from modelscope.outputs import OutputKeysfrom modelscope.pipelines import pipelinefrom modelscope.utils.constant import Tasks
p = pipeline(task=Tasks.document_segmentation,model='damo/nlp_bert_document-segmentation_chinese-base')
result = p(documents='移动端语音唤醒模型，检测关键词为“小云小云”。模型主体为4层FSMN结构，使用CTC训练准则，参数量750K，适用于移动端设备运行。模型输入为Fbank特征，输出为基于char建模的中文全集token预测，测试工具根据每一帧的预测数据进行后处理得到输入音频的实时检测结果。模型训练采用“basetrain + finetune”的模式，basetrain过程使用大量内部移动端数据，在此基础上，使用1万条设备端录制安静场景“小云小云”数据进行微调，得到最终面向业务的模型。后续用户可在basetrain模型基础上，使用其他关键词数据进行微调，得到新的语音唤醒模型，但暂时未开放模型finetune功能。')
print(result[OutputKeys.TEXT])# 输出''' 移动端语音唤醒模型，检测关键词为“小云小云”。模型主体为4层FSMN结构，使用CTC训练准则，参数量750K，适用于移动端设备运行。模型输入为Fbank特征，输出为基于char建模的中文全集token预测，测试工具根据每一帧的预测数据进行后处理得到输入音频的实时检测结果。 模型训练采用“basetrain + finetune”的模式，basetrain过程使用大量内部移动端数据，在此基础上，使用1万条设备端录制安静场景“小云小云”数据进行微调，得到最终面向业务的模型。后续用户可在basetrain模型基础上，使用其他关键词数据进行微调，得到新的语音唤醒模型，但暂时未开放模型finetune功能。'''

import cv2from paddleocr import PaddleOCRpaddleocr = PaddleOCR(lang='ch', show_log=False, enable_mkldnn=True) img = cv2.imread('1.jpg')result = paddleocr.ocr(img)for i in range(len(result[0])):print(result[0][i][1][0]) # 输出识别结果

import osimport cv2from paddleocr import PPStructure,save_structure_restable_engine = PPStructure(layout=False, show_log=True) save_folder = './output'img_path = 'table.jpg'img = cv2.imread(img_path)result = table_engine(img)save_structure_res(result, save_folder, os.path.basename(img_path).split('.')[0])for line in result:line.pop('img')print(line)

from PIL import Imagefrom pix2tex.cli import LatexOCR
model = LatexOCR()img = Image.open('1.jpg')print(model(img))

import numpy as np
def xy_cut(bboxes, direction="x"):result = []K = len(bboxes)indexes = range(K)if len(bboxes) <= 0:return resultif direction == "x":# x firstsorted_ids = sorted(indexes, key=lambda k: (bboxes[k][0], bboxes[k][1]))sorted_boxes = sorted(bboxes, key=lambda x: (x[0], x[1]))next_dir = "y"else:sorted_ids = sorted(indexes, key=lambda k: (bboxes[k][1], bboxes[k][0]))sorted_boxes = sorted(bboxes, key=lambda x: (x[1], x[0]))next_dir = "x"
curr = 0np_bboxes = np.array(sorted_boxes)for idx in range(len(sorted_boxes)):if direction == "x":# a new seg pathif idx != K - 1 and sorted_boxes[idx][2] < sorted_boxes[idx + 1][0]:rel_res = xy_cut(sorted_boxes[curr:idx + 1], next_dir)result += [sorted_ids[i + curr] for i in rel_res]curr = idx + 1else:# a new seg pathif idx != K - 1 and sorted_boxes[idx][3] < sorted_boxes[idx + 1][1]:rel_res = xy_cut(sorted_boxes[curr:idx + 1], next_dir)result += [sorted_ids[i + curr] for i in rel_res]curr = idx + 1
result += sorted_ids[curr:idx + 1]return result

def augment_xy_cut(bboxes, direction="x", lambda_x=0.5, lambda_y=0.5, theta=5, aug=False):if aug is True:for idx in range(len(bboxes)):vx = np.random.normal(loc=0, scale=1)vy = np.random.normal(loc=0, scale=1)if np.abs(vx) >= lambda_x:bboxes[idx][0] += round(theta * vx)bboxes[idx][2] += round(theta * vx)if np.abs(vy) >= lambda_y:bboxes[idx][1] += round(theta * vy)bboxes[idx][3] += round(theta * vy)bboxes[idx] = [max(0, i) for i in bboxes[idx]]res_idx = xy_cut(bboxes, direction=direction)res_bboxes = [bboxes[idx] for idx in res_idx]return res_idx, res_bboxes

