1.总结

按照惯例，先吐槽一下。反正也没人看我比比歪歪。做事全部藏着掖着，真有你们的。如果需求都不对技术开放，那还要技术干嘛，自己玩不是更好？？一天天的耍猴一样的耍我玩，真是够够的

2.需求

快递单的关键字ocr识别，那简单啊，直接使用paddleocr的代码不就好了。但是客户的需求显然没那么简单，不然也轮不到我。。。，总结下来难点有4个。
1.大视野
2.关键字提取
3.多种模版
4.多面单

3.方案

这么大的事业完全送去做ocr不现实，一个是速度太慢，二是精度不高。所以只能先分割面单，然后在ocr，最后再关键字匹配。
1.yolov5 面单定位
2.paddleocr 字符识别
大的技术架构就是这样，具体的细节下面再说

4.面单定位

老生常谈的yolov5，就不在过多的介绍。数据集需要的可以自取面单数据
因为我的使用的相机是2000w的所以，图片分辨率是5440*2800。所以选择的模型是yolov5n6
image_size =1280

4.1反转图片扩充数据集

import json
import os.path
import numpy as np
from labelme import utils
import cv2
file_path=r'G:\sick\420_1'
file_path2=r'G:\sick\420_2'

for file_name in os.listdir(file_path):
    if file_name.endswith('.json'):
        file_num=file_name.split('.')[0]+'_1'
        json_path=os.path.join(file_path,file_name)
        json_data = json.load(open(json_path))
        main_coord = np.array([5440, 2800])

        # 如果'points'是NumPy数组，先转换为列表
        json_data['shapes'][0]['points'] = (main_coord - json_data['shapes'][0]['points']).tolist()
        json_data['shapes'][0]['label'] = 'flip_order'



        img = utils.img_b64_to_arr(json_data['imageData'])
        mask_img = cv2.rotate(img, cv2.ROTATE_180)
        save_image_name = file_num+'.jpg'
        save_json_name = file_num+'.json'
        cv2.imwrite(os.path.join(file_path2,save_image_name),mask_img)
        json_data['imageData']=utils.img_arr_to_b64(mask_img)
        with open(os.path.join(file_path2,save_json_name), 'w') as json_file:
            json.dump(json_data, json_file)

4.2新的标注方式

segment anything 使用这个分割模型检测图片上所有的物体，然后通过长宽比和面积大小筛选我们需要的面单，生成对应json

# -*- coding: utf-8 -*-


import os

import cv2
import sys
import numpy as np
import base64
import json
import os.path as osp
import PIL.Image
import io
sys.path.append("..")
from segment_anything import sam_model_registry, SamAutomaticMaskGenerator, SamPredictor

def load_image_file(filename):
    try:
        image_pil = PIL.Image.open(filename)
    except IOError:
        return

    with io.BytesIO() as f:
        ext = osp.splitext(filename)[1].lower()
        if ext in [".jpg", ".jpeg"]:
            format = "JPEG"
        else:
            format = "PNG"
        image_pil.save(f, format=format)
        f.seek(0)
        return f.read()


def show_anns(anns, file_name):
    if len(anns) == 0:
        return
    sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)

    image = load_image_file(os.path.join(file_path, file_name))
    image_base64_str = base64.b64encode(image).decode('utf-8')
    shapes = []
    # 创建保存图片的字典
    data = {"version": "4.5.6", "flags": {}, 'shapes': shapes, "imagePath": file_name, "imageHeight": 2800,
            "imageWidth": 5440,
            "imageData": image_base64_str}
    for ann in sorted_anns:
        if ann['area'] > 20000 and ann['area'] < 30000:

            mask_array = ann['segmentation']
            # 寻找轮廓
            # 将布尔类型数组转换为灰度图像
            mask = np.uint8(mask_array) * 255
            contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

            # 获取最大轮廓
            max_contour = max(contours, key=cv2.contourArea)

            # 获取最小外接矩形
            rect = cv2.minAreaRect(max_contour)

            # 获取矩形的四个顶点
            box = cv2.boxPoints(rect)
            box = np.intp(box)
            box_abs = np.abs(box)  # 使用绝对值将坐标转换为正数
            # 将顶点坐标按比例缩放回原始图像大小
            box_restored = np.round(box_abs * [scale_y,scale_x]).astype(np.int32)

            box = {
                "label": "order",
                "points": box_restored.tolist(),
                "group_id": None,
                "shape_type": "polygon",
                "flags": {}
            }
            shapes.append(box)

