一、Flask简介
Flask是一个轻量级的可定制框架，使用Python语言编写，较其他同类型框架更为灵活、轻便、安全且容易上手。它可以很好地结合MVC模式进行开发，开发人员分工合作，小型团队在短时间内就可以完成功能丰富的中小型网站或Web服务的实现。另外，Flask还有很强的定制性，用户可以根据自己的需求来添加相应的功能，在保持核心功能简单的同时实现功能的丰富与扩展，其强大的插件库可以让用户实现个性化的网站定制，开发出功能强大的网站。
二、Flask启动代码
Flask的安装也比较简单，百度上有很多教程，建立一个app_VD.py，代码如下：
from flask import Flask
from flask_cors import CORS
from VideoDiagnose import vd_Process
#加载配置
app = Flask(__name__)
app.config.from_pyfile("config.py")
CORS(app)
app.register_blueprint(vd_Process, url_prefix="/")
#启动算法sever
if __name__ == '__main__':
app.run(host=app.config['HOST'], port=app.config['PORT'], debug=app.config['DEBUG'])其中， config.py为配置文件，内容参考代码段如下：
PORT = '5585'
HOST = '0.0.0.0'
DEBUG = True以上为Flask启动流程，直接使用python app_VD.py即可执行程序，执行后输出：
三、base64图像部分设计
3.1 json参数
通过postman以post的方式发送图像json文件，内容如下：
{
"algConfig": {
"b_colordistortion": 1,
"b_contrast": 1,
"b_definition": 1,
"b_noise": 1,
"b_shade": 1,
"b_signalloss": 1,
"b_strip": 1,
"b_highBrightness": 1,
"b_lowBrightness": 1,
"b_stationaryframe": 1,
"f_Quality": 0.2
},
"images": [
{
"base64Data": "/9j//gARTGF2YzU4LjEzNC4xMDAA/9sAQwAIBAQEBAQFBQUFBQUGBgYGBgYGBgYGBgYGBwcHCAgIBwcHBgYHBwgICAgJCQkICAgICQkKCgoMDAsLDg4OEREU/90ABAAU/8QBogAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoLAQADAQEBAQEBAQEBAAAAAAAAAQIDBAUGBwgJCgsQAAIBAwMCBAMFBQQEAAABfQECAwAEEQUSITFBBhNRYQcicRQygZGhCCNCscEVUtHwJDNicoIJChYXGBkaJSYnKCkqNDU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6g4SFhoeIiYqSk5SVlpeYmZqio6Slpqeoqaqys7S1tre4ubrCw8TFxsfIycrS09TV1tfY2drh4uPk5ebn6Onq8fLz9PX29/j5+hEAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/8AAEQgAtAFAAwEiAAIRAAMRAP/aAAwDAQACEQMRAD8A9VopaSgAopaSgBaKSlBoAOtFLSdaADFFLSYoAKSnUlABRiiigAxRRRQAlFGKKAClpKKAFoozRmgAoozRmgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAP/Q9WopBS0AFFFFABRRRigBDQKXFHagAooooAKKM0CgAwaKWkNABRRiigApKWigAxSYpeaTmgAopcUYoASilxRigBKM0tFABmjNFFABmjNFIaAFzRmkooAXNGaSigBc0ZpKKAFzRmkxRigBc0ZpMUYoA//R9VpaKKACiijFABRRiigBDS0mKWgBKKWigBKKXFAoASjvS0UABpKXijFACUUvFFABSUYoxQAc0UtFACUUuKMUAJRS4oxQAlFLijFACUUuKMUAJRilxRQAYpKWigBKWk5oxQAtFJijFAC0UlGKAP/S9WopMUYoAWikxRigBaKTFLQAUUUUAFFFFABRRRmgAo70UUAApaSloAKbinUnFABikpaKAEpaDSUALRSUUALRRijFABRRijFABRRRigAooooAKKKKACiiigAooooAKKKKAP/T9WooooAKKM0maAFoo4ooAKKKKAAUopOaU0ABpKKKACiiigAoFFFAAaKMUUAApeKSigAooooAKWiigBKKWjigBKKXijigBKKXiigBKKWigAxRiiigBMUUtJQAUUUUAFFGKXFAH//U9WpM0tGaADFFGaKACg0Cl5oASgUc0UAFFFGaACijFGKACilxRQAlLRSUAHNLRSUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFLSCloASil4o4oAMUhpaKACikooAWikooA//1fVqWkooAWkpeaOaACijmjmgApKKOaADFLRRQAnNFLijFACZopcUYoASloxRQAhopaMUAJRS4oxQAlFLijFACUUuKMUAJRS4oxQAlFLijFACUUtFABRRSUALRSUtACUtJRQAtJS0UAJRS0lAH//W9Wo5oooAWikooAXmikooAKKKKAFpKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKMUUUAGKKKKAFopKKAFpKKXFAH//X9WoooxQAUUUUAApeaMUlABRRmlzQAlFLmjNACUUuaM0AJRS5ozQAlFLmjNACUUuaM0AJRS5ozQAlFLmjNACUUuaM0AJRS5ozQAlFLmjNACUUuaM0AJS8UZozQAlFLmjNACUUuaM0AJRS5ozQAUlLmkoA/9D1ajmilxQAUUUUAFJQaKACiiigAooooAKKWigBKKWigBKKWigBKKWigBKKWigBKKWigBKKWigBKKWigBKKWigBKKWigBKKWigBKKWigBKKWigBKKWigBKKWkoA/9H1fNGaSg0ALSUUUAFFFFABS8UlFAC8UUlFAC0UlFAC0UlFAC0UlFAC0UlFAC0UlFAC0UlFAC0UlFAC0UlFAC0UlFAC0UlFAC0UlFAC0UUlAC0UlFAC0ZpKKAFpKKXFAH//0vV+9HajvR2oAKQ0tIaACiiigAooooAKKKKACiiigAooooABS0CigAooooAKKKKACiiigAooooAKKKKACiiigBDRQaKACiiigANLSGlFABijFFFABiiiigBD1o60HrQKAP/Z"
}
]
}3.2 接收部分代码
也就是base64部分的json数据接收和图像解析代码，解析文件为VideoDiagnose.py，代码内容如下：
import os
import cv2
import requests
import base64
import json
import threading
import numpy as np
import time
import datetime
from flask import Blueprint
from flask import request
from flask import current_app as app
import queue
vd_Process = Blueprint('vd_Process', __name__)
device = torch.device('cpu')
def image_to_base64(filepath):
"""将 JPG 文件转换为 Base64 编码字符串"""
with open(filepath, "rb") as image_file:
