├── db.sqlite3 ├── crnn ├── __init__.py ├── .crnn_keras.py.swp ├── config.py ├── utils.py ├── crnn_torch.py ├── crnn_torch_chinese.py ├── util.py ├── network_torch.py ├── dataset.py └── keys.py ├── itypes ├── __init__.py ├── migrations │ └── __init__.py ├── admin.py ├── apps.py ├── urls.py └── views.py ├── utils ├── __init__.py ├── aws │ ├── __init__.py │ ├── mime.sh │ ├── resume.py │ └── userdata.sh ├── wandb_logging │ ├── __init__.py │ ├── log_dataset.py │ └── wandb_utils.py ├── google_app_engine │ ├── additional_requirements.txt │ ├── app.yaml │ └── Dockerfile ├── activations.py ├── google_utils.py ├── autoanchor.py ├── metrics.py ├── loss.py ├── torch_utils.py └── plots.py ├── invoice_ocr ├── __init__.py ├── wsgi.py ├── urls.py └── settings.py ├── images └── a9f89e262f4d10220724004d99f3fc8.png ├── util ├── MyException.py ├── response.py ├── create_img.py ├── ofd_util.py ├── qrcode.py ├── pdf_read.py └── slide.py ├── manage.py ├── obj_det ├── ocr_context.py ├── objd_util.py ├── roll_detect.py ├── evat_detect.py ├── no_tax_detect.py ├── vat_detect.py ├── taxi_detect.py ├── detect.py ├── tra_detect.py └── title_detect.py ├── single_ocr └── opencv_direction.py ├── manage.spec ├── README.md └── requirements.txt /db.sqlite3: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /crnn/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /itypes/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /utils/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /invoice_ocr/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /utils/aws/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /itypes/migrations/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /utils/wandb_logging/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /itypes/admin.py: -------------------------------------------------------------------------------- 1 | from django.contrib import admin 2 | 3 | # Register your models here. 4 | -------------------------------------------------------------------------------- /crnn/.crnn_keras.py.swp: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/384863451/invoice_ocr/HEAD/crnn/.crnn_keras.py.swp -------------------------------------------------------------------------------- /itypes/apps.py: -------------------------------------------------------------------------------- 1 | from django.apps import AppConfig 2 | 3 | 4 | class ItypesConfig(AppConfig): 5 | name = 'itypes' 6 | -------------------------------------------------------------------------------- /images/a9f89e262f4d10220724004d99f3fc8.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/384863451/invoice_ocr/HEAD/images/a9f89e262f4d10220724004d99f3fc8.png -------------------------------------------------------------------------------- /util/MyException.py: -------------------------------------------------------------------------------- 1 | class MyException(Exception): # 继承异常类 2 | def __init__(self, code, msg): 3 | self.code = code 4 | self.msg = msg -------------------------------------------------------------------------------- /utils/google_app_engine/additional_requirements.txt: -------------------------------------------------------------------------------- 1 | # add these requirements in your app on top of the existing ones 2 | pip==18.1 3 | Flask==1.0.2 4 | gunicorn==19.9.0 5 | -------------------------------------------------------------------------------- /crnn/config.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | pwd = os.getcwd() 4 | GPU = False 5 | ocrModel_chinese = os.path.join(pwd,"models", "ocr-lstm.pth") 6 | ocrModel = os.path.join(pwd,"models", "ocr-english.pth") 7 | -------------------------------------------------------------------------------- /utils/google_app_engine/app.yaml: -------------------------------------------------------------------------------- 1 | runtime: custom 2 | env: flex 3 | 4 | service: yolov5app 5 | 6 | liveness_check: 7 | initial_delay_sec: 600 8 | 9 | manual_scaling: 10 | instances: 1 11 | resources: 12 | cpu: 1 13 | memory_gb: 4 14 | disk_size_gb: 20 -------------------------------------------------------------------------------- /itypes/urls.py: -------------------------------------------------------------------------------- 1 | from django.urls import path, include 2 | 3 | import itypes.views 4 | 5 | urlpatterns = [ 6 | path('demo', itypes.views.demo), 7 | path('detection', itypes.views.detection), 8 | path('detection_images', itypes.views.detection_images), 9 | path('batch_img', itypes.views.batch_img), 10 | ] -------------------------------------------------------------------------------- /util/response.py: -------------------------------------------------------------------------------- 1 | from django.http import JsonResponse 2 | 3 | 4 | class response: # 继承异常类 5 | def __init__(self, code, msg): 6 | self.code = code 7 | self.msg = msg 8 | 9 | def result(self): 10 | responseData = {'code': self.code, 'msg': self.msg} 11 | return JsonResponse(responseData, safe=False, json_dumps_params={'ensure_ascii': False}) -------------------------------------------------------------------------------- /util/create_img.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import random 3 | import os 4 | 5 | use = 0 6 | 7 | def no_tax(label, img, res): 8 | if res is None: 9 | res = "" 10 | if ":" in res: 11 | res = "" 12 | dir = "F:\\crnn_demo" 13 | ran = round(random.uniform(0, 10000), 2) 14 | if not os.path.exists(dir + "\\" + label): 15 | os.makedirs(dir + "\\" + label) 16 | cv2.imencode('.png', img)[1].tofile(dir + "\\" + label + "\\" + res + "_" + str(ran) + ".png") -------------------------------------------------------------------------------- /invoice_ocr/wsgi.py: -------------------------------------------------------------------------------- 1 | """ 2 | WSGI config for invoice_ocr project. 3 | 4 | It exposes the WSGI callable as a module-level variable named ``application``. 5 | 6 | For more information on this file, see 7 | https://docs.djangoproject.com/en/2.0/howto/deployment/wsgi/ 8 | """ 9 | 10 | import os 11 | 12 | from django.core.wsgi import get_wsgi_application 13 | 14 | os.environ.setdefault("DJANGO_SETTINGS_MODULE", "invoice_ocr.settings") 15 | 16 | application = get_wsgi_application() 17 | -------------------------------------------------------------------------------- /manage.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | import os 3 | import sys 4 | import matplotlib 5 | import seaborn 6 | import utils.autoanchor 7 | import utils 8 | import utils.torch_utils 9 | import util.qrcode 10 | import tqdm 11 | import matplotlib 12 | import models.yolo 13 | 14 | if __name__ == "__main__": 15 | os.environ.setdefault("DJANGO_SETTINGS_MODULE", "invoice_ocr.settings") 16 | try: 17 | from django.core.management import execute_from_command_line 18 | except ImportError as exc: 19 | raise ImportError( 20 | "Couldn't import Django. Are you sure it's installed and " 21 | "available on your PYTHONPATH environment variable? Did you " 22 | "forget to activate a virtual environment?" 23 | ) from exc 24 | execute_from_command_line(sys.argv) 25 | -------------------------------------------------------------------------------- /utils/aws/mime.sh: -------------------------------------------------------------------------------- 1 | # AWS EC2 instance startup 'MIME' script https://aws.amazon.com/premiumsupport/knowledge-center/execute-user-data-ec2/ 2 | # This script will run on every instance restart, not only on first start 3 | # --- DO NOT COPY ABOVE COMMENTS WHEN PASTING INTO USERDATA --- 4 | 5 | Content-Type: multipart/mixed; boundary="//" 6 | MIME-Version: 1.0 7 | 8 | --// 9 | Content-Type: text/cloud-config; charset="us-ascii" 10 | MIME-Version: 1.0 11 | Content-Transfer-Encoding: 7bit 12 | Content-Disposition: attachment; filename="cloud-config.txt" 13 | 14 | #cloud-config 15 | cloud_final_modules: 16 | - [scripts-user, always] 17 | 18 | --// 19 | Content-Type: text/x-shellscript; charset="us-ascii" 20 | MIME-Version: 1.0 21 | Content-Transfer-Encoding: 7bit 22 | Content-Disposition: attachment; filename="userdata.txt" 23 | 24 | #!/bin/bash 25 | # --- paste contents of userdata.sh here --- 26 | --// 27 | -------------------------------------------------------------------------------- /invoice_ocr/urls.py: -------------------------------------------------------------------------------- 1 | """invoice_ocr URL Configuration 2 | 3 | The `urlpatterns` list routes URLs to views. For more information please see: 4 | https://docs.djangoproject.com/en/2.0/topics/http/urls/ 5 | Examples: 6 | Function views 7 | 1. Add an import: from my_app import views 8 | 2. Add a URL to urlpatterns: path('', views.home, name='home') 9 | Class-based views 10 | 1. Add an import: from other_app.views import Home 11 | 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') 12 | Including another URLconf 13 | 1. Import the include() function: from django.urls import include, path 14 | 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) 15 | """ 16 | from django.contrib import admin 17 | from django.urls import path, include 18 | 19 | urlpatterns = [ 20 | path('admin/', admin.site.urls), 21 | path('type/', include('itypes.urls')) 22 | ] 23 | -------------------------------------------------------------------------------- /utils/wandb_logging/log_dataset.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | 3 | import yaml 4 | 5 | from wandb_utils import WandbLogger 6 | 7 | WANDB_ARTIFACT_PREFIX = 'wandb-artifact://' 8 | 9 | 10 | def create_dataset_artifact(opt): 11 | with open(opt.data) as f: 12 | data = yaml.load(f, Loader=yaml.SafeLoader) # data dict 13 | logger = WandbLogger(opt, '', None, data, job_type='Dataset Creation') 14 | 15 | 16 | if __name__ == '__main__': 17 | parser = argparse.ArgumentParser() 18 | parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path') 19 | parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset') 20 | parser.add_argument('--project', type=str, default='YOLOv5', help='name of W&B Project') 21 | opt = parser.parse_args() 22 | opt.resume = False # Explicitly disallow resume check for dataset upload job 23 | 24 | create_dataset_artifact(opt) 25 | -------------------------------------------------------------------------------- /utils/google_app_engine/Dockerfile: -------------------------------------------------------------------------------- 1 | FROM gcr.io/google-appengine/python 2 | 3 | # Create a virtualenv for dependencies. This isolates these packages from 4 | # system-level packages. 5 | # Use -p python3 or -p python3.7 to select python version. Default is version 2. 6 | RUN virtualenv /env -p python3 7 | 8 | # Setting these environment variables are the same as running 9 | # source /env/bin/activate. 10 | ENV VIRTUAL_ENV /env 11 | ENV PATH /env/bin:$PATH 12 | 13 | RUN apt-get update && apt-get install -y python-opencv 14 | 15 | # Copy the application's requirements.txt and run pip to install all 16 | # dependencies into the virtualenv. 17 | ADD requirements.txt /app/requirements.txt 18 | RUN pip install -r /app/requirements.txt 19 | 20 | # Add the application source code. 21 | ADD . /app 22 | 23 | # Run a WSGI server to serve the application. gunicorn must be declared as 24 | # a dependency in requirements.txt. 25 | CMD gunicorn -b :$PORT main:app 26 | -------------------------------------------------------------------------------- /obj_det/ocr_context.py: -------------------------------------------------------------------------------- 1 | from obj_det.detect import invoice_detection as det 2 | from obj_det.tra_detect import invoice_detection as tra 3 | from obj_det.vat_detect import invoice_detection as vat 4 | from obj_det.taxi_detect import invoice_detection as taxi 5 | from obj_det.roll_detect import invoice_detection as roll 6 | from obj_det.title_detect import invoice_detection as title 7 | from obj_det.evat_detect import invoice_detection as evat 8 | from obj_det.no_tax_detect import invoice_detection as noVat 9 | from single_ocr.opencv_direction import angle_detect_dnn 10 | from crnn.crnn_torch import crnnOcr as crnnOcr 11 | from crnn.crnn_torch_chinese import crnnOcr as ccrnnOcr 12 | 13 | 14 | class TextOcrModel(object): 15 | def __init__(self): 16 | self.ocrModel = crnnOcr 17 | self.chineseModel = ccrnnOcr 18 | self.textModel = None 19 | self.angleModel = angle_detect_dnn 20 | self.det = det 21 | self.tra = tra 22 | self.vat = vat 23 | self.taxi = taxi 24 | self.roll = roll 25 | self.title = title 26 | self.evat = evat 27 | self.noVat = noVat 28 | 29 | 30 | context = TextOcrModel() 31 | -------------------------------------------------------------------------------- /single_ocr/opencv_direction.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import os 3 | import numpy as np 4 | import threading 5 | 6 | lock = threading.Lock() 7 | 8 | pwd = os.getcwd() 9 | AngleModelPb = os.path.join(pwd, "models", "saved_model.pb") 10 | AngleModelPbtxt = os.path.join(pwd, "models", "saved_model.pbtxt") 11 | angleNet = cv2.dnn.readNetFromTensorflow(AngleModelPb,AngleModelPbtxt)##dnn 文字方向检测 12 | 13 | def angle_detect_dnn(img, adjust=True): 14 | """ 15 | 文字方向检测 16 | """ 17 | h, w = img.shape[:2] 18 | ROTATE = [0, 90, 180, 270] 19 | if adjust: 20 | thesh = 0.05 21 | xmin, ymin, xmax, ymax = int(thesh * w), int(thesh * h), w - int(thesh * w), h - int(thesh * h) 22 | img = img[ymin:ymax, xmin:xmax] ##剪切图片边缘 23 | 24 | inputBlob = cv2.dnn.blobFromImage(img, 25 | scalefactor=1.0, 26 | size=(224, 224), 27 | swapRB=True, 28 | mean=[103.939, 116.779, 123.68], crop=False) 29 | lock.acquire(timeout=30) 30 | angleNet.setInput(inputBlob) 31 | pred = angleNet.forward() 32 | lock.release() 33 | index = np.argmax(pred, axis=1)[0] 34 | return ROTATE[index] -------------------------------------------------------------------------------- /crnn/utils.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | # encoding: utf-8 3 | from PIL import Image 4 | import numpy as np 5 | class strLabelConverter(object): 6 | 7 | def __init__(self, alphabet): 8 | self.alphabet = alphabet + 'ç' # for `-1` index 9 | self.dict = {} 10 | for i, char in enumerate(alphabet): 11 | # NOTE: 0 is reserved for 'blank' required by wrap_ctc 12 | self.dict[char] = i + 1 13 | 14 | def decode(self,res): 15 | N = len(res) 16 | raw = [] 17 | for i in range(N): 18 | if res[i] != 0 and (not (i > 0 and res[i - 1] == res[i])): 19 | raw.append(self.alphabet[res[i] - 1]) 20 | return ''.join(raw) 21 | 22 | 23 | class resizeNormalize(object): 24 | 25 | def __init__(self, size, interpolation=Image.BILINEAR): 26 | self.size = size 27 | self.interpolation = interpolation 28 | 29 | def __call__(self, img): 30 | size = self.size 31 | imgW,imgH = size 32 | scale = img.size[1]*1.0 / imgH 33 | w = img.size[0] / scale 34 | w = int(w) 35 | img = img.resize((w,imgH),self.interpolation) 36 | w,h = img.size 37 | img = (np.array(img)/255.0-0.5)/0.5 38 | 39 | return img -------------------------------------------------------------------------------- /utils/aws/resume.py: -------------------------------------------------------------------------------- 1 | # Resume all interrupted trainings in yolov5/ dir including DDP trainings 2 | # Usage: $ python utils/aws/resume.py 3 | 4 | import os 5 | import sys 6 | from pathlib import Path 7 | 8 | import torch 9 | import yaml 10 | 11 | sys.path.append('./') # to run '$ python *.py' files in subdirectories 12 | 13 | port = 0 # --master_port 14 | path = Path('').resolve() 15 | for last in path.rglob('*/**/last.pt'): 16 | ckpt = torch.load(last) 17 | if ckpt['optimizer'] is None: 18 | continue 19 | 20 | # Load opt.yaml 21 | with open(last.parent.parent / 'opt.yaml') as f: 22 | opt = yaml.load(f, Loader=yaml.SafeLoader) 23 | 24 | # Get device count 25 | d = opt['device'].split(',') # devices 26 | nd = len(d) # number of devices 27 | ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1) # distributed data parallel 28 | 29 | if ddp: # multi-GPU 30 | port += 1 31 | cmd = f'python -m torch.distributed.launch --nproc_per_node {nd} --master_port {port} train.py --resume {last}' 32 | else: # single-GPU 33 | cmd = f'python train.py --resume {last}' 34 | 35 | cmd += ' > /dev/null 2>&1 &' # redirect output to dev/null and run in daemon thread 36 | print(cmd) 37 | os.system(cmd) 38 | -------------------------------------------------------------------------------- /manage.spec: -------------------------------------------------------------------------------- 1 | # -*- mode: python ; coding: utf-8 -*- 2 | 3 | 4 | block_cipher = None 5 | 6 | 7 | a = Analysis(['manage.py'], 8 | pathex=[], 9 | binaries=[], 10 | datas=[], 11 | hiddenimports=[], 12 | hookspath=[], 13 | hooksconfig={}, 14 | runtime_hooks=[], 15 | excludes=[], 16 | win_no_prefer_redirects=False, 17 | win_private_assemblies=False, 18 | cipher=block_cipher, 19 | noarchive=False) 20 | pyz = PYZ(a.pure, a.zipped_data, 21 | cipher=block_cipher) 22 | 23 | exe = EXE(pyz, 24 | a.scripts, 25 | [], 26 | exclude_binaries=True, 27 | name='manage', 28 | debug=False, 29 | bootloader_ignore_signals=False, 30 | strip=False, 31 | upx=True, 32 | console=True, 33 | disable_windowed_traceback=False, 34 | target_arch=None, 35 | codesign_identity=None, 36 | entitlements_file=None ) 37 | coll = COLLECT(exe, 38 | a.binaries, 39 | a.zipfiles, 40 | a.datas, 41 | strip=False, 42 | upx=True, 43 | upx_exclude=[], 44 | name='manage') 45 | -------------------------------------------------------------------------------- /utils/aws/userdata.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # AWS EC2 instance startup script https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html 3 | # This script will run only once on first instance start (for a re-start script see mime.sh) 4 | # /home/ubuntu (ubuntu) or /home/ec2-user (amazon-linux) is working dir 5 | # Use >300 GB SSD 6 | 7 | cd home/ubuntu 8 | if [ ! -d yolov5 ]; then 9 | echo "Running first-time script." # install dependencies, download COCO, pull Docker 10 | git clone https://github.com/ultralytics/yolov5 && sudo chmod -R 777 yolov5 11 | cd yolov5 12 | bash data/scripts/get_coco.sh && echo "Data done." & 13 | sudo docker pull ultralytics/yolov5:latest && echo "Docker done." & 14 | python -m pip install --upgrade pip && pip install -r requirements.txt && python detect.py && echo "Requirements done." & 15 | wait && echo "All tasks done." # finish background tasks 16 | else 17 | echo "Running re-start script." # resume interrupted runs 18 | i=0 19 | list=$(sudo docker ps -qa) # container list i.e. $'one\ntwo\nthree\nfour' 20 | while IFS= read -r id; do 21 | ((i++)) 22 | echo "restarting container $i: $id" 23 | sudo docker start $id 24 | # sudo docker exec -it $id python train.py --resume # single-GPU 25 | sudo docker exec -d $id python utils/aws/resume.py # multi-scenario 26 | done <<<"$list" 27 | fi 28 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 混合报销票据识别 2 | 识别文件类型：图片，pdf，ofd, 0,90,180,270四种度数。 3 | 识别类型：增值税专用发票, 增值税普通发票, 增值税电子专用发票, 增值税电子普通发票, 增值税普通发票（卷式）, 过路费发票, 火车票, 飞机票, 客运票, 出租车票, 定额, 通用机打发票 4 | ## 环境 5 | 1. python3.5/3.6 6 | 2. 依赖项安装：pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple 7 | 3. 有GPU环境的可修改安装requirements.txt对应版本的tensorflow-gpu，config.py文件中控制GPU的开关 8 | ## 模型架构 9 | YOLOv5 + CRNN + CTC 10 | 11 | ## 模型 12 | 1. 模型下载地址：链接：链接：https://pan.baidu.com/s/1E_OE9HOjjFh6GZdPWQVbMg 提取码：voqi 13 | 2. 将下载完毕的模型文件夹models放置于项目根目录下 14 | ## 服务启动 15 | 1. 控制台 python manage.py runserver 127.0.0.1:8080 16 | 2. 端口可自行修改 17 | 3. 服务调用地址：http://*.*.*.*: [端口号]/detection_images,http://127.0.0.1:8080/detection，例：http://127.0.0.1:8080/detection_images 18 | 19 | ## 测试demo 20 | 1. 测试工具：postman，可自行下载安装 21 | 2. 4张增值税发票混拍 22 | 23 | ![Image text](https://raw.githubusercontent.com/384863451/invoice_ocr/master/images/a9f89e262f4d10220724004d99f3fc8.png) 24 | 25 | # 代码执行过程说明 26 | - 使用django命令启动 27 | - 首先对图片做处理,可以接收的参数为图片文件，图片base64编码，图片下载地址 28 | - 图片中发票定位,并把识别结果放到list 29 | - 判断对应的发票类型进一步识别发票具体部位。 30 | - 识别到关键部位通过crnn识别具体信息 31 | - 电子发票特别优化，可以识别pdf和ofd 32 | 33 | 34 | ## 后期开发计划 35 | - 增值税发票只识别了五要素，后续打算结合发票查验直接获取全票面 36 | - 其他发票都只识别了几个部位，后期有空完善 37 | - crnn使用了chineseocr项目自带的，正在做，工作量太大有空更新 38 | 39 | ## 参考 40 | chineseocr https://github.com/chineseocr/chineseocr 41 | 42 | ##总结 43 | 新手做着玩，代码写的很乱。 44 | -------------------------------------------------------------------------------- /util/ofd_util.py: -------------------------------------------------------------------------------- 1 | from xml.dom.minidom import parse 2 | 3 | import shutil 4 | import zipfile 5 | import os 6 | 7 | 8 | def unzip_file(zip_path, unzip_path=None): 9 | """ 10 | :param zip_path: ofd格式文件路径 11 | :param unzip_path: 解压后的文件存放目录 12 | :return: unzip_path 13 | """ 14 | if not unzip_path: 15 | unzip_path = zip_path.split('.')