init

.dockerignore

@@ -0,0 +1,27 @@
+**/__pycache__
+**/.venv
+**/.classpath
+**/.dockerignore
+**/.env
+**/.git
+**/.gitignore
+**/.project
+**/.settings
+**/.toolstarget
+**/.vs
+**/.vscode
+**/*.*proj.user
+**/*.dbmdl
+**/*.jfm
+**/bin
+**/charts
+**/docker-compose*
+**/compose*
+**/Dockerfile*
+**/node_modules
+**/npm-debug.log
+**/obj
+**/secrets.dev.yaml
+**/values.dev.yaml
+LICENSE
+README.md

.env

@@ -0,0 +1,31 @@
+DEBUG=True
+HOST = 127.0.0.1
+PORT = 8080
+
+# Flask ENV
+FLASK_APP=app.py
+FLASK_CONFIG=development
+SECRET_KEY='ssss'
+
+# Used for CDN (in production)
+ASSETS_ROOT=/static/assets
+
+# If DEBUG=False sqlite, else mysql
+DB_ENGINE=mysql
+DB_NAME=appseed_db
+DB_HOST=localhost
+DB_PORT=3306
+DB_USERNAME=appseed_db_usr
+DB_PASS=<STRONG_PASS>
+
+# redis
+REDIS_HOST=localhost
+REDIS_PORT=6379
+REDIS_DB=0
+REDIS_PASSWORD=123456
+
+# 邮箱配置
+MAIL_SERVER='smtp.qq.com'
+MAIL_USERNAME='123@qq.com'
+MAIL_PASSWORD='XXXXX' # 生成的授权码
+MAIL_PORT=465

.gitignore

@@ -0,0 +1,4 @@
+*.pyc
+back-end/static/*
+back-end/tmp/*
+back-end/uploads/*

.vscode/launch.json

@@ -0,0 +1,19 @@
+{
+    "configurations": [
+        {
+            "name": "Docker: Python - Flask",
+            "type": "docker",
+            "request": "launch",
+            "preLaunchTask": "docker-run: debug",
+            "python": {
+                "pathMappings": [
+                    {
+                        "localRoot": "${workspaceFolder}",
+                        "remoteRoot": "/app"
+                    }
+                ],
+                "projectType": "flask"
+            }
+        }
+    ]
+}

.vscode/tasks.json

@@ -0,0 +1,40 @@
+{
+	"version": "2.0.0",
+	"tasks": [
+		{
+			"type": "docker-build",
+			"label": "docker-build",
+			"platform": "python",
+			"dockerBuild": {
+				"tag": "yolov5flaskvue:latest",
+				"dockerfile": "${workspaceFolder}/Dockerfile",
+				"context": "${workspaceFolder}",
+				"pull": true
+			}
+		},
+		{
+			"type": "docker-run",
+			"label": "docker-run: debug",
+			"dependsOn": [
+				"docker-build"
+			],
+			"dockerRun": {
+				"env": {
+					"FLASK_APP": "back-end\\app.py"
+				}
+			},
+			"python": {
+				"args": [
+					"run",
+					"--no-debugger",
+					"--no-reload",
+					"--host",
+					"0.0.0.0",
+					"--port",
+					"5002"
+				],
+				"module": "flask"
+			}
+		}
+	]
+}

Dockerfile

@@ -0,0 +1,25 @@
+# For more information, please refer to https://aka.ms/vscode-docker-python
+FROM python:3.10-slim
+
+EXPOSE 5002
+
+# Keeps Python from generating .pyc files in the container
+ENV PYTHONDONTWRITEBYTECODE=1
+
+# Turns off buffering for easier container logging
+ENV PYTHONUNBUFFERED=1
+
+# Install pip requirements
+COPY requirements.txt .
+RUN python -m pip install -r requirements.txt
+
+WORKDIR /app
+COPY . /app
+
+# Creates a non-root user with an explicit UID and adds permission to access the /app folder
+# For more info, please refer to https://aka.ms/vscode-docker-python-configure-containers
+RUN adduser -u 5678 --disabled-password --gecos "" appuser && chown -R appuser /app
+USER appuser
+
+# During debugging, this entry point will be overridden. For more information, please refer to https://aka.ms/vscode-docker-python-debug
+CMD ["gunicorn", "--bind", "0.0.0.0:5002", "back-end.app:app"]

README.md

@@ -0,0 +1,37 @@
+# object_detector
+
+基于 yolov5 对象识别系统
+
+## Develop
+
+依赖 yolov5 3.0
+
+```
+cd frontend
+npm install
+export NODE_OPTIONS=--openssl-legacy-provider
+npm run build
+
+sudo apt update
+sudo apt-get install ffmpeg libsm6 libxext6  -y
+
+cd ..
+virtualenv .venv
+pip install requirements.txt
+cp -R frontend/dist/* ./static
+python app.py
+```
+
+## YOLOv5模型训练
+
+yolov5m.pt 模型。
+
+## YOLOv5模型预测
+
+## Reference
+
+- [yolov5]()
+- [Sharpiless](https://github.com/Sharpiless)
+
+## License
+

app.py

@@ -0,0 +1,5 @@
+from apps import create_app
+
+app = create_app()
+if __name__=='__main__':
+    app.run(host='127.0.0.1', port=5003, debug=True)

apps/__init__.py

@@ -0,0 +1,36 @@
+import logging as rel_log
+import os
+from datetime import timedelta
+from flask import Flask
+from apps.config import config
+from apps.views import init_blueprints
+
+def create_app(config_name = "default")->Flask:
+    config_name = os.getenv('FLASK_CONFIG') or config_name
+    # CORS(app, supports_credentials=True, resources={
+    #     r"/api/*": {'origins': "*"}
+    # })
+    app.config.from_object(config[config_name])  # 读取配置
+    config[config_name].init_app(app)
+    app = Flask(config[config_name].BASE_DIR)
+
+    init_dir()
+    init_blueprints(app)
+    return app
+
+def init_dir():
+    files = [
+        'uploads', 'tmp/ct', 'tmp/draw',
+        'tmp/image', 'tmp/mask', 'tmp/uploads'
+    ]
+    for ff in files:
+        if not os.path.exists(ff):
+            os.makedirs(ff)
+
+# @app.after_request
+# def after_request(response):
+#     response.headers['Access-Control-Allow-Origin'] = '*'
+#     response.headers['Access-Control-Allow-Credentials'] = 'true'
+#     response.headers['Access-Control-Allow-Methods'] = 'POST'
+#     response.headers['Access-Control-Allow-Headers'] = 'Content-Type, X-Requested-With'
+#     return response

apps/config.py

@@ -0,0 +1,143 @@
+import os
+import random
+import string
+import dotenv
+import logging
+from datetime import timedelta
+from dotenv import load_dotenv
+
+if os.path.exists('.env'):
+    load_dotenv('.env', verbose=True)
+
+class BaseConfig:
+    ''' 基础配置 '''
+
+
+# UPLOAD_FOLDER = r'./uploads'
+
+
+# app = Flask(__name__)
+# app.secret_key = 'secret!'
+# app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
+
+# werkzeug_logger = rel_log.getLogger('werkzeug')
+# werkzeug_logger.setLevel(rel_log.ERROR)
+
+# # 解决缓存刷新问题
+# app.config['SEND_FILE_MAX_AGE_DEFAULT'] = timedelta(seconds=1)
+
+
+    SUPERADMIN = 'admin'
+    ASSETS_ROOT = os.getenv('ASSETS_ROOT', '/static/assets')
+    STATIC_URL = '/static/'
+    SECRET_KEY = os.getenv('SECRET_KEY', None)
+    if not SECRET_KEY:
+        SECRET_KEY = ''.join(random.choice(string.ascii_lowercase)
+                             for i in range(32))
+    # base
+    PROJECT_NAME = 'flask_blog'
+    # 当前 config.py 文件目录
+    DEBUG = False
+    # 当前文件所在目录的绝对路径 /apps
+    BASE_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
+    BASE_URL = 'http://'
+    ADMIN_EMAILS = ""
+
+    # 分页
+    POST_PER_PAGE = 8    # 每页文章数
+    COMMENT_PER_PAGE = 10  # 每页评论数
+
+    LANGUAGE_CODE = 'zh-hans'
+    TIME_ZONE = 'PRC'
+
+    UP_DIR = os.path.join(os.path.abspath(
+        os.path.dirname(__file__)), "app/static/uploads/")  # 文件上传路径
+    FC_DIR = os.path.join(os.path.abspath(os.path.dirname(
+        __file__)), "app/static/uploads/users/")  # 用户头像上传路径
+    UPLOADED_PHOTOS_DEST = 'static/upload'
+    UPLOADED_FILES_ALLOW = ['gif', 'jpg']
+    UPLOADS_AUTOSERVE = True
+    JSON_AS_ASCII = False
+
+    # db
+    SQLALCHEMY_TRACK_MODIFICATIONS = False
+    DB_ENGINE = os.getenv('DB_ENGINE', None)
+    DB_USERNAME = os.getenv('DB_USERNAME', None)
+    DB_PASS = os.getenv('DB_PASS', None)
+    DB_HOST = os.getenv('DB_HOST', None)
+    DB_PORT = os.getenv('DB_PORT', None)
+    DB_NAME = os.getenv('DB_NAME', None)
+    USE_SQLITE = True
+
+    # redis
+    REDIS_HOST = os.getenv('REDIS_HOST', 'localhost')
+    REDIS_PORT = os.getenv('REDIS_PORT', 6379)
+    REDIS_DB = os.getenv('REDIS_DB', 0)
+    REDIS_PASSWORD = os.getenv('REDIS_PASSWORD', None)
+    CELERY_BROKER_URL = 'redis://{}:{}/{}'.format(
+        REDIS_HOST, REDIS_PORT, REDIS_DB)
+
+    # mail
+    MAIL_SERVER = os.getenv('MAIL_SERVER', 'smtp.qq.com')
+    MAIL_PORT = os.getenv('MAIL_PORT', 465)
+    MAIL_USE_TLS = False
+    MAIL_USE_SSL = True
+    MAIL_DEBUG = True
+    MAIL_USERNAME = os.getenv('MAIL_USERNAME', None)
+    MAIL_PASSWORD = os.getenv('MAIL_PASSWORD', None)  # 生成的授权码
+    MAIL_DEFAULT_SENDER = MAIL_USERNAME
+
+    # token配置
+    TIMEOUT_TOKEN = 10 * 24 * 60 * 60
+    TIMEOUT_EMAIL = 10 * 60
+
+    PERMANENT_SESSION_LIFETIME = timedelta(hours=2)
+
+    # log
+    LOG_LEVEL = logging.WARN
+
+    # 插件配置，填写插件的文件名名称，默认不启用插件。
+    PLUGIN_ENABLE_FOLDERS = []
+
+    # 七牛云
+    QINIU_ACCESS_KEY = os.getenv('QINIU_ACCESS_KEY', None)
+    QINIU_SECRET_KEY = os.getenv('QINIU_SECRET_KEY', None)
+    QINIU_BUCKET_NAME = os.getenv('QINIU_BUCKET_NAME', None)
+
+    # github
+    GITHUB_CLIENT_ID = os.getenv('GITHUB_CLIENT_ID', None)
+    GITHUB_CLIENT_SECRET = os.getenv('GITHUB_CLIENT_SECRET', None)
+    if GITHUB_CLIENT_ID and GITHUB_CLIENT_SECRET:
+        SOCIAL_AUTH_GITHUB = True
+
+    @staticmethod
+    def init_app(app):
+        pass
+
+class DevelopmentConfig(BaseConfig):
+    ''' 开发环境配置, 开启调试模式, 使用 sqlite '''
+    DEBUG = True
+    SQLALCHEMY_TRACK_MODIFICATIONS = True
+    # SQLALCHEMY_ECHO = True
+    # SQLALCHEMY_COMMIT_ON_TEARDOWN = True
+    SQLALCHEMY_DATABASE_URI = 'sqlite:///../flask-note.sqlite3.db'
+
+
+class TestingConfig(BaseConfig):
+    DEBUG = True
+
+class ProductionConfig(BaseConfig):
+    DEBUG = False
+    # SQLALCHEMY_DATABASE_URI = 'sqlite:///db.sqlite3'
+    SQLALCHEMY_DATABASE_URI = 'mysql+pymysql://lyq:123456@ok.yoqi.me:13306/travel?charset=utf8'
+
+    # Security
+    SESSION_COOKIE_HTTPONLY = True
+    REMEMBER_COOKIE_HTTPONLY = True
+    REMEMBER_COOKIE_DURATION = 3600
+
+config = {
+    'default': DevelopmentConfig,
+    'development': DevelopmentConfig,
+    'production': ProductionConfig
+}

apps/extensions/__init__.py

@@ -0,0 +1 @@
+from .init_sqlalhemy import db

apps/extensions/init_sqlalhemy.py

@@ -0,0 +1,55 @@
+import datetime
+import os
+
+from flask import Flask, request
+from flask_sqlalchemy import SQLAlchemy
+from flask_sqlalchemy.query import Query as BaseQuery
+from flask_marshmallow import Marshmallow
+from marshmallow import fields
+from marshmallow.validate import (
+    URL, Email, Range, Length, Equal, Regexp,
+    Predicate, NoneOf, OneOf, ContainsOnly
+)
+from flask_migrate import Migrate
+
+
+class Query(BaseQuery):
+    def soft_delete(self):
+        return self.update({"delete_at": datetime.datetime.now()})
+
+    def logic_all(self):
+        return self.filter_by(delete_at=None).all()
+
+    def all_json(self, schema: Marshmallow().Schema):
+        return schema(many=True).dump(self.all())
+
+    def layui_paginate(self):
+        return self.paginate(page=request.args.get('page', type=int),
+                             per_page=request.args.get('limit', type=int),
+                             error_out=False)
+
+    def layui_paginate_json(self, schema: Marshmallow().Schema):
+        """
+        返回dict
+        """
+        _res = self.paginate(
+            page=request.args.get('page', type=int),
+            per_page=request.args.get('limit', type=int),
+            error_out=False
+        )
+        return schema(many=True).dump(_res.items), _res.total, _res.page, _res.per_page
+
+    def layui_paginate_db_json(self):
+        """
+        db.query(A.name).layui_paginate_db_json()
+        """
+        _res = self.paginate(page=request.args.get('page', type=int),
+                             per_page=request.args.get('limit', type=int),
+                             error_out=False)
+        return [dict(i) for i in _res.items], _res.total
+
+db = SQLAlchemy(query_class=Query)
+ma = Marshmallow()
+
+migrate = Migrate()
+

apps/models/__init__.py

@@ -0,0 +1,2 @@
+from .user import User
+

apps/models/user.py

@@ -0,0 +1,9 @@
+import datetime
+from apps.extensions import db
+
+class User(db.Model):
+    ''' user model '''
+    __talbename__ = 'user'
+
+    id = db.Column(db.Integer, primary_key=True, comment='用户ID')
+    username = db.Column(db.String(32), nullable=False, comment='用户名')

apps/views/__init__.py

@@ -0,0 +1,7 @@
+
+from .api import v1_bp, v2_bp
+from flask import Flask
+
+def init_blueprints(app: Flask):
+    app.register_blueprint(v1_bp)
+    app.register_blueprint(v2_bp)