    # 将字典保存为 JSON 文件
    with open(file_name.split('.')[0]+'.json', 'w') as json_file:
        json.dump(data, json_file)


# 加载模型
sam_checkpoint = "sam_vit_h_4b8939.pth"
model_type = "vit_h"

device = "cuda"

sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
sam.to(device=device)

mask_generator = SamAutomaticMaskGenerator(sam)

file_path = 'sick_ocr'

file_names = os.listdir(file_path)
scale_x = 4
scale_y = 4
# 单张图片推理
for file_name in file_names:
    if file_name.endswith('jpg'):
        image_file = os.path.join(file_path, file_name)
        image = cv2.imread(image_file)
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

        image_resize = cv2.resize(image, (1024, 1024))
        # 计算缩放比例

        masks = mask_generator.generate(image_resize)
        show_anns(masks,  file_name)
    # 筛选area为制定大小

4.3json2yolo

'''
Created on Aug 18, 2021

@author: xiaosonh
'''
import os
import sys
import argparse
import shutil
import math
from collections import OrderedDict

import json
import cv2
import PIL.Image

from sklearn.model_selection import train_test_split
from labelme import utils


class Labelme2YOLO(object):

    def __init__(self, json_dir, to_seg=False):
        self._json_dir = json_dir

        self._label_id_map = self._get_label_id_map(self._json_dir)
        self._to_seg = to_seg

        i = 'YOLODataset'
        i += '_seg/' if to_seg else '/'
        self._save_path_pfx = os.path.join(self._json_dir, i)

    def _make_train_val_dir(self):
        self._label_dir_path = os.path.join(self._save_path_pfx, 'labels/')
        self._image_dir_path = os.path.join(self._save_path_pfx, 'images/')

        for yolo_path in (os.path.join(self._label_dir_path + 'train/'),
                          os.path.join(self._label_dir_path + 'val/'),
                          os.path.join(self._image_dir_path + 'train/'),
                          os.path.join(self._image_dir_path + 'val/')):
            if os.path.exists(yolo_path):
                shutil.rmtree(yolo_path)

            os.makedirs(yolo_path)

    def _get_label_id_map(self, json_dir):
        label_set = set()

        for file_name in os.listdir(json_dir):
            if file_name.endswith('json'):
                json_path = os.path.join(json_dir, file_name)
                data = json.load(open(json_path))
                for shape in data['shapes']:
                    label_set.add(shape['label'])

        return OrderedDict([(label, label_id) \
                            for label_id, label in enumerate(label_set)])

    def _train_test_split(self, folders, json_names, val_size):
        if len(folders) > 0 and 'train' in folders and 'val' in folders:
            train_folder = os.path.join(self._json_dir, 'train/')
            train_json_names = [train_sample_name + '.json' \
                                for train_sample_name in os.listdir(train_folder) \
                                if os.path.isdir(os.path.join(train_folder, train_sample_name))]

            val_folder = os.path.join(self._json_dir, 'val/')
            val_json_names = [val_sample_name + '.json' \
                              for val_sample_name in os.listdir(val_folder) \
                              if os.path.isdir(os.path.join(val_folder, val_sample_name))]

            return train_json_names, val_json_names

        train_idxs, val_idxs = train_test_split(range(len(json_names)),
                                                test_size=val_size)
        train_json_names = [json_names[train_idx] for train_idx in train_idxs]
        val_json_names = [json_names[val_idx] for val_idx in val_idxs]

        return train_json_names, val_json_names

    def convert(self, val_size):
        json_names = [file_name for file_name in os.listdir(self._json_dir) \
                      if os.path.isfile(os.path.join(self._json_dir, file_name)) and \
                      file_name.endswith('.json')]
        folders = [file_name for file_name in os.listdir(self._json_dir) \
                   if os.path.isdir(os.path.join(self._json_dir, file_name))]
        train_json_names, val_json_names = self._train_test_split(folders, json_names, val_size)

        self._make_train_val_dir()

        # convert labelme object to yolo format object, and save them to files
        # also get image from labelme json file and save them under images folder
        for target_dir, json_names in zip(('train/', 'val/'),
                                          (train_json_names, val_json_names)):
            for json_name in json_names:
                json_path = os.path.join(self._json_dir, json_name)
                json_data = json.load(open(json_path))

                print('Converting %s for %s ...' % (json_name, target_dir.replace('/', '')))

                img_path = self._save_yolo_image(json_data,
                                                 json_name,
                                                 self._image_dir_path,
                                                 target_dir)

                yolo_obj_list = self._get_yolo_object_list(json_data, img_path)
                self._save_yolo_label(json_name,
                                      self._label_dir_path,
                                      target_dir,
                                      yolo_obj_list)

        print('Generating dataset.yaml file ...')
        self._save_dataset_yaml()

    def convert_one(self, json_name):
        json_path = os.path.join(self._json_dir, json_name)
        json_data = json.load(open(json_path))

        print('Converting %s ...' % json_name)

        img_path = self._save_yolo_image(json_data, json_name,
                                         self._json_dir, '')