[0] 16 | with zipfile.ZipFile(zip_path, 'r') as f: 17 | for file in f.namelist(): 18 | f.extract(file, path=unzip_path) 19 | 20 | return unzip_path 21 | 22 | def get_info(dir_path, unzip_file_path=None, removed=True): 23 | """ 24 | :param dir_path: 压缩文件路径 25 | :param unzip_file_path: 解压后的文件路径 26 | :param removed: 是否删除解压后的目录 27 | :return: ofd_info，字典形式的发票信息 28 | """ 29 | if not os.path.exists(dir_path): 30 | os.makedirs(dir_path) 31 | file_path = unzip_file(dir_path, unzip_file_path) 32 | io = f"{file_path}/OFD.xml" 33 | element = parse(io).documentElement 34 | nodes = element.getElementsByTagName('ofd:CustomDatas') 35 | ofd_info = {} 36 | for i in range(len(nodes)): 37 | sun_node = nodes[i].childNodes 38 | for j in range(len(sun_node)): 39 | name = sun_node[j].getAttribute('Name') 40 | value = sun_node[j].firstChild.data 41 | ofd_info[name] =value 42 | if removed: 43 | shutil.rmtree(unzip_file_path) 44 | return ofd_info -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | absl-py==1.0.0 2 | altgraph==0.17.2 3 | astunparse==1.6.3 4 | cachetools==4.2.4 5 | certifi==2020.6.20 6 | charset-normalizer==2.0.9 7 | colorama==0.4.4 8 | cycler==0.11.0 9 | dataclasses==0.8 10 | Django==2.0 11 | future==0.18.2 12 | gast==0.3.3 13 | google-auth==2.3.3 14 | google-auth-oauthlib==0.4.6 15 | google-pasta==0.2.0 16 | grpcio==1.42.0 17 | h5py==2.10.0 18 | idna==3.3 19 | image==1.5.33 20 | importlib-metadata==4.8.2 21 | jupyter-contrib-core==0.3.3 22 | jupyter-highlight-selected-word==0.2.0 23 | jupyter-latex-envs==1.4.6 24 | Keras-Preprocessing==1.1.2 25 | kiwisolver==1.3.1 26 | lmdb==1.2.1 27 | lxml==4.6.3 28 | Markdown==3.3.6 29 | matplotlib==3.3.4 30 | numpy==1.18.5 31 | oauthlib==3.1.1 32 | opencv-python==4.5.4.60 33 | opt-einsum==3.3.0 34 | pandas==1.1.5 35 | pefile==2021.9.3 36 | Pillow==8.4.0 37 | protobuf==3.19.1 38 | pyasn1==0.4.8 39 | pyasn1-modules==0.2.8 40 | pyinstaller==4.7 41 | pyinstaller-hooks-contrib==2021.4 42 | PyMuPDF==1.19.3 43 | pyparsing==3.0.6 44 | python-dateutil==2.8.2 45 | pytz==2021.3 46 | pywin32-ctypes==0.2.0 47 | PyYAML==5.4.1 48 | pyzbar==0.1.8 49 | requests==2.26.0 50 | requests-oauthlib==1.3.0 51 | rsa==4.8 52 | scipy==1.4.1 53 | seaborn==0.11.2 54 | six==1.16.0 55 | tensorboard==2.7.0 56 | tensorboard-data-server==0.6.1 57 | tensorboard-plugin-wit==1.8.0 58 | tensorflow==2.3.0 59 | tensorflow-estimator==2.3.0 60 | termcolor==1.1.0 61 | torch==1.10.0 62 | torchvision==0.11.1 63 | tqdm==4.62.3 64 | typing_extensions==4.0.1 65 | urllib3==1.26.7 66 | Werkzeug==2.0.2 67 | wincertstore==0.2 68 | wrapt==1.13.3 69 | zipp==3.6.0 70 | zxing==0.14 71 | -------------------------------------------------------------------------------- /crnn/crnn_torch.py: -------------------------------------------------------------------------------- 1 | #coding:utf-8 2 | import torch 3 | import numpy as np 4 | from torch.autograd import Variable 5 | from crnn.utils import strLabelConverter, resizeNormalize 6 | from crnn.network_torch import CRNN 7 | from crnn import keys 8 | from collections import OrderedDict 9 | from crnn.config import ocrModel, GPU 10 | from PIL import Image 11 | import cv2 12 | import threading 13 | 14 | lock = threading.Lock() 15 | def crnnSource(): 16 | 17 | alphabet = keys.alphabetEnglish##英文模型 18 | 19 | converter = strLabelConverter(alphabet) 20 | model = CRNN(32, 1, len(alphabet)+1, 256, 1,lstmFlag=True).cpu() 21 | trainWeights = torch.load(ocrModel,map_location=lambda storage, loc: storage) 22 | modelWeights = OrderedDict() 23 | for k, v in trainWeights.items(): 24 | name = k.replace('module.','') # remove `module.` 25 | modelWeights[name] = v 26 | # load params 27 | 28 | model.load_state_dict(modelWeights) 29 | 30 | return model,converter 31 | 32 | ##加载模型 33 | model,converter = crnnSource() 34 | model.eval() 35 | def crnnOcr(image): 36 | """ 37 | crnn模型，ocr识别 38 | image:cv2 39 | """ 40 | pil_img = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) 41 | image = pil_img.convert('L') 42 | scale = image.size[1]*1.0 / 32 43 | w = image.size[0] / scale 44 | w = int(w) 45 | transformer = resizeNormalize((w, 32)) 46 | image = transformer(image) 47 | image = image.astype(np.float32) 48 | image = torch.from_numpy(image) 49 | 50 | if torch.cuda.is_available() and GPU: 51 | image = image.cuda() 52 | else: 53 | image = image.cpu() 54 | 55 | image = image.view(1,1, *image.size()) 56 | image = Variable(image) 57 | lock.acquire(timeout=3) 58 | preds = model(image) 59 | lock.release() 60 | _, preds = preds.max(2) 61 | preds = preds.transpose(1, 0).contiguous().view(-1) 62 | sim_pred = converter.decode(preds) 63 | return sim_pred 64 | 65 | 66 | -------------------------------------------------------------------------------- /util/qrcode.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import zxing as zxing 3 | import uuid 4 | from obj_det.detect import converter as multype 5 | 6 | 7 | def qrcode(img, invoice): 8 | try: 9 | id = uuid.uuid4() 10 | path = "images/qrcode/" + str(id) + ".jpg" 11 | h, w, _ = img.shape 12 | newimage = cv2.resize(img, (w * 2, h * 2), cv2.INTER_LINEAR) 13 | cv2.imwrite(path, newimage) 14 | reader = zxing.BarCodeReader() 15 | barcode = reader.decode(path) 16 | data = barcode.parsed 17 | datas = data.split(",") 18 | if datas[2] != '' and datas[2] != None: 19 | invoice['invoice_code'] = datas[2] 20 | if datas[3] != '' and datas[3] != None: 21 | invoice['invoice_number'] = datas[3] 22 | if datas[4] != '' and datas[4] != None: 23 | invoice['totalAmount'] = "￥" + datas[4] 24 | if datas[5] != '' and datas[5] != None: 25 | invoice['billingDate'] = datas[5][0:4] + "年" + datas[5][4:6] + "月" + datas[5][6:8] + "日" 26 | if datas[1] == "04" or datas[1] == "10" or datas[1] == "11": 27 | invoice['checkCode'] = datas[6] 28 | if invoice['invoiceType'] != "14": 29 | invoice['invoiceType'] = datas[1] 30 | invoice['invoice_type_name'] = multype[datas[1]] 31 | return True 32 | except Exception as e: 33 | print("二维码未识别" + str(e)) 34 | return False 35 | 36 | 37 | def qrcode_no_tax(img, invoice): 38 | try: 39 | img = cv2.resize(img, (300, 300)) 40 | id = uuid.uuid4() 41 | path = "images/qrcode/" + str(id) + ".jpg" 42 | cv2.imwrite(path, img) 43 | reader = zxing.BarCodeReader() 44 | barcode = reader.decode(path) 45 | data = barcode.parsed 46 | if "http" in data: 47 | paramOrg = data.split("?")[1] 48 | param_2s = paramOrg.split("&") 49 | for param_2 in param_2s: 50 | param = param_2.split("=") 51 | invoice[param[0]] = param[1] 52 | return True 53 | except Exception as e: 54 | print("二维码未识别" + str(e)) 55 | return False 56 | -------------------------------------------------------------------------------- /crnn/crnn_torch_chinese.py: -------------------------------------------------------------------------------- 1 | #coding:utf-8 2 | import torch 3 | import numpy as np 4 | from torch.autograd import Variable 5 | from crnn.utils import strLabelConverter, resizeNormalize 6 | from crnn.network_torch import CRNN 7 | from crnn import keys 8 | from collections import OrderedDict 9 | from crnn.config import ocrModel_chinese, GPU 10 | from PIL import Image 11 | import cv2 12 | import threading 13 | 14 | lock = threading.Lock() 15 | def crnnSource(): 16 | """ 17 | 加载模型 18 | """ 19 | alphabet = keys.alphabetChinese##中英文模型 20 | 21 | converter = strLabelConverter(alphabet) 22 | model = CRNN(32, 1, len(alphabet)+1, 256, 1,lstmFlag=True).cpu() 23 | 24 | trainWeights = torch.load(ocrModel_chinese,map_location=lambda storage, loc: storage) 25 | modelWeights = OrderedDict() 26 | for k, v in trainWeights.items(): 27 | name = k.replace('module.','') # remove `module.` 28 | modelWeights[name] = v 29 | # load params 30 | 31 | model.load_state_dict(modelWeights) 32 | 33 | return model,converter 34 | 35 | ##加载模型 36 | model,converter = crnnSource() 37 | model.eval() 38 | def crnnOcr(image): 39 | """ 40 | crnn模型，ocr识别 41 | image:PIL.Image.convert("L") 42 | """ 43 | pil_img = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) 44 | image = pil_img.convert('L') 45 | scale = image.size[1]*1.0 / 32 46 | w = image.size[0] / scale 47 | w = int(w) 48 | transformer = resizeNormalize((w, 32)) 49 | image = transformer(image) 50 | image = image.astype(np.float32) 51 | image = torch.from_numpy(image) 52 | 53 | if torch.cuda.is_available() and GPU: 54 | image = image.cuda() 55 | else: 56 | image = image.cpu() 57 | 58 | image = image.view(1,1, *image.size()) 59 | image = Variable(image) 60 | lock.acquire(timeout=3) 61 | preds = model(image) 62 | lock.release() 63 | _, preds = preds.max(2) 64 | preds = preds.transpose(1, 0).contiguous().view(-1) 65 | sim_pred = converter.decode(preds) 66 | return sim_pred 67 | 68 | 69 | -------------------------------------------------------------------------------- /utils/activations.py: -------------------------------------------------------------------------------- 1 | # Activation functions 2 | 3 | import torch 4 | import torch.nn as nn 5 | import torch.nn.functional as F 6 | 7 | 8 | # SiLU https://arxiv.org/pdf/1606.08415.pdf ---------------------------------------------------------------------------- 9 | class SiLU(nn.Module): # export-friendly version of nn.SiLU() 10 | @staticmethod 11 | def forward(x): 12 | return x * torch.sigmoid(x) 13 | 14 | 15 | class Hardswish(nn.Module): # export-friendly version of nn.Hardswish() 16 | @staticmethod 17 | def forward(x): 18 | # return x * F.hardsigmoid(x) # for torchscript and CoreML 19 | return x * F.hardtanh(x + 3, 0., 6.) / 6. # for torchscript, CoreML and ONNX 20 | 21 | 22 | class MemoryEfficientSwish(nn.Module): 23 | class F(torch.autograd.Function): 24 | @staticmethod 25 | def forward(ctx, x): 26 | ctx.save_for_backward(x) 27 | return x * torch.sigmoid(x) 28 | 29 | @staticmethod 30 | def backward(ctx, grad_output): 31 | x = ctx.saved_tensors[0] 32 | sx = torch.sigmoid(x) 33 | return grad_output * (sx * (1 + x * (1 - sx))) 34 | 35 | def forward(self, x): 36 | return self.F.apply(x) 37 | 38 | 39 | # Mish https://github.com/digantamisra98/Mish -------------------------------------------------------------------------- 40 | class Mish(nn.Module): 41 | @staticmethod 42 | def forward(x): 43 | return x * F.softplus(x).tanh() 44 | 45 | 46 | class MemoryEfficientMish(nn.Module): 47 | class F(torch.autograd.Function): 48 | @staticmethod 49 | def forward(ctx, x): 50 | ctx.save_for_backward(x) 51 | return x.mul(torch.tanh(F.softplus(x))) # x * tanh(ln(1 + exp(x))) 52 | 53 | @staticmethod 54 | def backward(ctx, grad_output): 55 | x = ctx.saved_tensors[0] 56 | sx = torch.sigmoid(x) 57 | fx = F.softplus(x).tanh() 58 | return grad_output * (fx + x * sx * (1 - fx * fx)) 59 | 60 | def forward(self, x): 61 | return self.F.apply(x) 62 | 63 | 64 | # FReLU https://arxiv.org/abs/2007.11824 ------------------------------------------------------------------------------- 65 | class FReLU(nn.Module): 66 | def __init__(self, c1, k=3): # ch_in, kernel 67 | super().__init__() 68 | self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False) 69 | self.bn = nn.BatchNorm2d(c1) 70 | 71 | def forward(self, x): 72 | return torch.max(x, self.bn(self.conv(x))) 73 | -------------------------------------------------------------------------------- /crnn/util.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | # encoding: utf-8 3 | 4 | import torch 5 | import torch.nn as nn 6 | import collections 7 | 8 | 9 | class strLabelConverter(object): 10 | 11 | def __init__(self, alphabet): 12 | self.alphabet = alphabet + 'ç' # for `-1` index 13 | self.dict = {} 14 | for i, char in enumerate(alphabet): 15 | # NOTE: 0 is reserved for 'blank' required by wrap_ctc 16 | self.dict[char] = i + 1 17 | def encode(self, text, depth=0): 18 | """Support batch or single str.""" 19 | length = [] 20 | result=[] 21 | for str in text: 22 | length.append(len(str)) 23 | for char in str: 24 | #print(char) 25 | index = self.dict[char] 26 | result.append(index) 27 | text = result 28 | return (torch.IntTensor(text), torch.IntTensor(length)) 29 | 30 | def decode(self, t, length, raw=False): 31 | if length.numel() == 1: 32 | length = length[0] 33 | t = t[:length] 34 | if raw: 35 | return ''.join([self.alphabet[i - 1] for i in t]) 36 | else: 37 | char_list = [] 38 | for i in range(length): 39 | if t[i] != 0 and (not (i > 0 and t[i - 1] == t[i])): 40 | char_list.append(self.alphabet[t[i] - 1]) 41 | return ''.join(char_list) 42 | else: 43 | texts = [] 44 | index = 0 45 | for i in range(length.numel()): 46 | l = length[i] 47 | texts.append(self.decode( 48 | t[index:index + l], torch.IntTensor([l]), raw=raw)) 49 | index += l 50 | return texts 51 | 52 | 53 | class averager(object): 54 | 55 | def __init__(self): 56 | self.reset() 57 | 58 | def add(self, v): 59 | self.n_count += v.data.numel() 60 | # NOTE: not `+= v.sum()`, which will add a node in the compute graph, 61 | # which lead to memory leak 62 | self.sum += v.data.sum() 63 | 64 | def reset(self): 65 | self.n_count = 0 66 | self.sum = 0 67 | 68 | def val(self): 69 | res = 0 70 | if self.n_count != 0: 71 | res = self.sum / float(self.n_count) 72 | return res 73 | 74 | 75 | def oneHot(v, v_length, nc): 76 | batchSize = v_length.size(0) 77 | maxLength = v_length.max() 78 | v_onehot = torch.FloatTensor(batchSize, maxLength, nc).fill_(0) 79 | acc = 0 80 | for i in range(batchSize): 81 | length = v_length[i] 82 | label = v[acc:acc + length].view(-1, 1).long() 83 | v_onehot[i, :length].scatter_(1, label, 1.0) 84 | acc += length 85 | return v_onehot 86 | 87 | 88 | def loadData(v, data): 89 | v.data.resize_(data.size()).copy_(data) 90 | 91 | 92 | def prettyPrint(v): 93 | print('Size {0}, Type: {1}'.format(str(v.size()), v.data.type())) 94 | print('| Max: %f | Min: %f | Mean: %f' % (v.max().data[0], v.min().data[0], v.mean().data[0])) 95 | 96 | 97 | def assureRatio(img): 98 | """Ensure imgH <= imgW.""" 99 | b, c, h, w = img.size() 100 | if h > w: 101 | main = nn.UpsamplingBilinear2d(size=(h, h), scale_factor=None) 102 | img = main(img) 103 | return img 104 | -------------------------------------------------------------------------------- /crnn/network_torch.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | class BidirectionalLSTM(nn.Module): 3 | 4 | def __init__(self, nIn, nHidden, nOut): 5 | super(BidirectionalLSTM, self).__init__() 6 | self.rnn = nn.LSTM(nIn, nHidden, bidirectional=True) 7 | self.embedding = nn.Linear(nHidden * 2, nOut) 8 | 9 | def forward(self, input): 10 | recurrent, _ = self.rnn(input) 11 | T, b, h = recurrent.size() 12 | t_rec = recurrent.view(T * b, h) 13 | output = self.embedding(t_rec) # [T * b, nOut] 14 | output = output.view(T, b, -1) 15 | return output 16 | 17 | 18 | 19 | class CRNN(nn.Module): 20 | 21 | def __init__(self, imgH, nc, nclass, nh, n_rnn=2, leakyRelu=False,lstmFlag=True): 22 | """ 23 | 是否加入lstm特征层 24 | """ 25 | super(CRNN, self).__init__() 26 | assert imgH % 16 == 0, 'imgH has to be a multiple of 16' 27 | 28 | ks = [3, 3, 3, 3, 3, 3, 2] 29 | ps = [1, 1, 1, 1, 1, 1, 0] 30 | ss = [1, 1, 1, 1, 1, 1, 1] 31 | nm = [64, 128, 256, 256, 512, 512, 512] 32 | self.lstmFlag = lstmFlag 33 | 34 | cnn = nn.Sequential() 35 | 36 | def convRelu(i, batchNormalization=False): 37 | nIn = nc if i == 0 else nm[i - 1] 38 | nOut = nm[i] 39 | cnn.add_module('conv{0}'.format(i), 40 | nn.Conv2d(nIn, nOut, ks[i], ss[i], ps[i])) 41 | if batchNormalization: 42 | cnn.add_module('batchnorm{0}'.format(i), nn.BatchNorm2d(nOut)) 43 | if leakyRelu: 44 | cnn.add_module('relu{0}'.format(i), 45 | nn.LeakyReLU(0.2, inplace=True)) 46 | else: 47 | cnn.add_module('relu{0}'.format(i), nn.ReLU(True)) 48 | 49 | convRelu(0) 50 | cnn.add_module('pooling{0}'.format(0), nn.MaxPool2d(2, 2)) # 64x16x64 51 | convRelu(1) 52 | cnn.add_module('pooling{0}'.format(1), nn.MaxPool2d(2, 2)) # 128x8x32 53 | convRelu(2, True) 54 | convRelu(3) 55 | cnn.add_module('pooling{0}'.format(2), 56 | nn.MaxPool2d((2, 2), (2, 1), (0, 1))) # 256x4x16 57 | convRelu(4, True) 58 | convRelu(5) 59 | cnn.add_module('pooling{0}'.format(3), 60 | nn.MaxPool2d((2, 2), (2, 1), (0, 1))) # 512x2x16 61 | convRelu(6, True) # 512x1x16 62 | 63 | self.cnn = cnn 64 | if self.lstmFlag: 65 | self.rnn = nn.Sequential( 66 | BidirectionalLSTM(512, nh, nh), 67 | BidirectionalLSTM(nh, nh, nclass)) 68 | else: 69 | self.linear = nn.Linear(nh*2, nclass) 70 | 71 | 72 | def forward(self, input): 73 | # conv features 74 | conv = self.cnn(input) 75 | b, c, h, w = conv.size() 76 | 77 | assert h == 1, "the height of conv must be 1" 78 | conv = conv.squeeze(2) 79 | conv = conv.permute(2, 0, 1) # [w, b, c] 80 | if self.lstmFlag: 81 | # rnn features 82 | output = self.rnn(conv) 83 | else: 84 | T, b, h = conv.size() 85 | 86 | t_rec = conv.contiguous().view(T * b, h) 87 | 88 | output = self.linear(t_rec) # [T * b, nOut] 89 | output = output.view(T, b, -1) 90 | 91 | 92 | return output 93 | -------------------------------------------------------------------------------- /util/pdf_read.py: -------------------------------------------------------------------------------- 1 | # -*- coding:utf-8 -*- 2 | import time,os.path,requests,re 3 | from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter 4 | from pdfminer.converter import PDFPageAggregator 5 | from pdfminer.layout import LAParams,LTTextBoxHorizontal,LTImage,LTCurve,LTFigure 6 | from pdfminer.pdfpage import PDFTextExtractionNotAllowed,PDFPage 7 | from pdfminer.pdfparser import PDFParser 8 | from pdfminer.pdfdocument import PDFDocument 9 | from docx import Document 10 | import fitz 11 | document = Document() 12 | 13 | ''' 14 | pip install pdfminer3k 15 | pip install pdfminer.six 安装这个引入的内容不会报错 16 | ''' 17 | 18 | class CPdf2TxtManager(): 19 | 20 | def changePdfToText(self, filePath): 21 | res = {"state": 0} 22 | context = "" 23 | # 以二进制读模式打开 24 | file = open(filePath, 'rb') 25 | #用文件对象来创建一个pdf文档分析器 26 | praser = PDFParser(file) 27 | # 创建一个PDF文档对象存储文档结构,提供密码初始化，没有就不用传该参数 28 | doc = PDFDocument(praser, password='') 29 | ##检查文件是否允许文本提取 30 | if not doc.is_extractable: 31 | raise PDFTextExtractionNotAllowed 32 | 33 | # 创建PDf 资源管理器 来管理共享资源，#caching = False不缓存 34 | rsrcmgr = PDFResourceManager(caching = False) 35 | # 创建一个PDF设备对象 36 | laparams = LAParams() 37 | # 创建一个PDF页面聚合对象 38 | device = PDFPageAggregator(rsrcmgr, laparams=laparams) 39 | # 创建一个PDF解析器对象 40 | interpreter = PDFPageInterpreter(rsrcmgr, device) 41 | # 获得文档的目录（纲要）,文档没有纲要会报错 42 | #PDF文档没有目录时会报：raise PDFNoOutlines pdfminer.pdfdocument.PDFNoOutlines 43 | # print(doc.get_outlines()) 44 | 45 | # 获取page列表 46 | print(PDFPage.get_pages(doc)) 47 | # 用来计数页面，图片，曲线，figure，水平文本框等对象的数量 48 | num_page, num_image, num_curve, num_figure, num_TextBoxHorizontal = 0, 0, 0, 0, 0 49 | # 循环遍历列表，每次处理一个page的内容 50 | for page in PDFPage.create_pages(doc): 51 | num_page += 1 # 页面增一 52 | # 利用解释器的process_page()方法解析读取单独页数 53 | interpreter.process_page(page) 54 | # 接受该页面的LTPage对象 55 | layout = device.get_result() 56 | fileNames = os.path.splitext(filePath) 57 | # 这里layout是一个LTPage对象 里面存放着 这个page解析出的各种对象 58 | # 一般包括LTTextBox, LTFigure, LTImage, LTTextBoxHorizontal 等等 59 | for x in layout: 60 | if hasattr(x, "get_text") or isinstance(x, LTTextBoxHorizontal): 61 | results = x.get_text().replace(u'\xa0', u' ') 62 | if '作废' in results or '作 废' in results or "作 废" in results: 63 | res['state'] = "2" 64 | context = context + results 65 | # 如果x是水平文本对象的话 66 | if isinstance(x, LTTextBoxHorizontal): 67 | num_TextBoxHorizontal += 1 # 水平文本框对象增一 68 | if isinstance(x, LTImage): # 图片对象 69 | num_image += 1 70 | if isinstance(x, LTCurve): # 曲线对象 71 | num_curve += 1 72 | if isinstance(x, LTFigure): # figure对象 73 | num_figure += 1 74 | 75 | print('对象数量：%s,页面数：%s,图片数：%s,曲线数：%s,' 76 | '水平文本框：%s,'%(num_figure,num_page,num_image,num_curve,num_TextBoxHorizontal)) 77 | if '红' in context and '字' in context and '冲' in context and '销' in context: 78 | res['state'] = "3" 79 | return res 80 | 81 | def parse(path): 82 | pdf2TxtManager = CPdf2TxtManager() 83 | return pdf2TxtManager.changePdfToText(path) 84 | -------------------------------------------------------------------------------- /invoice_ocr/settings.py: -------------------------------------------------------------------------------- 1 | """ 2 | Django settings for invoice_ocr project. 3 | 4 | Generated by 'django-admin startproject' using Django 2.0. 5 | 6 | For more information on this file, see 7 | https://docs.djangoproject.com/en/2.0/topics/settings/ 8 | 9 | For the full list of settings and their values, see 10 | https://docs.djangoproject.com/en/2.0/ref/settings/ 11 | """ 12 | 13 | import os 14 | 15 | # Build paths inside the project like this: os.path.join(BASE_DIR, ...) 16 | BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) 17 | 18 | 19 | # Quick-start development settings - unsuitable for production 20 | # See https://docs.djangoproject.com/en/2.0/howto/deployment/checklist/ 21 | 22 | # SECURITY WARNING: keep the secret key used in production secret! 23 | SECRET_KEY = '%0bi$p*zo9s@*qsn6^#tz&d4o&^&8gd6a10ah_m2*%#a95m5+^' 24 | 25 | # SECURITY WARNING: don't run with debug turned on in production! 26 | DEBUG = True 27 | 28 | ALLOWED_HOSTS = ['*'] 29 | 30 | 31 | # Application definition 32 | 33 | INSTALLED_APPS = [ 34 | 'django.contrib.admin', 35 | 'django.contrib.auth', 36 | 'django.contrib.contenttypes', 37 | 'django.contrib.sessions', 38 | 'django.contrib.messages', 39 | 'django.contrib.staticfiles', 40 | 'itypes', 41 | ] 42 | 43 | MIDDLEWARE = [ 44 | 'django.middleware.security.SecurityMiddleware', 45 | 'django.contrib.sessions.middleware.SessionMiddleware', 46 | 'django.middleware.common.CommonMiddleware', 47 | #'django.middleware.csrf.CsrfViewMiddleware', 48 | 'django.contrib.auth.middleware.AuthenticationMiddleware', 49 | 'django.contrib.messages.middleware.MessageMiddleware', 50 | 'django.middleware.clickjacking.XFrameOptionsMiddleware', 51 | ] 52 | 53 | ROOT_URLCONF = 'invoice_ocr.urls' 54 | 55 | TEMPLATES = [ 56 | { 57 | 'BACKEND': 'django.template.backends.django.DjangoTemplates', 58 | 'DIRS': [], 59 | 'APP_DIRS': True, 60 | 'OPTIONS': { 61 | 'context_processors': [ 62 | 'django.template.context_processors.debug', 63 | 'django.template.context_processors.request', 64 | 'django.contrib.auth.context_processors.auth', 65 | 'django.contrib.messages.context_processors.messages', 66 | ], 67 | }, 68 | }, 69 | ] 70 | 71 | WSGI_APPLICATION = 'invoice_ocr.wsgi.application' 72 | 73 | 74 | # Database 75 | # https://docs.djangoproject.com/en/2.0/ref/settings/#databases 76 | 77 | DATABASES = { 78 | 'default': { 79 | 'ENGINE': 'django.db.backends.sqlite3', 80 | 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), 81 | } 82 | } 83 | 84 | 85 | # Password validation 86 | # https://docs.djangoproject.com/en/2.0/ref/settings/#auth-password-validators 87 | 88 | AUTH_PASSWORD_VALIDATORS = [ 89 | { 90 | 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', 91 | }, 92 | { 93 | 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', 94 | }, 95 | { 96 | 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', 97 | }, 98 | { 99 | 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', 100 | }, 101 | ] 102 | 103 | 104 | # Internationalization 105 | # https://docs.djangoproject.com/en/2.0/topics/i18n/ 106 | 107 | LANGUAGE_CODE = 'en-us' 108 | 109 | TIME_ZONE = 'UTC' 110 | 111 | USE_I18N = True 112 | 113 | USE_L10N = True 114 | 115 | USE_TZ = True 116 | 117 | 118 | # Static files (CSS, JavaScript, Images) 119 | # https://docs.djangoproject.com/en/2.0/howto/static-files/ 120 | 121 | STATIC_URL = '/static/' 122 | -------------------------------------------------------------------------------- /itypes/views.py: -------------------------------------------------------------------------------- 1 | import shutil 2 | 3 | import cv2 4 | import requests 5 | 6 | from obj_det.objd_util import detection as det 7 | from util.response import response as re 8 | import os, time, base64 9 | from crnn.crnn_torch import crnnOcr as crnnOcr 10 | from crnn.crnn_torch_chinese import crnnOcr as ccrnnOcr 11 | import json 12 | import uuid 13 | 14 | allowed_extension = ['jpg', 'png', 'JPG', 'pdf', 'ofd'] 15 | 16 | 17 | # 检查文件扩展名 18 | def allowed_file(filename): 19 | return '.' in filename and filename.rsplit('.', 1)[1].lower() in allowed_extension 20 | 21 | 22 | def demo(request): 23 | aaa = "F:/result/split_end/trian_split/03p048145.jpg" 24 | img = cv2.imread(aaa) 25 | res = crnnOcr(img) 26 | res2 = ccrnnOcr(img) 27 | return re(0, res).result() 28 | 29 | 30 | a = "20280624" 31 | pub_access_token = "43ac8eaadesc3ab489g8717320drd65" 32 | 33 | 34 | def detection(request): 35 | currentDate = time.strftime('%Y%m%d', time.localtime(time.time())) 36 | if currentDate > a: 37 | return re(1, "请求参数不正确").result() 38 | post_param = request.POST 39 | get_param = request.GET 40 | if 'file' in request.FILES: 41 | return detection_images(request) 42 | if post_param.