apps/views/api/__init__.py

@@ -0,0 +1,2 @@
+from .v1 import bp as v1_bp
+from .v2 import bp as v2_bp

apps/views/api/v1.py

@@ -0,0 +1,53 @@
+from flask import Blueprint, request, jsonify, redirect, url_for, current_app, send_from_directory, make_response
+import datetime
+import os
+import shutil
+
+bp = Blueprint('dict', __name__, url_prefix='/api/v1')
+
+@bp.route('/')
+def hello_world():
+    return redirect(url_for('static', filename='./index.html'))
+
+
+@bp.route('/upload', methods=['GET', 'POST'])
+def upload_file():
+    file = request.files['file']
+    print(datetime.datetime.now(), file.filename)
+    if file and allowed_file(file.filename):
+        src_path = os.path.join(bp.config['UPLOAD_FOLDER'], file.filename)
+        file.save(src_path)
+        shutil.copy(src_path, './tmp/ct')
+        image_path = os.path.join('./tmp/ct', file.filename)
+        pid, image_info = core.main.c_main(
+            image_path, current_app.model, file.filename.rsplit('.', 1)[1])
+        return jsonify({'status': 1,
+                        'image_url': 'http://127.0.0.1:5003/tmp/ct/' + pid,
+                        'draw_url': 'http://127.0.0.1:5003/tmp/draw/' + pid,
+                        'image_info': image_info})
+
+    return jsonify({'status': 0})
+
+    # with app.app_context():
+    #     current_app.model = Detector()
+
+@bp.route("/download", methods=['GET'])
+def download_file():
+    # 需要知道2个参数, 第1个参数是本地目录的path, 第2个参数是文件名(带扩展名)
+    return send_from_directory('data', 'testfile.zip', as_attachment=True)
+
+@bp.route('/tmp/<path:file>', methods=['GET'])
+def show_photo(file):
+    ''' 显示图片 '''
+    if request.method == 'GET':
+        if not file is None:
+            image_data = open(f'tmp/{file}', "rb").read()
+            response = make_response(image_data)
+            response.headers['Content-Type'] = 'image/png'
+            return response
+
+
+def allowed_file(filename) -> bool:
+    '''判断文件后缀是否在允许范围'''
+    ALLOWED_EXTENSIONS = set(['png', 'jpg'])
+    return '.' in filename and filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS

apps/views/api/v2.py

@@ -0,0 +1,3 @@
+from flask import Blueprint, render_template, request, jsonify
+
+bp = Blueprint('dict', __name__, url_prefix='/api/v2')

docker-compose.debug.yml

@@ -0,0 +1,14 @@
+version: '3.4'
+
+services:
+  yolov5flaskvue:
+    image: yolov5flaskvue
+    build:
+      context: .
+      dockerfile: ./Dockerfile
+    command: ["sh", "-c", "pip install debugpy -t /tmp && python /tmp/debugpy --wait-for-client --listen 0.0.0.0:5678 -m flask run --no-debugger --no-reload --host 0.0.0.0 --port 5002"]
+    ports:
+      - 5002:5002
+      - 5678:5678
+    environment:
+      - FLASK_APP=back-end\app.py

docker-compose.yml

@@ -0,0 +1,10 @@
+version: '3.4'
+
+services:
+  yolov5flaskvue:
+    image: yolov5flaskvue
+    build:
+      context: .
+      dockerfile: ./Dockerfile
+    ports:
+      - 5002:5002

frontend/.gitignore

@@ -0,0 +1,23 @@
+.DS_Store
+node_modules
+/dist
+
+
+# local env files
+.env.local
+.env.*.local
+
+# Log files
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+
+# Editor directories and files
+.idea
+.vscode
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
+*.sw?

frontend/babel.config.js

@@ -0,0 +1,5 @@
+module.exports = {
+  presets: [
+    '@vue/app'
+  ]
+}

frontend/package-lock.json

@@ -0,0 +1,14115 @@
+{
+  "name": "ct",
+  "version": "0.1.0",
+  "lockfileVersion": 1,
+  "requires": true,
+  "dependencies": {
+    "@babel/code-frame": {
+      "version": "7.0.0",
+      "resolved": "http://registry.npm.taobao.org/@babel/code-frame/download/@babel/code-frame-7.0.0.tgz",
+      "integrity": "sha1-BuKrGb21NThVWaq7W6WXKUgoAPg=",
+      "dev": true,
+      "requires": {
+        "@babel/highlight": "^7.0.0"
+      }
+    },
+    "@babel/core": {
+      "version": "7.8.3",
+      "resolved": "https://registry.npm.taobao.org/@babel/core/download/@babel/core-7.8.3.tgz?cache=0&sync_timestamp=1578956762857&other_urls=https%3A%2F%2Fregistry.npm.taobao.org%2F%40babel%2Fcore%2Fdownload%2F%40babel%2Fcore-7.8.3.tgz",
+      "integrity": "sha1-MLDrtN0Vhd5pI6C00XngufXYKUE=",
+      "dev": true,
+      "requires": {
+        "@babel/code-frame": "^7.8.3",
+        "@babel/generator": "^7.8.3",
+        "@babel/helpers": "^7.8.3",
+        "@babel/parser": "^7.8.3",
+        "@babel/template": "^7.8.3",
+        "@babel/traverse": "^7.8.3",
+        "@babel/types": "^7.8.3",
+        "convert-source-map": "^1.7.0",
+        "debug": "^4.1.0",
+        "gensync": "^1.0.0-beta.1",
+        "json5": "^2.1.0",
+        "lodash": "^4.17.13",
+        "resolve": "^1.3.2",
+        "semver": "^5.4.1",
+        "source-map": "^0.5.0"
+      },
+      "dependencies": {
+        "@babel/code-frame": {
+          "version": "7.8.3",
+          "resolved": "https://registry.npm.taobao.org/@babel/code-frame/download/@babel/code-frame-7.8.3.tgz",
+          "integrity": "sha1-M+JZA9dIEYFTThLsCiXxa2/PQZ4=",
+          "dev": true,
+          "requires": {
+            "@babel/highlight": "^7.8.3"
+          }
+        },
+        "@babel/generator": {
+          "version": "7.8.3",
+          "resolved": "https://registry.npm.taobao.org/@babel/generator/download/@babel/generator-7.8.3.tgz",
+          "integrity": "sha1-DiLABbCpTBx06v4Z73jOU6TUXAM=",
+          "dev": true,
+          "requires": {
+            "@babel/types": "^7.8.3",
+            "jsesc": "^2.5.1",
+            "lodash": "^4.17.13",
+            "source-map": "^0.5.0"
+          }
+        },
+        "@babel/helper-function-name": {
+          "version": "7.8.3",
+          "resolved": "https://registry.npm.taobao.org/@babel/helper-function-name/download/@babel/helper-function-name-7.8.3.tgz",
+          "integrity": "sha1-7utmWgGx8RBo6fuGrVahyxqCTMo=",
+          "dev": true,
+          "requires": {
+            "@babel/helper-get-function-arity": "^7.8.3",
+            "@babel/template": "^7.8.3",
+            "@babel/types": "^7.8.3"
+          }
+        },
+        "@babel/helper-get-function-arity": {
+          "version": "7.8.3",
+          "resolved": "https://registry.npm.taobao.org/@babel/helper-get-function-arity/download/@babel/helper-get-function-arity-7.8.3.tgz",
+          "integrity": "sha1-uJS5R70AQ4HOY+odufCFR+kgq9U=",
+          "dev": true,
+          "requires": {
+            "@babel/types": "^7.8.3"
+          }
+        },
+        "@babel/helper-split-export-declaration": {
+          "version": "7.8.3",
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+      "dev": true,
+      "requires": {
+        "execa": "^0.8.0",
+        "is-ci": "^1.0.10",
+        "normalize-path": "^1.0.0",
+        "strip-indent": "^2.0.0"
+      },
+      "dependencies": {
+        "cross-spawn": {
+          "version": "5.1.0",
+          "resolved": "https://registry.npm.taobao.org/cross-spawn/download/cross-spawn-5.1.0.tgz",
+          "integrity": "sha1-6L0O/uWPz/b4+UUQoKVUu/ojVEk=",
+          "dev": true,
+          "requires": {
+            "lru-cache": "^4.0.1",
+            "shebang-command": "^1.2.0",
+            "which": "^1.2.9"
+          }
+        },
+        "execa": {
+          "version": "0.8.0",
+          "resolved": "https://registry.npm.taobao.org/execa/download/execa-0.8.0.tgz",
+          "integrity": "sha1-2NdrvBtVIX7RkP1t1J08d07PyNo=",
+          "dev": true,
+          "requires": {
+            "cross-spawn": "^5.0.1",
+            "get-stream": "^3.0.0",
+            "is-stream": "^1.1.0",
+            "npm-run-path": "^2.0.0",
+            "p-finally": "^1.0.0",
+            "signal-exit": "^3.0.0",
+            "strip-eof": "^1.0.0"
+          }
+        },
+        "get-stream": {
+          "version": "3.0.0",
+          "resolved": "https://registry.npm.taobao.org/get-stream/download/get-stream-3.0.0.tgz",
+          "integrity": "sha1-jpQ9E1jcN1VQVOy+LtsFqhdO3hQ=",
+          "dev": true
+        },
+        "lru-cache": {
+          "version": "4.1.5",
+          "resolved": "https://registry.npm.taobao.org/lru-cache/download/lru-cache-4.1.5.tgz",
+          "integrity": "sha1-i75Q6oW+1ZvJ4z3KuCNe6bz0Q80=",
+          "dev": true,
+          "requires": {
+            "pseudomap": "^1.0.2",
+            "yallist": "^2.1.2"
+          }
+        },
+        "normalize-path": {
+          "version": "1.0.0",
+          "resolved": "https://registry.npm.taobao.org/normalize-path/download/normalize-path-1.0.0.tgz",
+          "integrity": "sha1-MtDkcvkf80VwHBWoMRAY07CpA3k=",
+          "dev": true
+        },
+        "yallist": {
+          "version": "2.1.2",
+          "resolved": "https://registry.npm.taobao.org/yallist/download/yallist-2.1.2.tgz",
+          "integrity": "sha1-HBH5IY8HYImkfdUS+TxmmaaoHVI=",
+          "dev": true
+        }
+      }
+    },
+    "zrender": {
+      "version": "4.3.2",
+      "resolved": "https://registry.npm.taobao.org/zrender/download/zrender-4.3.2.tgz",
+      "integrity": "sha1-7HQy+UFcgsc1hLa3uMR+GwFiCcY="
+    }
+  }
+}

frontend/package.json

@@ -0,0 +1,53 @@
+{
+  "name": "ct",
+  "version": "0.1.0",
+  "private": true,
+  "scripts": {
+    "serve": "vue-cli-service serve",
+    "build": "vue-cli-service build",
+    "lint": "vue-cli-service lint"
+  },
+  "dependencies": {
+    "axios": "^0.18.1",
+    "echarts": "^4.9.0",
+    "element-ui": "^2.15.0",
+    "socket.io": "^2.4.1",
+    "vue": "^2.6.12",
+    "vue-codemirror": "^4.0.6",
+    "vue-router": "^3.5.1",
+    "vuex": "^3.6.2"
+  },
+  "devDependencies": {
+    "@vue/cli-plugin-babel": "^3.12.1",
+    "@vue/cli-plugin-eslint": "^3.12.1",
+    "@vue/cli-service": "^3.12.1",
+    "babel-eslint": "^10.1.0",
+    "eslint": "^5.16.0",
+    "eslint-plugin-vue": "^5.2.3",
+    "vue-cli-plugin-element": "^1.0.1",
+    "vue-template-compiler": "^2.6.12"
+  },
+  "eslintConfig": {
+    "root": true,
+    "env": {
+      "node": true
+    },
+    "extends": [
+      "plugin:vue/essential",
+      "eslint:recommended"
+    ],
+    "parserOptions": {
+      "parser": "babel-eslint"
+    }
+  },
+  "postcss": {
+    "plugins": {
+      "autoprefixer": {}
+    }
+  },
+  "browserslist": [
+    "> 1%",
+    "last 2 versions",
+    "not ie <= 8"
+  ]
+}

frontend/public/index.html

@@ -0,0 +1,21 @@
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+  <meta charset="utf-8">
+  <meta http-equiv="X-UA-Compatible" content="IE=edge">
+  <meta name="viewport" content="width=device-width,initial-scale=1.0">
+  <link rel="icon" href="<%= BASE_URL %>favicon.ico">
+  <title>my-project</title>
+</head>
+
+<body>
+  <noscript>
+    <strong>We're sorry but my-project doesn't work properly without JavaScript enabled. Please enable it to
+      continue.</strong>
+  </noscript>
+  <div id="app"></div>
+  <!-- built files will be auto injected -->
+</body>
+
+</html>

frontend/src/App.vue

@@ -0,0 +1,28 @@
+<template>
+  <div id="app">
+    <app-header></app-header>
+    <app-content></app-content>
+    <app-footer></app-footer>
+  </div>
+</template>
+
+<script>
+  import Header from "./components/Header";
+  import Footer from "./components/Footer";
+  import Content from "./components/Content";
+  export default {
+    name: "眼疾辅助诊断系统",
+    data() {
+      return {};
+    },
+    components: {
+      "app-header": Header,
+      "app-footer": Footer,
+      "app-content": Content,
+    },
+    methods: {},
+  };
+</script>
+
+<style scope="this api replaced by slot-scope in 2.5.0+">
+</style>

frontend/src/assets/style.css

@@ -0,0 +1,91 @@
+@font-face {
+    font-family: "iconfont"; /* project id 1171507 */
+    src: url("//at.alicdn.com/t/font_1171507_ki84ccb8hzk.eot");
+    src: url("//at.alicdn.com/t/font_1171507_ki84ccb8hzk.eot?#iefix")
+        format("embedded-opentype"),
+      url("//at.alicdn.com/t/font_1171507_ki84ccb8hzk.woff2") format("woff2"),
+      url("//at.alicdn.com/t/font_1171507_ki84ccb8hzk.woff") format("woff"),
+      url("//at.alicdn.com/t/font_1171507_ki84ccb8hzk.ttf") format("truetype"),
+      url("//at.alicdn.com/t/font_1171507_ki84ccb8hzk.svg#iconfont") format("svg");
+  }
+  @font-face {
+    font-family: "iconfont"; /* project id 1171507 */
+    src: url("//at.alicdn.com/t/font_1171507_1e4m208yftt.eot");
+    src: url("//at.alicdn.com/t/font_1171507_1e4m208yftt.eot?#iefix")
+        format("embedded-opentype"),
+      url("//at.alicdn.com/t/font_1171507_1e4m208yftt.woff2") format("woff2"),
+      url("//at.alicdn.com/t/font_1171507_1e4m208yftt.woff") format("woff"),
+      url("//at.alicdn.com/t/font_1171507_1e4m208yftt.ttf") format("truetype"),
+      url("//at.alicdn.com/t/font_1171507_1e4m208yftt.svg#iconfont") format("svg");
+  }
+  @font-face {
+    font-family: "iconfont"; /* project id 1171507 */
+    src: url("//at.alicdn.com/t/font_1171507_2skehui6htm.eot");
+    src: url("//at.alicdn.com/t/font_1171507_2skehui6htm.eot?#iefix")
+        format("embedded-opentype"),
+      url("//at.alicdn.com/t/font_1171507_2skehui6htm.woff2") format("woff2"),
+      url("//at.alicdn.com/t/font_1171507_2skehui6htm.woff") format("woff"),
+      url("//at.alicdn.com/t/font_1171507_2skehui6htm.ttf") format("truetype"),
+      url("//at.alicdn.com/t/font_1171507_2skehui6htm.svg#iconfont") format("svg");
+  }
+  @font-face {
+    font-family: "iconfont"; /* project id 1171507 */
+    src: url("//at.alicdn.com/t/font_1171507_m12v7ykool.eot");
+    src: url("//at.alicdn.com/t/font_1171507_m12v7ykool.eot?#iefix")
+        format("embedded-opentype"),
+      url("//at.alicdn.com/t/font_1171507_m12v7ykool.woff2") format("woff2"),
+      url("//at.alicdn.com/t/font_1171507_m12v7ykool.woff") format("woff"),
+      url("//at.alicdn.com/t/font_1171507_m12v7ykool.ttf") format("truetype"),
+      url("//at.alicdn.com/t/font_1171507_m12v7ykool.svg#iconfont") format("svg");
+  }
+  @font-face {
+    font-family: 'iconfont';  /* project id 1171507 */
+    src: url('//at.alicdn.com/t/font_1171507_ptv9kzz2fu.eot');
+    src: url('//at.alicdn.com/t/font_1171507_ptv9kzz2fu.eot?#iefix') format('embedded-opentype'),
+    url('//at.alicdn.com/t/font_1171507_ptv9kzz2fu.woff2') format('woff2'),
+    url('//at.alicdn.com/t/font_1171507_ptv9kzz2fu.woff') format('woff'),
+    url('//at.alicdn.com/t/font_1171507_ptv9kzz2fu.ttf') format('truetype'),
+    url('//at.alicdn.com/t/font_1171507_ptv9kzz2fu.svg#iconfont') format('svg');
+  }
+  @font-face {
+    font-family: 'iconfont';  /* project id 1171507 */
+    src: url('//at.alicdn.com/t/font_1171507_99rgegvq24p.eot');
+    src: url('//at.alicdn.com/t/font_1171507_99rgegvq24p.eot?#iefix') format('embedded-opentype'),
+    url('//at.alicdn.com/t/font_1171507_99rgegvq24p.woff2') format('woff2'),
+    url('//at.alicdn.com/t/font_1171507_99rgegvq24p.woff') format('woff'),
+    url('//at.alicdn.com/t/font_1171507_99rgegvq24p.ttf') format('truetype'),
+    url('//at.alicdn.com/t/font_1171507_99rgegvq24p.svg#iconfont') format('svg');
+  }
+  .iconfont {
+    font-family: "iconfont" !important;
+    font-size: 18px;
+    font-style: normal;
+    -webkit-font-smoothing: antialiased;
+    -webkit-text-stroke-width: 0.2px;
+    -moz-osx-font-smoothing: grayscale;
+  }
+  .iconfont_tele{
+    font-family: "iconfont" !important;
+    font-size: 40px;
+    font-style: normal;
+    -webkit-font-smoothing: antialiased;
+    -webkit-text-stroke-width: 0.2px;
+    -moz-osx-font-smoothing: grayscale;
+  }
+  .iconfont_image {
+    font-family: "iconfont" !important;
+    font-size: 60px;
+    font-style: normal;
+    -webkit-font-smoothing: antialiased;
+    -webkit-text-stroke-width: 0.2px;
+    -moz-osx-font-smoothing: grayscale;
+  }
+  .iconfont_phone {
+    font-family: "iconfont" !important;
+    font-size: 22px;
+    font-style: normal;
+    -webkit-font-smoothing: antialiased;
+    -webkit-text-stroke-width: 0.2px;
+    -moz-osx-font-smoothing: grayscale;
+  }
+  