bboxes = [[58.54924774169922, 1379.6373291015625, 1112.8863525390625, 1640.0870361328125],[60.1091423034668, 483.88677978515625, 1117.4927978515625, 586.197021484375],[57.687435150146484, 1098.1053466796875, 387.9796142578125, 1216.916015625],[63.158992767333984, 311.2080993652344, 1116.2508544921875, 365.2145080566406],[138.85513305664062, 144.44039916992188, 845.18017578125, 198.04937744140625],[996.1032104492188, 1053.6279296875, 1126.1046142578125, 1071.3463134765625],[58.743492126464844, 634.3077392578125, 898.405029296875, 700.9544677734375],[61.35755920410156, 750.6771240234375, 1051.1060791015625, 850.3980712890625],[426.77691650390625, 70.69780731201172, 556.0884399414062, 109.58145141601562],[997.040283203125, 903.5933227539062, 1129.2984619140625, 921.10595703125],[59.40523910522461, 1335.1563720703125, 329.7382507324219, 1357.46533203125],[568.9025268554688, 14.365530967712402, 1087.898193359375, 32.60292434692383],[998.1250610351562, 752.936279296875, 1128.435546875, 770.4116821289062],[59.6968879699707, 947.9129638671875, 601.4513549804688, 999.4548950195312],[58.91489028930664, 1049.8773193359375, 487.3372497558594, 1072.2935791015625],[60.49456024169922, 902.8802490234375, 600.7571411132812, 1000.3502197265625],[60.188941955566406, 247.99755859375, 155.72970581054688, 272.1385192871094],[996.873291015625, 637.3861694335938, 1128.3558349609375, 655.1572875976562],[59.74936294555664, 1272.98828125, 154.8768310546875, 1295.870361328125],[58.835716247558594, 1050.5926513671875, 481.59027099609375, 1071.966796875],[60.60163116455078, 750.1132202148438, 376.1781921386719, 771.8764038085938],[57.982513427734375, 419.16058349609375, 155.35882568359375, 444.25115966796875],[1017.0194091796875, 1336.21826171875, 1128.002197265625, 1355.67724609375],[1019.8740844726562, 486.90814208984375, 1127.482421875, 504.61767578125]]
res_idx, res_bboxes = augment_xy_cut(bboxes, direction="y")print(res_idx)# res_idx, res_bboxes = augment_xy_cut(bboxes, direction="x")# print(res_idx)
new_boxs = []for i in res_idx:# print(i)
new_boxs.append(bboxes[i])
print(new_boxs)

import torchfrom model import LayoutLMv3ForBboxClassificationfrom collections import defaultdict
CLS_TOKEN_ID = 0UNK_TOKEN_ID = 3EOS_TOKEN_ID = 2

def BboxesMasks(boxes):bbox = [[0, 0, 0, 0]] + boxes + [[0, 0, 0, 0]]input_ids = [CLS_TOKEN_ID] + [UNK_TOKEN_ID] * len(boxes) + [EOS_TOKEN_ID]attention_mask = [1] + [1] * len(boxes) + [1]return {"bbox": torch.tensor([bbox]),"attention_mask": torch.tensor([attention_mask]),"input_ids": torch.tensor([input_ids]),}

def decode(logits, length):logits = logits[1: length + 1, :length]orders = logits.argsort(descending=False).tolist()ret = [o.pop() for o in orders]while True:order_to_idxes = defaultdict(list)for idx, order in enumerate(ret):order_to_idxes[order].append(idx)order_to_idxes = {k: v for k, v in order_to_idxes.items() if len(v) > 1}if not order_to_idxes:breakfor order, idxes in order_to_idxes.items():idxes_to_logit = {}for idx in idxes:idxes_to_logit[idx] = logits[idx, order]idxes_to_logit = sorted(idxes_to_logit.items(), key=lambda x: x[1], reverse=True)for idx, _ in idxes_to_logit[1:]:ret[idx] = orders[idx].pop()return ret

def layoutreader(bboxes):inputs = BboxesMasks(bboxes)logits = model(**inputs).logits.cpu().squeeze(0)orders = decode(logits, len(bboxes))return orders

if __name__ == '__main__':bboxes = [[584, 0, 595, 1], [35, 120, 89, 133],[35, 140, 75, 152]]model_path = ""model = LayoutLMv3ForBboxClassification.from_pretrained()
print(layoutreader(bboxes))# [1, 2, 0]