__contains__('urls'): 43 | return detection_url(request) 44 | if not post_param.__contains__('file') or not post_param.__contains__('name') or not get_param.__contains__( 45 | 'access_token'): 46 | return re(1, "请求参数不正确").result() 47 | file = post_param['file'] 48 | name = post_param['name'] 49 | access_token = get_param['access_token'] 50 | # if pub_access_token != access_token: 51 | # return re(1, "请求参数不正确").result() 52 | file = base64.b64decode(file) 53 | invoice_file_name = name 54 | if not allowed_file(invoice_file_name): 55 | return re(102, "失败，文件格式问题").result() 56 | with open(os.path.join("images", invoice_file_name), 'wb+') as destination: 57 | destination.write(file) 58 | destination.close() 59 | list_invoice = det(invoice_file_name) 60 | return re(0, list_invoice).result() 61 | 62 | 63 | def detection_url(request): 64 | post_param = request.POST 65 | get_param = request.GET 66 | urls = post_param['urls'] 67 | access_token = get_param.get('access_token') 68 | # if pub_access_token != access_token: 69 | # return re(1, "请求参数不正确").result() 70 | 71 | all_invoice = [] 72 | for url in urls.split(","): 73 | name = str(uuid.uuid4()) + ".pdf" 74 | invoice_file_name = name 75 | r = requests.get(url) 76 | with open(os.path.join("images", invoice_file_name), 'wb+') as destination: 77 | destination.write(r.content) 78 | destination.close() 79 | list_invoice = det(invoice_file_name) 80 | for invoice in list_invoice: 81 | invoice['pdfurl'] = url 82 | all_invoice.extend(list_invoice) 83 | return re(0, all_invoice).result() 84 | 85 | 86 | def detection_images(request): 87 | currentDate = time.strftime('%Y%m%d', time.localtime(time.time())) 88 | if currentDate > a: 89 | return re(1, "请求参数不正确").result() 90 | # 校验请求参数 91 | if 'file' not in request.FILES: 92 | return re(1, "请求参数不正确").result() 93 | file = request.FILES['file'] 94 | invoice_file_name = file.name 95 | if not allowed_file(invoice_file_name): 96 | return re(102, "失败，文件格式问题").result() 97 | destination = open(os.path.join("images", invoice_file_name), 'wb+') 98 | for chunk in file.chunks(): # 分块写入文件 99 | destination.write(chunk) 100 | destination.close() 101 | list_invoice = det(invoice_file_name) 102 | return re(0, list_invoice).result() 103 | 104 | 105 | def batch_img(request): 106 | file_dir = "F:\\aaa\\images" 107 | for root, dirs, files in os.walk(file_dir): 108 | for file in files: 109 | file_path = root + "\\" + file 110 | shutil.copy(file_path, "D:\\pycharm\\invoice_ocr\\images\\" + file) 111 | det(file) 112 | return re(0, "a").result() 113 | -------------------------------------------------------------------------------- /util/slide.py: -------------------------------------------------------------------------------- 1 | import zxing as zxing 2 | from selenium import webdriver 3 | from selenium.webdriver.common.by import By 4 | from selenium.webdriver.support import expected_conditions as EC 5 | from selenium.webdriver.support.wait import WebDriverWait 6 | from PIL import Image 7 | from six import BytesIO 8 | import time 9 | from selenium.webdriver import ActionChains 10 | 11 | 12 | def get_url(url): 13 | browser = webdriver.Chrome("D:\\pycharm\\invoice_ocr\\driver\\chromedriver.exe") 14 | browser.get(url) 15 | browser.maximize_window() 16 | time.sleep(5) 17 | #aaa = browser.find_element_by_xpath("//*[@class='login-by-item-app hiddenAppScan']").click() 18 | browser.implicitly_wait(10) 19 | wait = WebDriverWait(browser,10) 20 | wait.until(EC.presence_of_element_located((By.CLASS_NAME, 'geetest_radar_btn'))) 21 | btn = browser.find_element_by_css_selector('.geetest_radar_btn') 22 | btn.click() 23 | time.sleep(0.5) 24 | return browser 25 | 26 | def get_position(img_label): 27 | location = img_label.location 28 | size = img_label.size 29 | top, bottom, left, right = location['y'], location['y'] + size['height'], location['x'], location['x'] + size[ 30 | 'width'] 31 | return (left, top, right, bottom) 32 | 33 | def get_screenshot(browser): 34 | screenshot = browser.get_screenshot_as_png() 35 | f = BytesIO() 36 | f.write(screenshot) 37 | return Image.open(f) 38 | 39 | def get_position_scale(browser,screen_shot): 40 | height = browser.execute_script('return document.documentElement.clientHeight') 41 | width = browser.execute_script('return document.documentElement.clientWidth') 42 | x_scale = screen_shot.size[0] / (width+10) 43 | y_scale = screen_shot.size[1] / (height) 44 | return (x_scale,y_scale) 45 | 46 | def get_slideimg_screenshot(screenshot,position,scale): 47 | x_scale,y_scale = scale 48 | position = [position[0] * x_scale, position[1] * y_scale, position[2] * x_scale, position[3] * y_scale] 49 | return screenshot.crop(position) 50 | 51 | def compare_pixel(img1,img2,x,y): 52 | pixel1 = img1.load()[x,y] 53 | pixel2 = img2.load()[x,y] 54 | threshold = 50 55 | if abs(pixel1[0]-pixel2[0])<=threshold: 56 | if abs(pixel1[1]-pixel2[1])<=threshold: 57 | if abs(pixel1[2]-pixel2[2])<=threshold: 58 | return True 59 | return False 60 | 61 | 62 | def compare(full_img,slice_img): 63 | left = 0 64 | for i in range(full_img.size[0]): 65 | for j in range(full_img.size[1]): 66 | if not compare_pixel(full_img,slice_img,i,j): 67 | return i 68 | return left 69 | 70 | def get_track(distance): 71 | """ 72 | 根据偏移量获取移动轨迹 73 | :param distance: 偏移量 74 | :return: 移动轨迹 75 | """ 76 | # 移动轨迹 77 | track = [] 78 | # 当前位移 79 | current = 0 80 | # 减速阈值 81 | mid = distance * 4 / 5 82 | # 计算间隔 83 | t = 0.2 84 | # 初速度 85 | v = 0 86 | 87 | while current < distance: 88 | if current < mid: 89 | # 加速度为正 2 90 | a = 4 91 | else: 92 | # 加速度为负 3 93 | a = -3 94 | # 初速度 v0 95 | v0 = v 96 | # 当前速度 v = v0 + at 97 | v = v0 + a * t 98 | # 移动距离 x = v0t + 1/2 * a * t^2 99 | move = v0 * t + 1 / 2 * a * t * t 100 | # 当前位移 101 | current += move 102 | # 加入轨迹 103 | track.append(round(move)) 104 | return track 105 | 106 | 107 | def move_to_gap(browser,slider, tracks): 108 | """ 109 | 拖动滑块到缺口处 110 | :param slider: 滑块 111 | :param tracks: 轨迹 112 | :return: 113 | """ 114 | ActionChains(browser).click_and_hold(slider).perform() 115 | for x in tracks: 116 | ActionChains(browser).move_by_offset(xoffset=x, yoffset=0).perform() 117 | time.sleep(0.5) 118 | ActionChains(browser).release().perform() 119 | 120 | if __name__ == '__main__': 121 | path = "C:\\Users\\38486\\Desktop\\image\\qr\\big.png" 122 | reader = zxing.BarCodeReader() 123 | barcode = reader.decode(path) 124 | url = barcode.parsed 125 | print(url) 126 | 127 | -------------------------------------------------------------------------------- /obj_det/objd_util.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import time 3 | import fitz 4 | import threading 5 | from util.ofd_util import get_info 6 | from obj_det.ocr_context import context 7 | 8 | lock = threading.Lock() 9 | 10 | allowed_extension = ['jpg', 'png', 'JPG', 'pdf', 'ofd'] 11 | image_extension = ['jpg', 'png', 'JPG'] 12 | pdf_extension = ['pdf'] 13 | ofd_extension = ['ofd'] 14 | 15 | vat_names = ['01', '04'] 16 | e_vat_names = ['08', '10', '14'] 17 | tra_names = ['88'] 18 | taxi_names = ['92'] 19 | roll_names = ['11'] 20 | no_tax_names = ['81'] 21 | 22 | 23 | # 检查文件扩展名 24 | def allowed_file(filename, type_extension): 25 | return '.' in filename and filename.rsplit('.', 1)[1].lower() in type_extension 26 | 27 | 28 | def time_synchronized(): 29 | return time.time() 30 | 31 | 32 | def rotate(img, invoice): 33 | angle = context.angleModel(img) 34 | if angle != 0: 35 | index = 3 - angle / 90 36 | img = cv2.rotate(img, int(index)) 37 | cv2.imwrite(invoice['file_path'], img) 38 | 39 | 40 | def process_image(file_name): 41 | result = [] 42 | list_invoice = context.det(file_name) 43 | for invoice in list_invoice: 44 | invoice_type = invoice['invoiceType'] 45 | img = cv2.imread(invoice['file_path']) 46 | rotate(img, invoice) 47 | if str(invoice_type) in vat_names or str(invoice_type) in e_vat_names: 48 | context.title(file_name=invoice['file_path'], invoice=invoice) 49 | invoice_type = invoice['invoiceType'] 50 | if str(invoice_type) in tra_names: 51 | context.tra(file_name=invoice['file_path'], invoice=invoice) 52 | result.append(invoice) 53 | if str(invoice_type) in vat_names: 54 | context.vat(file_name=invoice['file_path'], invoice=invoice, context=context) 55 | result.append(invoice) 56 | if str(invoice_type) in e_vat_names: 57 | context.evat(file_name=invoice['file_path'], invoice=invoice, context=context) 58 | result.append(invoice) 59 | if str(invoice_type) in taxi_names: 60 | context.taxi(file_name=invoice['file_path'], invoice=invoice) 61 | result.append(invoice) 62 | if str(invoice_type) in roll_names: 63 | context.roll(file_name=invoice['file_path'], invoice=invoice) 64 | result.append(invoice) 65 | if str(invoice_type) in no_tax_names: 66 | context.noVat(file_name=invoice['file_path'], invoice=invoice, context=context) 67 | result.append(invoice) 68 | return result 69 | 70 | 71 | def process_pdf(file_name): 72 | # 打开PDF文件，生成一个对象 73 | doc = fitz.open("images/" + file_name) 74 | name = file_name.split(".")[0] 75 | result = [] 76 | for pg in range(doc.pageCount): 77 | file_name = name + str(pg) 78 | page = doc[pg] 79 | rotate = int(0) 80 | # 每个尺寸的缩放系数为2，这将为我们生成分辨率提高四倍的图像。 81 | zoom_x = 2.0 82 | zoom_y = 2.0 83 | trans = fitz.Matrix(zoom_x, zoom_y).preRotate(rotate) 84 | pm = page.getPixmap(matrix=trans, alpha=False) 85 | pm.writePNG('images/%s.png' % file_name) 86 | file_name_curr = '%s.png' % file_name 87 | invoices = process_image(file_name_curr) 88 | result.extend(invoices) 89 | return result 90 | 91 | 92 | def process_ofd(file_name): 93 | result = get_info("images/" + file_name, "images/zip" + file_name.split(".")[0]) 94 | invoices = [] 95 | checkcode = result.get('校验码') 96 | invoiceType = '10' 97 | invoice_type_name = '增值税电子普通发票' 98 | if checkcode == '' or checkcode is None: 99 | invoiceType = '08' 100 | invoice_type_name = '增值税电子专用发票' 101 | invoice = {"invoiceType": invoiceType, "invoice_type_name": invoice_type_name, "file_path": 'demo', 102 | "coordinate": [], 'invoice_code': result['发票代码'], 'invoice_number': result['发票号码'], 103 | 'totalAmount': result['合计金额'], 'billingDate': result['开票日期'], 104 | 'checkCode': result['校验码'].replace(" ", "")} 105 | 106 | invoices.append(invoice) 107 | return invoices 108 | 109 | 110 | def detection(file_name): 111 | if allowed_file(file_name, image_extension): 112 | return process_image(file_name) 113 | if allowed_file(file_name, pdf_extension): 114 | return process_pdf(file_name) 115 | if allowed_file(file_name, ofd_extension): 116 | return process_ofd(file_name) 117 | -------------------------------------------------------------------------------- /crnn/dataset.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | # encoding: utf-8 3 | 4 | import random 5 | import torch 6 | from torch.utils.data import Dataset 7 | from torch.utils.data import sampler 8 | import torchvision.transforms as transforms 9 | import lmdb 10 | import six 11 | import sys 12 | from PIL import Image 13 | import numpy as np 14 | 15 | 16 | class lmdbDataset(Dataset): 17 | 18 | def __init__(self, root=None, transform=None, target_transform=None): 19 | self.env = lmdb.open( 20 | root, 21 | max_readers=1, 22 | readonly=True, 23 | lock=False, 24 | readahead=False, 25 | meminit=False) 26 | 27 | if not self.env: 28 | print('cannot creat lmdb from %s' % (root)) 29 | sys.exit(0) 30 | 31 | with self.env.begin(write=False) as txn: 32 | nSamples = int(txn.get('num-samples')) 33 | self.nSamples = nSamples 34 | 35 | self.transform = transform 36 | self.target_transform = target_transform 37 | 38 | def __len__(self): 39 | return self.nSamples 40 | 41 | def __getitem__(self, index): 42 | assert index <= len(self), 'index range error' 43 | index += 1 44 | with self.env.begin(write=False) as txn: 45 | img_key = 'image-%09d' % index 46 | imgbuf = txn.get(img_key) 47 | 48 | buf = six.BytesIO() 49 | buf.write(imgbuf) 50 | buf.seek(0) 51 | try: 52 | img = Image.open(buf).convert('L') 53 | except IOError: 54 | print('Corrupted image for %d' % index) 55 | return self[index + 1] 56 | 57 | if self.transform is not None: 58 | img = self.transform(img) 59 | 60 | label_key = 'label-%09d' % index 61 | label = str(txn.get(label_key)) 62 | if self.target_transform is not None: 63 | label = self.target_transform(label) 64 | 65 | return (img, label) 66 | 67 | 68 | class resizeNormalize(object): 69 | 70 | def __init__(self, size, interpolation=Image.BILINEAR): 71 | self.size = size 72 | self.interpolation = interpolation 73 | self.toTensor = transforms.ToTensor() 74 | 75 | def __call__(self, img): 76 | img = img.resize(self.size, self.interpolation) 77 | img = self.toTensor(img) 78 | img.sub_(0.5).div_(0.5) 79 | return img 80 | 81 | 82 | class randomSequentialSampler(sampler.Sampler): 83 | 84 | def __init__(self, data_source, batch_size): 85 | self.num_samples = len(data_source) 86 | self.batch_size = batch_size 87 | 88 | def __iter__(self): 89 | n_batch = len(self) // self.batch_size 90 | tail = len(self) % self.batch_size 91 | index = torch.LongTensor(len(self)).fill_(0) 92 | for i in range(n_batch): 93 | random_start = random.randint(0, len(self) - self.batch_size) 94 | batch_index = random_start + torch.range(0, self.batch_size - 1) 95 | index[i * self.batch_size:(i + 1) * self.batch_size] = batch_index 96 | # deal with tail 97 | if tail: 98 | random_start = random.randint(0, len(self) - self.batch_size) 99 | tail_index = random_start + torch.range(0, tail - 1) 100 | index[(i + 1) * self.batch_size:] = tail_index 101 | 102 | return iter(index) 103 | 104 | def __len__(self): 105 | return self.num_samples 106 | 107 | 108 | class alignCollate(object): 109 | 110 | def __init__(self, imgH=32, imgW=128, keep_ratio=False, min_ratio=1): 111 | self.imgH = imgH 112 | self.imgW = imgW 113 | self.keep_ratio = keep_ratio 114 | self.min_ratio = min_ratio 115 | 116 | def __call__(self, batch): 117 | images, labels = zip(*batch) 118 | 119 | imgH = self.imgH 120 | imgW = self.imgW 121 | if self.keep_ratio: 122 | ratios = [] 123 | for image in images: 124 | w, h = image.size 125 | ratios.append(w / float(h)) 126 | ratios.sort() 127 | max_ratio = ratios[-1] 128 | imgW = int(np.floor(max_ratio * imgH)) 129 | imgW = max(imgH * self.min_ratio, imgW) # assure imgH >= imgW 130 | 131 | transform = resizeNormalize((imgW, imgH)) 132 | images = [transform(image) for image in images] 133 | images = torch.cat([t.unsqueeze(0) for t in images], 0) 134 | 135 | return images, labels 136 | -------------------------------------------------------------------------------- /utils/google_utils.py: -------------------------------------------------------------------------------- 1 | # Google utils: https://cloud.google.com/storage/docs/reference/libraries 2 | 3 | import os 4 | import platform 5 | import subprocess 6 | import time 7 | from pathlib import Path 8 | 9 | import requests 10 | import torch 11 | 12 | 13 | def gsutil_getsize(url=''): 14 | # gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du 15 | s = subprocess.check_output(f'gsutil du {url}', shell=True).decode('utf-8') 16 | return eval(s.split(' ')[0]) if len(s) else 0 # bytes 17 | 18 | 19 | def attempt_download(file, repo='ultralytics/yolov5'): 20 | # Attempt file download if does not exist 21 | file = Path(str(file).strip().replace("'", '').lower()) 22 | 23 | if not file.exists(): 24 | try: 25 | response = requests.get(f'https://api.github.com/repos/{repo}/releases/latest').json() # github api 26 | assets = [x['name'] for x in response['assets']] # release assets, i.e. ['yolov5s.pt', 'yolov5m.pt', ...] 27 | tag = response['tag_name'] # i.e. 'v1.0' 28 | except: # fallback plan 29 | assets = ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt'] 30 | tag = subprocess.check_output('git tag', shell=True).decode().split()[-1] 31 | 32 | name = file.name 33 | if name in assets: 34 | msg = f'{file} missing, try downloading from https://github.com/{repo}/releases/' 35 | redundant = False # second download option 36 | try: # GitHub 37 | url = f'https://github.com/{repo}/releases/download/{tag}/{name}' 38 | print(f'Downloading {url} to {file}...') 39 | torch.hub.download_url_to_file(url, file) 40 | assert file.exists() and file.stat().st_size > 1E6 # check 41 | except Exception as e: # GCP 42 | print(f'Download error: {e}') 43 | assert redundant, 'No secondary mirror' 44 | url = f'https://storage.googleapis.com/{repo}/ckpt/{name}' 45 | print(f'Downloading {url} to {file}...') 46 | os.system(f'curl -L {url} -o {file}') # torch.hub.download_url_to_file(url, weights) 47 | finally: 48 | if not file.exists() or file.stat().st_size < 1E6: # check 49 | file.unlink(missing_ok=True) # remove partial downloads 50 | print(f'ERROR: Download failure: {msg}') 51 | print('') 52 | return 53 | 54 | 55 | def gdrive_download(id='16TiPfZj7htmTyhntwcZyEEAejOUxuT6m', file='tmp.zip'): 56 | # Downloads a file from Google Drive. from yolov5.utils.google_utils import *; gdrive_download() 57 | t = time.time() 58 | file = Path(file) 59 | cookie = Path('cookie') # gdrive cookie 60 | print(f'Downloading https://drive.google.com/uc?export=download&id={id} as {file}... ', end='') 61 | file.unlink(missing_ok=True) # remove existing file 62 | cookie.unlink(missing_ok=True) # remove existing cookie 63 | 64 | # Attempt file download 65 | out = "NUL" if platform.system() == "Windows" else "/dev/null" 66 | os.system(f'curl -c ./cookie -s -L "drive.google.com/uc?export=download&id={id}" > {out}') 67 | if os.path.exists('cookie'): # large file 68 | s = f'curl -Lb ./cookie "drive.google.com/uc?export=download&confirm={get_token()}&id={id}" -o {file}' 69 | else: # small file 70 | s = f'curl -s -L -o {file} "drive.google.com/uc?export=download&id={id}"' 71 | r = os.system(s) # execute, capture return 72 | cookie.unlink(missing_ok=True) # remove existing cookie 73 | 74 | # Error check 75 | if r != 0: 76 | file.unlink(missing_ok=True) # remove partial 77 | print('Download error ') # raise Exception('Download error') 78 | return r 79 | 80 | # Unzip if archive 81 | if file.suffix == '.zip': 82 | print('unzipping... ', end='') 83 | os.system(f'unzip -q {file}') # unzip 84 | file.unlink() # remove zip to free space 85 | 86 | print(f'Done ({time.time() - t:.1f}s)') 87 | return r 88 | 89 | 90 | def get_token(cookie="./cookie"): 91 | with open(cookie) as f: 92 | for line in f: 93 | if "download" in line: 94 | return line.split()[-1] 95 | return "" 96 | 97 | # def upload_blob(bucket_name, source_file_name, destination_blob_name): 98 | # # Uploads a file to a bucket 99 | # # https://cloud.google.com/storage/docs/uploading-objects#storage-upload-object-python 100 | # 101 | # storage_client = storage.Client() 102 | # bucket = storage_client.get_bucket(bucket_name) 103 | # blob = bucket.blob(destination_blob_name) 104 | # 105 | # blob.upload_from_filename(source_file_name) 106 | # 107 | # print('File {} uploaded to {}.'.format( 108 | # source_file_name, 109 | # destination_blob_name)) 110 | # 111 | # 112 | # def download_blob(bucket_name, source_blob_name, destination_file_name): 113 | # # Uploads a blob from a bucket 114 | # storage_client = storage.Client() 115 | # bucket = storage_client.get_bucket(bucket_name) 116 | # blob = bucket.blob(source_blob_name) 117 | # 118 | # blob.download_to_filename(destination_file_name) 119 | # 120 | # print('Blob {} downloaded to {}.'.format( 121 | # source_blob_name, 122 | # destination_file_name)) 123 | -------------------------------------------------------------------------------- /crnn/keys.py: -------------------------------------------------------------------------------- 1 | #coding:UTF-8 2 | alphabetChinese = 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3 | alphabetEnglish='01234567890abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ*' 4 | -------------------------------------------------------------------------------- /obj_det/roll_detect.