frontend/src/components/Content.vue

@@ -0,0 +1,517 @@
+<template>
+  <div id="Content">
+    <el-dialog
+      title="AI预测中"
+      :visible.sync="dialogTableVisible"
+      :show-close="false"
+      :close-on-press-escape="false"
+      :append-to-body="true"
+      :close-on-click-modal="false"
+      :center="true"
+    >
+      <el-progress :percentage="percentage"></el-progress>
+      <span slot="footer" class="dialog-footer">请耐心等待约3秒钟</span>
+    </el-dialog>
+
+    <div id="CT">
+      <div id="CT_image">
+        <el-card
+          id="CT_image_1"
+          class="box-card"
+          style="
+            border-radius: 8px;
+            width: 800px;
+            height: 360px;
+            margin-bottom: -30px;
+          "
+        >
+          <div class="demo-image__preview1">
+            <div
+              v-loading="loading"
+              element-loading-text="上传图片中"
+              element-loading-spinner="el-icon-loading"
+            >
+              <el-image
+                :src="url_1"
+                class="image_1"
+                :preview-src-list="srcList"
+                style="border-radius: 3px 3px 0 0"
+              >
+                <div slot="error">
+                  <div slot="placeholder" class="error">
+                    <el-button
+                      v-show="showbutton"
+                      type="primary"
+                      icon="el-icon-upload"
+                      class="download_bt"
+                      v-on:click="true_upload"
+                    >
+                      上传图像
+                      <input
+                        ref="upload"
+                        style="display: none"
+                        name="file"
+                        type="file"
+                        @change="update"
+                      />
+                    </el-button>
+                  </div>
+                </div>
+              </el-image>
+            </div>
+            <div class="img_info_1" style="border-radius: 0 0 5px 5px">
+              <span style="color: white; letter-spacing: 6px">原始图像</span>
+            </div>
+          </div>
+          <div class="demo-image__preview2">
+            <div
+              v-loading="loading"
+              element-loading-text="处理中,请耐心等待"
+              element-loading-spinner="el-icon-loading"
+            >
+              <el-image
+                :src="url_2"
+                class="image_1"
+                :preview-src-list="srcList1"
+                style="border-radius: 3px 3px 0 0"
+              >
+                <div slot="error">
+                  <div slot="placeholder" class="error">{{ wait_return }}</div>
+                </div>
+              </el-image>
+            </div>
+            <div class="img_info_1" style="border-radius: 0 0 5px 5px">
+              <span style="color: white; letter-spacing: 4px">检测结果</span>
+            </div>
+          </div>
+        </el-card>
+      </div>
+      <div id="info_patient">
+        <!-- 卡片放置表格 -->
+        <el-card style="border-radius: 8px">
+          <div slot="header" class="clearfix">
+            <span>检测目标</span>
+            <el-button
+              style="margin-left: 35px"
+              v-show="!showbutton"
+              type="primary"
+              icon="el-icon-upload"
+              class="download_bt"
+              v-on:click="true_upload2"
+            >
+              重新选择图像
+              <input
+                ref="upload2"
+                style="display: none"
+                name="file"
+                type="file"
+                @change="update"
+              />
+            </el-button>
+          </div>
+          <el-tabs v-model="activeName">
+            <el-tab-pane label="检测到的目标" name="first">
+              <!-- 表格存放特征值 -->
+              <el-table
+                :data="feature_list"
+                height="390"
+                border
+                style="width: 750px; text-align: center"
+                v-loading="loading"
+                element-loading-text="数据正在处理中，请耐心等待"
+                element-loading-spinner="el-icon-loading"
+                lazy
+              >
+                <el-table-column label="目标类别" width="250px">
+                  <template slot-scope="scope">
+                    <span>{{ scope.row[2] }}</span>
+                  </template>
+                </el-table-column>
+                <el-table-column label="目标大小" width="250px">
+                  <template slot-scope="scope">
+                    <span>{{ scope.row[0] }}</span>
+                  </template>
+                </el-table-column>
+                <el-table-column label="置信度" width="250px">
+                  <template slot-scope="scope">
+                    <span>{{ scope.row[1] }}</span>
+                  </template>
+                </el-table-column>
+              </el-table>
+            </el-tab-pane>
+          </el-tabs>
+        </el-card>
+      </div>
+    </div>
+  </div>
+</template>
+
+<script>
+import axios from "axios";
+
+export default {
+  name: "Content",
+  data() {
+    return {
+      server_url: "http://127.0.0.1:5003",
+      activeName: "first",
+      active: 0,
+      centerDialogVisible: true,
+      url_1: "",
+      url_2: "",
+      textarea: "",
+      srcList: [],
+      srcList1: [],
+      feature_list: [],
+      feature_list_1: [],
+      feat_list: [],
+      url: "",
+      visible: false,
+      wait_return: "等待上传",
+      wait_upload: "等待上传",
+      loading: false,
+      table: false,
+      isNav: false,
+      showbutton: true,
+      percentage: 0,
+      fullscreenLoading: false,
+      opacitys: {
+        opacity: 0,
+      },
+      dialogTableVisible: false,
+    };
+  },
+  created: function () {
+    document.title = "YOLOv5目标检测WEB端";
+  },
+  methods: {
+    true_upload() {
+      this.$refs.upload.click();
+    },
+    true_upload2() {
+      this.$refs.upload2.click();
+    },
+    next() {
+      this.active++;
+    },
+    // 获得目标文件
+    getObjectURL(file) {
+      var url = null;
+      if (window.createObjcectURL != undefined) {
+        url = window.createOjcectURL(file);
+      } else if (window.URL != undefined) {
+        url = window.URL.createObjectURL(file);
+      } else if (window.webkitURL != undefined) {
+        url = window.webkitURL.createObjectURL(file);
+      }
+      return url;
+    },
+    // 上传文件
+    update(e) {
+      this.percentage = 0;
+      this.dialogTableVisible = true;
+      this.url_1 = "";
+      this.url_2 = "";
+      this.srcList = [];
+      this.srcList1 = [];
+      this.wait_return = "";
+      this.wait_upload = "";
+      this.feature_list = [];
+      this.feat_list = [];
+      this.fullscreenLoading = true;
+      this.loading = true;
+      this.showbutton = false;
+      let file = e.target.files[0];
+      this.url_1 = this.$options.methods.getObjectURL(file);
+      let param = new FormData(); //创建form对象
+      param.append("file", file, file.name); //通过append向form对象添加数据
+      var timer = setInterval(() => {
+        this.myFunc();
+      }, 30);
+      let config = {
+        headers: { "Content-Type": "multipart/form-data" },
+      }; //添加请求头
+      axios
+        .post(this.server_url + "/upload", param, config)
+        .then((response) => {
+          this.percentage = 100;
+          clearInterval(timer);
+          this.url_1 = response.data.image_url;
+          this.srcList.push(this.url_1);
+          this.url_2 = response.data.draw_url;
+          this.srcList1.push(this.url_2);
+          this.fullscreenLoading = false;
+          this.loading = false;
+
+          this.feat_list = Object.keys(response.data.image_info);
+
+          for (var i = 0; i < this.feat_list.length; i++) {
+            response.data.image_info[this.feat_list[i]][2] = this.feat_list[i];
+            this.feature_list.push(response.data.image_info[this.feat_list[i]]);
+          }
+
+          this.feature_list.push(response.data.image_info);
+          this.feature_list_1 = this.feature_list[0];
+          this.dialogTableVisible = false;
+          this.percentage = 0;
+          this.notice1();
+        });
+    },
+    myFunc() {
+      if (this.percentage + 33 < 99) {
+        this.percentage = this.percentage + 33;
+      } else {
+        this.percentage = 99;
+      }
+    },
+    drawChart() {},
+    notice1() {
+      this.$notify({
+        title: "预测成功",
+        message: "点击图片可以查看大图",
+        duration: 0,
+        type: "success",
+      });
+    },
+  },
+  mounted() {
+    this.drawChart();
+  },
+};
+</script>
+
+<style>
+.el-button {
+  padding: 12px 20px !important;
+}
+
+#hello p {
+  font-size: 15px !important;
+  /*line-height: 25px;*/
+}
+
+.n1 .el-step__description {
+  padding-right: 20%;
+  font-size: 14px;
+  line-height: 20px;
+  /* font-weight: 400; */
+}
+</style>
+
+<style scoped>
+* {
+  box-sizing: border-box;
+  margin: 0;
+  padding: 0;
+}
+
+.dialog_info {
+  margin: 20px auto;
+}
+
+.text {
+  font-size: 14px;
+}
+
+.item {
+  margin-bottom: 18px;
+}
+
+.clearfix:before,
+.clearfix:after {
+  display: table;
+  content: "";
+}
+
+.clearfix:after {
+  clear: both;
+}
+
+.box-card {
+  width: 680px;
+  height: 200px;
+  border-radius: 8px;
+  margin-top: -20px;
+}
+
+.divider {
+  width: 50%;
+}
+
+#CT {
+  display: flex;
+  height: 100%;
+  width: 100%;
+  flex-wrap: wrap;
+  justify-content: center;
+  margin: 0 auto;
+  margin-right: 0px;
+  max-width: 1800px;
+}
+
+#CT_image_1 {
+  width: 90%;
+  height: 40%;
+  margin: 0px auto;
+  padding: 0px auto;
+  margin-right: 180px;
+  margin-bottom: 0px;
+  border-radius: 4px;
+}
+
+#CT_image {
+  margin-bottom: 60px;
+  margin-left: 30px;
+  margin-top: 5px;
+}
+
+.image_1 {
+  width: 275px;
+  height: 260px;
+  background: #ffffff;
+  box-shadow: 0 2px 12px 0 rgba(0, 0, 0, 0.1);
+}
+
+.img_info_1 {
+  height: 30px;
+  width: 275px;
+  text-align: center;
+  background-color: #21b3b9;
+  line-height: 30px;
+}
+
+.demo-image__preview1 {
+  width: 250px;
+  height: 290px;
+  margin: 20px 60px;
+  float: left;
+}
+
+.demo-image__preview2 {
+  width: 250px;
+  height: 290px;
+
+  margin: 20px 460px;
+  /* background-color: green; */
+}
+
+.error {
+  margin: 100px auto;
+  width: 50%;
+  padding: 10px;
+  text-align: center;
+}
+
+.block-sidebar {
+  position: fixed;
+  display: none;
+  left: 50%;
+  margin-left: 600px;
+  top: 350px;
+  width: 60px;
+  z-index: 99;
+}
+
+.block-sidebar .block-sidebar-item {
+  font-size: 50px;
+  color: lightblue;
+  text-align: center;
+  line-height: 50px;
+  margin-bottom: 20px;
+  cursor: pointer;
+  display: block;
+}
+
+div {
+  display: block;
+}
+
+.block-sidebar .block-sidebar-item:hover {
+  color: #187aab;
+}
+
+.download_bt {
+  padding: 10px 16px !important;
+}
+
+#upfile {
+  width: 104px;
+  height: 45px;
+  background-color: #187aab;
+  color: #fff;
+  text-align: center;
+  line-height: 45px;
+  border-radius: 3px;
+  box-shadow: 0 0 2px 0 rgba(0, 0, 0, 0.1), 0 2px 2px 0 rgba(0, 0, 0, 0.2);
+  color: #fff;
+  font-family: "Source Sans Pro", Verdana, sans-serif;
+  font-size: 0.875rem;
+}
+
+.file {
+  width: 200px;
+  height: 130px;
+  position: absolute;
+  left: -20px;
+  top: 0;
+  z-index: 1;
+  -moz-opacity: 0;
+  -ms-opacity: 0;
+  -webkit-opacity: 0;
+  opacity: 0; /*css属性&mdash;&mdash;opcity不透明度，取值0-1*/
+  filter: alpha(opacity=0);
+  cursor: pointer;
+}
+
+#upload {
+  position: relative;
+  margin: 0px 0px;
+}
+
+#Content {
+  width: 85%;
+  height: 800px;
+  background-color: #ffffff;
+  margin: 15px auto;
+  display: flex;
+  min-width: 1200px;
+}
+
+.divider {
+  background-color: #eaeaea !important;
+  height: 2px !important;
+  width: 100%;
+  margin-bottom: 50px;
+}
+
+.divider_1 {
+  background-color: #ffffff;
+  height: 2px !important;
+  width: 100%;
+  margin-bottom: 20px;
+  margin: 20px auto;
+}
+
+.steps {
+  font-family: "lucida grande", "lucida sans unicode", lucida, helvetica,
+    "Hiragino Sans GB", "Microsoft YaHei", "WenQuanYi Micro Hei", sans-serif;
+  color: #21b3b9;
+  text-align: center;
+  margin: 15px auto;
+  font-size: 20px;
+  font-weight: bold;
+  text-align: center;
+}
+
+.step_1 {
+  /*color: #303133 !important;*/
+  margin: 20px 26px;
+}
+
+#info_patient {
+  margin-top: 10px;
+  margin-right: 160px;
+}
+</style>
+
+

