from typing import List

from .pdf_detection import Pipeline
from utils import non_max_suppression, merge_text_and_title_boxes, LayoutBox, PageDetectionResult
from tqdm import tqdm
from ..constants import PageDetectionEnum as E
from ..image_helper import remove_watermark


"""
    0 - Text
    1 - Title
    2 - Figure
    3 - Figure caption
    4 - Table
    5 - Table caption
    6 - Header
    7 - Footer
    8 - Reference
    9 - Equation
    使用训练后的权重时，id需要+1，即TEXT从1开始
"""
pipeline = Pipeline('./models/PaddleDetection/inference_model/picodet_lcnet_x1_0_fgd_layout_cdla_infer_v2')

effective_labels = [E.TEXT.value, E.TITLE.value, E.TABLE.value, E.TABLE_CAPTION.value, E.SCANNED_DOCUMENT.value]
# nms优先级，索引越低优先级越低
label_scores = [E.TITLE.value, E.TABLE_CAPTION.value, E.TEXT.value, E.TABLE.value, E.SCANNED_DOCUMENT.value]
expand_pixel = 10


def layout_analysis(images) -> List[PageDetectionResult]:
    layout_analysis_results = []
    for image in tqdm(images, '版面分析'):
        page_detecion_outputs = pipeline(image)

        layout_boxes = []
        is_scanned_document = False
        for o in page_detecion_outputs:
            clsid, box, confidence = o
            if clsid in effective_labels:
                layout_boxes.append(LayoutBox(clsid, box, confidence))
            if clsid == E.SCANNED_DOCUMENT.value:
                is_scanned_document = True
                image = remove_watermark(image)

        if is_scanned_document:
            # 扫描件需要去水印后重新进行版面分析来识别出标题，因为训练的图片是去水印之后的
            _page_detecion_outputs = pipeline(image)
            for o in _page_detecion_outputs:
                clsid, box, confidence = o
                if clsid == E.TABLE_CAPTION.value:
                    layout_boxes.append(LayoutBox(clsid, box, confidence))

        page_detecion_outputs = PageDetectionResult(layout_boxes, image)

        scores = []
        poses = []
        for box in page_detecion_outputs.boxes:
            # 相同的label重叠时，保留面积更大的
            area = (box.pos[3] - box.pos[1]) * (box.pos[2] - box.pos[0])
            area_score = area / 5000000
            scores.append(label_scores.index(box.clsid) + area_score)
            poses.append(box.pos)
        indices = non_max_suppression(poses, scores, 0.2)
        _boxes = []
        for i in indices:
            _boxes.append(page_detecion_outputs.boxes[i])
        page_detecion_outputs.boxes = _boxes

        if not is_scanned_document:
            for i in range(len(page_detecion_outputs.boxes) - 1, -1, -1):
                # 移除Table box和Figure box中的Table caption box和Text box (有些扫描件会被识别为Figure)
                box = page_detecion_outputs.boxes[i]
                if box.clsid in (E.TEXT.value, E.TABLE_CAPTION.value):
                    for _box in page_detecion_outputs.boxes:
                        if _box.clsid != E.FIGURE.value and _box.clsid != E.TABLE.value:
                            continue
                        if box.pos[0] > _box.pos[0] and box.pos[1] > _box.pos[1] and box.pos[2] < _box.pos[2] and box.pos[3] < _box.pos[3]:
                            page_detecion_outputs.boxes.remove(box)

        # 将text和title合并起来，便于转成markdown格式
        merged_labels = [E.TEXT.value, E.TITLE.value]
        other_labels = list(set(effective_labels) - set(merged_labels))
        page_detecion_outputs.boxes = merge_text_and_title_boxes(page_detecion_outputs.boxes, merged_labels, other_labels, E.TEXT.value)
        
        # 对box进行排序
        page_detecion_outputs.boxes.sort(key=lambda x: (x.pos[1], x.pos[0]))

        # box外扩，便于后续的ocr
        h, w = image.shape[:2]
        for layout in page_detecion_outputs.boxes:
            if layout.clsid != E.TEXT.value:
                continue
            layout.pos[0] -= expand_pixel
            layout.pos[1] -= expand_pixel
            layout.pos[2] += expand_pixel
            layout.pos[3] += expand_pixel

            layout.pos[0] = max(0, layout.pos[0])
            layout.pos[1] = max(0, layout.pos[1])
            layout.pos[2] = min(w, layout.pos[2])
            layout.pos[3] = min(h, layout.pos[3])

        layout_analysis_results.append(page_detecion_outputs)

    return layout_analysis_results