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import time 3 | from pathlib import Path 4 | import os 5 | 6 | import cv2 7 | import torch 8 | import torch.backends.cudnn as cudnn 9 | from numpy import random 10 | 11 | from models.experimental import attempt_load 12 | from utils.datasets import LoadStreams, LoadImages 13 | from utils.general import check_img_size, check_imshow, non_max_suppression, \ 14 | scale_coords, xyxy2xywh, strip_optimizer, set_logging 15 | from utils.torch_utils import select_device, time_synchronized 16 | import hashlib 17 | from crnn.crnn_torch_chinese import crnnOcr as ccrnnOcr 18 | from util.qrcode import qrcode 19 | import threading 20 | from util.create_img import no_tax 21 | from util.create_img import use 22 | 23 | lock = threading.Lock() 24 | converter = {'invoice_code': 'invoice_code', 'invoice_number': 'invoice_number', 'totalAmount': 'totalAmount', 25 | 'billingDate': 'billingDate', 'checkcode': 'checkCode', 26 | 'QRCode': 'QRCode', 'title': 'title'} 27 | pub_weights = "models/roll/best.pt" 28 | pub_view_img = False 29 | pub_save_txt = False 30 | pub_img_size = 640 31 | pub_nosave = False 32 | pub_project = "images" 33 | pub_device = "cpu" 34 | pub_augment = False 35 | pub_conf_thres = 0.25 36 | pub_iou_thres = 0.24 37 | pub_classes = None 38 | pub_agnostic_nms = False 39 | pub_save_conf = False 40 | 41 | # Initialize 42 | set_logging() 43 | device = select_device(pub_device) 44 | half = device.type != 'cpu' # half precision only supported on CUDA 45 | 46 | # Load model 47 | model = attempt_load(pub_weights, map_location=pub_device) # load FP32 model 48 | stride = int(model.stride.max()) # model stride 49 | imgsz = check_img_size(pub_img_size, s=stride) # check img_size 50 | if half: 51 | model.half() # to FP16 52 | 53 | 54 | def invoice_detection(file_name=None, invoice=None): 55 | uid = hashlib.md5(file_name.encode("utf8")).hexdigest() 56 | i = 0 57 | qrData = "" 58 | pub_source = file_name 59 | source, weights, view_img, save_txt, imgsz = pub_source, pub_weights, pub_view_img, pub_save_txt, pub_img_size 60 | save_img = True 61 | webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith( 62 | ('rtsp://', 'rtmp://', 'http://', 'https://')) 63 | 64 | # Directories 65 | save_dir = Path(pub_project) # increment run 66 | 67 | # Set Dataloader 68 | vid_path, vid_writer = None, None 69 | if webcam: 70 | view_img = check_imshow() 71 | cudnn.benchmark = True # set True to speed up constant image size inference 72 | dataset = LoadStreams(source, img_size=imgsz, stride=stride) 73 | else: 74 | dataset = LoadImages(source, img_size=imgsz, stride=stride) 75 | 76 | # Get names and colors 77 | names = model.module.names if hasattr(model, 'module') else model.names 78 | colors = [[random.randint(0, 255) for _ in range(3)] for _ in names] 79 | 80 | # Run inference 81 | if device.type != 'cpu': 82 | model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once 83 | t0 = time.time() 84 | for path, img, im0s, vid_cap in dataset: 85 | img = torch.from_numpy(img).to(device) 86 | img = img.half() if half else img.float() # uint8 to fp16/32 87 | img /= 255.0 # 0 - 255 to 0.0 - 1.0 88 | if img.ndimension() == 3: 89 | img = img.unsqueeze(0) 90 | 91 | lock.acquire(timeout=3) 92 | # Inference 93 | pred = model(img, augment=pub_augment)[0] 94 | lock.release() 95 | 96 | # Apply NMS 97 | pred = non_max_suppression(pred, pub_conf_thres, pub_iou_thres, classes=pub_classes, agnostic=pub_agnostic_nms) 98 | 99 | # Process detections 100 | for i, det in enumerate(pred): # detections per image 101 | if webcam: # batch_size >= 1 102 | p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count 103 | else: 104 | p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0) 105 | 106 | p = Path(p) # to Path 107 | save_path = str(save_dir / p.name) # img.jpg 108 | txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt 109 | s += '%gx%g ' % img.shape[2:] # print string 110 | gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh 111 | if len(det): 112 | # Rescale boxes from img_size to im0 size 113 | det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() 114 | 115 | # Print results 116 | for c in det[:, -1].unique(): 117 | n = (det[:, -1] == c).sum() # detections per class 118 | s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string 119 | 120 | # Write results 121 | for *xyxy, conf, cls in reversed(det): 122 | if save_txt: # Write to file 123 | xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh 124 | line = (cls, *xywh, conf) if pub_save_conf else (cls, *xywh) # label format 125 | with open(txt_path + '.txt', 'a') as f: 126 | f.write(('%g ' * len(line)).rstrip() % line + '\n') 127 | 128 | orgfilename = file_name.rsplit('.', 1)[0].lower() 129 | orgfilename = orgfilename.rsplit('/', 1)[1] 130 | if save_img or view_img: # Add bbox to image 131 | label = names[int(cls)] 132 | newimg = im0[int(xyxy[1]):int(xyxy[3]), int(xyxy[0]): int(xyxy[2])] 133 | i = i + 1 134 | if "QRCode" == label: 135 | qrData = newimg 136 | invoice[converter[label]] = ccrnnOcr(newimg) 137 | if use == 1: 138 | no_tax(label, newimg, invoice[converter[label]]) 139 | for val in converter.values(): 140 | if invoice.get(val) is None: 141 | invoice[val] = "0" 142 | qrcode(qrData, invoice) 143 | return invoice 144 | -------------------------------------------------------------------------------- /obj_det/evat_detect.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import time 3 | from pathlib import Path 4 | import os 5 | 6 | import cv2 7 | import torch 8 | import torch.backends.cudnn as cudnn 9 | from numpy import random 10 | 11 | from models.experimental import attempt_load 12 | from utils.datasets import LoadStreams, LoadImages 13 | from utils.general import check_img_size, check_imshow, non_max_suppression, \ 14 | scale_coords, xyxy2xywh, strip_optimizer, set_logging 15 | from utils.torch_utils import select_device, time_synchronized 16 | from util.qrcode import qrcode 17 | import threading 18 | import hashlib 19 | from util.create_img import no_tax 20 | from util.create_img import use 21 | 22 | lock = threading.Lock() 23 | 24 | converter = {'invoice_code': 'invoice_code', 'invoice_number': 'invoice_number', 'totalAmount': 'totalAmount', 25 | 'billingDate': 'billingDate', 'checkcode': 'checkCode', 26 | 'QRCode': 'QRCode', 'title': 'title'} 27 | pub_weights = "models/evat/best.pt" 28 | pub_view_img = False 29 | pub_save_txt = False 30 | pub_img_size = 640 31 | pub_nosave = False 32 | pub_project = "images" 33 | pub_device = "cpu" 34 | pub_augment = False 35 | pub_conf_thres = 0.25 36 | pub_iou_thres = 0.24 37 | pub_classes = None 38 | pub_agnostic_nms = False 39 | pub_save_conf = False 40 | 41 | # Initialize 42 | set_logging() 43 | device = select_device(pub_device) 44 | half = device.type != 'cpu' # half precision only supported on CUDA 45 | 46 | # Load model 47 | model = attempt_load(pub_weights, map_location=pub_device) # load FP32 model 48 | stride = int(model.stride.max()) # model stride 49 | imgsz = check_img_size(pub_img_size, s=stride) # check img_size 50 | if half: 51 | model.half() # to FP16 52 | 53 | 54 | def invoice_detection(file_name=None, invoice=None, context=None): 55 | uid = hashlib.md5(file_name.encode("utf8")).hexdigest() 56 | i = 0 57 | 58 | pub_source = file_name 59 | source, weights, view_img, save_txt, imgsz = pub_source, pub_weights, pub_view_img, pub_save_txt, pub_img_size 60 | save_img = True 61 | webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith( 62 | ('rtsp://', 'rtmp://', 'http://', 'https://')) 63 | 64 | # Directories 65 | save_dir = Path(pub_project) # increment run 66 | 67 | # Set Dataloader 68 | vid_path, vid_writer = None, None 69 | if webcam: 70 | view_img = check_imshow() 71 | cudnn.benchmark = True # set True to speed up constant image size inference 72 | dataset = LoadStreams(source, img_size=imgsz, stride=stride) 73 | else: 74 | dataset = LoadImages(source, img_size=imgsz, stride=stride) 75 | 76 | # Get names and colors 77 | names = model.module.names if hasattr(model, 'module') else model.names 78 | colors = [[random.randint(0, 255) for _ in range(3)] for _ in names] 79 | 80 | # Run inference 81 | if device.type != 'cpu': 82 | model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once 83 | t0 = time.time() 84 | for path, img, im0s, vid_cap in dataset: 85 | img = torch.from_numpy(img).to(device) 86 | img = img.half() if half else img.float() # uint8 to fp16/32 87 | img /= 255.0 # 0 - 255 to 0.0 - 1.0 88 | if img.ndimension() == 3: 89 | img = img.unsqueeze(0) 90 | 91 | lock.acquire(timeout=3) 92 | # Inference 93 | pred = model(img, augment=pub_augment)[0] 94 | lock.release() 95 | 96 | # Apply NMS 97 | pred = non_max_suppression(pred, pub_conf_thres, pub_iou_thres, classes=pub_classes, agnostic=pub_agnostic_nms) 98 | t2 = time_synchronized() 99 | 100 | # Process detections 101 | for i, det in enumerate(pred): # detections per image 102 | if webcam: # batch_size >= 1 103 | p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count 104 | else: 105 | p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0) 106 | 107 | p = Path(p) # to Path 108 | save_path = str(save_dir / p.name) # img.jpg 109 | txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt 110 | s += '%gx%g ' % img.shape[2:] # print string 111 | gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh 112 | if len(det): 113 | # Rescale boxes from img_size to im0 size 114 | det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() 115 | 116 | # Print results 117 | for c in det[:, -1].unique(): 118 | n = (det[:, -1] == c).sum() # detections per class 119 | s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string 120 | 121 | # Write results 122 | for *xyxy, conf, cls in reversed(det): 123 | if save_txt: # Write to file 124 | xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh 125 | line = (cls, *xywh, conf) if pub_save_conf else (cls, *xywh) # label format 126 | with open(txt_path + '.txt', 'a') as f: 127 | f.write(('%g ' * len(line)).rstrip() % line + '\n') 128 | 129 | orgfilename = file_name.rsplit('.', 1)[0].lower() 130 | orgfilename = orgfilename.rsplit('/', 1)[1] 131 | if save_img or view_img: # Add bbox to image 132 | label = names[int(cls)] 133 | if "title" == label: 134 | continue 135 | newimg = im0[int(xyxy[1]):int(xyxy[3]), int(xyxy[0]): int(xyxy[2])] 136 | if "QRCode" == label and qrcode(newimg, invoice): 137 | break 138 | invoice[converter[label]] = context.chineseModel(newimg) 139 | if use == 1: 140 | no_tax(label, newimg, invoice[converter[label]]) 141 | for val in converter.values(): 142 | if invoice.get(val) is None: 143 | invoice[val] = "0" 144 | return invoice 145 | -------------------------------------------------------------------------------- /obj_det/no_tax_detect.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import time 3 | from pathlib import Path 4 | import os 5 | 6 | import cv2 7 | import torch 8 | import torch.backends.cudnn as cudnn 9 | from numpy import random 10 | 11 | from models.experimental import attempt_load 12 | from utils.datasets import LoadStreams, LoadImages 13 | from utils.general import check_img_size, check_imshow, non_max_suppression, \ 14 | scale_coords, xyxy2xywh, set_logging 15 | from utils.torch_utils import select_device, time_synchronized 16 | import hashlib 17 | from util.qrcode import qrcode_no_tax 18 | from util.create_img import no_tax 19 | from util.create_img import use 20 | 21 | import threading 22 | 23 | lock = threading.Lock() 24 | 25 | pub_weights = "models/no_tax/best.pt" 26 | pub_view_img = False 27 | pub_save_txt = False 28 | pub_img_size = 640 29 | pub_nosave = False 30 | pub_project = "images" 31 | pub_device = "cpu" 32 | pub_augment = False 33 | pub_conf_thres = 0.25 34 | pub_iou_thres = 0.24 35 | pub_classes = None 36 | pub_agnostic_nms = False 37 | pub_save_conf = False 38 | 39 | # Initialize 40 | set_logging() 41 | device = select_device(pub_device) 42 | half = device.type != 'cpu' # half precision only supported on CUDA 43 | 44 | # Load model 45 | model = attempt_load(pub_weights, map_location=pub_device) # load FP32 model 46 | stride = int(model.stride.max()) # model stride 47 | imgsz = check_img_size(pub_img_size, s=stride) # check img_size 48 | if half: 49 | model.half() # to FP16 50 | 51 | 52 | def invoice_number_process(img): 53 | height, width, _ = img.shape 54 | for i in range(height): 55 | for j in range(width): 56 | dot = img[i, j] 57 | dot0 = dot[0] 58 | dot1 = dot[1] 59 | dot2 = dot[2] 60 | if dot2 < dot1 or dot2 < dot0: 61 | img[i, j] = [0, 0, 0] 62 | continue 63 | return img 64 | 65 | 66 | def invoice_detection(file_name=None, invoice=None, context=None): 67 | uid = hashlib.md5(file_name.encode("utf8")).hexdigest() 68 | i = 0 69 | 70 | pub_source = file_name 71 | source, weights, view_img, save_txt, imgsz = pub_source, pub_weights, pub_view_img, pub_save_txt, pub_img_size 72 | save_img = True 73 | webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith( 74 | ('rtsp://', 'rtmp://', 'http://', 'https://')) 75 | 76 | # Directories 77 | save_dir = Path(pub_project) # increment run 78 | 79 | # Set Dataloader 80 | vid_path, vid_writer = None, None 81 | if webcam: 82 | view_img = check_imshow() 83 | cudnn.benchmark = True # set True to speed up constant image size inference 84 | dataset = LoadStreams(source, img_size=imgsz, stride=stride) 85 | else: 86 | dataset = LoadImages(source, img_size=imgsz, stride=stride) 87 | 88 | # Get names and colors 89 | names = model.module.names if hasattr(model, 'module') else model.names 90 | colors = [[random.randint(0, 255) for _ in range(3)] for _ in names] 91 | 92 | # Run inference 93 | if device.type != 'cpu': 94 | model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once 95 | t0 = time.time() 96 | for path, img, im0s, vid_cap in dataset: 97 | img = torch.from_numpy(img).to(device) 98 | img = img.half() if half else img.float() # uint8 to fp16/32 99 | img /= 255.0 # 0 - 255 to 0.0 - 1.0 100 | if img.ndimension() == 3: 101 | img = img.unsqueeze(0) 102 | 103 | lock.acquire(timeout=3) 104 | # Inference 105 | pred = model(img, augment=pub_augment)[0] 106 | lock.release() 107 | 108 | # Apply NMS 109 | pred = non_max_suppression(pred, pub_conf_thres, pub_iou_thres, classes=pub_classes, agnostic=pub_agnostic_nms) 110 | t2 = time_synchronized() 111 | 112 | # Process detections 113 | for i, det in enumerate(pred): # detections per image 114 | if webcam: # batch_size >= 1 115 | p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count 116 | else: 117 | p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0) 118 | 119 | p = Path(p) # to Path 120 | save_path = str(save_dir / p.name) # img.jpg 121 | txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt 122 | s += '%gx%g ' % img.shape[2:] # print string 123 | gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh 124 | if len(det): 125 | # Rescale boxes from img_size to im0 size 126 | det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() 127 | 128 | # Print results 129 | for c in det[:, -1].unique(): 130 | n = (det[:, -1] == c).sum() # detections per class 131 | s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string 132 | 133 | # Write results 134 | for *xyxy, conf, cls in reversed(det): 135 | if save_txt: # Write to file 136 | xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh 137 | line = (cls, *xywh, conf) if pub_save_conf else (cls, *xywh) # label format 138 | with open(txt_path + '.txt', 'a') as f: 139 | f.write(('%g ' * len(line)).rstrip() % line + '\n') 140 | 141 | orgfilename = file_name.rsplit('.', 1)[0].lower() 142 | orgfilename = orgfilename.rsplit('/', 1)[1] 143 | if save_img or view_img: # Add bbox to image 144 | label = names[int(cls)] 145 | newimg = im0[int(xyxy[1]):int(xyxy[3]), int(xyxy[0]): int(xyxy[2])] 146 | i = i + 1 147 | if "qrcode" == label and qrcode_no_tax(newimg, invoice): 148 | break 149 | invoice[label] = context.chineseModel(newimg) 150 | if use == 1: 151 | no_tax(label, newimg, invoice[label]) 152 | 153 | return invoice 154 | -------------------------------------------------------------------------------- /obj_det/vat_detect.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import time 3 | from pathlib import Path 4 | import os 5 | 6 | import cv2 7 | import torch 8 | import torch.backends.cudnn as cudnn 9 | from numpy import random 10 | 11 | from models.experimental import attempt_load 12 | from utils.datasets import LoadStreams, LoadImages 13 | from utils.general import check_img_size, check_imshow, non_max_suppression, \ 14 | scale_coords, xyxy2xywh, set_logging 15 | from utils.torch_utils import select_device, time_synchronized 16 | import hashlib 17 | from util.qrcode import qrcode 18 | from util.create_img import no_tax 19 | from util.create_img import use 20 | 21 | import threading 22 | 23 | lock = threading.Lock() 24 | 25 | converter = {'invoice_code': 'invoice_code', 'invoice_number': 'invoice_number', 'totalAmount': 'totalAmount', 26 | 'billingDate': 'billingDate', 'checkcode': 'checkCode', 27 | 'QRCode': 'QRCode', 'title': 'title'} 28 | pub_weights = "models/vat/best.pt" 29 | pub_view_img = False 30 | pub_save_txt = False 31 | pub_img_size = 640 32 | pub_nosave = False 33 | pub_project = "images" 34 | pub_device = "cpu" 35 | pub_augment = False 36 | pub_conf_thres = 0.25 37 | pub_iou_thres = 0.24 38 | pub_classes = None 39 | pub_agnostic_nms = False 40 | pub_save_conf = False 41 | 42 | # Initialize 43 | set_logging() 44 | device = select_device(pub_device) 45 | half = device.type != 'cpu' # half precision only supported on CUDA 46 | 47 | # Load model 48 | model = attempt_load(pub_weights, map_location=pub_device) # load FP32 model 49 | stride = int(model.stride.max()) # model stride 50 | imgsz = check_img_size(pub_img_size, s=stride) # check img_size 51 | if half: 52 | model.half() # to FP16 53 | 54 | 55 | def invoice_detection(file_name=None, invoice=None, context=None): 56 | uid = hashlib.md5(file_name.encode("utf8")).hexdigest() 57 | i = 0 58 | 59 | pub_source = file_name 60 | source, weights, view_img, save_txt, imgsz = pub_source, pub_weights, pub_view_img, pub_save_txt, pub_img_size 61 | save_img = True 62 | webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith( 63 | ('rtsp://', 'rtmp://', 'http://', 'https://')) 64 | 65 | # Directories 66 | save_dir = Path(pub_project) # increment run 67 | 68 | # Set Dataloader 69 | vid_path, vid_writer = None, None 70 | if webcam: 71 | view_img = check_imshow() 72 | cudnn.benchmark = True # set True to speed up constant image size inference 73 | dataset = LoadStreams(source, img_size=imgsz, stride=stride) 74 | else: 75 | dataset = LoadImages(source, img_size=imgsz, stride=stride) 76 | 77 | # Get names and colors 78 | names = model.module.names if hasattr(model, 'module') else model.names 79 | colors = [[random.randint(0, 255) for _ in range(3)] for _ in names] 80 | 81 | # Run inference 82 | if device.type != 'cpu': 83 | model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once 84 | t0 = time.time() 85 | for path, img, im0s, vid_cap in dataset: 86 | img = torch.from_numpy(img).to(device) 87 | img = img.half() if half else img.float() # uint8 to fp16/32 88 | img /= 255.0 # 0 - 255 to 0.0 - 1.0 89 | if img.ndimension() == 3: 90 | img = img.unsqueeze(0) 91 | 92 | lock.acquire(timeout=3) 93 | # Inference 94 | pred = model(img, augment=pub_augment)[0] 95 | lock.release() 96 | 97 | # Apply NMS 98 | pred = non_max_suppression(pred, pub_conf_thres, pub_iou_thres, classes=pub_classes, agnostic=pub_agnostic_nms) 99 | t2 = time_synchronized() 100 | 101 | # Process detections 102 | for i, det in enumerate(pred): # detections per image 103 | if webcam: # batch_size >= 1 104 | p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count 105 | else: 106 | p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0) 107 | 108 | p = Path(p) # to Path 109 | save_path = str(save_dir / p.name) # img.jpg 110 | txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt 111 | s += '%gx%g ' % img.shape[2:] # print string 112 | gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh 113 | if len(det): 114 | # Rescale boxes from img_size to im0 size 115 | det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() 116 | 117 | # Print results 118 | for c in det[:, -1].unique(): 119 | n = (det[:, -1] == c).sum() # detections per class 120 | s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string 121 | 122 | # Write results 123 | for *xyxy, conf, cls in reversed(det): 124 | if save_txt: # Write to file 125 | xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh 126 | line = (cls, *xywh, conf) if pub_save_conf else (cls, *xywh) # label format 127 | with open(txt_path + '.txt', 'a') as f: 128 | f.write(('%g ' * len(line)).rstrip() % line + '\n') 129 | 130 | orgfilename = file_name.rsplit('.', 1)[0].lower() 131 | orgfilename = orgfilename.rsplit('/', 1)[1] 132 | if save_img or view_img: # Add bbox to image 133 | label = names[int(cls)] 134 | if "title" == label: 135 | continue 136 | newimg = im0[int(xyxy[1]):int(xyxy[3]), int(xyxy[0]): int(xyxy[2])] 137 | i = i + 1 138 | if "QRCode" == label and qrcode(newimg, invoice): 139 | break 140 | invoice[converter[label]] = context.chineseModel(newimg) 141 | if use == 1: 142 | no_tax(label, newimg, invoice[converter[label]]) 143 | for val in converter.values(): 144 | if invoice.get(val) is None: 145 | invoice[val] = "0" 146 | return invoice 147 | -------------------------------------------------------------------------------- /obj_det/taxi_detect.