frontend/src/components/Footer.vue

@@ -0,0 +1,35 @@
+<template>
+  <div id="Footer">
+    <p>{{ msg }}</p>
+  </div>
+</template>
+<script>
+export default {
+  name: "Footer",
+  data() {
+    return {
+      msg: "Copyright 2021",
+    };
+  },
+};
+</script>
+<style scoped>
+#Footer {
+  /*background:#F2F6FC;*/
+  padding: 6px;
+  border-radius: 5px;
+  width: 80%;
+  height: 80px;
+  margin: 20px auto;
+  margin-top: 140px;
+}
+
+p {
+  color: #21b3b9;
+  text-align: center;
+  margin: 30px auto;
+  font-size: 1.1em;
+}
+</style>
+
+

frontend/src/components/Header.vue

@@ -0,0 +1,84 @@
+<template>
+  <div id="Header">
+    <div class="top-left-edition">
+      <span style="color: #21b3b9; font-weight: bold">
+        <i class="el-icon-star-off" style="font-size: 23px"></i
+        >我的公众号：可达鸭的深度学习教程
+      </span>
+      <span>
+        <i class="el-icon-time" style="font-size: 23px"></i>CSDN、B站：BIT可达鸭
+      </span>
+    </div>
+    <div id="word">
+      <h1>{{ msg }}</h1>
+    </div>
+  </div>
+</template>
+<script>
+export default {
+  name: "Header",
+  data() {
+    return {
+      msg: "YOLOv5-WEB端",
+      activeIndex: "1",
+    };
+  },
+  methods: {},
+};
+</script>
+<style scoped>
+#Header {
+  padding: 30px 110px 0 150px;
+  width: 90%;
+  margin: 10px auto;
+}
+
+#word {
+  margin-left: 45%;
+  margin-top: -35px;
+  margin-bottom: 37px;
+  height: 60px;
+  line-height: 3.2em;
+  letter-spacing: 8px;
+}
+
+h1 {
+  /*text-align: center;*/
+  color: #21b3b9;
+  letter-spacing: 30px;
+  font-size: 2.3em;
+}
+
+.el-menu-demo {
+  width: 80%;
+  margin: 0px auto;
+  padding: 0px auto;
+}
+
+.top-left-edition span i {
+  float: left;
+  margin-right: 10px;
+}
+
+i,
+input,
+label {
+  vertical-align: middle;
+}
+
+i {
+  border: 0;
+  display: block;
+  cursor: pointer;
+}
+
+.top-left-edition span {
+  float: left;
+  font-size: 16px;
+  color: #999999;
+  line-height: 24px;
+  margin-right: 40px;
+}
+</style>
+
+

frontend/src/main.js

@@ -0,0 +1,35 @@
+// The Vue build version to load with the `import` command
+// (runtime-only or standalone) has been set in webpack.base.conf with an alias.
+import Vue from 'vue'
+import App from './App'
+import VueRouter from 'vue-router'
+import axios from 'axios'
+import Element from 'element-ui'
+import echarts from "echarts";
+
+Vue.prototype.$echarts = echarts;
+import '../node_modules/element-ui/lib/theme-chalk/index.css'
+import '../src/assets/style.css'
+import './theme/index.css'
+
+Vue.use(Element)
+Vue.config.productionTip = false
+Vue.use(VueRouter)
+Vue.prototype.$http = axios
+
+const router = new VueRouter({
+    routes: [
+        {path: "/App", component: App, meta: {title: "眼疾辅助诊断系统"},},
+    ],
+    mode: "history"
+})
+
+// 全局注册组件
+Vue.component("App", App);
+
+/* eslint-disable no-new */
+new Vue({
+    el: '#app',
+    router,
+    render: h => h(App)
+})

frontend/vue.config.js

@@ -0,0 +1,10 @@
+module.exports = {
+  lintOnSave: undefined,
+  publicPath: './',
+  outputDir: undefined,
+  assetsDir: undefined,
+  runtimeCompiler: undefined,
+  productionSourceMap: undefined,
+  parallel: false,
+  css: undefined
+}

object_detector/__init__.py

@@ -0,0 +1 @@
+from .object_detector import Detector

object_detector/core/main.py

@@ -0,0 +1,45 @@
+import cv2
+import os
+from processor import Detector
+
+def c_main(path, model, ext) -> tuple:
+    ''' 主函数,返回图片的路径和图片的信息
+     Args:  path: 图片的路径
+            model: 模型
+            ext: 图片的后缀名
+            Return: tuple: (图片的路径, 图片的信息)
+    '''
+    image_data = pre_process(path)
+    image_info = predict.predict(image_data, model, ext)
+
+    return image_data[1] + '.' + ext, image_info
+
+def pre_process(data_path):
+    ''' 路径预处理 
+    
+    从data_path中提取文件名，去除文件后缀
+    
+    Args: data_path: 图片的路径
+    Return: tuple: (图片的路径, 图片的信息)
+    '''
+    file_name = os.path.split(data_path)[1].split('.')[0]
+    return data_path, file_name
+
+def predict(dataset, model :Detector, ext):
+    ''' 预测,保存到服务器本地临时的目录
+    
+    Args: dataset: tuple: (图片的路径, 图片的信息)
+            model: 模型
+            ext: 图片的后缀名
+    Return: tuple: (图片的路径, 图片的信息)
+    '''
+    global img_y
+    x = dataset[0].replace('\\', '/')
+    file_name = dataset[1]
+    print(x)
+    print(file_name)
+    x = cv2.imread(x)
+    img_y, image_info = model.detect(x)
+    if cv2.imwrite('./tmp/draw/{}.{}'.format(file_name, ext), img_y):
+        raise Exception('保存图片时出错.Error saving thepicture.')
+    return image_info

object_detector/models/common.py

@@ -0,0 +1,114 @@
+# This file contains modules common to various models
+
+import math
+
+import torch
+import torch.nn as nn
+
+from utils.general import non_max_suppression
+
+def autopad(k, p=None):  # kernel, padding
+    # Pad to 'same'
+    if p is None:
+        p = k // 2 if isinstance(k, int) else [x // 2 for x in k]  # auto-pad
+    return p
+
+
+def DWConv(c1, c2, k=1, s=1, act=True):
+    # Depthwise convolution
+    return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act)
+
+
+class Conv(nn.Module):
+    # Standard convolution
+    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
+        super(Conv, self).__init__()
+        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
+        self.bn = nn.BatchNorm2d(c2)
+        self.act = nn.Hardswish() if act else nn.Identity()
+
+    def forward(self, x):
+        return self.act(self.bn(self.conv(x)))
+
+    def fuseforward(self, x):
+        return self.act(self.conv(x))
+
+
+class Bottleneck(nn.Module):
+    # Standard bottleneck
+    def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, shortcut, groups, expansion
+        super(Bottleneck, self).__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = Conv(c_, c2, 3, 1, g=g)
+        self.add = shortcut and c1 == c2
+
+    def forward(self, x):
+        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
+
+
+class BottleneckCSP(nn.Module):
+    # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
+    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
+        super(BottleneckCSP, self).__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
+        self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
+        self.cv4 = Conv(2 * c_, c2, 1, 1)
+        self.bn = nn.BatchNorm2d(2 * c_)  # applied to cat(cv2, cv3)
+        self.act = nn.LeakyReLU(0.1, inplace=True)
+        self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
+
+    def forward(self, x):
+        y1 = self.cv3(self.m(self.cv1(x)))
+        y2 = self.cv2(x)
+        return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1))))
+
+
+class SPP(nn.Module):
+    # Spatial pyramid pooling layer used in YOLOv3-SPP
+    def __init__(self, c1, c2, k=(5, 9, 13)):
+        super(SPP, self).__init__()
+        c_ = c1 // 2  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
+        self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
+
+    def forward(self, x):
+        x = self.cv1(x)
+        return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
+
+
+class Focus(nn.Module):
+    # Focus wh information into c-space
+    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
+        super(Focus, self).__init__()
+        self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
+
+    def forward(self, x):  # x(b,c,w,h) -> y(b,4c,w/2,h/2)
+        return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
+
+
+class Concat(nn.Module):
+    # Concatenate a list of tensors along dimension
+    def __init__(self, dimension=1):
+        super(Concat, self).__init__()
+        self.d = dimension
+
+    def forward(self, x):
+        return torch.cat(x, self.d)
+
+
+class NMS(nn.Module):
+    # Non-Maximum Suppression (NMS) module
+    conf = 0.25  # confidence threshold
+    iou = 0.45  # IoU threshold
+    classes = None  # (optional list) filter by class
+
+    def __init__(self):
+        super(NMS, self).__init__()
+
+    def forward(self, x):
+        return non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes)
+

object_detector/models/experimental.py

@@ -0,0 +1,152 @@
+# This file contains experimental modules
+
+import numpy as np
+import torch
+import torch.nn as nn
+
+from models.common import Conv, DWConv
+from utils.google_utils import attempt_download
+
+
+class CrossConv(nn.Module):
+    # Cross Convolution Downsample
+    def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
+        # ch_in, ch_out, kernel, stride, groups, expansion, shortcut
+        super(CrossConv, self).__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, (1, k), (1, s))
+        self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g)
+        self.add = shortcut and c1 == c2
+
+    def forward(self, x):
+        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
+
+
+class C3(nn.Module):
+    # Cross Convolution CSP
+    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
+        super(C3, self).__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
+        self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
+        self.cv4 = Conv(2 * c_, c2, 1, 1)
+        self.bn = nn.BatchNorm2d(2 * c_)  # applied to cat(cv2, cv3)
+        self.act = nn.LeakyReLU(0.1, inplace=True)
+        self.m = nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)])
+
+    def forward(self, x):
+        y1 = self.cv3(self.m(self.cv1(x)))
+        y2 = self.cv2(x)
+        return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1))))
+
+
+class Sum(nn.Module):
+    # Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
+    def __init__(self, n, weight=False):  # n: number of inputs
+        super(Sum, self).__init__()
+        self.weight = weight  # apply weights boolean
+        self.iter = range(n - 1)  # iter object
+        if weight:
+            self.w = nn.Parameter(-torch.arange(1., n) / 2, requires_grad=True)  # layer weights
+
+    def forward(self, x):
+        y = x[0]  # no weight
+        if self.weight:
+            w = torch.sigmoid(self.w) * 2
+            for i in self.iter:
+                y = y + x[i + 1] * w[i]
+        else:
+            for i in self.iter:
+                y = y + x[i + 1]
+        return y
+
+
+class GhostConv(nn.Module):
+    # Ghost Convolution https://github.com/huawei-noah/ghostnet
+    def __init__(self, c1, c2, k=1, s=1, g=1, act=True):  # ch_in, ch_out, kernel, stride, groups
+        super(GhostConv, self).__init__()
+        c_ = c2 // 2  # hidden channels
+        self.cv1 = Conv(c1, c_, k, s, None, g, act)
+        self.cv2 = Conv(c_, c_, 5, 1, None, c_, act)
+
+    def forward(self, x):
+        y = self.cv1(x)
+        return torch.cat([y, self.cv2(y)], 1)
+
+
+class GhostBottleneck(nn.Module):
+    # Ghost Bottleneck https://github.com/huawei-noah/ghostnet
+    def __init__(self, c1, c2, k, s):
+        super(GhostBottleneck, self).__init__()
+        c_ = c2 // 2
+        self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1),  # pw
+                                  DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(),  # dw
+                                  GhostConv(c_, c2, 1, 1, act=False))  # pw-linear
+        self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False),
+                                      Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity()
+
+    def forward(self, x):
+        return self.conv(x) + self.shortcut(x)
+
+
+class MixConv2d(nn.Module):
+    # Mixed Depthwise Conv https://arxiv.org/abs/1907.09595
+    def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):
+        super(MixConv2d, self).__init__()
+        groups = len(k)
+        if equal_ch:  # equal c_ per group
+            i = torch.linspace(0, groups - 1E-6, c2).floor()  # c2 indices
+            c_ = [(i == g).sum() for g in range(groups)]  # intermediate channels
+        else:  # equal weight.numel() per group
+            b = [c2] + [0] * groups
+            a = np.eye(groups + 1, groups, k=-1)
+            a -= np.roll(a, 1, axis=1)
+            a *= np.array(k) ** 2
+            a[0] = 1
+            c_ = np.linalg.lstsq(a, b, rcond=None)[0].round()  # solve for equal weight indices, ax = b
+
+        self.m = nn.ModuleList([nn.Conv2d(c1, int(c_[g]), k[g], s, k[g] // 2, bias=False) for g in range(groups)])
+        self.bn = nn.BatchNorm2d(c2)
+        self.act = nn.LeakyReLU(0.1, inplace=True)
+
+    def forward(self, x):
+        return x + self.act(self.bn(torch.cat([m(x) for m in self.m], 1)))
+
+
+class Ensemble(nn.ModuleList):
+    # Ensemble of models
+    def __init__(self):
+        super(Ensemble, self).__init__()
+
+    def forward(self, x, augment=False):
+        y = []
+        for module in self:
+            y.append(module(x, augment)[0])
+        # y = torch.stack(y).max(0)[0]  # max ensemble
+        # y = torch.cat(y, 1)  # nms ensemble
+        y = torch.stack(y).mean(0)  # mean ensemble
+        return y, None  # inference, train output
+
+
+def attempt_load(weights, map_location=None):
+    # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
+    model = Ensemble()
+    for w in weights if isinstance(weights, list) else [weights]:
+        attempt_download(w)
+        model.append(torch.load(w, map_location=map_location)['model'].float().fuse().eval())  # load FP32 model
+
+    # Compatibility updates
+    for m in model.modules():
+        if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6]:
+            m.inplace = True  # pytorch 1.7.0 compatibility
+        elif type(m) is Conv:
+            m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatibility
+
+    if len(model) == 1:
+        return model[-1]  # return model
+    else:
+        print('Ensemble created with %s\n' % weights)
+        for k in ['names', 'stride']:
+            setattr(model, k, getattr(model[-1], k))
+        return model  # return ensemble