        yolo_obj_list = self._get_yolo_object_list(json_data, img_path)
        self._save_yolo_label(json_name, self._json_dir,
                              '', yolo_obj_list)

    def _get_yolo_object_list(self, json_data, img_path):
        yolo_obj_list = []

        img_h, img_w, _ = cv2.imread(img_path).shape
        for shape in json_data['shapes']:
            # labelme circle shape is different from others
            # it only has 2 points, 1st is circle center, 2nd is drag end point
            if shape['shape_type'] == 'circle':
                yolo_obj = self._get_circle_shape_yolo_object(shape, img_h, img_w)
            else:
                yolo_obj = self._get_other_shape_yolo_object(shape, img_h, img_w)

            yolo_obj_list.append(yolo_obj)

        return yolo_obj_list

    def _get_circle_shape_yolo_object(self, shape, img_h, img_w):
        label_id = self._label_id_map[shape['label']]
        obj_center_x, obj_center_y = shape['points'][0]

        radius = math.sqrt((obj_center_x - shape['points'][1][0]) ** 2 +
                           (obj_center_y - shape['points'][1][1]) ** 2)

        if self._to_seg:
            retval = [label_id]

            n_part = radius / 10
            n_part = int(n_part) if n_part > 4 else 4
            n_part2 = n_part << 1

            pt_quad = [None for i in range(0, 4)]
            pt_quad[0] = [[obj_center_x + math.cos(i * math.pi / n_part2) * radius,
                           obj_center_y - math.sin(i * math.pi / n_part2) * radius]
                          for i in range(1, n_part)]
            pt_quad[1] = [[obj_center_x * 2 - x1, y1] for x1, y1 in pt_quad[0]]
            pt_quad[1].reverse()
            pt_quad[3] = [[x1, obj_center_y * 2 - y1] for x1, y1 in pt_quad[0]]
            pt_quad[3].reverse()
            pt_quad[2] = [[obj_center_x * 2 - x1, y1] for x1, y1 in pt_quad[3]]
            pt_quad[2].reverse()

            pt_quad[0].append([obj_center_x, obj_center_y - radius])
            pt_quad[1].append([obj_center_x - radius, obj_center_y])
            pt_quad[2].append([obj_center_x, obj_center_y + radius])
            pt_quad[3].append([obj_center_x + radius, obj_center_y])

            for i in pt_quad:
                for j in i:
                    j[0] = round(float(j[0]) / img_w, 6)
                    j[1] = round(float(j[1]) / img_h, 6)
                    retval.extend(j)
            return retval

        obj_w = 2 * radius
        obj_h = 2 * radius

        yolo_center_x = round(float(obj_center_x / img_w), 6)
        yolo_center_y = round(float(obj_center_y / img_h), 6)
        yolo_w = round(float(obj_w / img_w), 6)
        yolo_h = round(float(obj_h / img_h), 6)

        return label_id, yolo_center_x, yolo_center_y, yolo_w, yolo_h

    def _get_other_shape_yolo_object(self, shape, img_h, img_w):
        label_id = self._label_id_map[shape['label']]

        if self._to_seg:
            retval = [label_id]
            for i in shape['points']:
                i[0] = round(float(i[0]) / img_w, 6)
                i[1] = round(float(i[1]) / img_h, 6)
                retval.extend(i)
            return retval

        def __get_object_desc(obj_port_list):
            __get_dist = lambda int_list: max(int_list) - min(int_list)

            x_lists = [port[0] for port in obj_port_list]
            y_lists = [port[1] for port in obj_port_list]

            return min(x_lists), __get_dist(x_lists), min(y_lists), __get_dist(y_lists)

        obj_x_min, obj_w, obj_y_min, obj_h = __get_object_desc(shape['points'])

        yolo_center_x = round(float((obj_x_min + obj_w / 2.0) / img_w), 6)
        yolo_center_y = round(float((obj_y_min + obj_h / 2.0) / img_h), 6)
        yolo_w = round(float(obj_w / img_w), 6)
        yolo_h = round(float(obj_h / img_h), 6)

        return label_id, yolo_center_x, yolo_center_y, yolo_w, yolo_h

    def _save_yolo_label(self, json_name, label_dir_path, target_dir, yolo_obj_list):
        txt_path = os.path.join(label_dir_path,
                                target_dir,
                                json_name.replace('.json', '.txt'))

        with open(txt_path, 'w+') as f:
            for yolo_obj_idx, yolo_obj in enumerate(yolo_obj_list):
                yolo_obj_line = ""
                for i in yolo_obj:
                    yolo_obj_line += f'{i} '
                yolo_obj_line = yolo_obj_line[:-1]
                if yolo_obj_idx != len(yolo_obj_list) - 1:
                    yolo_obj_line += '\n'
                f.write(yolo_obj_line)