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import time 3 | from pathlib import Path 4 | import os 5 | 6 | import cv2 7 | import torch 8 | import torch.backends.cudnn as cudnn 9 | from numpy import random 10 | 11 | from models.experimental import attempt_load 12 | from utils.datasets import LoadStreams, LoadImages 13 | from utils.general import check_img_size, check_imshow, non_max_suppression, \ 14 | scale_coords, xyxy2xywh, strip_optimizer, set_logging 15 | from utils.torch_utils import select_device, time_synchronized 16 | import hashlib 17 | from crnn.crnn_torch_chinese import crnnOcr as ccrnnOcr 18 | from util.create_img import no_tax 19 | from util.create_img import use 20 | 21 | import threading 22 | 23 | lock = threading.Lock() 24 | 25 | converter = {'invoice_code': 'invoice_code', 'invoice_number': 'invoice_number', 'totalAmount': 'amountTax'} 26 | pub_weights = "models/taxi/best.pt" 27 | pub_view_img = False 28 | pub_save_txt = False 29 | pub_img_size = 640 30 | pub_nosave = False 31 | pub_project = "images" 32 | pub_device = "cpu" 33 | pub_augment = False 34 | pub_conf_thres = 0.25 35 | pub_iou_thres = 0.24 36 | pub_classes = None 37 | pub_agnostic_nms = False 38 | pub_save_conf = False 39 | 40 | # Initialize 41 | set_logging() 42 | device = select_device(pub_device) 43 | half = device.type != 'cpu' # half precision only supported on CUDA 44 | 45 | # Load model 46 | model = attempt_load(pub_weights, map_location=pub_device) # load FP32 model 47 | stride = int(model.stride.max()) # model stride 48 | imgsz = check_img_size(pub_img_size, s=stride) # check img_size 49 | if half: 50 | model.half() # to FP16 51 | 52 | 53 | def invoice_number_process(img): 54 | height, width, _ = img.shape 55 | for i in range(height): 56 | for j in range(width): 57 | dot = img[i, j] 58 | dot0 = dot[0] 59 | dot1 = dot[1] 60 | dot2 = dot[2] 61 | if dot2 < dot1 or dot2 < dot0: 62 | img[i, j] = [0, 0, 0] 63 | continue 64 | return img 65 | 66 | 67 | def invoice_detection(file_name=None, invoice=None): 68 | uid = hashlib.md5(file_name.encode("utf8")).hexdigest() 69 | i = 0 70 | 71 | pub_source = file_name 72 | source, weights, view_img, save_txt, imgsz = pub_source, pub_weights, pub_view_img, pub_save_txt, pub_img_size 73 | save_img = True 74 | webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith( 75 | ('rtsp://', 'rtmp://', 'http://', 'https://')) 76 | 77 | # Directories 78 | save_dir = Path(pub_project) # increment run 79 | 80 | # Set Dataloader 81 | vid_path, vid_writer = None, None 82 | if webcam: 83 | view_img = check_imshow() 84 | cudnn.benchmark = True # set True to speed up constant image size inference 85 | dataset = LoadStreams(source, img_size=imgsz, stride=stride) 86 | else: 87 | dataset = LoadImages(source, img_size=imgsz, stride=stride) 88 | 89 | # Get names and colors 90 | names = model.module.names if hasattr(model, 'module') else model.names 91 | colors = [[random.randint(0, 255) for _ in range(3)] for _ in names] 92 | 93 | # Run inference 94 | if device.type != 'cpu': 95 | model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once 96 | t0 = time.time() 97 | for path, img, im0s, vid_cap in dataset: 98 | img = torch.from_numpy(img).to(device) 99 | img = img.half() if half else img.float() # uint8 to fp16/32 100 | img /= 255.0 # 0 - 255 to 0.0 - 1.0 101 | if img.ndimension() == 3: 102 | img = img.unsqueeze(0) 103 | 104 | lock.acquire(timeout=3) 105 | # Inference 106 | pred = model(img, augment=pub_augment)[0] 107 | lock.release() 108 | 109 | # Apply NMS 110 | pred = non_max_suppression(pred, pub_conf_thres, pub_iou_thres, classes=pub_classes, agnostic=pub_agnostic_nms) 111 | t2 = time_synchronized() 112 | 113 | # Process detections 114 | for i, det in enumerate(pred): # detections per image 115 | if webcam: # batch_size >= 1 116 | p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count 117 | else: 118 | p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0) 119 | 120 | p = Path(p) # to Path 121 | save_path = str(save_dir / p.name) # img.jpg 122 | txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt 123 | s += '%gx%g ' % img.shape[2:] # print string 124 | gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh 125 | if len(det): 126 | # Rescale boxes from img_size to im0 size 127 | det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() 128 | 129 | # Print results 130 | for c in det[:, -1].unique(): 131 | n = (det[:, -1] == c).sum() # detections per class 132 | s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string 133 | 134 | # Write results 135 | for *xyxy, conf, cls in reversed(det): 136 | if save_txt: # Write to file 137 | xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh 138 | line = (cls, *xywh, conf) if pub_save_conf else (cls, *xywh) # label format 139 | with open(txt_path + '.txt', 'a') as f: 140 | f.write(('%g ' * len(line)).rstrip() % line + '\n') 141 | 142 | orgfilename = file_name.rsplit('.', 1)[0].lower() 143 | orgfilename = orgfilename.rsplit('/', 1)[1] 144 | if save_img or view_img: # Add bbox to image 145 | label = names[int(cls)] 146 | newimg = im0[int(xyxy[1]):int(xyxy[3]), int(xyxy[0]): int(xyxy[2])] 147 | i = i + 1 148 | invoice[converter[label]] = ccrnnOcr(newimg) 149 | if use == 1: 150 | no_tax(label, newimg, invoice[converter[label]]) 151 | for val in converter.values(): 152 | if invoice.get(val) is None: 153 | invoice[val] = "0" 154 | return invoice 155 | -------------------------------------------------------------------------------- /obj_det/detect.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import time 3 | from pathlib import Path 4 | import os 5 | 6 | import cv2 7 | import torch 8 | import torch.backends.cudnn as cudnn 9 | from numpy import random 10 | 11 | from models.experimental import attempt_load 12 | from utils.datasets import LoadStreams, LoadImages 13 | from utils.general import check_img_size, check_imshow, non_max_suppression, \ 14 | scale_coords, xyxy2xywh, strip_optimizer, set_logging 15 | from utils.torch_utils import select_device, time_synchronized 16 | import threading 17 | 18 | lock = threading.Lock() 19 | 20 | converter = {'01':'增值税专用发票', '04': '增值税普通发票', '08': '增值税电子专用发票', '10': '增值税电子普通发票', '11': '增值税普通发票（卷式）', '81': '非税财政电子票据', '86': '过路费发票', '88': '火车票', '89': '飞机票', '90':'客运票', '92':'出租车票', '93':'定额', '95':'通用机打发票'} 21 | pub_weights = "models/mutipart/best.pt" 22 | pub_view_img = False 23 | pub_save_txt = False 24 | pub_img_size = 640 25 | pub_nosave = False 26 | pub_project = "images" 27 | pub_device = "cpu" 28 | pub_augment = False 29 | pub_conf_thres = 0.25 30 | pub_iou_thres = 0.24 31 | pub_classes = None 32 | pub_agnostic_nms = False 33 | pub_save_conf = False 34 | 35 | # Initialize 36 | set_logging() 37 | device = select_device(pub_device) 38 | half = device.type != 'cpu' # half precision only supported on CUDA 39 | 40 | # Load model 41 | model = attempt_load(pub_weights, map_location=pub_device) # load FP32 model 42 | stride = int(model.stride.max()) # model stride 43 | imgsz = check_img_size(pub_img_size, s=stride) # check img_size 44 | if half: 45 | model.half() # to FP16 46 | 47 | 48 | def invoice_number_process(img): 49 | height, width, _ = img.shape 50 | for i in range(height): 51 | for j in range(width): 52 | dot = img[i, j] 53 | dot0 = dot[0] 54 | dot1 = dot[1] 55 | dot2 = dot[2] 56 | if dot2 < dot1 or dot2 < dot0: 57 | img[i, j] = [0, 0, 0] 58 | continue 59 | return img 60 | 61 | 62 | def invoice_detection(file_name=None): 63 | i = 0 64 | invoices = [] 65 | pub_source = "images/" + file_name 66 | source, weights, view_img, save_txt, imgsz = pub_source, pub_weights, pub_view_img, pub_save_txt, pub_img_size 67 | save_img = True 68 | webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith( 69 | ('rtsp://', 'rtmp://', 'http://', 'https://')) 70 | 71 | # Directories 72 | save_dir = Path(pub_project) # increment run 73 | 74 | # Set Dataloader 75 | vid_path, vid_writer = None, None 76 | if webcam: 77 | view_img = check_imshow() 78 | cudnn.benchmark = True # set True to speed up constant image size inference 79 | dataset = LoadStreams(source, img_size=imgsz, stride=stride) 80 | else: 81 | dataset = LoadImages(source, img_size=imgsz, stride=stride) 82 | 83 | # Get names and colors 84 | names = model.module.names if hasattr(model, 'module') else model.names 85 | colors = [[random.randint(0, 255) for _ in range(3)] for _ in names] 86 | 87 | # Run inference 88 | if device.type != 'cpu': 89 | model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once 90 | t0 = time.time() 91 | for path, img, im0s, vid_cap in dataset: 92 | img = torch.from_numpy(img).to(device) 93 | img = img.half() if half else img.float() # uint8 to fp16/32 94 | img /= 255.0 # 0 - 255 to 0.0 - 1.0 95 | if img.ndimension() == 3: 96 | img = img.unsqueeze(0) 97 | 98 | # Inference 99 | lock.acquire(timeout=3) 100 | pred = model(img, augment=pub_augment)[0] 101 | lock.release() 102 | 103 | # Apply NMS 104 | pred = non_max_suppression(pred, pub_conf_thres, pub_iou_thres, classes=pub_classes, agnostic=pub_agnostic_nms) 105 | 106 | # Process detections 107 | for i, det in enumerate(pred): # detections per image 108 | if webcam: # batch_size >= 1 109 | p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count 110 | else: 111 | p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0) 112 | 113 | p = Path(p) # to Path 114 | save_path = str(save_dir / p.name) # img.jpg 115 | txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt 116 | s += '%gx%g ' % img.shape[2:] # print string 117 | gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh 118 | if len(det): 119 | # Rescale boxes from img_size to im0 size 120 | det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() 121 | 122 | # Print results 123 | for c in det[:, -1].unique(): 124 | n = (det[:, -1] == c).sum() # detections per class 125 | s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string 126 | 127 | # Write results 128 | for *xyxy, conf, cls in reversed(det): 129 | if save_txt: # Write to file 130 | xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh 131 | line = (cls, *xywh, conf) if pub_save_conf else (cls, *xywh) # label format 132 | with open(txt_path + '.txt', 'a') as f: 133 | f.write(('%g ' * len(line)).rstrip() % line + '\n') 134 | 135 | orgfilename = file_name.rsplit('.', 1)[0].lower() 136 | type = file_name.rsplit('.', 1)[1].lower() 137 | if save_img or view_img: # Add bbox to image 138 | label = names[int(cls)] 139 | newimg = im0[int(xyxy[1]):int(xyxy[3]), int(xyxy[0]): int(xyxy[2])] 140 | dir = "images/" + orgfilename 141 | if not os.path.exists(dir): 142 | os.makedirs(dir) 143 | newPath = dir + "/" + str(i) + "_" + orgfilename + "." + type 144 | cv2.imwrite(newPath, newimg) 145 | i = i + 1 146 | coordinate = [int(xyxy[0]), int(xyxy[1]), int(xyxy[2]), int(xyxy[3]), int(xyxy[0]), int(xyxy[1]), int(xyxy[2]), int(xyxy[3])] 147 | json = {"invoiceType": label, "invoice_type_name": converter[label], "file_path": newPath, "coordinate": coordinate} 148 | invoices.append(json) 149 | return invoices 150 | -------------------------------------------------------------------------------- /obj_det/tra_detect.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import time 3 | from pathlib import Path 4 | import os 5 | 6 | import cv2 7 | import torch 8 | import torch.backends.cudnn as cudnn 9 | from numpy import random 10 | 11 | from models.experimental import attempt_load 12 | from utils.datasets import LoadStreams, LoadImages 13 | from utils.general import check_img_size, check_imshow, non_max_suppression, \ 14 | scale_coords, xyxy2xywh, strip_optimizer, set_logging 15 | from utils.torch_utils import select_device, time_synchronized 16 | import hashlib 17 | from crnn.crnn_torch import crnnOcr as crnnOcr 18 | from crnn.crnn_torch_chinese import crnnOcr as ccrnnOcr 19 | from util.create_img import no_tax 20 | from util.create_img import use 21 | 22 | import threading 23 | 24 | lock = threading.Lock() 25 | 26 | converter = {'invoice_number': 'invoice_number', 'totalAmount': 'amountTax', 'ids': 'ids', 'date': 'billingDate', 'checi': 'checi', 27 | 'start': 'starStation', 'end': 'endStation'} 28 | pub_weights = "models/huochepiao/best.pt" 29 | pub_view_img = False 30 | pub_save_txt = False 31 | pub_img_size = 640 32 | pub_nosave = False 33 | pub_project = "images" 34 | pub_device = "cpu" 35 | pub_augment = False 36 | pub_conf_thres = 0.25 37 | pub_iou_thres = 0.24 38 | pub_classes = None 39 | pub_agnostic_nms = False 40 | pub_save_conf = False 41 | 42 | # Initialize 43 | set_logging() 44 | device = select_device(pub_device) 45 | half = device.type != 'cpu' # half precision only supported on CUDA 46 | 47 | # Load model 48 | model = attempt_load(pub_weights, map_location=pub_device) # load FP32 model 49 | stride = int(model.stride.max()) # model stride 50 | imgsz = check_img_size(pub_img_size, s=stride) # check img_size 51 | if half: 52 | model.half() # to FP16 53 | 54 | 55 | def invoice_number_process(img): 56 | height, width, _ = img.shape 57 | for i in range(height): 58 | for j in range(width): 59 | dot = img[i, j] 60 | dot0 = dot[0] 61 | dot1 = dot[1] 62 | dot2 = dot[2] 63 | if dot2 < dot1 or dot2 < dot0: 64 | img[i, j] = [0, 0, 0] 65 | continue 66 | return img 67 | 68 | 69 | def invoice_detection(file_name=None, invoice=None): 70 | uid = hashlib.md5(file_name.encode("utf8")).hexdigest() 71 | i = 0 72 | 73 | pub_source = file_name 74 | source, weights, view_img, save_txt, imgsz = pub_source, pub_weights, pub_view_img, pub_save_txt, pub_img_size 75 | save_img = True 76 | webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith( 77 | ('rtsp://', 'rtmp://', 'http://', 'https://')) 78 | 79 | # Directories 80 | save_dir = Path(pub_project) # increment run 81 | 82 | # Set Dataloader 83 | vid_path, vid_writer = None, None 84 | if webcam: 85 | view_img = check_imshow() 86 | cudnn.benchmark = True # set True to speed up constant image size inference 87 | dataset = LoadStreams(source, img_size=imgsz, stride=stride) 88 | else: 89 | dataset = LoadImages(source, img_size=imgsz, stride=stride) 90 | 91 | # Get names and colors 92 | names = model.module.names if hasattr(model, 'module') else model.names 93 | colors = [[random.randint(0, 255) for _ in range(3)] for _ in names] 94 | 95 | # Run inference 96 | if device.type != 'cpu': 97 | model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once 98 | t0 = time.time() 99 | for path, img, im0s, vid_cap in dataset: 100 | img = torch.from_numpy(img).to(device) 101 | img = img.half() if half else img.float() # uint8 to fp16/32 102 | img /= 255.0 # 0 - 255 to 0.0 - 1.0 103 | if img.ndimension() == 3: 104 | img = img.unsqueeze(0) 105 | 106 | lock.acquire(timeout=3) 107 | # Inference 108 | pred = model(img, augment=pub_augment)[0] 109 | lock.release() 110 | 111 | # Apply NMS 112 | pred = non_max_suppression(pred, pub_conf_thres, pub_iou_thres, classes=pub_classes, agnostic=pub_agnostic_nms) 113 | t2 = time_synchronized() 114 | 115 | # Process detections 116 | for i, det in enumerate(pred): # detections per image 117 | if webcam: # batch_size >= 1 118 | p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count 119 | else: 120 | p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0) 121 | 122 | p = Path(p) # to Path 123 | save_path = str(save_dir / p.name) # img.jpg 124 | txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt 125 | s += '%gx%g ' % img.shape[2:] # print string 126 | gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh 127 | if len(det): 128 | # Rescale boxes from img_size to im0 size 129 | det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() 130 | 131 | # Print results 132 | for c in det[:, -1].unique(): 133 | n = (det[:, -1] == c).sum() # detections per class 134 | s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string 135 | 136 | # Write results 137 | for *xyxy, conf, cls in reversed(det): 138 | if save_txt: # Write to file 139 | xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh 140 | line = (cls, *xywh, conf) if pub_save_conf else (cls, *xywh) # label format 141 | with open(txt_path + '.txt', 'a') as f: 142 | f.write(('%g ' * len(line)).rstrip() % line + '\n') 143 | 144 | orgfilename = file_name.rsplit('.', 1)[0].lower() 145 | orgfilename = orgfilename.rsplit('/', 1)[1] 146 | if save_img or view_img: # Add bbox to image 147 | label = names[int(cls)] 148 | newimg = im0[int(xyxy[1]):int(xyxy[3]), int(xyxy[0]): int(xyxy[2])] 149 | i = i + 1 150 | if "checi" == label or "invoice_number" == label: 151 | invoice[converter[label]] = crnnOcr(newimg) 152 | else: 153 | invoice[converter[label]] = ccrnnOcr(newimg) 154 | if use == 1: 155 | no_tax(label, newimg, invoice[converter[label]]) 156 | 157 | for val in converter.values(): 158 | if invoice.get(val) is None: 159 | invoice[val] = "0" 160 | return invoice 161 | -------------------------------------------------------------------------------- /obj_det/title_detect.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import time 3 | from pathlib import Path 4 | import os 5 | 6 | import cv2 7 | import torch 8 | import torch.backends.cudnn as cudnn 9 | from numpy import random 10 | 11 | from models.experimental import attempt_load 12 | from utils.datasets import LoadStreams, LoadImages 13 | from utils.general import check_img_size, check_imshow, non_max_suppression, \ 14 | scale_coords, xyxy2xywh, strip_optimizer, set_logging 15 | from utils.torch_utils import select_device, time_synchronized 16 | import hashlib 17 | from crnn.crnn_torch_chinese import crnnOcr as ccrnnOcr 18 | 19 | import threading 20 | 21 | lock = threading.Lock() 22 | 23 | converter_name = {'01':'增值税专用发票', '04': '增值税普通发票', '08': '增值税电子专用发票', '10': '增值税电子普通发票', '11': '增值税普通发票（卷式）', '14': '增值税电子普通发票（通行费）'} 24 | converter = {'增值税专用发票':'01', '增值税普通发票': '04', '增值税电子专用发票': '08', '增值税电子普通发票': '10', '增值税普通发票（卷式）': '11', '增值税电子普通发票（通行费）': '14'} 25 | pub_weights = "models/title/best.pt" 26 | pub_view_img = False 27 | pub_save_txt = False 28 | pub_img_size = 640 29 | pub_nosave = False 30 | pub_project = "images" 31 | pub_device = "cpu" 32 | pub_augment = False 33 | pub_conf_thres = 0.25 34 | pub_iou_thres = 0.24 35 | pub_classes = None 36 | pub_agnostic_nms = False 37 | pub_save_conf = False 38 | 39 | # Initialize 40 | set_logging() 41 | device = select_device(pub_device) 42 | half = device.type != 'cpu' # half precision only supported on CUDA 43 | 44 | # Load model 45 | model = attempt_load(pub_weights, map_location=pub_device) # load FP32 model 46 | stride = int(model.stride.max()) # model stride 47 | imgsz = check_img_size(pub_img_size, s=stride) # check img_size 48 | if half: 49 | model.half() # to FP16 50 | 51 | 52 | def invoice_number_process(img): 53 | height, width, _ = img.shape 54 | for i in range(height): 55 | for j in range(width): 56 | dot = img[i, j] 57 | dot0 = dot[0] 58 | dot1 = dot[1] 59 | dot2 = dot[2] 60 | if dot2 < dot1 or dot2 < dot0: 61 | img[i, j] = [0, 0, 0] 62 | continue 63 | return img 64 | 65 | 66 | def invoice_detection(file_name=None, invoice=None): 67 | title = "" 68 | pub_source = file_name 69 | source, weights, view_img, save_txt, imgsz = pub_source, pub_weights, pub_view_img, pub_save_txt, pub_img_size 70 | save_img = True 71 | webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith( 72 | ('rtsp://', 'rtmp://', 'http://', 'https://')) 73 | 74 | # Directories 75 | save_dir = Path(pub_project) # increment run 76 | 77 | # Set Dataloader 78 | vid_path, vid_writer = None, None 79 | if webcam: 80 | view_img = check_imshow() 81 | cudnn.benchmark = True # set True to speed up constant image size inference 82 | dataset = LoadStreams(source, img_size=imgsz, stride=stride) 83 | else: 84 | dataset = LoadImages(source, img_size=imgsz, stride=stride) 85 | 86 | # Get names and colors 87 | names = model.module.names if hasattr(model, 'module') else model.names 88 | colors = [[random.randint(0, 255) for _ in range(3)] for _ in names] 89 | 90 | # Run inference 91 | if device.type != 'cpu': 92 | model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once 93 | t0 = time.time() 94 | for path, img, im0s, vid_cap in dataset: 95 | img = torch.from_numpy(img).to(device) 96 | img = img.half() if half else img.float() # uint8 to fp16/32 97 | img /= 255.0 # 0 - 255 to 0.0 - 1.0 98 | if img.ndimension() == 3: 99 | img = img.unsqueeze(0) 100 | 101 | lock.acquire(timeout=3) 102 | # Inference 103 | pred = model(img, augment=pub_augment)[0] 104 | lock.release() 105 | 106 | # Apply NMS 107 | pred = non_max_suppression(pred, pub_conf_thres, pub_iou_thres, classes=pub_classes, agnostic=pub_agnostic_nms) 108 | t2 = time_synchronized() 109 | 110 | # Process detections 111 | for i, det in enumerate(pred): # detections per image 112 | if webcam: # batch_size >= 1 113 | p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count 114 | else: 115 | p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0) 116 | 117 | p = Path(p) # to Path 118 | save_path = str(save_dir / p.name) # img.jpg 119 | txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt 120 | s += '%gx%g ' % img.shape[2:] # print string 121 | gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh 122 | if len(det): 123 | # Rescale boxes from img_size to im0 size 124 | det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() 125 | 126 | # Print results 127 | for c in det[:, -1].unique(): 128 | n = (det[:, -1] == c).sum() # detections per class 129 | s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string 130 | 131 | # Write results 132 | for *xyxy, conf, cls in reversed(det): 133 | if save_txt: # Write to file 134 | xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh 135 | line = (cls, *xywh, conf) if pub_save_conf else (cls, *xywh) # label format 136 | with open(txt_path + '.txt', 'a') as f: 137 | f.write(('%g ' * len(line)).rstrip() % line + '\n') 138 | 139 | orgfilename = file_name.rsplit('.', 1)[0].lower() 140 | orgfilename = orgfilename.rsplit('/', 1)[1] 141 | if save_img or view_img: # Add bbox to image 142 | label = names[int(cls)] 143 | newimg = im0[int(xyxy[1]):int(xyxy[3]), int(xyxy[0]): int(xyxy[2])] 144 | if "title" == label: 145 | title = ccrnnOcr(newimg) 146 | if "go" == label: 147 | go = ccrnnOcr(newimg) 148 | title = "增值税电子普通发票（通行费）" 149 | if "专用发票" in title: 150 | if "电子" in title: 151 | invoice['invoiceType'] = "08" 152 | else: 153 | invoice['invoiceType'] = "01" 154 | if "普通发票" in title: 155 | if "电子" in title: 156 | invoice['invoiceType'] = "10" 157 | else: 158 | invoice['invoiceType'] = "04" 159 | if "增值税电子普通发票（通行费）" in title: 160 | invoice['invoiceType'] = "14" 161 | invoice['invoice_type_name'] = converter_name[invoice['invoiceType']] 162 | return invoice 163 | -------------------------------------------------------------------------------- /utils/autoanchor.py: -------------------------------------------------------------------------------- 1 | # Auto-anchor utils 2 | 3 | import numpy as np 4 | import torch 5 | import yaml 6 | from scipy.cluster.vq import kmeans 7 | from tqdm import tqdm 8 | 9 | from utils.general import colorstr 10 | 11 | 12 | def check_anchor_order(m): 13 | # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary 14 | a = m.anchor_grid.prod(-1).view(-1) # anchor area 15 | da = a[-1] - a[0] # delta a 16 | ds = m.stride[-1] - m.stride[0] # delta s 17 | if da.sign() != ds.sign(): # same order 18 | print('Reversing anchor order') 19 | m.anchors[:] = m.anchors.flip(0) 20 | m.anchor_grid[:] = m.anchor_grid.flip(0) 21 | 22 | 23 | def check_anchors(dataset, model, thr=4.0, imgsz=640): 24 | # Check anchor fit to data, recompute if necessary 25 | prefix = colorstr('autoanchor: ') 26 | print(f'\n{prefix}Analyzing anchors... ', end='') 27 | m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1] # Detect() 28 | shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True) 29 | scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1)) # augment scale 30 | wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float() # wh 31 | 32 | def metric(k): # compute metric 33 | r = wh[:, None] / k[None] 34 | x = torch.min(r, 1. / r).min(2)[0] # ratio metric 35 | best = x.max(1)[0] # best_x 36 | aat = (x > 1. / thr).float().sum(1).mean() # anchors above threshold 37 | bpr = (best > 1. / thr).float().mean() # best possible recall 38 | return bpr, aat 39 | 40 | anchors = m.anchor_grid.clone().cpu().view(-1, 2) # current anchors 41 | bpr, aat = metric(anchors) 42 | print(f'anchors/target = {aat:.2f}, Best Possible Recall (BPR) = {bpr:.4f}', end='') 43 | if bpr < 0.98: # threshold to recompute 44 | print('. Attempting to improve anchors, please wait...') 45 | na = m.anchor_grid.numel() // 2 # number of anchors 46 | try: 47 | anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False) 48 | except Exception as e: 49 | print(f'{prefix}ERROR: {e}') 50 | new_bpr = metric(anchors)[0] 51 | if new_bpr > bpr: # replace anchors 52 | anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors) 53 | m.anchor_grid[:] = anchors.clone().view_as(m.anchor_grid) # for inference 54 | m.anchors[:] = anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1) # loss 55 | check_anchor_order(m) 56 | print(f'{prefix}New anchors saved to model. Update model *.yaml to use these anchors in the future.') 