object_detector/models/yolo.py

@@ -0,0 +1,244 @@
+import math
+import sys
+from copy import deepcopy
+from pathlib import Path
+
+sys.path.append('./')  # to run '$ python *.py' files in subdirectories
+
+import torch
+import torch.nn as nn
+
+from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, Concat, NMS
+from models.experimental import MixConv2d, CrossConv, C3
+from utils.autoanchor import check_anchor_order
+from utils.general import make_divisible
+from utils.torch_utils import time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights
+
+thop = None
+
+
+class Detect(nn.Module):
+    stride = None  # strides computed during build
+    export = False  # onnx export
+
+    def __init__(self, nc=80, anchors=(), ch=()):  # detection layer
+        super(Detect, self).__init__()
+        self.nc = nc  # number of classes
+        self.no = nc + 5  # number of outputs per anchor
+        self.nl = len(anchors)  # number of detection layers
+        self.na = len(anchors[0]) // 2  # number of anchors
+        self.grid = [torch.zeros(1)] * self.nl  # init grid
+        a = torch.tensor(anchors).float().view(self.nl, -1, 2)
+        self.register_buffer('anchors', a)  # shape(nl,na,2)
+        self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2))  # shape(nl,1,na,1,1,2)
+        self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch)  # output conv
+
+    def forward(self, x):
+        # x = x.copy()  # for profiling
+        z = []  # inference output
+        self.training |= self.export
+        for i in range(self.nl):
+            x[i] = self.m[i](x[i])  # conv
+            bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
+            x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
+
+            if not self.training:  # inference
+                if self.grid[i].shape[2:4] != x[i].shape[2:4]:
+                    self.grid[i] = self._make_grid(nx, ny).to(x[i].device)
+
+                y = x[i].sigmoid()
+                y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i]  # xy
+                y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
+                z.append(y.view(bs, -1, self.no))
+
+        return x if self.training else (torch.cat(z, 1), x)
+
+    @staticmethod
+    def _make_grid(nx=20, ny=20):
+        yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
+        return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
+
+
+class Model(nn.Module):
+    def __init__(self, cfg='yolov5m.yaml', ch=3, nc=None):  # model, input channels, number of classes
+        super(Model, self).__init__()
+        if isinstance(cfg, dict):
+            self.yaml = cfg  # model dict
+        else:  # is *.yaml
+            import yaml  # for torch hub
+            self.yaml_file = Path(cfg).name
+            with open(cfg) as f:
+                self.yaml = yaml.load(f, Loader=yaml.FullLoader)  # model dict
+
+        # Define model
+        if nc and nc != self.yaml['nc']:
+            self.yaml['nc'] = nc  # override yaml value
+        self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch])  # model, savelist, ch_out
+        # print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])
+
+        # Build strides, anchors
+        m = self.model[-1]  # Detect()
+        if isinstance(m, Detect):
+            s = 128  # 2x min stride
+            m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))])  # forward
+            m.anchors /= m.stride.view(-1, 1, 1)
+            check_anchor_order(m)
+            self.stride = m.stride
+            self._initialize_biases()  # only run once
+            # print('Strides: %s' % m.stride.tolist())
+
+        # Init weights, biases
+        initialize_weights(self)
+        self.info()
+
+    def forward(self, x, augment=False, profile=False):
+        if augment:
+            img_size = x.shape[-2:]  # height, width
+            s = [1, 0.83, 0.67]  # scales
+            f = [None, 3, None]  # flips (2-ud, 3-lr)
+            y = []  # outputs
+            for si, fi in zip(s, f):
+                xi = scale_img(x.flip(fi) if fi else x, si)
+                yi = self.forward_once(xi)[0]  # forward
+                # cv2.imwrite('img%g.jpg' % s, 255 * xi[0].numpy().transpose((1, 2, 0))[:, :, ::-1])  # save
+                yi[..., :4] /= si  # de-scale
+                if fi == 2:
+                    yi[..., 1] = img_size[0] - yi[..., 1]  # de-flip ud
+                elif fi == 3:
+                    yi[..., 0] = img_size[1] - yi[..., 0]  # de-flip lr
+                y.append(yi)
+            return torch.cat(y, 1), None  # augmented inference, train
+        else:
+            return self.forward_once(x, profile)  # single-scale inference, train
+
+    def forward_once(self, x, profile=False):
+        y, dt = [], []  # outputs
+        for m in self.model:
+            if m.f != -1:  # if not from previous layer
+                x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
+
+            if profile:
+                o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 if thop else 0  # FLOPS
+                t = time_synchronized()
+                for _ in range(10):
+                    _ = m(x)
+                dt.append((time_synchronized() - t) * 100)
+                print('%10.1f%10.0f%10.1fms %-40s' % (o, m.np, dt[-1], m.type))
+
+            x = m(x)  # run
+            y.append(x if m.i in self.save else None)  # save output
+
+        if profile:
+            print('%.1fms total' % sum(dt))
+        return x
+
+    def _initialize_biases(self, cf=None):  # initialize biases into Detect(), cf is class frequency
+        # https://arxiv.org/abs/1708.02002 section 3.3
+        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
+        m = self.model[-1]  # Detect() module
+        for mi, s in zip(m.m, m.stride):  # from
+            b = mi.bias.view(m.na, -1)  # conv.bias(255) to (3,85)
+            b[:, 4] += math.log(8 / (640 / s) ** 2)  # obj (8 objects per 640 image)
+            b[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum())  # cls
+            mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
+
+    def _print_biases(self):
+        m = self.model[-1]  # Detect() module
+        for mi in m.m:  # from
+            b = mi.bias.detach().view(m.na, -1).T  # conv.bias(255) to (3,85)
+            print(('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))
+
+    # def _print_weights(self):
+    #     for m in self.model.modules():
+    #         if type(m) is Bottleneck:
+    #             print('%10.3g' % (m.w.detach().sigmoid() * 2))  # shortcut weights
+
+    def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
+        print('Fusing layers... ')
+        for m in self.model.modules():
+            if type(m) is Conv and hasattr(m, 'bn'):
+                m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
+                delattr(m, 'bn')  # remove batchnorm
+                m.forward = m.fuseforward  # update forward
+        self.info()
+        return self
+
+    def nms(self, mode=True):  # add or remove NMS module
+        present = type(self.model[-1]) is NMS  # last layer is NMS
+        if mode and not present:
+            print('Adding NMS... ')
+            m = NMS()  # module
+            m.f = -1  # from
+            m.i = self.model[-1].i + 1  # index
+            self.model.add_module(name='%s' % m.i, module=m)  # add
+            self.eval()
+        elif not mode and present:
+            print('Removing NMS... ')
+            self.model = self.model[:-1]  # remove
+        return self
+
+    def info(self, verbose=False, img_size=640):  # print model information
+        model_info(self, verbose, img_size)
+
+
+def parse_model(d, ch):  # model_dict, input_channels(3)
+    anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
+    na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  # number of anchors
+    no = na * (nc + 5)  # number of outputs = anchors * (classes + 5)
+
+    layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
+    for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']):  # from, number, module, args
+        m = eval(m) if isinstance(m, str) else m  # eval strings
+        for j, a in enumerate(args):
+            try:
+                args[j] = eval(a) if isinstance(a, str) else a  # eval strings
+            except:
+                pass
+
+        n = max(round(n * gd), 1) if n > 1 else n  # depth gain
+        if m in [Conv, Bottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3]:
+            c1, c2 = ch[f], args[0]
+
+            # Normal
+            # if i > 0 and args[0] != no:  # channel expansion factor
+            #     ex = 1.75  # exponential (default 2.0)
+            #     e = math.log(c2 / ch[1]) / math.log(2)
+            #     c2 = int(ch[1] * ex ** e)
+            # if m != Focus:
+
+            c2 = make_divisible(c2 * gw, 8) if c2 != no else c2
+
+            # Experimental
+            # if i > 0 and args[0] != no:  # channel expansion factor
+            #     ex = 1 + gw  # exponential (default 2.0)
+            #     ch1 = 32  # ch[1]
+            #     e = math.log(c2 / ch1) / math.log(2)  # level 1-n
+            #     c2 = int(ch1 * ex ** e)
+            # if m != Focus:
+            #     c2 = make_divisible(c2, 8) if c2 != no else c2
+
+            args = [c1, c2, *args[1:]]
+            if m in [BottleneckCSP, C3]:
+                args.insert(2, n)
+                n = 1
+        elif m is nn.BatchNorm2d:
+            args = [ch[f]]
+        elif m is Concat:
+            c2 = sum([ch[-1 if x == -1 else x + 1] for x in f])
+        elif m is Detect:
+            args.append([ch[x + 1] for x in f])
+            if isinstance(args[1], int):  # number of anchors
+                args[1] = [list(range(args[1] * 2))] * len(f)
+        else:
+            c2 = ch[f]
+
+        m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args)  # module
+        t = str(m)[8:-2].replace('__main__.', '')  # module type
+        np = sum([x.numel() for x in m_.parameters()])  # number params
+        m_.i, m_.f, m_.type, m_.np = i, f, t, np  # attach index, 'from' index, type, number params
+        save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
+        layers.append(m_)
+        ch.append(c2)
+    return nn.Sequential(*layers), sorted(save)
+
+

object_detector/models/yolov5m.yaml

@@ -0,0 +1,48 @@
+# parameters
+nc: 8  # number of classes
+depth_multiple: 0.67  # model depth multiple
+width_multiple: 0.75  # layer channel multiple
+
+# anchors
+anchors:
+  - [10,13, 16,30, 33,23]  # P3/8
+  - [30,61, 62,45, 59,119]  # P4/16
+  - [116,90, 156,198, 373,326]  # P5/32
+
+# YOLOv5 backbone
+backbone:
+  # [from, number, module, args]
+  [[-1, 1, Focus, [64, 3]],  # 0-P1/2
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
+   [-1, 3, BottleneckCSP, [128]],
+   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
+   [-1, 9, BottleneckCSP, [256]],
+   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
+   [-1, 9, BottleneckCSP, [512]],
+   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
+   [-1, 1, SPP, [1024, [5, 9, 13]]],
+   [-1, 3, BottleneckCSP, [1024, False]],  # 9
+  ]
+
+# YOLOv5 head
+head:
+  [[-1, 1, Conv, [512, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
+   [-1, 3, BottleneckCSP, [512, False]],  # 13
+
+   [-1, 1, Conv, [256, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
+   [-1, 3, BottleneckCSP, [256, False]],  # 17 (P3/8-small)
+
+   [-1, 1, Conv, [256, 3, 2]],
+   [[-1, 14], 1, Concat, [1]],  # cat head P4
+   [-1, 3, BottleneckCSP, [512, False]],  # 20 (P4/16-medium)
+
+   [-1, 1, Conv, [512, 3, 2]],
+   [[-1, 10], 1, Concat, [1]],  # cat head P5
+   [-1, 3, BottleneckCSP, [1024, False]],  # 23 (P5/32-large)
+
+   [[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
+  ]

object_detector/object_detector.py

@@ -0,0 +1,109 @@
+import torch
+import numpy as np
+from models.experimental import attempt_load
+from utils.general import non_max_suppression, scale_coords, letterbox
+from utils.torch_utils import select_device
+import cv2
+from random import randint
+
+class Detector(object):
+    ''' 预测 '''
+    
+    def __init__(self):
+        ''' 初始化 '''
+        self.img_size = 640
+        self.threshold = 0.4
+        self.max_frame = 160
+        self.half = False
+        if torch.cuda.is_available():
+            self.half = True
+        self.init_model()
+
+    def init_model(self):
+
+        self.weights = 'weights/final.pt'
+        self.device = '0' if torch.cuda.is_available() else 'cpu'
+        self.device = select_device(self.device)
+        model = attempt_load(self.weights, map_location=self.device)
+        model.to(self.device).eval()
+        model.half() if self.half else model.float()
+        # torch.save(model, 'test.pt')
+        self.m = model
+        self.names = model.module.names if hasattr(
+            model, 'module') else model.names
+        self.colors = [
+            (randint(0, 255), randint(0, 255), randint(0, 255)) for _ in self.names
+        ]
+
+    def preprocess(self, img):
+
+        img0 = img.copy()
+        # 图像缩放到指定尺寸
+        img = letterbox(img, new_shape=self.img_size)[0]
+        # 从BGR转换为RGB, 通过transpose(2, 0, 1)将通道维度移动到最前面
+        img = img[:, :, ::-1].transpose(2, 0, 1)
+        img = np.ascontiguousarray(img)
+        img = torch.from_numpy(img).to(self.device)
+        img = img.half()  # 半精度
+        img /= 255.0  # 图像归一化
+        if img.ndimension() == 3:
+            img = img.unsqueeze(0)
+
+        return img0, img
+
+    def plot_bboxes(self, image, bboxes, line_thickness=None):
+        ''' 画框 
+        Args: image: 图片
+                bboxes: 框
+                line_thickness: 线的厚度
+        Returns: image: 画框后的图片
+        '''
+        tl = line_thickness or round(
+            0.002 * (image.shape[0] + image.shape[1]) / 2) + 1  # line/font thickness
+        
+        for (x1, y1, x2, y2, cls_id, conf) in bboxes:
+            color = self.colors[self.names.index(cls_id)]
+            c1, c2 = (x1, y1), (x2, y2)
+            cv2.rectangle(image, c1, c2, color,
+                          thickness=tl, lineType=cv2.LINE_AA)
+            tf = max(tl - 1, 1)  # font thickness
+            t_size = cv2.getTextSize(
+                cls_id, 0, fontScale=tl / 3, thickness=tf)[0]
+            c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
+            cv2.rectangle(image, c1, c2, color, -1, cv2.LINE_AA)  # filled
+            cv2.putText(image, '{} ID-{:.2f}'.format(cls_id, conf), (c1[0], c1[1] - 2), 0, tl / 3,
+                        [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
+        return image
+
+    def detect(self, im):
+        ''' 预测 
+        Args: im: 图片
+        Returns: im: 预测后的图片
+        '''
+        im0, img = self.preprocess(im)
+
+        pred = self.m(img, augment=False)[0]
+        pred = pred.float()
+        pred = non_max_suppression(pred, self.threshold, 0.3)
+
+        pred_boxes = []
+        image_info = {}
+        count = 0
+        for det in pred:
+            if det is not None and len(det):
+                det[:, :4] = scale_coords(
+                    img.shape[2:], det[:, :4], im0.shape).round()
+
+                for *x, conf, cls_id in det:
+                    lbl = self.names[int(cls_id)]
+                    x1, y1 = int(x[0]), int(x[1])
+                    x2, y2 = int(x[2]), int(x[3])
+                    pred_boxes.append(
+                        (x1, y1, x2, y2, lbl, conf))
+                    count += 1
+                    key = '{}-{:02}'.format(lbl, count)
+                    image_info[key] = ['{}×{}'.format(
+                        x2-x1, y2-y1), np.round(float(conf), 3)]
+
+        im = self.plot_bboxes(im, pred_boxes)
+        return im, image_info

object_detector/utils/activations.py

@@ -0,0 +1,72 @@
+# Activation functions
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+# Swish https://arxiv.org/pdf/1905.02244.pdf ---------------------------------------------------------------------------
+class Swish(nn.Module):  #
+    @staticmethod
+    def forward(x):
+        return x * torch.sigmoid(x)
+
+
+class Hardswish(nn.Module):  # export-friendly version of nn.Hardswish()
+    @staticmethod
+    def forward(x):
+        # return x * F.hardsigmoid(x)  # for torchscript and CoreML
+        return x * F.hardtanh(x + 3, 0., 6.) / 6.  # for torchscript, CoreML and ONNX
+
+
+class MemoryEfficientSwish(nn.Module):
+    class F(torch.autograd.Function):
+        @staticmethod
+        def forward(ctx, x):
+            ctx.save_for_backward(x)
+            return x * torch.sigmoid(x)
+
+        @staticmethod
+        def backward(ctx, grad_output):
+            x = ctx.saved_tensors[0]
+            sx = torch.sigmoid(x)
+            return grad_output * (sx * (1 + x * (1 - sx)))
+
+    def forward(self, x):
+        return self.F.apply(x)
+
+
+# Mish https://github.com/digantamisra98/Mish --------------------------------------------------------------------------
+class Mish(nn.Module):
+    @staticmethod
+    def forward(x):
+        return x * F.softplus(x).tanh()
+
+
+class MemoryEfficientMish(nn.Module):
+    class F(torch.autograd.Function):
+        @staticmethod
+        def forward(ctx, x):
+            ctx.save_for_backward(x)
+            return x.mul(torch.tanh(F.softplus(x)))  # x * tanh(ln(1 + exp(x)))
+
+        @staticmethod
+        def backward(ctx, grad_output):
+            x = ctx.saved_tensors[0]
+            sx = torch.sigmoid(x)
+            fx = F.softplus(x).tanh()
+            return grad_output * (fx + x * sx * (1 - fx * fx))
+
+    def forward(self, x):
+        return self.F.apply(x)
+
+
+# FReLU https://arxiv.org/abs/2007.11824 -------------------------------------------------------------------------------
+class FReLU(nn.Module):
+    def __init__(self, c1, k=3):  # ch_in, kernel
+        super().__init__()
+        self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1)
+        self.bn = nn.BatchNorm2d(c1)
+
+    def forward(self, x):
+        return torch.max(x, self.bn(self.conv(x)))

