[0]
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if hasattr(detection_result, 'boxes') and len(detection_result.boxes) > 0:
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# 先收集所有图像区域
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image_regions = []
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for i, box in enumerate(detection_result.boxes):
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class_id = int(box.cls[0])
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# 只处理图像类别
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if class_id == IMAGE_CLASS_ID:
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# 提取边界框坐标
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x1, y1, x2, y2 = box.xyxy[0].tolist()
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x1, y1, x2, y2 = map(int, [x1, y1, x2, y2])
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# 扩展边界
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x1_extended = max(0, x1 - EXTEND_PIXELS)
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y1_extended = max(0, y1 - EXTEND_PIXELS)
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x2_extended = min(original_image.shape[1], x2 + EXTEND_PIXELS)
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y2_extended = min(original_image.shape[0], y2 + EXTEND_PIXELS)
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image_regions.append((x1_extended, y1_extended, x2_extended, y2_extended))
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# 按y坐标排序图像区域（从上到下）
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image_regions.sort(key=lambda r: r[1])
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# 处理每个图像区域
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for x1_extended, y1_extended, x2_extended, y2_extended in image_regions:
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# 裁剪扩展后的区域
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cropped_region = original_image[y1_extended:y2_extended, x1_extended:x2_extended]
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# 增加计数器
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image_counter += 1
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# 生成保存路径
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save_path = os.path.join(output_dir, f"image_{image_counter}.png")
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save_path = save_path.replace('\\', '/')
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img_list.append(save_path)
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# 保存截取的图片
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cv2.imwrite(save_path, cropped_region)
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# 在处理后的图像中用白色填充该区域
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cv2.rectangle(processed_image, (x1_extended, y1_extended), (x2_extended, y2_extended), (255, 255, 255),
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-1)
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# 保存处理后的图像
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cv2.imwrite(processed_image_path, processed_image)
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else:
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logger.info("未检测到任何区域")
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return output_dir, processed_image_path, img_list
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