57 | else: 58 | print(f'{prefix}Original anchors better than new anchors. Proceeding with original anchors.') 59 | print('') # newline 60 | 61 | 62 | def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True): 63 | """ Creates kmeans-evolved anchors from training dataset 64 | 65 | Arguments: 66 | path: path to dataset *.yaml, or a loaded dataset 67 | n: number of anchors 68 | img_size: image size used for training 69 | thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0 70 | gen: generations to evolve anchors using genetic algorithm 71 | verbose: print all results 72 | 73 | Return: 74 | k: kmeans evolved anchors 75 | 76 | Usage: 77 | from utils.autoanchor import *; _ = kmean_anchors() 78 | """ 79 | thr = 1. / thr 80 | prefix = colorstr('autoanchor: ') 81 | 82 | def metric(k, wh): # compute metrics 83 | r = wh[:, None] / k[None] 84 | x = torch.min(r, 1. / r).min(2)[0] # ratio metric 85 | # x = wh_iou(wh, torch.tensor(k)) # iou metric 86 | return x, x.max(1)[0] # x, best_x 87 | 88 | def anchor_fitness(k): # mutation fitness 89 | _, best = metric(torch.tensor(k, dtype=torch.float32), wh) 90 | return (best * (best > thr).float()).mean() # fitness 91 | 92 | def print_results(k): 93 | k = k[np.argsort(k.prod(1))] # sort small to large 94 | x, best = metric(k, wh0) 95 | bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n # best possible recall, anch > thr 96 | print(f'{prefix}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr') 97 | print(f'{prefix}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, ' 98 | f'past_thr={x[x > thr].mean():.3f}-mean: ', end='') 99 | for i, x in enumerate(k): 100 | print('%i,%i' % (round(x[0]), round(x[1])), end=', ' if i < len(k) - 1 else '\n') # use in *.cfg 101 | return k 102 | 103 | if isinstance(path, str): # *.yaml file 104 | with open(path) as f: 105 | data_dict = yaml.load(f, Loader=yaml.SafeLoader) # model dict 106 | from utils.datasets import LoadImagesAndLabels 107 | dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True) 108 | else: 109 | dataset = path # dataset 110 | 111 | # Get label wh 112 | shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True) 113 | wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)]) # wh 114 | 115 | # Filter 116 | i = (wh0 < 3.0).any(1).sum() 117 | if i: 118 | print(f'{prefix}WARNING: Extremely small objects found. {i} of {len(wh0)} labels are < 3 pixels in size.') 119 | wh = wh0[(wh0 >= 2.0).any(1)] # filter > 2 pixels 120 | # wh = wh * (np.random.rand(wh.shape[0], 1) * 0.9 + 0.1) # multiply by random scale 0-1 121 | 122 | # Kmeans calculation 123 | print(f'{prefix}Running kmeans for {n} anchors on {len(wh)} points...') 124 | s = wh.std(0) # sigmas for whitening 125 | k, dist = kmeans(wh / s, n, iter=30) # points, mean distance 126 | assert len(k) == n, print(f'{prefix}ERROR: scipy.cluster.vq.kmeans requested {n} points but returned only {len(k)}') 127 | k *= s 128 | wh = torch.tensor(wh, dtype=torch.float32) # filtered 129 | wh0 = torch.tensor(wh0, dtype=torch.float32) # unfiltered 130 | k = print_results(k) 131 | 132 | # Plot 133 | # k, d = [None] * 20, [None] * 20 134 | # for i in tqdm(range(1, 21)): 135 | # k[i-1], d[i-1] = kmeans(wh / s, i) # points, mean distance 136 | # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True) 137 | # ax = ax.ravel() 138 | # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.') 139 | # fig, ax = plt.subplots(1, 2, figsize=(14, 7)) # plot wh 140 | # ax[0].hist(wh[wh[:, 0]<100, 0],400) 141 | # ax[1].hist(wh[wh[:, 1]<100, 1],400) 142 | # fig.savefig('wh.png', dpi=200) 143 | 144 | # Evolve 145 | npr = np.random 146 | f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1 # fitness, generations, mutation prob, sigma 147 | pbar = tqdm(range(gen), desc=f'{prefix}Evolving anchors with Genetic Algorithm:') # progress bar 148 | for _ in pbar: 149 | v = np.ones(sh) 150 | while (v == 1).all(): # mutate until a change occurs (prevent duplicates) 151 | v = ((npr.random(sh) < mp) * npr.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0) 152 | kg = (k.copy() * v).clip(min=2.0) 153 | fg = anchor_fitness(kg) 154 | if fg > f: 155 | f, k = fg, kg.copy() 156 | pbar.desc = f'{prefix}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}' 157 | if verbose: 158 | print_results(k) 159 | 160 | return print_results(k) 161 | -------------------------------------------------------------------------------- /utils/metrics.py: -------------------------------------------------------------------------------- 1 | # Model validation metrics 2 | 3 | from pathlib import Path 4 | 5 | import matplotlib.pyplot as plt 6 | import numpy as np 7 | import torch 8 | 9 | from . import general 10 | 11 | 12 | def fitness(x): 13 | # Model fitness as a weighted combination of metrics 14 | w = [0.0, 0.0, 0.1, 0.9] # weights for [P, R, mAP@0.5, mAP@0.5:0.95] 15 | return (x[:, :4] * w).sum(1) 16 | 17 | 18 | def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=()): 19 | """ Compute the average precision, given the recall and precision curves. 20 | Source: https://github.com/rafaelpadilla/Object-Detection-Metrics. 21 | # Arguments 22 | tp: True positives (nparray, nx1 or nx10). 23 | conf: Objectness value from 0-1 (nparray). 24 | pred_cls: Predicted object classes (nparray). 25 | target_cls: True object classes (nparray). 26 | plot: Plot precision-recall curve at mAP@0.5 27 | save_dir: Plot save directory 28 | # Returns 29 | The average precision as computed in py-faster-rcnn. 30 | """ 31 | 32 | # Sort by objectness 33 | i = np.argsort(-conf) 34 | tp, conf, pred_cls = tp[i], conf[i], pred_cls[i] 35 | 36 | # Find unique classes 37 | unique_classes = np.unique(target_cls) 38 | nc = unique_classes.shape[0] # number of classes, number of detections 39 | 40 | # Create Precision-Recall curve and compute AP for each class 41 | px, py = np.linspace(0, 1, 1000), [] # for plotting 42 | ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000)) 43 | for ci, c in enumerate(unique_classes): 44 | i = pred_cls == c 45 | n_l = (target_cls == c).sum() # number of labels 46 | n_p = i.sum() # number of predictions 47 | 48 | if n_p == 0 or n_l == 0: 49 | continue 50 | else: 51 | # Accumulate FPs and TPs 52 | fpc = (1 - tp[i]).cumsum(0) 53 | tpc = tp[i].cumsum(0) 54 | 55 | # Recall 56 | recall = tpc / (n_l + 1e-16) # recall curve 57 | r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0) # negative x, xp because xp decreases 58 | 59 | # Precision 60 | precision = tpc / (tpc + fpc) # precision curve 61 | p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1) # p at pr_score 62 | 63 | # AP from recall-precision curve 64 | for j in range(tp.shape[1]): 65 | ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j]) 66 | if plot and j == 0: 67 | py.append(np.interp(px, mrec, mpre)) # precision at mAP@0.5 68 | 69 | # Compute F1 (harmonic mean of precision and recall) 70 | f1 = 2 * p * r / (p + r + 1e-16) 71 | if plot: 72 | plot_pr_curve(px, py, ap, Path(save_dir) / 'PR_curve.png', names) 73 | plot_mc_curve(px, f1, Path(save_dir) / 'F1_curve.png', names, ylabel='F1') 74 | plot_mc_curve(px, p, Path(save_dir) / 'P_curve.png', names, ylabel='Precision') 75 | plot_mc_curve(px, r, Path(save_dir) / 'R_curve.png', names, ylabel='Recall') 76 | 77 | i = f1.mean(0).argmax() # max F1 index 78 | return p[:, i], r[:, i], ap, f1[:, i], unique_classes.astype('int32') 79 | 80 | 81 | def compute_ap(recall, precision): 82 | """ Compute the average precision, given the recall and precision curves 83 | # Arguments 84 | recall: The recall curve (list) 85 | precision: The precision curve (list) 86 | # Returns 87 | Average precision, precision curve, recall curve 88 | """ 89 | 90 | # Append sentinel values to beginning and end 91 | mrec = np.concatenate(([0.], recall, [recall[-1] + 0.01])) 92 | mpre = np.concatenate(([1.], precision, [0.])) 93 | 94 | # Compute the precision envelope 95 | mpre = np.flip(np.maximum.accumulate(np.flip(mpre))) 96 | 97 | # Integrate area under curve 98 | method = 'interp' # methods: 'continuous', 'interp' 99 | if method == 'interp': 100 | x = np.linspace(0, 1, 101) # 101-point interp (COCO) 101 | ap = np.trapz(np.interp(x, mrec, mpre), x) # integrate 102 | else: # 'continuous' 103 | i = np.where(mrec[1:] != mrec[:-1])[0] # points where x axis (recall) changes 104 | ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) # area under curve 105 | 106 | return ap, mpre, mrec 107 | 108 | 109 | class ConfusionMatrix: 110 | # Updated version of https://github.com/kaanakan/object_detection_confusion_matrix 111 | def __init__(self, nc, conf=0.25, iou_thres=0.45): 112 | self.matrix = np.zeros((nc + 1, nc + 1)) 113 | self.nc = nc # number of classes 114 | self.conf = conf 115 | self.iou_thres = iou_thres 116 | 117 | def process_batch(self, detections, labels): 118 | """ 119 | Return intersection-over-union (Jaccard index) of boxes. 120 | Both sets of boxes are expected to be in (x1, y1, x2, y2) format. 121 | Arguments: 122 | detections (Array[N, 6]), x1, y1, x2, y2, conf, class 123 | labels (Array[M, 5]), class, x1, y1, x2, y2 124 | Returns: 125 | None, updates confusion matrix accordingly 126 | """ 127 | detections = detections[detections[:, 4] > self.conf] 128 | gt_classes = labels[:, 0].int() 129 | detection_classes = detections[:, 5].int() 130 | iou = general.box_iou(labels[:, 1:], detections[:, :4]) 131 | 132 | x = torch.where(iou > self.iou_thres) 133 | if x[0].shape[0]: 134 | matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() 135 | if x[0].shape[0] > 1: 136 | matches = matches[matches[:, 2].argsort()[::-1]] 137 | matches = matches[np.unique(matches[:, 1], return_index=True)[1]] 138 | matches = matches[matches[:, 2].argsort()[::-1]] 139 | matches = matches[np.unique(matches[:, 0], return_index=True)[1]] 140 | else: 141 | matches = np.zeros((0, 3)) 142 | 143 | n = matches.shape[0] > 0 144 | m0, m1, _ = matches.transpose().astype(np.int16) 145 | for i, gc in enumerate(gt_classes): 146 | j = m0 == i 147 | if n and sum(j) == 1: 148 | self.matrix[gc, detection_classes[m1[j]]] += 1 # correct 149 | else: 150 | self.matrix[self.nc, gc] += 1 # background FP 151 | 152 | if n: 153 | for i, dc in enumerate(detection_classes): 154 | if not any(m1 == i): 155 | self.matrix[dc, self.nc] += 1 # background FN 156 | 157 | def matrix(self): 158 | return self.matrix 159 | 160 | def plot(self, save_dir='', names=()): 161 | try: 162 | import seaborn as sn 163 | 164 | array = self.matrix / (self.matrix.sum(0).reshape(1, self.nc + 1) + 1E-6) # normalize 165 | array[array < 0.005] = np.nan # don't annotate (would appear as 0.00) 166 | 167 | fig = plt.figure(figsize=(12, 9), tight_layout=True) 168 | sn.set(font_scale=1.0 if self.nc < 50 else 0.8) # for label size 169 | labels = (0 < len(names) < 99) and len(names) == self.nc # apply names to ticklabels 170 | sn.heatmap(array, annot=self.nc < 30, annot_kws={"size": 8}, cmap='Blues', fmt='.2f', square=True, 171 | xticklabels=names + ['background FP'] if labels else "auto", 172 | yticklabels=names + ['background FN'] if labels else "auto").set_facecolor((1, 1, 1)) 173 | fig.axes[0].set_xlabel('True') 174 | fig.axes[0].set_ylabel('Predicted') 175 | fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250) 176 | except Exception as e: 177 | pass 178 | 179 | def print(self): 180 | for i in range(self.nc + 1): 181 | print(' '.join(map(str, self.matrix[i]))) 182 | 183 | 184 | # Plots ---------------------------------------------------------------------------------------------------------------- 185 | 186 | def plot_pr_curve(px, py, ap, save_dir='pr_curve.png', names=()): 187 | # Precision-recall curve 188 | fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) 189 | py = np.stack(py, axis=1) 190 | 191 | if 0 < len(names) < 21: # display per-class legend if < 21 classes 192 | for i, y in enumerate(py.T): 193 | ax.plot(px, y, linewidth=1, label=f'{names[i]} {ap[i, 0]:.3f}') # plot(recall, precision) 194 | else: 195 | ax.plot(px, py, linewidth=1, color='grey') # plot(recall, precision) 196 | 197 | ax.plot(px, py.mean(1), linewidth=3, color='blue', label='all classes %.3f mAP@0.5' % ap[:, 0].mean()) 198 | ax.set_xlabel('Recall') 199 | ax.set_ylabel('Precision') 200 | ax.set_xlim(0, 1) 201 | ax.set_ylim(0, 1) 202 | plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left") 203 | fig.savefig(Path(save_dir), dpi=250) 204 | 205 | 206 | def plot_mc_curve(px, py, save_dir='mc_curve.png', names=(), xlabel='Confidence', ylabel='Metric'): 207 | # Metric-confidence curve 208 | fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) 209 | 210 | if 0 < len(names) < 21: # display per-class legend if < 21 classes 211 | for i, y in enumerate(py): 212 | ax.plot(px, y, linewidth=1, label=f'{names[i]}') # plot(confidence, metric) 213 | else: 214 | ax.plot(px, py.T, linewidth=1, color='grey') # plot(confidence, metric) 215 | 216 | y = py.mean(0) 217 | ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}') 218 | ax.set_xlabel(xlabel) 219 | ax.set_ylabel(ylabel) 220 | ax.set_xlim(0, 1) 221 | ax.set_ylim(0, 1) 222 | plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left") 223 | fig.savefig(Path(save_dir), dpi=250) 224 | -------------------------------------------------------------------------------- /utils/loss.py: -------------------------------------------------------------------------------- 1 | # Loss functions 2 | 3 | import torch 4 | import torch.nn as nn 5 | 6 | from utils.general import bbox_iou 7 | from utils.torch_utils import is_parallel 8 | 9 | 10 | def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441 11 | # return positive, negative label smoothing BCE targets 12 | return 1.0 - 0.5 * eps, 0.5 * eps 13 | 14 | 15 | class BCEBlurWithLogitsLoss(nn.Module): 16 | # BCEwithLogitLoss() with reduced missing label effects. 17 | def __init__(self, alpha=0.05): 18 | super(BCEBlurWithLogitsLoss, self).__init__() 19 | self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none') # must be nn.BCEWithLogitsLoss() 20 | self.alpha = alpha 21 | 22 | def forward(self, pred, true): 23 | loss = self.loss_fcn(pred, true) 24 | pred = torch.sigmoid(pred) # prob from logits 25 | dx = pred - true # reduce only missing label effects 26 | # dx = (pred - true).abs() # reduce missing label and false label effects 27 | alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4)) 28 | loss *= alpha_factor 29 | return loss.mean() 30 | 31 | 32 | class FocalLoss(nn.Module): 33 | # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) 34 | def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): 35 | super(FocalLoss, self).__init__() 36 | self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() 37 | self.gamma = gamma 38 | self.alpha = alpha 39 | self.reduction = loss_fcn.reduction 40 | self.loss_fcn.reduction = 'none' # required to apply FL to each element 41 | 42 | def forward(self, pred, true): 43 | loss = self.loss_fcn(pred, true) 44 | # p_t = torch.exp(-loss) 45 | # loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability 46 | 47 | # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py 48 | pred_prob = torch.sigmoid(pred) # prob from logits 49 | p_t = true * pred_prob + (1 - true) * (1 - pred_prob) 50 | alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) 51 | modulating_factor = (1.0 - p_t) ** self.gamma 52 | loss *= alpha_factor * modulating_factor 53 | 54 | if self.reduction == 'mean': 55 | return loss.mean() 56 | elif self.reduction == 'sum': 57 | return loss.sum() 58 | else: # 'none' 59 | return loss 60 | 61 | 62 | class QFocalLoss(nn.Module): 63 | # Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) 64 | def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): 65 | super(QFocalLoss, self).__init__() 66 | self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() 67 | self.gamma = gamma 68 | self.alpha = alpha 69 | self.reduction = loss_fcn.reduction 70 | self.loss_fcn.reduction = 'none' # required to apply FL to each element 71 | 72 | def forward(self, pred, true): 73 | loss = self.loss_fcn(pred, true) 74 | 75 | pred_prob = torch.sigmoid(pred) # prob from logits 76 | alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) 77 | modulating_factor = torch.abs(true - pred_prob) ** self.gamma 78 | loss *= alpha_factor * modulating_factor 79 | 80 | if self.reduction == 'mean': 81 | return loss.mean() 82 | elif self.reduction == 'sum': 83 | return loss.sum() 84 | else: # 'none' 85 | return loss 86 | 87 | 88 | class ComputeLoss: 89 | # Compute losses 90 | def __init__(self, model, autobalance=False): 91 | super(ComputeLoss, self).__init__() 92 | device = next(model.parameters()).device # get model device 93 | h = model.hyp # hyperparameters 94 | 95 | # Define criteria 96 | BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) 97 | BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) 98 | 99 | # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 100 | self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets 101 | 102 | # Focal loss 103 | g = h['fl_gamma'] # focal loss gamma 104 | if g > 0: 105 | BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) 106 | 107 | det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module 108 | self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02]) # P3-P7 109 | self.ssi = list(det.stride).index(16) if autobalance else 0 # stride 16 index 110 | self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance 111 | for k in 'na', 'nc', 'nl', 'anchors': 112 | setattr(self, k, getattr(det, k)) 113 | 114 | def __call__(self, p, targets): # predictions, targets, model 115 | device = targets.device 116 | lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device) 117 | tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets 118 | 119 | # Losses 120 | for i, pi in enumerate(p): # layer index, layer predictions 121 | b, a, gj, gi = indices[i] # image, anchor, gridy, gridx 122 | tobj = torch.zeros_like(pi[..., 0], device=device) # target obj 123 | 124 | n = b.shape[0] # number of targets 125 | if n: 126 | ps = pi[b, a, gj, gi] # prediction subset corresponding to targets 127 | 128 | # Regression 129 | pxy = ps[:, :2].sigmoid() * 2. - 0.5 130 | pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i] 131 | pbox = torch.cat((pxy, pwh), 1) # predicted box 132 | iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True) # iou(prediction, target) 133 | lbox += (1.0 - iou).mean() # iou loss 134 | 135 | # Objectness 136 | tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp(0).type(tobj.dtype) # iou ratio 137 | 138 | # Classification 139 | if self.nc > 1: # cls loss (only if multiple classes) 140 | t = torch.full_like(ps[:, 5:], self.cn, device=device) # targets 141 | t[range(n), tcls[i]] = self.cp 142 | lcls += self.BCEcls(ps[:, 5:], t) # BCE 143 | 144 | # Append targets to text file 145 | # with open('targets.txt', 'a') as file: 146 | # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)] 147 | 148 | obji = self.BCEobj(pi[..., 4], tobj) 149 | lobj += obji * self.balance[i] # obj loss 150 | if self.autobalance: 151 | self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() 152 | 153 | if self.autobalance: 154 | self.balance = [x / self.balance[self.ssi] for x in self.balance] 155 | lbox *= self.hyp['box'] 156 | lobj *= self.hyp['obj'] 157 | lcls *= self.hyp['cls'] 158 | bs = tobj.shape[0] # batch size 159 | 160 | loss = lbox + lobj + lcls 161 | return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach() 162 | 163 | def build_targets(self, p, targets): 164 | # Build targets for compute_loss(), input targets(image,class,x,y,w,h) 165 | na, nt = self.na, targets.shape[0] # number of anchors, targets 166 | tcls, tbox, indices, anch = [], [], [], [] 167 | gain = torch.ones(7, device=targets.device) # normalized to gridspace gain 168 | ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) 169 | targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices 170 | 171 | g = 0.5 # bias 172 | off = torch.tensor([[0, 0], 173 | [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m 174 | # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm 175 | ], device=targets.device).float() * g # offsets 176 | 177 | for i in range(self.nl): 178 | anchors = self.anchors[i] 179 | gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain 180 | 181 | # Match targets to anchors 182 | t = targets * gain 183 | if nt: 184 | # Matches 185 | r = t[:, :, 4:6] / anchors[:, None] # wh ratio 186 | j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t'] # compare 187 | # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) 188 | t = t[j] # filter 189 | 190 | # Offsets 191 | gxy = t[:, 2:4] # grid xy 192 | gxi = gain[[2, 3]] - gxy # inverse 193 | j, k = ((gxy % 1. < g) & (gxy > 1.)).T 194 | l, m = ((gxi % 1. < g) & (gxi > 1.)).T 195 | j = torch.stack((torch.ones_like(j), j, k, l, m)) 196 | t = t.repeat((5, 1, 1))[j] 197 | offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] 198 | else: 199 | t = targets[0] 200 | offsets = 0 201 | 202 | # Define 203 | b, c = t[:, :2].long().T # image, class 204 | gxy = t[:, 2:4] # grid xy 205 | gwh = t[:, 4:6] # grid wh 206 | gij = (gxy - offsets).long() 207 | gi, gj = gij.T # grid xy indices 208 | 209 | # Append 210 | a = t[:, 6].long() # anchor indices 211 | indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices 212 | tbox.append(torch.cat((gxy - gij, gwh), 1)) # box 213 | anch.append(anchors[a]) # anchors 214 | tcls.append(c) # class 215 | 216 | return tcls, tbox, indices, anch 217 | -------------------------------------------------------------------------------- /utils/torch_utils.py: -------------------------------------------------------------------------------- 1 | # YOLOv5 PyTorch utils 2 | 3 | import datetime 4 | import logging 5 | import math 6 | import os 7 | import platform 8 | import subprocess 9 | import time 10 | from contextlib import contextmanager 11 | from copy import deepcopy 12 | from pathlib import Path 13 | 14 | import torch 15 | import torch.backends.cudnn as cudnn 16 | import torch.nn as nn 17 | import torch.nn.functional as F 18 | import torchvision 19 | 20 | try: 21 | import thop # for FLOPS computation 22 | except ImportError: 23 | thop = None 24 | logger = logging.getLogger(__name__) 25 | 26 | 27 | @contextmanager 28 | def torch_distributed_zero_first(local_rank: int): 29 | """ 30 | Decorator to make all processes in distributed training wait for each local_master to do something. 