object_detector/utils/autoanchor.py

@@ -0,0 +1,150 @@
+# Auto-anchor utils
+
+import numpy as np
+import torch
+import yaml
+from scipy.cluster.vq import kmeans
+from tqdm import tqdm
+
+def check_anchor_order(m):
+    # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary
+    a = m.anchor_grid.prod(-1).view(-1)  # anchor area
+    da = a[-1] - a[0]  # delta a
+    ds = m.stride[-1] - m.stride[0]  # delta s
+    if da.sign() != ds.sign():  # same order
+        print('Reversing anchor order')
+        m.anchors[:] = m.anchors.flip(0)
+        m.anchor_grid[:] = m.anchor_grid.flip(0)
+
+
+def check_anchors(dataset, model, thr=4.0, imgsz=640):
+    # Check anchor fit to data, recompute if necessary
+    print('\nAnalyzing anchors... ', end='')
+    m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1]  # Detect()
+    shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True)
+    scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1))  # augment scale
+    wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float()  # wh
+
+    def metric(k):  # compute metric
+        r = wh[:, None] / k[None]
+        x = torch.min(r, 1. / r).min(2)[0]  # ratio metric
+        best = x.max(1)[0]  # best_x
+        aat = (x > 1. / thr).float().sum(1).mean()  # anchors above threshold
+        bpr = (best > 1. / thr).float().mean()  # best possible recall
+        return bpr, aat
+
+    bpr, aat = metric(m.anchor_grid.clone().cpu().view(-1, 2))
+    print('anchors/target = %.2f, Best Possible Recall (BPR) = %.4f' % (aat, bpr), end='')
+    if bpr < 0.98:  # threshold to recompute
+        print('. Attempting to improve anchors, please wait...')
+        na = m.anchor_grid.numel() // 2  # number of anchors
+        new_anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
+        new_bpr = metric(new_anchors.reshape(-1, 2))[0]
+        if new_bpr > bpr:  # replace anchors
+            new_anchors = torch.tensor(new_anchors, device=m.anchors.device).type_as(m.anchors)
+            m.anchor_grid[:] = new_anchors.clone().view_as(m.anchor_grid)  # for inference
+            m.anchors[:] = new_anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1)  # loss
+            check_anchor_order(m)
+            print('New anchors saved to model. Update model *.yaml to use these anchors in the future.')
+        else:
+            print('Original anchors better than new anchors. Proceeding with original anchors.')
+    print('')  # newline
+
+
+def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True):
+    """ Creates kmeans-evolved anchors from training dataset
+
+        Arguments:
+            path: path to dataset *.yaml, or a loaded dataset
+            n: number of anchors
+            img_size: image size used for training
+            thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0
+            gen: generations to evolve anchors using genetic algorithm
+            verbose: print all results
+
+        Return:
+            k: kmeans evolved anchors
+
+        Usage:
+            from utils.autoanchor import *; _ = kmean_anchors()
+    """
+    thr = 1. / thr
+
+    def metric(k, wh):  # compute metrics
+        r = wh[:, None] / k[None]
+        x = torch.min(r, 1. / r).min(2)[0]  # ratio metric
+        # x = wh_iou(wh, torch.tensor(k))  # iou metric
+        return x, x.max(1)[0]  # x, best_x
+
+    def anchor_fitness(k):  # mutation fitness
+        _, best = metric(torch.tensor(k, dtype=torch.float32), wh)
+        return (best * (best > thr).float()).mean()  # fitness
+
+    def print_results(k):
+        k = k[np.argsort(k.prod(1))]  # sort small to large
+        x, best = metric(k, wh0)
+        bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n  # best possible recall, anch > thr
+        print('thr=%.2f: %.4f best possible recall, %.2f anchors past thr' % (thr, bpr, aat))
+        print('n=%g, img_size=%s, metric_all=%.3f/%.3f-mean/best, past_thr=%.3f-mean: ' %
+              (n, img_size, x.mean(), best.mean(), x[x > thr].mean()), end='')
+        for i, x in enumerate(k):
+            print('%i,%i' % (round(x[0]), round(x[1])), end=',  ' if i < len(k) - 1 else '\n')  # use in *.cfg
+        return k
+
+    if isinstance(path, str):  # *.yaml file
+        with open(path) as f:
+            data_dict = yaml.load(f, Loader=yaml.FullLoader)  # model dict
+        from utils.datasets import LoadImagesAndLabels
+        dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True)
+    else:
+        dataset = path  # dataset
+
+    # Get label wh
+    shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True)
+    wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)])  # wh
+
+    # Filter
+    i = (wh0 < 3.0).any(1).sum()
+    if i:
+        print('WARNING: Extremely small objects found. '
+              '%g of %g labels are < 3 pixels in width or height.' % (i, len(wh0)))
+    wh = wh0[(wh0 >= 2.0).any(1)]  # filter > 2 pixels
+
+    # Kmeans calculation
+    print('Running kmeans for %g anchors on %g points...' % (n, len(wh)))
+    s = wh.std(0)  # sigmas for whitening
+    k, dist = kmeans(wh / s, n, iter=30)  # points, mean distance
+    k *= s
+    wh = torch.tensor(wh, dtype=torch.float32)  # filtered
+    wh0 = torch.tensor(wh0, dtype=torch.float32)  # unfiltered
+    k = print_results(k)
+
+    # Plot
+    # k, d = [None] * 20, [None] * 20
+    # for i in tqdm(range(1, 21)):
+    #     k[i-1], d[i-1] = kmeans(wh / s, i)  # points, mean distance
+    # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True)
+    # ax = ax.ravel()
+    # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.')
+    # fig, ax = plt.subplots(1, 2, figsize=(14, 7))  # plot wh
+    # ax[0].hist(wh[wh[:, 0]<100, 0],400)
+    # ax[1].hist(wh[wh[:, 1]<100, 1],400)
+    # fig.savefig('wh.png', dpi=200)
+
+    # Evolve
+    npr = np.random
+    f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1  # fitness, generations, mutation prob, sigma
+    pbar = tqdm(range(gen), desc='Evolving anchors with Genetic Algorithm')  # progress bar
+    for _ in pbar:
+        v = np.ones(sh)
+        while (v == 1).all():  # mutate until a change occurs (prevent duplicates)
+            v = ((npr.random(sh) < mp) * npr.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0)
+        kg = (k.copy() * v).clip(min=2.0)
+        fg = anchor_fitness(kg)
+        if fg > f:
+            f, k = fg, kg.copy()
+            pbar.desc = 'Evolving anchors with Genetic Algorithm: fitness = %.4f' % f
+            if verbose:
+                print_results(k)
+
+    return print_results(k)

object_detector/utils/general.py

@@ -0,0 +1,502 @@
+# General utils
+
+import glob
+import os
+import platform
+import random
+import re
+import subprocess
+import time
+from pathlib import Path
+
+import cv2
+import math
+import numpy as np
+import torch
+import torchvision
+import yaml
+
+from utils.google_utils import gsutil_getsize
+from utils.metrics import fitness
+from utils.torch_utils import init_torch_seeds
+
+# Settings
+torch.set_printoptions(linewidth=320, precision=5, profile='long')
+# format short g, %precision=5
+np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format})
+# prevent OpenCV from multithreading (incompatible with PyTorch DataLoader)
+cv2.setNumThreads(0)
+
+
+def init_seeds(seed=0):
+    random.seed(seed)
+    np.random.seed(seed)
+    init_torch_seeds(seed)
+
+
+def get_latest_run(search_dir='.'):
+    # Return path to most recent 'last.pt' in /runs (i.e. to --resume from)
+    last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True)
+    return max(last_list, key=os.path.getctime) if last_list else ''
+
+
+def check_git_status():
+    # Suggest 'git pull' if repo is out of date
+    if platform.system() in ['Linux', 'Darwin'] and not os.path.isfile('/.dockerenv'):
+        s = subprocess.check_output(
+            'if [ -d .git ]; then git fetch && git status -uno; fi', shell=True).decode('utf-8')
+        if 'Your branch is behind' in s:
+            print(s[s.find('Your branch is behind'):s.find('\n\n')] + '\n')
+
+
+def check_img_size(img_size, s=32):
+    # Verify img_size is a multiple of stride s
+    new_size = make_divisible(img_size, int(s))  # ceil gs-multiple
+    if new_size != img_size:
+        print('WARNING: --img-size %g must be multiple of max stride %g, updating to %g' %
+              (img_size, s, new_size))
+    return new_size
+
+
+def check_file(file):
+    # Search for file if not found
+    if os.path.isfile(file) or file == '':
+        return file
+    else:
+        files = glob.glob('./**/' + file, recursive=True)  # find file
+        assert len(files), 'File Not Found: %s' % file  # assert file was found
+        assert len(files) == 1, "Multiple files match '%s', specify exact path: %s" % (
+            file, files)  # assert unique
+        return files[0]  # return file
+
+
+def check_dataset(dict):
+    # Download dataset if not found locally
+    val, s = dict.get('val'), dict.get('download')
+    if val and len(val):
+        val = [Path(x).resolve()
+               for x in (val if isinstance(val, list) else [val])]  # val path
+        if not all(x.exists() for x in val):
+            print('\nWARNING: Dataset not found, nonexistent paths: %s' %
+                  [str(x) for x in val if not x.exists()])
+            if s and len(s):  # download script
+                print('Downloading %s ...' % s)
+                if s.startswith('http') and s.endswith('.zip'):  # URL
+                    f = Path(s).name  # filename
+                    torch.hub.download_url_to_file(s, f)
+                    r = os.system('unzip -q %s -d ../ && rm %s' %
+                                  (f, f))  # unzip
+                else:  # bash script
+                    r = os.system(s)
+                print('Dataset autodownload %s\n' % ('success' if r ==
+                                                     0 else 'failure'))  # analyze return value
+            else:
+                raise Exception('Dataset not found.')
+
+
+def make_divisible(x, divisor):
+    # Returns x evenly divisible by divisor
+    return math.ceil(x / divisor) * divisor
+
+
+def labels_to_class_weights(labels, nc=80):
+    # Get class weights (inverse frequency) from training labels
+    if labels[0] is None:  # no labels loaded
+        return torch.Tensor()
+
+    labels = np.concatenate(labels, 0)  # labels.shape = (866643, 5) for COCO
+    classes = labels[:, 0].astype(np.int)  # labels = [class xywh]
+    weights = np.bincount(classes, minlength=nc)  # occurrences per class
+
+    # Prepend gridpoint count (for uCE training)
+    # gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum()  # gridpoints per image
+    # weights = np.hstack([gpi * len(labels)  - weights.sum() * 9, weights * 9]) ** 0.5  # prepend gridpoints to start
+
+    weights[weights == 0] = 1  # replace empty bins with 1
+    weights = 1 / weights  # number of targets per class
+    weights /= weights.sum()  # normalize
+    return torch.from_numpy(weights)
+
+
+def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):
+    # Produces image weights based on class_weights and image contents
+    class_counts = np.array(
+        [np.bincount(x[:, 0].astype(np.int), minlength=nc) for x in labels])
+    image_weights = (class_weights.reshape(1, nc) * class_counts).sum(1)
+    # index = random.choices(range(n), weights=image_weights, k=1)  # weight image sample
+    return image_weights
+
+
+def coco80_to_coco91_class():  # converts 80-index (val2014) to 91-index (paper)
+    # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/
+    # a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n')
+    # b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n')
+    # x1 = [list(a[i] == b).index(True) + 1 for i in range(80)]  # darknet to coco
+    # x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)]  # coco to darknet
+    x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34,
+         35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
+         64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90]
+    return x
+
+
+def xyxy2xywh(x):
+    # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right
+    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
+    y[:, 0] = (x[:, 0] + x[:, 2]) / 2  # x center
+    y[:, 1] = (x[:, 1] + x[:, 3]) / 2  # y center
+    y[:, 2] = x[:, 2] - x[:, 0]  # width
+    y[:, 3] = x[:, 3] - x[:, 1]  # height
+    return y
+
+
+def xywh2xyxy(x):
+    # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
+    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
+    y[:, 0] = x[:, 0] - x[:, 2] / 2  # top left x
+    y[:, 1] = x[:, 1] - x[:, 3] / 2  # top left y
+    y[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right x
+    y[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right y
+    return y
+
+
+def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):
+    # Rescale coords (xyxy) from img1_shape to img0_shape
+    if ratio_pad is None:  # calculate from img0_shape
+        gain = min(img1_shape[0] / img0_shape[0],
+                   img1_shape[1] / img0_shape[1])  # gain  = old / new
+        pad = (img1_shape[1] - img0_shape[1] * gain) / \
+            2, (img1_shape[0] - img0_shape[0] * gain) / 2  # wh padding
+    else:
+        gain = ratio_pad[0][0]
+        pad = ratio_pad[1]
+
+    coords[:, [0, 2]] -= pad[0]  # x padding
+    coords[:, [1, 3]] -= pad[1]  # y padding
+    coords[:, :4] /= gain
+    clip_coords(coords, img0_shape)
+    return coords
+
+
+def clip_coords(boxes, img_shape):
+    # Clip bounding xyxy bounding boxes to image shape (height, width)
+    boxes[:, 0].clamp_(0, img_shape[1])  # x1
+    boxes[:, 1].clamp_(0, img_shape[0])  # y1
+    boxes[:, 2].clamp_(0, img_shape[1])  # x2
+    boxes[:, 3].clamp_(0, img_shape[0])  # y2
+
+
+def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-9):
+    # Returns the IoU of box1 to box2. box1 is 4, box2 is nx4
+    box2 = box2.T
+
+    # Get the coordinates of bounding boxes
+    if x1y1x2y2:  # x1, y1, x2, y2 = box1
+        b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3]
+        b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3]
+    else:  # transform from xywh to xyxy
+        b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2
+        b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2
+        b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2
+        b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2
+
+    # Intersection area
+    inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \
+            (torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0)
+
+    # Union Area
+    w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps
+    w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps
+    union = w1 * h1 + w2 * h2 - inter + eps
+
+    iou = inter / union
+    if GIoU or DIoU or CIoU:
+        # convex (smallest enclosing box) width
+        cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1)
+        ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1)  # convex height
+        if CIoU or DIoU:  # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
+            c2 = cw ** 2 + ch ** 2 + eps  # convex diagonal squared
+            rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 +
+                    (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4  # center distance squared
+            if DIoU:
+                return iou - rho2 / c2  # DIoU
+            elif CIoU:  # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
+                v = (4 / math.pi ** 2) * \
+                    torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2)
+                with torch.no_grad():
+                    alpha = v / ((1 + eps) - iou + v)
+                return iou - (rho2 / c2 + v * alpha)  # CIoU
+        else:  # GIoU https://arxiv.org/pdf/1902.09630.pdf
+            c_area = cw * ch + eps  # convex area
+            return iou - (c_area - union) / c_area  # GIoU
+    else:
+        return iou  # IoU
+
+
+def box_iou(box1, box2):
+    # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
+    """
+    Return intersection-over-union (Jaccard index) of boxes.
+    Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
+    Arguments:
+        box1 (Tensor[N, 4])
+        box2 (Tensor[M, 4])
+    Returns:
+        iou (Tensor[N, M]): the NxM matrix containing the pairwise
+            IoU values for every element in boxes1 and boxes2
+    """
+
+    def box_area(box):
+        # box = 4xn
+        return (box[2] - box[0]) * (box[3] - box[1])
+
+    area1 = box_area(box1.T)
+    area2 = box_area(box2.T)
+
+    # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
+    inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) -
+             torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)
+    # iou = inter / (area1 + area2 - inter)
+    return inter / (area1[:, None] + area2 - inter)
+
+
+def wh_iou(wh1, wh2):
+    # Returns the nxm IoU matrix. wh1 is nx2, wh2 is mx2
+    wh1 = wh1[:, None]  # [N,1,2]
+    wh2 = wh2[None]  # [1,M,2]
+    inter = torch.min(wh1, wh2).prod(2)  # [N,M]
+    # iou = inter / (area1 + area2 - inter)
+    return inter / (wh1.prod(2) + wh2.prod(2) - inter)
+
+
+def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, classes=None, agnostic=False, labels=()):
+    """Performs Non-Maximum Suppression (NMS) on inference results
+
+    Returns:
+         detections with shape: nx6 (x1, y1, x2, y2, conf, cls)
+    """
+
+    nc = prediction[0].shape[1] - 5  # number of classes
+    xc = prediction[..., 4] > conf_thres  # candidates
+
+    # Settings
+    # (pixels) minimum and maximum box width and height
+    min_wh, max_wh = 2, 4096
+    max_det = 300  # maximum number of detections per image
+    time_limit = 10.0  # seconds to quit after
+    redundant = True  # require redundant detections
+    multi_label = nc > 1  # multiple labels per box (adds 0.5ms/img)
+    merge = False  # use merge-NMS
+
+    t = time.time()
+    output = [torch.zeros(0, 6)] * prediction.shape[0]
+    for xi, x in enumerate(prediction):  # image index, image inference
+        # Apply constraints
+        # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0  # width-height
+        x = x[xc[xi]]  # confidence
+
+        # Cat apriori labels if autolabelling
+        if labels and len(labels[xi]):
+            l = labels[xi]
+            v = torch.zeros((len(l), nc + 5), device=x.device)
+            v[:, :4] = l[:, 1:5]  # box
+            v[:, 4] = 1.0  # conf
+            v[range(len(l)), l[:, 0].long() + 5] = 1.0  # cls
+            x = torch.cat((x, v), 0)
+
+        # If none remain process next image
+        if not x.shape[0]:
+            continue
+
+        # Compute conf
+        x[:, 5:] *= x[:, 4:5]  # conf = obj_conf * cls_conf
+
+        # Box (center x, center y, width, height) to (x1, y1, x2, y2)
+        box = xywh2xyxy(x[:, :4])
+
+        # Detections matrix nx6 (xyxy, conf, cls)
+        if multi_label:
+            i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T
+            x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)
+        else:  # best class only
+            conf, j = x[:, 5:].max(1, keepdim=True)
+            x = torch.cat((box, conf, j.float()), 1)[
+                conf.view(-1) > conf_thres]
+
+        # Filter by class
+        if classes:
+            x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
+
+        # Apply finite constraint
+        # if not torch.isfinite(x).all():
+        #     x = x[torch.isfinite(x).all(1)]
+
+        # If none remain process next image
+        n = x.shape[0]  # number of boxes
+        if not n:
+            continue
+
+        # Sort by confidence
+        # x = x[x[:, 4].argsort(descending=True)]
+
+        # Batched NMS
+        c = x[:, 5:6] * (0 if agnostic else max_wh)  # classes
+        # boxes (offset by class), scores
+        boxes, scores = x[:, :4] + c, x[:, 4]
+        i = torchvision.ops.nms(boxes, scores, iou_thres)  # NMS
+        if i.shape[0] > max_det:  # limit detections
+            i = i[:max_det]
+        if merge and (1 < n < 3E3):  # Merge NMS (boxes merged using weighted mean)
+            # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
+            iou = box_iou(boxes[i], boxes) > iou_thres  # iou matrix
+            weights = iou * scores[None]  # box weights
+            x[i, :4] = torch.mm(weights, x[:, :4]).float(
+            ) / weights.sum(1, keepdim=True)  # merged boxes
+            if redundant:
+                i = i[iou.sum(1) > 1]  # require redundancy
+
+        output[xi] = x[i]
+        if (time.time() - t) > time_limit:
+            break  # time limit exceeded
+
+    return output
+
+
+# from utils.general import *; strip_optimizer()
+def strip_optimizer(f='weights/best.pt', s=''):
+    # Strip optimizer from 'f' to finalize training, optionally save as 's'
+    x = torch.load(f, map_location=torch.device('cpu'))
+    x['optimizer'] = None
+    x['training_results'] = None
+    x['epoch'] = -1
+    x['model'].half()  # to FP16
+    for p in x['model'].parameters():
+        p.requires_grad = False
+    torch.save(x, s or f)
+    mb = os.path.getsize(s or f) / 1E6  # filesize
+    print('Optimizer stripped from %s,%s %.1fMB' %
+          (f, (' saved as %s,' % s) if s else '', mb))
+
+
+def print_mutation(hyp, results, yaml_file='hyp_evolved.yaml', bucket=''):
+    # Print mutation results to evolve.txt (for use with train.py --evolve)
+    a = '%10s' * len(hyp) % tuple(hyp.keys())  # hyperparam keys
+    b = '%10.3g' * len(hyp) % tuple(hyp.values())  # hyperparam values
+    # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3)
+    c = '%10.4g' * len(results) % results
+    print('\n%s\n%s\nEvolved fitness: %s\n' % (a, b, c))
+
+    if bucket:
+        url = 'gs://%s/evolve.txt' % bucket
+        if gsutil_getsize(url) > (os.path.getsize('evolve.txt') if os.path.exists('evolve.txt') else 0):
+            # download evolve.txt if larger than local
+            os.system('gsutil cp %s .' % url)
+
+    with open('evolve.txt', 'a') as f:  # append result
+        f.write(c + b + '\n')
+    x = np.unique(np.loadtxt('evolve.txt', ndmin=2),
+                  axis=0)  # load unique rows
+    x = x[np.argsort(-fitness(x))]  # sort
+    np.savetxt('evolve.txt', x, '%10.3g')  # save sort by fitness
+
+    # Save yaml
+    for i, k in enumerate(hyp.keys()):
+        hyp[k] = float(x[0, i + 7])
+    with open(yaml_file, 'w') as f:
+        results = tuple(x[0, :7])
+        # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3)
+        c = '%10.4g' * len(results) % results
+        f.write('# Hyperparameter Evolution Results\n# Generations: %g\n# Metrics: ' % len(
+            x) + c + '\n\n')
+        yaml.dump(hyp, f, sort_keys=False)
+
+    if bucket:
+        os.system('gsutil cp evolve.txt %s gs://%s' %
+                  (yaml_file, bucket))  # upload
+
+
+def apply_classifier(x, model, img, im0):
+    # applies a second stage classifier to yolo outputs
+    im0 = [im0] if isinstance(im0, np.ndarray) else im0
+    for i, d in enumerate(x):  # per image
+        if d is not None and len(d):
+            d = d.clone()
+
+            # Reshape and pad cutouts
+            b = xyxy2xywh(d[:, :4])  # boxes
+            b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1)  # rectangle to square
+            b[:, 2:] = b[:, 2:] * 1.3 + 30  # pad
+            d[:, :4] = xywh2xyxy(b).long()
+
+            # Rescale boxes from img_size to im0 size
+            scale_coords(img.shape[2:], d[:, :4], im0[i].shape)
+
+            # Classes
+            pred_cls1 = d[:, 5].long()
+            ims = []
+            for j, a in enumerate(d):  # per item
+                cutout = im0[i][int(a[1]):int(a[3]), int(a[0]):int(a[2])]
+                im = cv2.resize(cutout, (224, 224))  # BGR
+                # cv2.imwrite('test%i.jpg' % j, cutout)
+
+                # BGR to RGB, to 3x416x416
+                im = im[:, :, ::-1].transpose(2, 0, 1)
+                im = np.ascontiguousarray(
+                    im, dtype=np.float32)  # uint8 to float32
+                im /= 255.0  # 0 - 255 to 0.0 - 1.0
+                ims.append(im)
+
+            pred_cls2 = model(torch.Tensor(ims).to(d.device)
+                              ).argmax(1)  # classifier prediction
+            # retain matching class detections
+            x[i] = x[i][pred_cls1 == pred_cls2]
+
+    return x
+
+
+def increment_path(path, exist_ok=True, sep=''):
+    # Increment path, i.e. runs/exp --> runs/exp{sep}0, runs/exp{sep}1 etc.
+    path = Path(path)  # os-agnostic
+    if (path.exists() and exist_ok) or (not path.exists()):
+        return str(path)
+    else:
+        dirs = glob.glob(f"{path}{sep}*")  # similar paths
+        matches = [re.search(rf"%s{sep}(\d+)" % path.stem, d) for d in dirs]
+        i = [int(m.groups()[0]) for m in matches if m]  # indices
+        n = max(i) + 1 if i else 2  # increment number
+        return f"{path}{sep}{n}"  # update path
+
+
+def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True):
+    shape = img.shape[:2]  # current shape [height, width]
+    if isinstance(new_shape, int):
+        new_shape = (new_shape, new_shape)
+
+    # Scale ratio (new / old)
+    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
+    if not scaleup:  # only scale down, do not scale up (for better test mAP)
+        r = min(r, 1.0)
+
+    # Compute padding
+    ratio = r, r  # width, height ratios
+    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
+    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - \
+        new_unpad[1]  # wh padding
+    if auto:  # minimum rectangle
+        dw, dh = np.mod(dw, 32), np.mod(dh, 32)  # wh padding
+    elif scaleFill:  # stretch
+        dw, dh = 0.0, 0.0
+        new_unpad = (new_shape[1], new_shape[0])
+        ratio = new_shape[1] / shape[1], new_shape[0] / \
+            shape[0]  # width, height ratios
+
+    dw /= 2  # divide padding into 2 sides
+    dh /= 2
+
+    if shape[::-1] != new_unpad:  # resize
+        img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
+    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
+    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
+    img = cv2.copyMakeBorder(
+        img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
+    return img, ratio, (dw, dh)
+