    def _save_yolo_image(self, json_data, json_name, image_dir_path, target_dir):
        img_name = json_name.replace('.json', '.png')
        img_path = os.path.join(image_dir_path, target_dir, img_name)

        if not os.path.exists(img_path):
            img = utils.img_b64_to_arr(json_data['imageData'])
            PIL.Image.fromarray(img).save(img_path)

        return img_path

    def _save_dataset_yaml(self):
        yaml_path = os.path.join(self._save_path_pfx, 'dataset.yaml')

        with open(yaml_path, 'w+') as yaml_file:
            yaml_file.write('train: %s\n' % \
                            os.path.join(self._image_dir_path, 'train/'))
            yaml_file.write('val: %s\n\n' % \
                            os.path.join(self._image_dir_path, 'val/'))
            yaml_file.write('nc: %i\n\n' % len(self._label_id_map))

            names_str = ''
            for label, _ in self._label_id_map.items():
                names_str += "'%s', " % label
            names_str = names_str.rstrip(', ')
            yaml_file.write('names: [%s]' % names_str)


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--json_dir', type=str,
                        help='Please input the path of the labelme json files.')
    parser.add_argument('--val_size', type=float, nargs='?', default=0.1,
                        help='Please input the validation dataset size, for example 0.1 ')
    parser.add_argument('--json_name', type=str, nargs='?', default=None,
                        help='If you put json name, it would convert only one json file to YOLO.')
    parser.add_argument('--seg', action='store_true',
                        help='Convert to YOLOv5 v7.0 segmentation dataset')
    args = parser.parse_args(sys.argv[1:])

    convertor = Labelme2YOLO(args.json_dir, to_seg=args.seg)
    if args.json_name is None:
        convertor.convert(val_size=args.val_size)
    else:
        convertor.convert_one(args.json_name)

4.4yolov5推理

模型链接

import time
from typing import List, Optional

import cv2
import numpy as np
import onnxruntime as ort
import random
def letterbox(img: Optional[np.arange], new_shape: List = (1280, 1280), color: object = (114, 114, 114), ) -> np.arange:
    """Resize and pad image while meeting stride-multiple constraints

    Args:
        img (_type_): _description_
        new_shape (tuple, optional): _description_. Defaults to (256, 256).
        color (tuple, optional): _description_. Defaults to (114, 114, 114).

    Returns:
        _type_: _description_
    """
    shape = img.shape[:2]  # current shape [height, width]
    if isinstance(new_shape, int):
        new_shape = (new_shape, new_shape)
    # Scale ratio (new / old)
    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])

    # Compute padding
    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding

    dw /= 2  # divide padding into 2 sides
    dh /= 2

    if shape[::-1] != new_unpad:  # resize
        img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
    img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
    return img, r, (dw, dh)


class Model:
    def __init__(self, model_path: str, cuda: bool) -> None:
        """Load model

        Args:
            model_path (str): _description_
            cuda (str): _description_
        """
        providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider']
        self.session = ort.InferenceSession(model_path, providers=providers)


    def detect(self, img: Optional[np.array], shape: List[int] = None, ) -> Optional[np.array]:
        """检测

        Args:
            img (Optional[np.array]): 图片
            shape (List[int], optional): 图片大小. Defaults to None.

        Returns:
            Optional[np.array]: 一个大的box和2个小的box为一组
        """

        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        image = img.copy()
        if shape is None:
            shape = [1280, 1280]
        # 图片缩放
        image, ratio, dwdh = letterbox(image, shape)
        # Convert
        # img=np.repeat(img[:, :, np.newaxis], 3, axis=2)
        image = image.transpose((2, 0, 1))  # 3x416x416
        image = np.expand_dims(image, 0)
        image = np.ascontiguousarray(image)
        im = image.astype(np.float32)
        im /= 255

        outname = ['output']

        inp = {'images': im}

        outputs = self.session.run(outname, inp)[0]
        outputs = outputs[:, 1:]  # 去掉batch
        #  少于3个
        # box还原原图大小
        boxes = outputs[:, 0:4]  # xyxy
        boxes -= np.array(dwdh * 2)
        boxes /= ratio

        return outputs

5.paddleocr

5.1 数据标注

使用paddleocr提供的标注工具paddlelabel，这个是我自己标注的，我只标注了和业务相关的检测区域，这样训练可以减少时间。
面单

5.2 文本检测训练

模型：ch_PP-OCR_V3_det_student
权重：en_PP-OCRv3_det_distill_train/student.pdparams

5.3 文本识别训练

模型：en_PP-OCRv4_rec.yml
权重：en_PP-OCRv4_rec_train/best_accuracy.pdparams

检测结果