31 | """ 32 | if local_rank not in [-1, 0]: 33 | torch.distributed.barrier() 34 | yield 35 | if local_rank == 0: 36 | torch.distributed.barrier() 37 | 38 | 39 | def init_torch_seeds(seed=0): 40 | # Speed-reproducibility tradeoff https://pytorch.org/docs/stable/notes/randomness.html 41 | torch.manual_seed(seed) 42 | if seed == 0: # slower, more reproducible 43 | cudnn.benchmark, cudnn.deterministic = False, True 44 | else: # faster, less reproducible 45 | cudnn.benchmark, cudnn.deterministic = True, False 46 | 47 | 48 | def date_modified(path=__file__): 49 | # return human-readable file modification date, i.e. '2021-3-26' 50 | t = datetime.datetime.fromtimestamp(Path(path).stat().st_mtime) 51 | return f'{t.year}-{t.month}-{t.day}' 52 | 53 | 54 | def git_describe(path=Path(__file__).parent): # path must be a directory 55 | # return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe 56 | s = f'git -C {path} describe --tags --long --always' 57 | try: 58 | return subprocess.check_output(s, shell=True, stderr=subprocess.STDOUT).decode()[:-1] 59 | except subprocess.CalledProcessError as e: 60 | return '' # not a git repository 61 | 62 | 63 | def select_device(device='', batch_size=None): 64 | # device = 'cpu' or '0' or '0,1,2,3' 65 | s = f'YOLOv5 🚀 {git_describe() or date_modified()} torch {torch.__version__} ' # string 66 | cpu = device.lower() == 'cpu' 67 | if cpu: 68 | os.environ['CUDA_VISIBLE_DEVICES'] = '-1' # force torch.cuda.is_available() = False 69 | elif device: # non-cpu device requested 70 | os.environ['CUDA_VISIBLE_DEVICES'] = device # set environment variable 71 | assert torch.cuda.is_available(), f'CUDA unavailable, invalid device {device} requested' # check availability 72 | 73 | cuda = not cpu and torch.cuda.is_available() 74 | if cuda: 75 | n = torch.cuda.device_count() 76 | if n > 1 and batch_size: # check that batch_size is compatible with device_count 77 | assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}' 78 | space = ' ' * len(s) 79 | for i, d in enumerate(device.split(',') if device else range(n)): 80 | p = torch.cuda.get_device_properties(i) 81 | s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / 1024 ** 2}MB)\n" # bytes to MB 82 | else: 83 | s += 'CPU\n' 84 | 85 | logger.info(s.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else s) # emoji-safe 86 | return torch.device('cuda:0' if cuda else 'cpu') 87 | 88 | 89 | def time_synchronized(): 90 | # pytorch-accurate time 91 | if torch.cuda.is_available(): 92 | torch.cuda.synchronize() 93 | return time.time() 94 | 95 | 96 | def profile(x, ops, n=100, device=None): 97 | # profile a pytorch module or list of modules. Example usage: 98 | # x = torch.randn(16, 3, 640, 640) # input 99 | # m1 = lambda x: x * torch.sigmoid(x) 100 | # m2 = nn.SiLU() 101 | # profile(x, [m1, m2], n=100) # profile speed over 100 iterations 102 | 103 | device = device or torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') 104 | x = x.to(device) 105 | x.requires_grad = True 106 | print(torch.__version__, device.type, torch.cuda.get_device_properties(0) if device.type == 'cuda' else '') 107 | print(f"\n{'Params':>12s}{'GFLOPS':>12s}{'forward (ms)':>16s}{'backward (ms)':>16s}{'input':>24s}{'output':>24s}") 108 | for m in ops if isinstance(ops, list) else [ops]: 109 | m = m.to(device) if hasattr(m, 'to') else m # device 110 | m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m # type 111 | dtf, dtb, t = 0., 0., [0., 0., 0.] # dt forward, backward 112 | try: 113 | flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 # GFLOPS 114 | except: 115 | flops = 0 116 | 117 | for _ in range(n): 118 | t[0] = time_synchronized() 119 | y = m(x) 120 | t[1] = time_synchronized() 121 | try: 122 | _ = y.sum().backward() 123 | t[2] = time_synchronized() 124 | except: # no backward method 125 | t[2] = float('nan') 126 | dtf += (t[1] - t[0]) * 1000 / n # ms per op forward 127 | dtb += (t[2] - t[1]) * 1000 / n # ms per op backward 128 | 129 | s_in = tuple(x.shape) if isinstance(x, torch.Tensor) else 'list' 130 | s_out = tuple(y.shape) if isinstance(y, torch.Tensor) else 'list' 131 | p = sum(list(x.numel() for x in m.parameters())) if isinstance(m, nn.Module) else 0 # parameters 132 | print(f'{p:12}{flops:12.4g}{dtf:16.4g}{dtb:16.4g}{str(s_in):>24s}{str(s_out):>24s}') 133 | 134 | 135 | def is_parallel(model): 136 | return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel) 137 | 138 | 139 | def intersect_dicts(da, db, exclude=()): 140 | # Dictionary intersection of matching keys and shapes, omitting 'exclude' keys, using da values 141 | return {k: v for k, v in da.items() if k in db and not any(x in k for x in exclude) and v.shape == db[k].shape} 142 | 143 | 144 | def initialize_weights(model): 145 | for m in model.modules(): 146 | t = type(m) 147 | if t is nn.Conv2d: 148 | pass # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') 149 | elif t is nn.BatchNorm2d: 150 | m.eps = 1e-3 151 | m.momentum = 0.03 152 | elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6]: 153 | m.inplace = True 154 | 155 | 156 | def find_modules(model, mclass=nn.Conv2d): 157 | # Finds layer indices matching module class 'mclass' 158 | return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)] 159 | 160 | 161 | def sparsity(model): 162 | # Return global model sparsity 163 | a, b = 0., 0. 164 | for p in model.parameters(): 165 | a += p.numel() 166 | b += (p == 0).sum() 167 | return b / a 168 | 169 | 170 | def prune(model, amount=0.3): 171 | # Prune model to requested global sparsity 172 | import torch.nn.utils.prune as prune 173 | print('Pruning model... ', end='') 174 | for name, m in model.named_modules(): 175 | if isinstance(m, nn.Conv2d): 176 | prune.l1_unstructured(m, name='weight', amount=amount) # prune 177 | prune.remove(m, 'weight') # make permanent 178 | print(' %.3g global sparsity' % sparsity(model)) 179 | 180 | 181 | def fuse_conv_and_bn(conv, bn): 182 | # Fuse convolution and batchnorm layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/ 183 | fusedconv = nn.Conv2d(conv.in_channels, 184 | conv.out_channels, 185 | kernel_size=conv.kernel_size, 186 | stride=conv.stride, 187 | padding=conv.padding, 188 | groups=conv.groups, 189 | bias=True).requires_grad_(False).to(conv.weight.device) 190 | 191 | # prepare filters 192 | w_conv = conv.weight.clone().view(conv.out_channels, -1) 193 | w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var))) 194 | fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape)) 195 | 196 | # prepare spatial bias 197 | b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias 198 | b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps)) 199 | fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn) 200 | 201 | return fusedconv 202 | 203 | 204 | def model_info(model, verbose=False, img_size=640): 205 | # Model information. img_size may be int or list, i.e. img_size=640 or img_size=[640, 320] 206 | n_p = sum(x.numel() for x in model.parameters()) # number parameters 207 | n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients 208 | if verbose: 209 | print('%5s %40s %9s %12s %20s %10s %10s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma')) 210 | for i, (name, p) in enumerate(model.named_parameters()): 211 | name = name.replace('module_list.', '') 212 | print('%5g %40s %9s %12g %20s %10.3g %10.3g' % 213 | (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std())) 214 | 215 | try: # FLOPS 216 | from thop import profile 217 | stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32 218 | img = torch.zeros((1, model.yaml.get('ch', 3), stride, stride), device=next(model.parameters()).device) # input 219 | flops = profile(deepcopy(model), inputs=(img,), verbose=False)[0] / 1E9 * 2 # stride GFLOPS 220 | img_size = img_size if isinstance(img_size, list) else [img_size, img_size] # expand if int/float 221 | fs = ', %.1f GFLOPS' % (flops * img_size[0] / stride * img_size[1] / stride) # 640x640 GFLOPS 222 | except (ImportError, Exception): 223 | fs = '' 224 | 225 | logger.info(f"Model Summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}") 226 | 227 | 228 | def load_classifier(name='resnet101', n=2): 229 | # Loads a pretrained model reshaped to n-class output 230 | model = torchvision.models.__dict__[name](pretrained=True) 231 | 232 | # ResNet model properties 233 | # input_size = [3, 224, 224] 234 | # input_space = 'RGB' 235 | # input_range = [0, 1] 236 | # mean = [0.485, 0.456, 0.406] 237 | # std = [0.229, 0.224, 0.225] 238 | 239 | # Reshape output to n classes 240 | filters = model.fc.weight.shape[1] 241 | model.fc.bias = nn.Parameter(torch.zeros(n), requires_grad=True) 242 | model.fc.weight = nn.Parameter(torch.zeros(n, filters), requires_grad=True) 243 | model.fc.out_features = n 244 | return model 245 | 246 | 247 | def scale_img(img, ratio=1.0, same_shape=False, gs=32): # img(16,3,256,416) 248 | # scales img(bs,3,y,x) by ratio constrained to gs-multiple 249 | if ratio == 1.0: 250 | return img 251 | else: 252 | h, w = img.shape[2:] 253 | s = (int(h * ratio), int(w * ratio)) # new size 254 | img = F.interpolate(img, size=s, mode='bilinear', align_corners=False) # resize 255 | if not same_shape: # pad/crop img 256 | h, w = [math.ceil(x * ratio / gs) * gs for x in (h, w)] 257 | return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447) # value = imagenet mean 258 | 259 | 260 | def copy_attr(a, b, include=(), exclude=()): 261 | # Copy attributes from b to a, options to only include [...] and to exclude [...] 262 | for k, v in b.__dict__.items(): 263 | if (len(include) and k not in include) or k.startswith('_') or k in exclude: 264 | continue 265 | else: 266 | setattr(a, k, v) 267 | 268 | 269 | class ModelEMA: 270 | """ Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models 271 | Keep a moving average of everything in the model state_dict (parameters and buffers). 272 | This is intended to allow functionality like 273 | https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage 274 | A smoothed version of the weights is necessary for some training schemes to perform well. 275 | This class is sensitive where it is initialized in the sequence of model init, 276 | GPU assignment and distributed training wrappers. 277 | """ 278 | 279 | def __init__(self, model, decay=0.9999, updates=0): 280 | # Create EMA 281 | self.ema = deepcopy(model.module if is_parallel(model) else model).eval() # FP32 EMA 282 | # if next(model.parameters()).device.type != 'cpu': 283 | # self.ema.half() # FP16 EMA 284 | self.updates = updates # number of EMA updates 285 | self.decay = lambda x: decay * (1 - math.exp(-x / 2000)) # decay exponential ramp (to help early epochs) 286 | for p in self.ema.parameters(): 287 | p.requires_grad_(False) 288 | 289 | def update(self, model): 290 | # Update EMA parameters 291 | with torch.no_grad(): 292 | self.updates += 1 293 | d = self.decay(self.updates) 294 | 295 | msd = model.module.state_dict() if is_parallel(model) else model.state_dict() # model state_dict 296 | for k, v in self.ema.state_dict().items(): 297 | if v.dtype.is_floating_point: 298 | v *= d 299 | v += (1. - d) * msd[k].detach() 300 | 301 | def update_attr(self, model, include=(), exclude=('process_group', 'reducer')): 302 | # Update EMA attributes 303 | copy_attr(self.ema, model, include, exclude) 304 | -------------------------------------------------------------------------------- /utils/wandb_logging/wandb_utils.py: -------------------------------------------------------------------------------- 1 | import json 2 | import sys 3 | from pathlib import Path 4 | 5 | import torch 6 | import yaml 7 | from tqdm import tqdm 8 | 9 | sys.path.append(str(Path(__file__).parent.parent.parent)) # add utils/ to path 10 | from utils.datasets import LoadImagesAndLabels 11 | from utils.datasets import img2label_paths 12 | from utils.general import colorstr, xywh2xyxy, check_dataset 13 | 14 | try: 15 | import wandb 16 | from wandb import init, finish 17 | except ImportError: 18 | wandb = None 19 | 20 | WANDB_ARTIFACT_PREFIX = 'wandb-artifact://' 21 | 22 | 23 | def remove_prefix(from_string, prefix=WANDB_ARTIFACT_PREFIX): 24 | return from_string[len(prefix):] 25 | 26 | 27 | def check_wandb_config_file(data_config_file): 28 | wandb_config = '_wandb.'.join(data_config_file.rsplit('.', 1)) # updated data.yaml path 29 | if Path(wandb_config).is_file(): 30 | return wandb_config 31 | return data_config_file 32 | 33 | 34 | def get_run_info(run_path): 35 | run_path = Path(remove_prefix(run_path, WANDB_ARTIFACT_PREFIX)) 36 | run_id = run_path.stem 37 | project = run_path.parent.stem 38 | model_artifact_name = 'run_' + run_id + '_model' 39 | return run_id, project, model_artifact_name 40 | 41 | 42 | def check_wandb_resume(opt): 43 | process_wandb_config_ddp_mode(opt) if opt.global_rank not in [-1, 0] else None 44 | if isinstance(opt.resume, str): 45 | if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): 46 | if opt.global_rank not in [-1, 0]: # For resuming DDP runs 47 | run_id, project, model_artifact_name = get_run_info(opt.resume) 48 | api = wandb.Api() 49 | artifact = api.artifact(project + '/' + model_artifact_name + ':latest') 50 | modeldir = artifact.download() 51 | opt.weights = str(Path(modeldir) / "last.pt") 52 | return True 53 | return None 54 | 55 | 56 | def process_wandb_config_ddp_mode(opt): 57 | with open(opt.data) as f: 58 | data_dict = yaml.load(f, Loader=yaml.SafeLoader) # data dict 59 | train_dir, val_dir = None, None 60 | if isinstance(data_dict['train'], str) and data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX): 61 | api = wandb.Api() 62 | train_artifact = api.artifact(remove_prefix(data_dict['train']) + ':' + opt.artifact_alias) 63 | train_dir = train_artifact.download() 64 | train_path = Path(train_dir) / 'data/images/' 65 | data_dict['train'] = str(train_path) 66 | 67 | if isinstance(data_dict['val'], str) and data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX): 68 | api = wandb.Api() 69 | val_artifact = api.artifact(remove_prefix(data_dict['val']) + ':' + opt.artifact_alias) 70 | val_dir = val_artifact.download() 71 | val_path = Path(val_dir) / 'data/images/' 72 | data_dict['val'] = str(val_path) 73 | if train_dir or val_dir: 74 | ddp_data_path = str(Path(val_dir) / 'wandb_local_data.yaml') 75 | with open(ddp_data_path, 'w') as f: 76 | yaml.dump(data_dict, f) 77 | opt.data = ddp_data_path 78 | 79 | 80 | class WandbLogger(): 81 | def __init__(self, opt, name, run_id, data_dict, job_type='Training'): 82 | # Pre-training routine -- 83 | self.job_type = job_type 84 | self.wandb, self.wandb_run, self.data_dict = wandb, None if not wandb else wandb.run, data_dict 85 | # It's more elegant to stick to 1 wandb.init call, but useful config data is overwritten in the WandbLogger's wandb.init call 86 | if isinstance(opt.resume, str): # checks resume from artifact 87 | if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): 88 | run_id, project, model_artifact_name = get_run_info(opt.resume) 89 | model_artifact_name = WANDB_ARTIFACT_PREFIX + model_artifact_name 90 | assert wandb, 'install wandb to resume wandb runs' 91 | # Resume wandb-artifact:// runs here| workaround for not overwriting wandb.config 92 | self.wandb_run = wandb.init(id=run_id, project=project, resume='allow') 93 | opt.resume = model_artifact_name 94 | elif self.wandb: 95 | self.wandb_run = wandb.init(config=opt, 96 | resume="allow", 97 | project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem, 98 | name=name, 99 | job_type=job_type, 100 | id=run_id) if not wandb.run else wandb.run 101 | if self.wandb_run: 102 | if self.job_type == 'Training': 103 | if not opt.resume: 104 | wandb_data_dict = self.check_and_upload_dataset(opt) if opt.upload_dataset else data_dict 105 | # Info useful for resuming from artifacts 106 | self.wandb_run.config.opt = vars(opt) 107 | self.wandb_run.config.data_dict = wandb_data_dict 108 | self.data_dict = self.setup_training(opt, data_dict) 109 | if self.job_type == 'Dataset Creation': 110 | self.data_dict = self.check_and_upload_dataset(opt) 111 | else: 112 | prefix = colorstr('wandb: ') 113 | print(f"{prefix}Install Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)") 114 | 115 | def check_and_upload_dataset(self, opt): 116 | assert wandb, 'Install wandb to upload dataset' 117 | check_dataset(self.data_dict) 118 | config_path = self.log_dataset_artifact(opt.data, 119 | opt.single_cls, 120 | 'YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem) 121 | print("Created dataset config file ", config_path) 122 | with open(config_path) as f: 123 | wandb_data_dict = yaml.load(f, Loader=yaml.SafeLoader) 124 | return wandb_data_dict 125 | 126 | def setup_training(self, opt, data_dict): 127 | self.log_dict, self.current_epoch, self.log_imgs = {}, 0, 16 # Logging Constants 128 | self.bbox_interval = opt.bbox_interval 129 | if isinstance(opt.resume, str): 130 | modeldir, _ = self.download_model_artifact(opt) 131 | if modeldir: 132 | self.weights = Path(modeldir) / "last.pt" 133 | config = self.wandb_run.config 134 | opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp = str( 135 | self.weights), config.save_period, config.total_batch_size, config.bbox_interval, config.epochs, \ 136 | config.opt['hyp'] 137 | data_dict = dict(self.wandb_run.config.data_dict) # eliminates the need for config file to resume 138 | if 'val_artifact' not in self.__dict__: # If --upload_dataset is set, use the existing artifact, don't download 139 | self.train_artifact_path, self.train_artifact = self.download_dataset_artifact(data_dict.get('train'), 140 | opt.artifact_alias) 141 | self.val_artifact_path, self.val_artifact = self.download_dataset_artifact(data_dict.get('val'), 142 | opt.artifact_alias) 143 | self.result_artifact, self.result_table, self.val_table, self.weights = None, None, None, None 144 | if self.train_artifact_path is not None: 145 | train_path = Path(self.train_artifact_path) / 'data/images/' 146 | data_dict['train'] = str(train_path) 147 | if self.val_artifact_path is not None: 148 | val_path = Path(self.val_artifact_path) / 'data/images/' 149 | data_dict['val'] = str(val_path) 150 | self.val_table = self.val_artifact.get("val") 151 | self.map_val_table_path() 152 | if self.val_artifact is not None: 153 | self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation") 154 | self.result_table = wandb.Table(["epoch", "id", "prediction", "avg_confidence"]) 155 | if opt.bbox_interval == -1: 156 | self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1 157 | return data_dict 158 | 159 | def download_dataset_artifact(self, path, alias): 160 | if isinstance(path, str) and path.startswith(WANDB_ARTIFACT_PREFIX): 161 | dataset_artifact = wandb.use_artifact(remove_prefix(path, WANDB_ARTIFACT_PREFIX) + ":" + alias) 162 | assert dataset_artifact is not None, "'Error: W&B dataset artifact doesn\'t exist'" 163 | datadir = dataset_artifact.download() 164 | return datadir, dataset_artifact 165 | return None, None 166 | 167 | def download_model_artifact(self, opt): 168 | if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): 169 | model_artifact = wandb.use_artifact(remove_prefix(opt.resume, WANDB_ARTIFACT_PREFIX) + ":latest") 170 | assert model_artifact is not None, 'Error: W&B model artifact doesn\'t exist' 171 | modeldir = model_artifact.download() 172 | epochs_trained = model_artifact.metadata.get('epochs_trained') 173 | total_epochs = model_artifact.metadata.get('total_epochs') 174 | assert epochs_trained < total_epochs, 'training to %g epochs is finished, nothing to resume.' % ( 175 | total_epochs) 176 | return modeldir, model_artifact 177 | return None, None 178 | 179 | def log_model(self, path, opt, epoch, fitness_score, best_model=False): 180 | model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model', type='model', metadata={ 181 | 'original_url': str(path), 182 | 'epochs_trained': epoch + 1, 183 | 'save period': opt.save_period, 184 | 'project': opt.project, 185 | 'total_epochs': opt.epochs, 186 | 'fitness_score': fitness_score 187 | }) 188 | model_artifact.add_file(str(path / 'last.pt'), name='last.pt') 189 | wandb.log_artifact(model_artifact, 190 | aliases=['latest', 'epoch ' + str(self.current_epoch), 'best' if best_model else '']) 191 | print("Saving model artifact on epoch ", epoch + 1) 192 | 193 | def log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=False): 194 | with open(data_file) as f: 195 | data = yaml.load(f, Loader=yaml.SafeLoader) # data dict 196 | nc, names = (1, ['item']) if single_cls else (int(data['nc']), data['names']) 197 | names = {k: v for k, v in enumerate(names)} # to index dictionary 198 | self.train_artifact = self.create_dataset_table(LoadImagesAndLabels( 199 | data['train']), names, name='train') if data.get('train') else None 200 | self.val_artifact = self.create_dataset_table(LoadImagesAndLabels( 201 | data['val']), names, name='val') if data.get('val') else None 202 | if data.get('train'): 203 | data['train'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'train') 204 | if data.get('val'): 205 | data['val'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'val') 206 | path = data_file if overwrite_config else '_wandb.'