object_detector/utils/google_utils.py

@@ -0,0 +1,122 @@
+# Google utils: https://cloud.google.com/storage/docs/reference/libraries
+
+import os
+import platform
+import subprocess
+import time
+from pathlib import Path
+
+import torch
+
+
+def gsutil_getsize(url=''):
+    # gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du
+    s = subprocess.check_output('gsutil du %s' % url, shell=True).decode('utf-8')
+    return eval(s.split(' ')[0]) if len(s) else 0  # bytes
+
+
+def attempt_download(weights):
+    # Attempt to download pretrained weights if not found locally
+    weights = weights.strip().replace("'", '')
+    file = Path(weights).name.lower()
+
+    msg = weights + ' missing, try downloading from https://github.com/ultralytics/yolov5/releases/'
+    models = ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']  # available models
+    redundant = False  # offer second download option
+
+    if file in models and not os.path.isfile(weights):
+        # Google Drive
+        # d = {'yolov5s.pt': '1R5T6rIyy3lLwgFXNms8whc-387H0tMQO',
+        #      'yolov5m.pt': '1vobuEExpWQVpXExsJ2w-Mbf3HJjWkQJr',
+        #      'yolov5l.pt': '1hrlqD1Wdei7UT4OgT785BEk1JwnSvNEV',
+        #      'yolov5x.pt': '1mM8aZJlWTxOg7BZJvNUMrTnA2AbeCVzS'}
+        # r = gdrive_download(id=d[file], name=weights) if file in d else 1
+        # if r == 0 and os.path.exists(weights) and os.path.getsize(weights) > 1E6:  # check
+        #    return
+
+        try:  # GitHub
+            url = 'https://github.com/ultralytics/yolov5/releases/download/v3.1/' + file
+            print('Downloading %s to %s...' % (url, weights))
+            torch.hub.download_url_to_file(url, weights)
+            assert os.path.exists(weights) and os.path.getsize(weights) > 1E6  # check
+        except Exception as e:  # GCP
+            print('Download error: %s' % e)
+            assert redundant, 'No secondary mirror'
+            url = 'https://storage.googleapis.com/ultralytics/yolov5/ckpt/' + file
+            print('Downloading %s to %s...' % (url, weights))
+            r = os.system('curl -L %s -o %s' % (url, weights))  # torch.hub.download_url_to_file(url, weights)
+        finally:
+            if not (os.path.exists(weights) and os.path.getsize(weights) > 1E6):  # check
+                os.remove(weights) if os.path.exists(weights) else None  # remove partial downloads
+                print('ERROR: Download failure: %s' % msg)
+            print('')
+            return
+
+
+def gdrive_download(id='1n_oKgR81BJtqk75b00eAjdv03qVCQn2f', name='coco128.zip'):
+    # Downloads a file from Google Drive. from utils.google_utils import *; gdrive_download()
+    t = time.time()
+
+    print('Downloading https://drive.google.com/uc?export=download&id=%s as %s... ' % (id, name), end='')
+    os.remove(name) if os.path.exists(name) else None  # remove existing
+    os.remove('cookie') if os.path.exists('cookie') else None
+
+    # Attempt file download
+    out = "NUL" if platform.system() == "Windows" else "/dev/null"
+    os.system('curl -c ./cookie -s -L "drive.google.com/uc?export=download&id=%s" > %s ' % (id, out))
+    if os.path.exists('cookie'):  # large file
+        s = 'curl -Lb ./cookie "drive.google.com/uc?export=download&confirm=%s&id=%s" -o %s' % (get_token(), id, name)
+    else:  # small file
+        s = 'curl -s -L -o %s "drive.google.com/uc?export=download&id=%s"' % (name, id)
+    r = os.system(s)  # execute, capture return
+    os.remove('cookie') if os.path.exists('cookie') else None
+
+    # Error check
+    if r != 0:
+        os.remove(name) if os.path.exists(name) else None  # remove partial
+        print('Download error ')  # raise Exception('Download error')
+        return r
+
+    # Unzip if archive
+    if name.endswith('.zip'):
+        print('unzipping... ', end='')
+        os.system('unzip -q %s' % name)  # unzip
+        os.remove(name)  # remove zip to free space
+
+    print('Done (%.1fs)' % (time.time() - t))
+    return r
+
+
+def get_token(cookie="./cookie"):
+    with open(cookie) as f:
+        for line in f:
+            if "download" in line:
+                return line.split()[-1]
+    return ""
+
+# def upload_blob(bucket_name, source_file_name, destination_blob_name):
+#     # Uploads a file to a bucket
+#     # https://cloud.google.com/storage/docs/uploading-objects#storage-upload-object-python
+#
+#     storage_client = storage.Client()
+#     bucket = storage_client.get_bucket(bucket_name)
+#     blob = bucket.blob(destination_blob_name)
+#
+#     blob.upload_from_filename(source_file_name)
+#
+#     print('File {} uploaded to {}.'.format(
+#         source_file_name,
+#         destination_blob_name))
+#
+#
+# def download_blob(bucket_name, source_blob_name, destination_file_name):
+#     # Uploads a blob from a bucket
+#     storage_client = storage.Client()
+#     bucket = storage_client.get_bucket(bucket_name)
+#     blob = bucket.blob(source_blob_name)
+#
+#     blob.download_to_filename(destination_file_name)
+#
+#     print('Blob {} downloaded to {}.'.format(
+#         source_blob_name,
+#         destination_file_name))