# 读取文件内容到字节串
encoded_string = base64.b64encode(image_file.read())
# 将字节串解码为 UTF-8 字符串
return encoded_string.decode('utf-8')
def base64_to_image(base64_string):
# Base64 字符串解码为字节
img_data = base64.b64decode(base64_string)
# 将字节转换为 NumPy 数组
nparr = np.frombuffer(img_data, np.uint8)
# 解码图像
img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
# cv2.imshow("img",img)
# cv2.waitKey(0)
return img
def vqd_detect(json_data):
images_Array = []
try:#检测参数是否正确
b_colordistortion = json_data["algConfig"]['b_colordistortion']#1
b_contrast = json_data["algConfig"]['b_contrast']#2
b_definition = json_data["algConfig"]['b_definition']#3
b_noise = json_data["algConfig"]['b_noise']#4
b_shade = json_data["algConfig"]['b_shade']#5
b_signalloss = json_data["algConfig"]['b_signalloss']#6
b_strip = json_data["algConfig"]['b_strip']#7
b_highBrightness = json_data["algConfig"]['b_highBrightness']#8
b_lowBrightness = json_data["algConfig"]['b_lowBrightness']#9
b_stationaryframe = json_data["algConfig"]['b_stationaryframe']#10
f_Quality = json_data["algConfig"]['f_Quality']
images = json_data['images']
for image_data in images:
base64_data = image_data["base64Data"]
if len(base64_data) > 2000:
images_Array.append(base64_data)
except Exception as e:
return "Json_Params_Error",{}
if len(images_Array) == 0 or len(images_Array) > 3:
return "No_images_Error",{}
decodeImageArray = []
try:#图像转换，变成CV图像
for image_data in images_Array:
cvImage = base64_to_image(image_data)
decodeImageArray.append(cvImage)
except Exception as e:
return "Image_Params_Error",{}
out_Result = {}
out_Result["b_normal"] = 0#0
#out_Result["n_normal"] = 1.0
out_Result["b_colordistortion"] = 0#1
out_Result["n_colordistortion"] = 1.0
out_Result["b_contrast"] = 0#2
out_Result["n_contrast"] = 1.0
out_Result["b_definition"] = 0#3
out_Result["n_definition"] = 1.0
out_Result["b_noise"] = 0#4
out_Result["n_noise"] = 1.0
out_Result["b_shade"] = 0#5
out_Result["n_shade"] = 1.0
out_Result["b_signalloss"] = 0#6
out_Result["n_signalloss"] = 1.0
out_Result["b_strip"] = 0#7
out_Result["n_strip"] = 1.0
out_Result["b_highBrightness"] = 0#8
out_Result["n_highBrightness"] = 1.0
out_Result["b_lowBrightness"] = 0#9
out_Result["n_lowBrightness"] = 1.0
out_Result["b_stationaryframe"] = 0#10
out_Result["n_stationaryframe"] = 1.0
cvResizeImage = cv2.resize(decodeImageArray[len(decodeImageArray) - 1], [480, 270])
gray_image = cv2.cvtColor(cvResizeImage, cv2.COLOR_BGR2GRAY)
cv2.imshow("image", cvResizeImage)
cv2.imshow("cut", cvResizeImage)
cv2.waitKey(0)
return "success",out_Result
@vd_Process.route('/vd_Process', methods=['POST'])
def handel_VD_Process():
timestamp = time.time()
format_time = time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(timestamp))
print(format_time)
try:
json_data = request.json
except Exception as e:
headers = {"Content-Type": "application/json"}
json_send_data = {
"data": {
"status": "Json_GET_Error",
"executeTime": format_time,
"result": {}
}
}
return json_send_data, 200, headers
status,out_Result = vqd_detect(json_data)
headers = {"Content-Type": "application/json"}
json_send_data = {
"data": {
"status": status,
"executeTime": format_time,
"result": out_Result
}
}
return json_send_data, 200, headers
if __name__ == '__main__':
print()四、运行结果
Flask执行后的状态得到一张灰白图像。 postman输出的结果如下内容所示：
{
"data": {
"executeTime": "2024-07-03 20:06:30",
"result": {
"b_colordistortion": 0,
"b_contrast": 0,
"b_definition": 0,
"b_highBrightness": 0,
"b_lowBrightness": 0,
"b_noise": 0,
"b_normal": 0,
"b_shade": 0,
"b_signalloss": 0,
"b_stationaryframe": 0,
"b_strip": 0,
"n_colordistortion": 1.0,
"n_contrast": 1.0,
"n_definition": 1.0,
"n_highBrightness": 1.0,
"n_lowBrightness": 1.0,
"n_noise": 1.0,
"n_shade": 1.0,
"n_signalloss": 1.0,
"n_stationaryframe": 1.0,
"n_strip": 1.0
},
"status": "success"
}
}