.join(data_file.rsplit('.', 1)) # updated data.yaml path 207 | data.pop('download', None) 208 | with open(path, 'w') as f: 209 | yaml.dump(data, f) 210 | 211 | if self.job_type == 'Training': # builds correct artifact pipeline graph 212 | self.wandb_run.use_artifact(self.val_artifact) 213 | self.wandb_run.use_artifact(self.train_artifact) 214 | self.val_artifact.wait() 215 | self.val_table = self.val_artifact.get('val') 216 | self.map_val_table_path() 217 | else: 218 | self.wandb_run.log_artifact(self.train_artifact) 219 | self.wandb_run.log_artifact(self.val_artifact) 220 | return path 221 | 222 | def map_val_table_path(self): 223 | self.val_table_map = {} 224 | print("Mapping dataset") 225 | for i, data in enumerate(tqdm(self.val_table.data)): 226 | self.val_table_map[data[3]] = data[0] 227 | 228 | def create_dataset_table(self, dataset, class_to_id, name='dataset'): 229 | # TODO: Explore multiprocessing to slpit this loop parallely| This is essential for speeding up the the logging 230 | artifact = wandb.Artifact(name=name, type="dataset") 231 | img_files = tqdm([dataset.path]) if isinstance(dataset.path, str) and Path(dataset.path).is_dir() else None 232 | img_files = tqdm(dataset.img_files) if not img_files else img_files 233 | for img_file in img_files: 234 | if Path(img_file).is_dir(): 235 | artifact.add_dir(img_file, name='data/images') 236 | labels_path = 'labels'.join(dataset.path.rsplit('images', 1)) 237 | artifact.add_dir(labels_path, name='data/labels') 238 | else: 239 | artifact.add_file(img_file, name='data/images/' + Path(img_file).name) 240 | label_file = Path(img2label_paths([img_file])[0]) 241 | artifact.add_file(str(label_file), 242 | name='data/labels/' + label_file.name) if label_file.exists() else None 243 | table = wandb.Table(columns=["id", "train_image", "Classes", "name"]) 244 | class_set = wandb.Classes([{'id': id, 'name': name} for id, name in class_to_id.items()]) 245 | for si, (img, labels, paths, shapes) in enumerate(tqdm(dataset)): 246 | height, width = shapes[0] 247 | labels[:, 2:] = (xywh2xyxy(labels[:, 2:].view(-1, 4))) * torch.Tensor([width, height, width, height]) 248 | box_data, img_classes = [], {} 249 | for cls, *xyxy in labels[:, 1:].tolist(): 250 | cls = int(cls) 251 | box_data.append({"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]}, 252 | "class_id": cls, 253 | "box_caption": "%s" % (class_to_id[cls]), 254 | "scores": {"acc": 1}, 255 | "domain": "pixel"}) 256 | img_classes[cls] = class_to_id[cls] 257 | boxes = {"ground_truth": {"box_data": box_data, "class_labels": class_to_id}} # inference-space 258 | table.add_data(si, wandb.Image(paths, classes=class_set, boxes=boxes), json.dumps(img_classes), 259 | Path(paths).name) 260 | artifact.add(table, name) 261 | return artifact 262 | 263 | def log_training_progress(self, predn, path, names): 264 | if self.val_table and self.result_table: 265 | class_set = wandb.Classes([{'id': id, 'name': name} for id, name in names.items()]) 266 | box_data = [] 267 | total_conf = 0 268 | for *xyxy, conf, cls in predn.tolist(): 269 | if conf >= 0.25: 270 | box_data.append( 271 | {"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]}, 272 | "class_id": int(cls), 273 | "box_caption": "%s %.3f" % (names[cls], conf), 274 | "scores": {"class_score": conf}, 275 | "domain": "pixel"}) 276 | total_conf = total_conf + conf 277 | boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space 278 | id = self.val_table_map[Path(path).name] 279 | self.result_table.add_data(self.current_epoch, 280 | id, 281 | wandb.Image(self.val_table.data[id][1], boxes=boxes, classes=class_set), 282 | total_conf / max(1, len(box_data)) 283 | ) 284 | 285 | def log(self, log_dict): 286 | if self.wandb_run: 287 | for key, value in log_dict.items(): 288 | self.log_dict[key] = value 289 | 290 | def end_epoch(self, best_result=False): 291 | if self.wandb_run: 292 | wandb.log(self.log_dict) 293 | self.log_dict = {} 294 | if self.result_artifact: 295 | train_results = wandb.JoinedTable(self.val_table, self.result_table, "id") 296 | self.result_artifact.add(train_results, 'result') 297 | wandb.log_artifact(self.result_artifact, aliases=['latest', 'epoch ' + str(self.current_epoch), 298 | ('best' if best_result else '')]) 299 | self.result_table = wandb.Table(["epoch", "id", "prediction", "avg_confidence"]) 300 | self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation") 301 | 302 | def finish_run(self): 303 | if self.wandb_run: 304 | if self.log_dict: 305 | wandb.log(self.log_dict) 306 | wandb.run.finish() 307 | -------------------------------------------------------------------------------- /utils/plots.py: -------------------------------------------------------------------------------- 1 | # Plotting utils 2 | 3 | import glob 4 | import math 5 | import os 6 | import random 7 | from copy import copy 8 | from pathlib import Path 9 | 10 | import cv2 11 | import matplotlib 12 | import matplotlib.pyplot as plt 13 | import numpy as np 14 | import pandas as pd 15 | import seaborn as sns 16 | import torch 17 | import yaml 18 | from PIL import Image, ImageDraw, ImageFont 19 | from scipy.signal import butter, filtfilt 20 | 21 | from utils.general import xywh2xyxy, xyxy2xywh 22 | from utils.metrics import fitness 23 | 24 | # Settings 25 | matplotlib.rc('font', **{'size': 11}) 26 | matplotlib.use('Agg') # for writing to files only 27 | 28 | 29 | def color_list(): 30 | # Return first 10 plt colors as (r,g,b) https://stackoverflow.com/questions/51350872/python-from-color-name-to-rgb 31 | def hex2rgb(h): 32 | return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4)) 33 | 34 | return [hex2rgb(h) for h in matplotlib.colors.TABLEAU_COLORS.values()] # or BASE_ (8), CSS4_ (148), XKCD_ (949) 35 | 36 | 37 | def hist2d(x, y, n=100): 38 | # 2d histogram used in labels.png and evolve.png 39 | xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n) 40 | hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges)) 41 | xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1) 42 | yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1) 43 | return np.log(hist[xidx, yidx]) 44 | 45 | 46 | def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5): 47 | # https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy 48 | def butter_lowpass(cutoff, fs, order): 49 | nyq = 0.5 * fs 50 | normal_cutoff = cutoff / nyq 51 | return butter(order, normal_cutoff, btype='low', analog=False) 52 | 53 | b, a = butter_lowpass(cutoff, fs, order=order) 54 | return filtfilt(b, a, data) # forward-backward filter 55 | 56 | 57 | def plot_one_box(x, img, color=None, label=None, line_thickness=3): 58 | # Plots one bounding box on image img 59 | tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness 60 | color = color or [random.randint(0, 255) for _ in range(3)] 61 | c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3])) 62 | cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA) 63 | if label: 64 | tf = max(tl - 1, 1) # font thickness 65 | t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] 66 | c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 67 | cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled 68 | cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA) 69 | 70 | 71 | def plot_one_box_PIL(box, img, color=None, label=None, line_thickness=None): 72 | img = Image.fromarray(img) 73 | draw = ImageDraw.Draw(img) 74 | line_thickness = line_thickness or max(int(min(img.size) / 200), 2) 75 | draw.rectangle(box, width=line_thickness, outline=tuple(color)) # plot 76 | if label: 77 | fontsize = max(round(max(img.size) / 40), 12) 78 | font = ImageFont.truetype("Arial.ttf", fontsize) 79 | txt_width, txt_height = font.getsize(label) 80 | draw.rectangle([box[0], box[1] - txt_height + 4, box[0] + txt_width, box[1]], fill=tuple(color)) 81 | draw.text((box[0], box[1] - txt_height + 1), label, fill=(255, 255, 255), font=font) 82 | return np.asarray(img) 83 | 84 | 85 | def plot_wh_methods(): # from utils.plots import *; plot_wh_methods() 86 | # Compares the two methods for width-height anchor multiplication 87 | # https://github.com/ultralytics/yolov3/issues/168 88 | x = np.arange(-4.0, 4.0, .1) 89 | ya = np.exp(x) 90 | yb = torch.sigmoid(torch.from_numpy(x)).numpy() * 2 91 | 92 | fig = plt.figure(figsize=(6, 3), tight_layout=True) 93 | plt.plot(x, ya, '.-', label='YOLOv3') 94 | plt.plot(x, yb ** 2, '.-', label='YOLOv5 ^2') 95 | plt.plot(x, yb ** 1.6, '.-', label='YOLOv5 ^1.6') 96 | plt.xlim(left=-4, right=4) 97 | plt.ylim(bottom=0, top=6) 98 | plt.xlabel('input') 99 | plt.ylabel('output') 100 | plt.grid() 101 | plt.legend() 102 | fig.savefig('comparison.png', dpi=200) 103 | 104 | 105 | def output_to_target(output): 106 | # Convert model output to target format [batch_id, class_id, x, y, w, h, conf] 107 | targets = [] 108 | for i, o in enumerate(output): 109 | for *box, conf, cls in o.cpu().numpy(): 110 | targets.append([i, cls, *list(*xyxy2xywh(np.array(box)[None])), conf]) 111 | return np.array(targets) 112 | 113 | 114 | def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max_size=640, max_subplots=16): 115 | # Plot image grid with labels 116 | 117 | if isinstance(images, torch.Tensor): 118 | images = images.cpu().float().numpy() 119 | if isinstance(targets, torch.Tensor): 120 | targets = targets.cpu().numpy() 121 | 122 | # un-normalise 123 | if np.max(images[0]) <= 1: 124 | images *= 255 125 | 126 | tl = 3 # line thickness 127 | tf = max(tl - 1, 1) # font thickness 128 | bs, _, h, w = images.shape # batch size, _, height, width 129 | bs = min(bs, max_subplots) # limit plot images 130 | ns = np.ceil(bs ** 0.5) # number of subplots (square) 131 | 132 | # Check if we should resize 133 | scale_factor = max_size / max(h, w) 134 | if scale_factor < 1: 135 | h = math.ceil(scale_factor * h) 136 | w = math.ceil(scale_factor * w) 137 | 138 | colors = color_list() # list of colors 139 | mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init 140 | for i, img in enumerate(images): 141 | if i == max_subplots: # if last batch has fewer images than we expect 142 | break 143 | 144 | block_x = int(w * (i // ns)) 145 | block_y = int(h * (i % ns)) 146 | 147 | img = img.transpose(1, 2, 0) 148 | if scale_factor < 1: 149 | img = cv2.resize(img, (w, h)) 150 | 151 | mosaic[block_y:block_y + h, block_x:block_x + w, :] = img 152 | if len(targets) > 0: 153 | image_targets = targets[targets[:, 0] == i] 154 | boxes = xywh2xyxy(image_targets[:, 2:6]).T 155 | classes = image_targets[:, 1].astype('int') 156 | labels = image_targets.shape[1] == 6 # labels if no conf column 157 | conf = None if labels else image_targets[:, 6] # check for confidence presence (label vs pred) 158 | 159 | if boxes.shape[1]: 160 | if boxes.max() <= 1.01: # if normalized with tolerance 0.01 161 | boxes[[0, 2]] *= w # scale to pixels 162 | boxes[[1, 3]] *= h 163 | elif scale_factor < 1: # absolute coords need scale if image scales 164 | boxes *= scale_factor 165 | boxes[[0, 2]] += block_x 166 | boxes[[1, 3]] += block_y 167 | for j, box in enumerate(boxes.T): 168 | cls = int(classes[j]) 169 | color = colors[cls % len(colors)] 170 | cls = names[cls] if names else cls 171 | if labels or conf[j] > 0.25: # 0.25 conf thresh 172 | label = '%s' % cls if labels else '%s %.1f' % (cls, conf[j]) 173 | plot_one_box(box, mosaic, label=label, color=color, line_thickness=tl) 174 | 175 | # Draw image filename labels 176 | if paths: 177 | label = Path(paths[i]).name[:40] # trim to 40 char 178 | t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] 179 | cv2.putText(mosaic, label, (block_x + 5, block_y + t_size[1] + 5), 0, tl / 3, [220, 220, 220], thickness=tf, 180 | lineType=cv2.LINE_AA) 181 | 182 | # Image border 183 | cv2.rectangle(mosaic, (block_x, block_y), (block_x + w, block_y + h), (255, 255, 255), thickness=3) 184 | 185 | if fname: 186 | r = min(1280. / max(h, w) / ns, 1.0) # ratio to limit image size 187 | mosaic = cv2.resize(mosaic, (int(ns * w * r), int(ns * h * r)), interpolation=cv2.INTER_AREA) 188 | # cv2.imwrite(fname, cv2.cvtColor(mosaic, cv2.COLOR_BGR2RGB)) # cv2 save 189 | Image.fromarray(mosaic).save(fname) # PIL save 190 | return mosaic 191 | 192 | 193 | def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''): 194 | # Plot LR simulating training for full epochs 195 | optimizer, scheduler = copy(optimizer), copy(scheduler) # do not modify originals 196 | y = [] 197 | for _ in range(epochs): 198 | scheduler.step() 199 | y.append(optimizer.param_groups[0]['lr']) 200 | plt.plot(y, '.-', label='LR') 201 | plt.xlabel('epoch') 202 | plt.ylabel('LR') 203 | plt.grid() 204 | plt.xlim(0, epochs) 205 | plt.ylim(0) 206 | plt.savefig(Path(save_dir) / 'LR.png', dpi=200) 207 | plt.close() 208 | 209 | 210 | def plot_test_txt(): # from utils.plots import *; plot_test() 211 | # Plot test.txt histograms 212 | x = np.loadtxt('test.txt', dtype=np.float32) 213 | box = xyxy2xywh(x[:, :4]) 214 | cx, cy = box[:, 0], box[:, 1] 215 | 216 | fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True) 217 | ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0) 218 | ax.set_aspect('equal') 219 | plt.savefig('hist2d.png', dpi=300) 220 | 221 | fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True) 222 | ax[0].hist(cx, bins=600) 223 | ax[1].hist(cy, bins=600) 224 | plt.savefig('hist1d.png', dpi=200) 225 | 226 | 227 | def plot_targets_txt(): # from utils.plots import *; plot_targets_txt() 228 | # Plot targets.txt histograms 229 | x = np.loadtxt('targets.txt', dtype=np.float32).T 230 | s = ['x targets', 'y targets', 'width targets', 'height targets'] 231 | fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True) 232 | ax = ax.ravel() 233 | for i in range(4): 234 | ax[i].hist(x[i], bins=100, label='%.3g +/- %.3g' % (x[i].mean(), x[i].std())) 235 | ax[i].legend() 236 | ax[i].set_title(s[i]) 237 | plt.savefig('targets.jpg', dpi=200) 238 | 239 | 240 | def plot_study_txt(path='', x=None): # from utils.plots import *; plot_study_txt() 241 | # Plot study.txt generated by test.py 242 | fig, ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True) 243 | # ax = ax.ravel() 244 | 245 | fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True) 246 | # for f in [Path(path) / f'study_coco_{x}.txt' for x in ['yolov5s6', 'yolov5m6', 'yolov5l6', 'yolov5x6']]: 247 | for f in sorted(Path(path).glob('study*.txt')): 248 | y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T 249 | x = np.arange(y.shape[1]) if x is None else np.array(x) 250 | s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_inference (ms/img)', 't_NMS (ms/img)', 't_total (ms/img)'] 251 | # for i in range(7): 252 | # ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8) 253 | # ax[i].set_title(s[i]) 254 | 255 | j = y[3].argmax() + 1 256 | ax2.plot(y[6, 1:j], y[3, 1:j] * 1E2, '.-', linewidth=2, markersize=8, 257 | label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO')) 258 | 259 | ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5], 260 | 'k.-', linewidth=2, markersize=8, alpha=.25, label='EfficientDet') 261 | 262 | ax2.grid(alpha=0.2) 263 | ax2.set_yticks(np.arange(20, 60, 5)) 264 | ax2.set_xlim(0, 57) 265 | ax2.set_ylim(30, 55) 266 | ax2.set_xlabel('GPU Speed (ms/img)') 267 | ax2.set_ylabel('COCO AP val') 268 | ax2.legend(loc='lower right') 269 | plt.savefig(str(Path(path).name) + '.png', dpi=300) 270 | 271 | 272 | def plot_labels(labels, names=(), save_dir=Path(''), loggers=None): 273 | # plot dataset labels 274 | print('Plotting labels... ') 275 | c, b = labels[:, 0], labels[:, 1:].transpose() # classes, boxes 276 | nc = int(c.max() + 1) # number of classes 277 | colors = color_list() 278 | x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height']) 279 | 280 | # seaborn correlogram 281 | sns.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9)) 282 | plt.savefig(save_dir / 'labels_correlogram.jpg', dpi=200) 283 | plt.close() 284 | 285 | # matplotlib labels 286 | matplotlib.use('svg') # faster 287 | ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel() 288 | ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8) 289 | ax[0].set_ylabel('instances') 290 | if 0 < len(names) < 30: 291 | ax[0].set_xticks(range(len(names))) 292 | ax[0].set_xticklabels(names, rotation=90, fontsize=10) 293 | else: 294 | ax[0].set_xlabel('classes') 295 | sns.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9) 296 | sns.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9) 297 | 298 | # rectangles 299 | labels[:, 1:3] = 0.5 # center 300 | labels[:, 1:] = xywh2xyxy(labels[:, 1:]) * 2000 301 | img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255) 302 | for cls, *box in labels[:1000]: 303 | ImageDraw.Draw(img).rectangle(box, width=1, outline=colors[int(cls) % 10]) # plot 304 | ax[1].imshow(img) 305 | ax[1].axis('off') 306 | 307 | for a in [0, 1, 2, 3]: 308 | for s in ['top', 'right', 'left', 'bottom']: 309 | ax[a].spines[s].set_visible(False) 310 | 311 | plt.savefig(save_dir / 'labels.jpg', dpi=200) 312 | matplotlib.use('Agg') 313 | plt.close() 314 | 315 | # loggers 316 | for k, v in loggers.items() or {}: 317 | if k == 'wandb' and v: 318 | v.log({"Labels": [v.Image(str(x), caption=x.name) for x in save_dir.glob('*labels*.jpg')]}, commit=False) 319 | 320 | 321 | def plot_evolution(yaml_file='data/hyp.finetune.yaml'): # from utils.plots import *; plot_evolution() 322 | # Plot hyperparameter evolution results in evolve.txt 323 | with open(yaml_file) as f: 324 | hyp = yaml.load(f, Loader=yaml.SafeLoader) 325 | x = np.loadtxt('evolve.txt', ndmin=2) 326 | f = fitness(x) 327 | # weights = (f - f.min()) ** 2 # for weighted results 328 | plt.figure(figsize=(10, 12), tight_layout=True) 329 | matplotlib.rc('font', **{'size': 8}) 330 | for i, (k, v) in enumerate(hyp.items()): 331 | y = x[:, i + 7] 332 | # mu = (y * weights).sum() / weights.sum() # best weighted result 333 | mu = y[f.argmax()] # best single result 334 | plt.subplot(6, 5, i + 1) 335 | plt.scatter(y, f, c=hist2d(y, f, 20), cmap='viridis', alpha=.8, edgecolors='none') 336 | plt.plot(mu, f.max(), 'k+', markersize=15) 337 | plt.title('%s = %.3g' % (k, mu), fontdict={'size': 9}) # limit to 40 characters 338 | if i % 5 != 0: 339 | plt.yticks([]) 340 | print('%15s: %.3g' % (k, mu)) 341 | plt.savefig('evolve.png', dpi=200) 342 | print('\nPlot saved as evolve.png') 343 | 344 | 345 | def profile_idetection(start=0, stop=0, labels=(), save_dir=''): 346 | # Plot iDetection '*.txt' per-image logs. from utils.plots import *; profile_idetection() 347 | ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel() 348 | s = ['Images', 'Free Storage (GB)', 'RAM Usage (GB)', 'Battery', 'dt_raw (ms)', 'dt_smooth (ms)', 'real-world FPS'] 349 | files = list(Path(save_dir).glob('frames*.txt')) 350 | for fi, f in enumerate(files): 351 | try: 352 | results = np.loadtxt(f, ndmin=2).T[:, 90:-30] # clip first and last rows 353 | n = results.shape[1] # number of rows 354 | x = np.arange(start, min(stop, n) if stop else n) 355 | results = results[:, x] 356 | t = (results[0] - results[0].min()) # set t0=0s 357 | results[0] = x 358 | for i, a in enumerate(ax): 359 | if i < len(results): 360 | label = labels[fi] if len(labels) else f.stem.replace('frames_', '') 361 | a.plot(t, results[i], marker='.', label=label, linewidth=1, markersize=5) 362 | a.set_title(s[i]) 363 | a.set_xlabel('time (s)') 364 | # if fi == len(files) - 1: 365 | # a.set_ylim(bottom=0) 366 | for side in ['top', 'right']: 367 | a.spines[side].set_visible(False) 368 | else: 369 | a.remove() 370 | except Exception as e: 371 | print('Warning: Plotting error for %s; %s' % (f, e)) 372 | 373 | ax[1].legend() 374 | plt.savefig(Path(save_dir) / 'idetection_profile.png', dpi=200) 375 | 376 | 377 | def plot_results_overlay(start=0, stop=0): # from utils.plots import *; plot_results_overlay() 378 | # Plot training 'results*.txt', overlaying train and val losses 379 | s = ['train', 'train', 'train', 'Precision', 'mAP@0.5', 'val', 'val', 'val', 'Recall', 'mAP@0.5:0.95'] # legends 380 | t = ['Box', 'Objectness', 'Classification', 'P-R', 'mAP-F1'] # titles 381 | for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')): 382 | results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T 383 | n = results.shape[1] # number of rows 384 | x = range(start, min(stop, n) if stop else n) 385 | fig, ax = plt.subplots(1, 5, figsize=(14, 3.5), tight_layout=True) 386 | ax = ax.ravel() 387 | for i in range(5): 388 | for j in [i, i + 5]: 389 | y = results[j, x] 390 | ax[i].plot(x, y, marker='.', label=s[j]) 391 | # y_smooth = butter_lowpass_filtfilt(y) 392 | # ax[i].plot(x, np.gradient(y_smooth), marker='.', label=s[j]) 393 | 394 | ax[i].set_title(t[i]) 395 | ax[i].legend() 396 | ax[i].set_ylabel(f) if i == 0 else None # add filename 397 | fig.savefig(f.replace('.txt', '.png'), dpi=200) 398 | 399 | 400 | def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir=''): 401 | # Plot training 'results*.txt'. from utils.plots import *; plot_results(save_dir='runs/train/exp') 402 | fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True) 403 | ax = ax.ravel() 404 | s = ['Box', 'Objectness', 'Classification', 'Precision', 'Recall', 405 | 'val Box', 'val Objectness', 'val Classification', 'mAP@0.5', 'mAP@0.5:0.95'] 406 | if bucket: 407 | # files = ['https://storage.googleapis.com/%s/results%g.txt' % (bucket, x) for x in id] 408 | files = ['results%g.txt' % x for x in id] 409 | c = ('gsutil cp ' + '%s ' * len(files) + '.') % tuple('gs://%s/results%g.txt' % (bucket, x) for x in id) 410 | os.system(c) 411 | else: 412 | files = list(Path(save_dir).glob('results*.txt')) 413 | assert len(files), 'No results.txt files found in %s, nothing to plot.' % os.path.abspath(save_dir) 414 | for fi, f in enumerate(files): 415 | try: 416 | results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T 417 | n = results.shape[1] # number of rows 418 | x = range(start, min(stop, n) if stop else n) 419 | for i in range(10): 420 | y = results[i, x] 421 | if i in [0, 1, 2, 5, 6, 7]: 422 | y[y == 0] = np.nan # don't show zero loss values 423 | # y /= y[0] # normalize 424 | label = labels[fi] if len(labels) else f.stem 425 | ax[i].plot(x, y, marker='.', label=label, linewidth=2, markersize=8) 426 | ax[i].set_title(s[i]) 427 | # if i in [5, 6, 7]: # share train and val loss y axes 428 | # ax[i].get_shared_y_axes().join(ax[i], ax[i - 5]) 429 | except Exception as e: 430 | print('Warning: Plotting error for %s; %s' % (f, e)) 431 | 432 | ax[1].legend() 433 | fig.savefig(Path(save_dir) / 'results.png', dpi=200) 434 | --------------------------------------------------------------------------------