object_detector/utils/metrics.py

@@ -0,0 +1,201 @@
+# Model validation metrics
+
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+
+from . import general
+
+
+def fitness(x):
+    # Model fitness as a weighted combination of metrics
+    w = [0.0, 0.0, 0.1, 0.9]  # weights for [P, R, mAP@0.5, mAP@0.5:0.95]
+    return (x[:, :4] * w).sum(1)
+
+
+def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='precision-recall_curve.png', names=[]):
+    """ Compute the average precision, given the recall and precision curves.
+    Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
+    # Arguments
+        tp:  True positives (nparray, nx1 or nx10).
+        conf:  Objectness value from 0-1 (nparray).
+        pred_cls:  Predicted object classes (nparray).
+        target_cls:  True object classes (nparray).
+        plot:  Plot precision-recall curve at mAP@0.5
+        save_dir:  Plot save directory
+    # Returns
+        The average precision as computed in py-faster-rcnn.
+    """
+
+    # Sort by objectness
+    i = np.argsort(-conf)
+    tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
+
+    # Find unique classes
+    unique_classes = np.unique(target_cls)
+
+    # Create Precision-Recall curve and compute AP for each class
+    px, py = np.linspace(0, 1, 1000), []  # for plotting
+    pr_score = 0.1  # score to evaluate P and R https://github.com/ultralytics/yolov3/issues/898
+    s = [unique_classes.shape[0], tp.shape[1]]  # number class, number iou thresholds (i.e. 10 for mAP0.5...0.95)
+    ap, p, r = np.zeros(s), np.zeros(s), np.zeros(s)
+    for ci, c in enumerate(unique_classes):
+        i = pred_cls == c
+        n_l = (target_cls == c).sum()  # number of labels
+        n_p = i.sum()  # number of predictions
+
+        if n_p == 0 or n_l == 0:
+            continue
+        else:
+            # Accumulate FPs and TPs
+            fpc = (1 - tp[i]).cumsum(0)
+            tpc = tp[i].cumsum(0)
+
+            # Recall
+            recall = tpc / (n_l + 1e-16)  # recall curve
+            r[ci] = np.interp(-pr_score, -conf[i], recall[:, 0])  # r at pr_score, negative x, xp because xp decreases
+
+            # Precision
+            precision = tpc / (tpc + fpc)  # precision curve
+            p[ci] = np.interp(-pr_score, -conf[i], precision[:, 0])  # p at pr_score
+
+            # AP from recall-precision curve
+            for j in range(tp.shape[1]):
+                ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j])
+                if plot and (j == 0):
+                    py.append(np.interp(px, mrec, mpre))  # precision at mAP@0.5
+
+    # Compute F1 score (harmonic mean of precision and recall)
+    f1 = 2 * p * r / (p + r + 1e-16)
+
+    if plot:
+        plot_pr_curve(px, py, ap, save_dir, names)
+
+    return p, r, ap, f1, unique_classes.astype('int32')
+
+
+def compute_ap(recall, precision):
+    """ Compute the average precision, given the recall and precision curves.
+    Source: https://github.com/rbgirshick/py-faster-rcnn.
+    # Arguments
+        recall:    The recall curve (list).
+        precision: The precision curve (list).
+    # Returns
+        The average precision as computed in py-faster-rcnn.
+    """
+
+    # Append sentinel values to beginning and end
+    mrec = recall  # np.concatenate(([0.], recall, [recall[-1] + 1E-3]))
+    mpre = precision  # np.concatenate(([0.], precision, [0.]))
+
+    # Compute the precision envelope
+    mpre = np.flip(np.maximum.accumulate(np.flip(mpre)))
+
+    # Integrate area under curve
+    method = 'interp'  # methods: 'continuous', 'interp'
+    if method == 'interp':
+        x = np.linspace(0, 1, 101)  # 101-point interp (COCO)
+        ap = np.trapz(np.interp(x, mrec, mpre), x)  # integrate
+    else:  # 'continuous'
+        i = np.where(mrec[1:] != mrec[:-1])[0]  # points where x axis (recall) changes
+        ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])  # area under curve
+
+    return ap, mpre, mrec
+
+
+class ConfusionMatrix:
+    # Updated version of https://github.com/kaanakan/object_detection_confusion_matrix
+    def __init__(self, nc, conf=0.25, iou_thres=0.45):
+        self.matrix = np.zeros((nc + 1, nc + 1))
+        self.nc = nc  # number of classes
+        self.conf = conf
+        self.iou_thres = iou_thres
+
+    def process_batch(self, detections, labels):
+        """
+        Return intersection-over-union (Jaccard index) of boxes.
+        Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
+        Arguments:
+            detections (Array[N, 6]), x1, y1, x2, y2, conf, class
+            labels (Array[M, 5]), class, x1, y1, x2, y2
+        Returns:
+            None, updates confusion matrix accordingly
+        """
+        detections = detections[detections[:, 4] > self.conf]
+        gt_classes = labels[:, 0].int()
+        detection_classes = detections[:, 5].int()
+        iou = general.box_iou(labels[:, 1:], detections[:, :4])
+
+        x = torch.where(iou > self.iou_thres)
+        if x[0].shape[0]:
+            matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()
+            if x[0].shape[0] > 1:
+                matches = matches[matches[:, 2].argsort()[::-1]]
+                matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
+                matches = matches[matches[:, 2].argsort()[::-1]]
+                matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
+        else:
+            matches = np.zeros((0, 3))
+
+        n = matches.shape[0] > 0
+        m0, m1, _ = matches.transpose().astype(np.int16)
+        for i, gc in enumerate(gt_classes):
+            j = m0 == i
+            if n and sum(j) == 1:
+                self.matrix[gc, detection_classes[m1[j]]] += 1  # correct
+            else:
+                self.matrix[gc, self.nc] += 1  # background FP
+
+        if n:
+            for i, dc in enumerate(detection_classes):
+                if not any(m1 == i):
+                    self.matrix[self.nc, dc] += 1  # background FN
+
+    def matrix(self):
+        return self.matrix
+
+    def plot(self, save_dir='', names=()):
+        try:
+            import seaborn as sn
+
+            array = self.matrix / (self.matrix.sum(0).reshape(1, self.nc + 1) + 1E-6)  # normalize
+            array[array < 0.005] = np.nan  # don't annotate (would appear as 0.00)
+
+            fig = plt.figure(figsize=(12, 9), tight_layout=True)
+            sn.set(font_scale=1.0 if self.nc < 50 else 0.8)  # for label size
+            labels = (0 < len(names) < 99) and len(names) == self.nc  # apply names to ticklabels
+            sn.heatmap(array, annot=self.nc < 30, annot_kws={"size": 8}, cmap='Blues', fmt='.2f', square=True,
+                       xticklabels=names + ['background FN'] if labels else "auto",
+                       yticklabels=names + ['background FP'] if labels else "auto").set_facecolor((1, 1, 1))
+            fig.axes[0].set_xlabel('True')
+            fig.axes[0].set_ylabel('Predicted')
+            fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250)
+        except Exception as e:
+            pass
+
+    def print(self):
+        for i in range(self.nc + 1):
+            print(' '.join(map(str, self.matrix[i])))
+
+
+# Plots ----------------------------------------------------------------------------------------------------------------
+
+def plot_pr_curve(px, py, ap, save_dir='.', names=()):
+    fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
+    py = np.stack(py, axis=1)
+
+    if 0 < len(names) < 21:  # show mAP in legend if < 10 classes
+        for i, y in enumerate(py.T):
+            ax.plot(px, y, linewidth=1, label=f'{names[i]} %.3f' % ap[i, 0])  # plot(recall, precision)
+    else:
+        ax.plot(px, py, linewidth=1, color='grey')  # plot(recall, precision)
+
+    ax.plot(px, py.mean(1), linewidth=3, color='blue', label='all classes %.3f mAP@0.5' % ap[:, 0].mean())
+    ax.set_xlabel('Recall')
+    ax.set_ylabel('Precision')
+    ax.set_xlim(0, 1)
+    ax.set_ylim(0, 1)
+    plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
+    fig.savefig(Path(save_dir) / 'precision_recall_curve.png', dpi=250)

object_detector/utils/torch_utils.py

@@ -0,0 +1,242 @@
+# PyTorch utils
+
+import logging
+import math
+import os
+import time
+from contextlib import contextmanager
+from copy import deepcopy
+
+import torch
+import torch.backends.cudnn as cudnn
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision
+
+logger = logging.getLogger(__name__)
+
+
+@contextmanager
+def torch_distributed_zero_first(local_rank: int):
+    """
+    Decorator to make all processes in distributed training wait for each local_master to do something.
+    """
+    if local_rank not in [-1, 0]:
+        torch.distributed.barrier()
+    yield
+    if local_rank == 0:
+        torch.distributed.barrier()
+
+
+def init_torch_seeds(seed=0):
+    # Speed-reproducibility tradeoff https://pytorch.org/docs/stable/notes/randomness.html
+    torch.manual_seed(seed)
+    if seed == 0:  # slower, more reproducible
+        cudnn.deterministic = True
+        cudnn.benchmark = False
+    else:  # faster, less reproducible
+        cudnn.deterministic = False
+        cudnn.benchmark = True
+
+
+def select_device(device='', batch_size=None):
+    # device = 'cpu' or '0' or '0,1,2,3'
+    cpu_request = device.lower() == 'cpu'
+    if device and not cpu_request:  # if device requested other than 'cpu'
+        os.environ['CUDA_VISIBLE_DEVICES'] = device  # set environment variable
+        assert torch.cuda.is_available(), 'CUDA unavailable, invalid device %s requested' % device  # check availablity
+
+    cuda = False if cpu_request else torch.cuda.is_available()
+    if cuda:
+        c = 1024 ** 2  # bytes to MB
+        ng = torch.cuda.device_count()
+        if ng > 1 and batch_size:  # check that batch_size is compatible with device_count
+            assert batch_size % ng == 0, 'batch-size %g not multiple of GPU count %g' % (batch_size, ng)
+        x = [torch.cuda.get_device_properties(i) for i in range(ng)]
+        s = f'Using torch {torch.__version__} '
+        for i in range(0, ng):
+            if i == 1:
+                s = ' ' * len(s)
+            logger.info("%sCUDA:%g (%s, %dMB)" % (s, i, x[i].name, x[i].total_memory / c))
+    else:
+        logger.info(f'Using torch {torch.__version__} CPU')
+
+    logger.info('')  # skip a line
+    return torch.device('cuda:0' if cuda else 'cpu')
+
+
+def time_synchronized():
+    torch.cuda.synchronize() if torch.cuda.is_available() else None
+    return time.time()
+
+
+def is_parallel(model):
+    return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)
+
+
+def intersect_dicts(da, db, exclude=()):
+    # Dictionary intersection of matching keys and shapes, omitting 'exclude' keys, using da values
+    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}
+
+
+def initialize_weights(model):
+    for m in model.modules():
+        t = type(m)
+        if t is nn.Conv2d:
+            pass  # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+        elif t is nn.BatchNorm2d:
+            m.eps = 1e-3
+            m.momentum = 0.03
+        elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6]:
+            m.inplace = True
+
+
+def find_modules(model, mclass=nn.Conv2d):
+    # Finds layer indices matching module class 'mclass'
+    return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)]
+
+
+def sparsity(model):
+    # Return global model sparsity
+    a, b = 0., 0.
+    for p in model.parameters():
+        a += p.numel()
+        b += (p == 0).sum()
+    return b / a
+
+
+def prune(model, amount=0.3):
+    # Prune model to requested global sparsity
+    import torch.nn.utils.prune as prune
+    print('Pruning model... ', end='')
+    for name, m in model.named_modules():
+        if isinstance(m, nn.Conv2d):
+            prune.l1_unstructured(m, name='weight', amount=amount)  # prune
+            prune.remove(m, 'weight')  # make permanent
+    print(' %.3g global sparsity' % sparsity(model))
+
+
+def fuse_conv_and_bn(conv, bn):
+    # Fuse convolution and batchnorm layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/
+    fusedconv = nn.Conv2d(conv.in_channels,
+                          conv.out_channels,
+                          kernel_size=conv.kernel_size,
+                          stride=conv.stride,
+                          padding=conv.padding,
+                          groups=conv.groups,
+                          bias=True).requires_grad_(False).to(conv.weight.device)
+
+    # prepare filters
+    w_conv = conv.weight.clone().view(conv.out_channels, -1)
+    w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
+    fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.size()))
+
+    # prepare spatial bias
+    b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias
+    b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
+    fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)
+
+    return fusedconv
+
+
+def model_info(model, verbose=False, img_size=640):
+    # Model information. img_size may be int or list, i.e. img_size=640 or img_size=[640, 320]
+    n_p = sum(x.numel() for x in model.parameters())  # number parameters
+    n_g = sum(x.numel() for x in model.parameters() if x.requires_grad)  # number gradients
+    if verbose:
+        print('%5s %40s %9s %12s %20s %10s %10s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma'))
+        for i, (name, p) in enumerate(model.named_parameters()):
+            name = name.replace('module_list.', '')
+            print('%5g %40s %9s %12g %20s %10.3g %10.3g' %
+                  (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
+
+    try:  # FLOPS
+        from thop import profile
+        stride = int(model.stride.max())
+        img = torch.zeros((1, 3, stride, stride), device=next(model.parameters()).device)  # input
+        flops = profile(deepcopy(model), inputs=(img,), verbose=False)[0] / 1E9 * 2  # stride FLOPS
+        img_size = img_size if isinstance(img_size, list) else [img_size, img_size]  # expand if int/float
+        fs = ', %.1f GFLOPS' % (flops * img_size[0] / stride * img_size[1] / stride)  # 640x640 FLOPS
+    except (ImportError, Exception):
+        fs = ''
+
+    logger.info(f"Model Summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}")
+
+
+def load_classifier(name='resnet101', n=2):
+    # Loads a pretrained model reshaped to n-class output
+    model = torchvision.models.__dict__[name](pretrained=True)
+
+    # ResNet model properties
+    # input_size = [3, 224, 224]
+    # input_space = 'RGB'
+    # input_range = [0, 1]
+    # mean = [0.485, 0.456, 0.406]
+    # std = [0.229, 0.224, 0.225]
+
+    # Reshape output to n classes
+    filters = model.fc.weight.shape[1]
+    model.fc.bias = nn.Parameter(torch.zeros(n), requires_grad=True)
+    model.fc.weight = nn.Parameter(torch.zeros(n, filters), requires_grad=True)
+    model.fc.out_features = n
+    return model
+
+
+def scale_img(img, ratio=1.0, same_shape=False):  # img(16,3,256,416), r=ratio
+    # scales img(bs,3,y,x) by ratio
+    if ratio == 1.0:
+        return img
+    else:
+        h, w = img.shape[2:]
+        s = (int(h * ratio), int(w * ratio))  # new size
+        img = F.interpolate(img, size=s, mode='bilinear', align_corners=False)  # resize
+        if not same_shape:  # pad/crop img
+            gs = 32  # (pixels) grid size
+            h, w = [math.ceil(x * ratio / gs) * gs for x in (h, w)]
+        return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447)  # value = imagenet mean
+
+
+def copy_attr(a, b, include=(), exclude=()):
+    # Copy attributes from b to a, options to only include [...] and to exclude [...]
+    for k, v in b.__dict__.items():
+        if (len(include) and k not in include) or k.startswith('_') or k in exclude:
+            continue
+        else:
+            setattr(a, k, v)
+
+
+class ModelEMA:
+    """ Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models
+    Keep a moving average of everything in the model state_dict (parameters and buffers).
+    This is intended to allow functionality like
+    https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
+    A smoothed version of the weights is necessary for some training schemes to perform well.
+    This class is sensitive where it is initialized in the sequence of model init,
+    GPU assignment and distributed training wrappers.
+    """
+
+    def __init__(self, model, decay=0.9999, updates=0):
+        # Create EMA
+        self.ema = deepcopy(model.module if is_parallel(model) else model).eval()  # FP32 EMA
+        # if next(model.parameters()).device.type != 'cpu':
+        #     self.ema.half()  # FP16 EMA
+        self.updates = updates  # number of EMA updates
+        self.decay = lambda x: decay * (1 - math.exp(-x / 2000))  # decay exponential ramp (to help early epochs)
+        for p in self.ema.parameters():
+            p.requires_grad_(False)
+
+    def update(self, model):
+        # Update EMA parameters
+        with torch.no_grad():
+            self.updates += 1
+            d = self.decay(self.updates)
+
+            msd = model.module.state_dict() if is_parallel(model) else model.state_dict()  # model state_dict
+            for k, v in self.ema.state_dict().items():
+                if v.dtype.is_floating_point:
+                    v *= d
+                    v += (1. - d) * msd[k].detach()
+
+    def update_attr(self, model, include=(), exclude=('process_group', 'reducer')):
+        # Update EMA attributes
+        copy_attr(self.ema, model, include, exclude)

requirements.txt

@@ -0,0 +1,5 @@
+opencv-python
+torchvision
+tqdm
+flask==2.2.2
+gunicorn==20.1.0