版面分析使用说明

pip install -U https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl

import cv2
import layoutparser as lp
image = cv2.imread("doc/table/layout.jpg")
image = image[..., ::-1]

# 加载模型
model = lp.PaddleDetectionLayoutModel(config_path="lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config",
                                threshold=0.5,
                                label_map={0: "Text", 1: "Title", 2: "List", 3:"Table", 4:"Figure"},
                                enforce_cpu=False,
                                enable_mkldnn=True)
# 检测
layout = model.detect(image)

# 显示结果
show_img = lp.draw_box(image, layout, box_width=3, show_element_type=True)
show_img.show()

# 接上面代码
# 首先过滤特定文本类型的区域
text_blocks = lp.Layout([b for b in layout if b.type=='Text'])
figure_blocks = lp.Layout([b for b in layout if b.type=='Figure'])

# 因为在图像区域内可能检测到文本区域，所以只需要删除它们
text_blocks = lp.Layout([b for b in text_blocks \
                   if not any(b.is_in(b_fig) for b_fig in figure_blocks)])

# 对文本区域排序并分配id
h, w = image.shape[:2]

left_interval = lp.Interval(0, w/2*1.05, axis='x').put_on_canvas(image)

left_blocks = text_blocks.filter_by(left_interval, center=True)
left_blocks.sort(key = lambda b:b.coordinates[1])

right_blocks = [b for b in text_blocks if b not in left_blocks]
right_blocks.sort(key = lambda b:b.coordinates[1])

# 最终合并两个列表，并按顺序添加索引
text_blocks = lp.Layout([b.set(id = idx) for idx, b in enumerate(left_blocks + right_blocks)])

# 显示结果
show_img = lp.draw_box(image, text_blocks,
            box_width=3,
            show_element_id=True)
show_img.show()