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463 lines
17 KiB
463 lines
17 KiB
9 months ago
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import datetime
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import math
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import os
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import signal
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import sys
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import threading
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import re
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import uuid
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import tempfile
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import pyexiv2
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import numpy as np
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import requests
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from PIL import Image
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from numpy import arctan
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import cv2
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from math import tan, cos, sin
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import oss2
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import paho.mqtt.client as mqtt
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import concurrent.futures
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import json
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# 客户端id
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client_id='python_gengbao'
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# 订阅主题
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longandlat_mqtt ='task/image/gps/custon'
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# 订阅主题
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appTwo ='task/image/disobey/handle'
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# 订阅主题
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weijian ='task/image/pixel/custon'
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# 发布通知主题
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pushNoticeTopic ='task/image/disobey/confirm'
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access_key = "LTAI5tMDFvYZRvrJfK7HeuEm"
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secret_key = "kP7ueRlhLbM9mvkaLtHfcje3GxgawH"
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endpoint = "oss-cn-hangzhou.aliyuncs.com"
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bucket_name = "yq-dajiang"
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auths = oss2.Auth(access_key, secret_key)
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bucket = oss2.Bucket(auths, endpoint, bucket_name)
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# 根据像素坐标生成图片和经纬度，放入集合中，返回到前端
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client = mqtt.Client(client_id=client_id)
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#截图
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def crop_image(input_image,center_x,center_y):
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print(center_x,center_y)
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#固定截图尺寸 1000*1000
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l_x = 0
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l_y=0
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r_x=1000
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r_y=1000
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if center_x>500:
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l_x=center_x-500
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r_x=center_x+500
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if r_x>=4000:
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r_x=4000
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l_x=3000
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if center_y>500:
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l_y=center_y-500
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r_y=center_y+500
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if r_y>=3000:
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r_y=3000
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l_y=2000
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print((l_x,l_y),(r_x,r_y))
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# 截取指定区域图像
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cropped_image = input_image[l_y:r_y, l_x:r_x]
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return np.array(cropped_image)
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#计算两个经纬度之间的距离，用于计算长宽
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def dimension(jingduA, weiduA,jingduB, weiduB):
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R = 6371.393
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Pi = math.pi
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a = (math.sin(math.radians(weiduA / 2 - weiduB / 2))) ** 2
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b = math.cos(weiduA * Pi / 180) * math.cos(weiduB * Pi / 180) * (
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math.sin((jingduA / 2 - jingduB / 2) * Pi / 180)) ** 2
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dimension = 2 * R * math.asin((a + b) ** 0.5) * 1000
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return dimension
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def sum(focalLength,x,altitude):
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width=4000
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#靶面宽
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ba=6.2
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#实际长度
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wr=(x*altitude*ba)/(focalLength*width)
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return wr
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def longandlat(pixel_x,pixel_y,imageMap):
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# 焦距
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focalLength =float(imageMap["focalLength"])
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# 经纬度
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longitude_str=imageMap["longitude"]
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latitude_str = imageMap["latitude"]
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# 使用正则表达式提取度、分和秒的数值
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pattern = r"(\d+)° (\d+)' ([\d.]+)\""
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longitude_match = re.match(pattern, longitude_str)
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latitude_match = re.match(pattern, latitude_str)
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print(longitude_match)
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print(type(longitude_match))
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# 度
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lg_du =int(longitude_match.group(1))
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# 分
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lg_fen =int(longitude_match.group(2))
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# 秒
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lg_seconds = float(longitude_match.group(3))
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# 度
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la_du = int(latitude_match.group(1))
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# 分
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la_fen = int(latitude_match.group(2))
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# 秒
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la_seconds =float(latitude_match.group(3))
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# 海拔
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altitude = float(imageMap["altitude"])
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# 图片宽高
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width =float(imageMap["width"])
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height =float(imageMap["height"])
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# 中点坐标
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x, y = width // 2, height // 2
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# 通过勾股定理可以计算出 1/2.3 英寸对角线的长度大约为 4.54 毫米。
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D = 2 * tan(arctan(9.826 / 2 / focalLength) / 2) * altitude
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# 因此，每个像素所代表的实际距离为(米)：
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# p =(9.826*altitude)/(width*focalLength)
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p = D / width
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# 经度每秒代表的距离 m
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# 纬度每秒代表的距离 m
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lat_p = 23.6
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long_p = 30.9
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pie=float(imageMap["xmpProperties"])
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if pie < 0:
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pie = -180 + abs(pie)
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else:
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pie = 180 - pie
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# z转换已中心点为原点的坐标体系
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px = (pixel_x - x)
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py = (y - pixel_y)
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a = math.radians(pie)
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# 坐标体系旋转后所得到的坐标
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c_a_x = cos(a) * px - sin(a) * py
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c_a_y = sin(a) * px + cos(a) * py
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ys = c_a_y * p / lat_p
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xs = c_a_x * p / long_p
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la_seconds += ys
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lg_seconds += xs
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longitude_x = lg_du + lg_fen / 60.0 + lg_seconds / 3600.0
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latitude_y = la_du + la_fen / 60.0 + la_seconds / 3600.0
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return longitude_x ,latitude_y,focalLength,altitude
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def upload_image_to_oss(imgeName,data):
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# imgeName 文件名 列：python/img.jpg
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bucket.put_object(imgeName, data)
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# 将对象权限设置为公共读，以便通过URL访问上传的图像
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bucket.put_object_acl(imgeName, oss2.OBJECT_ACL_PUBLIC_READ)
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# 获取对象URL
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url = bucket.sign_url("GET", imgeName,3600) # URL有效期为1小时
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return url.split('?')[0]
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# 连接回调函数
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def on_connect(client, userdata, flags, rc):
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"""一旦连接成功, 回调此方法"""
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rc_status = ["连接成功", "协议版本错误", "无效的客户端标识", "服务器无法使用", "用户名或密码错误", "无授权"]
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print("connect：", rc_status[rc])
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topics = [str(weijian), str(longandlat_mqtt), str(appTwo)]
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# 要订阅的主题列表
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for topic in topics:
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# 订阅主题
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print("订阅主题：", topic)
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client.subscribe(topic)
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def compute(re):
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if "sourceImgUrl" in re and "pixelCoordinate" in re:
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sourceImgUrl = re["sourceImgUrl"]
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mateSourceImgUrl = re["mateSourceImgUrl"]
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pixelCoordinate = re["pixelCoordinate"]
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imageMap = re["imageMap"]
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maplist=[]
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# 如果image1和image2都不为空，执行相关操作
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# 指向算法比对，获取图片z
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if pixelCoordinate:
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coordinateList = json.loads(pixelCoordinate)
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# 指向算法比对，获取图片
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if len(coordinateList) != 0:
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# 从URL加载图像
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resp = requests.get(sourceImgUrl)
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# 创建临时文件
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# 创建临时文件
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temp_file = tempfile.NamedTemporaryFile(delete=False)
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temp_file.write(resp.content)
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temp_file.close()
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# 前端缩放了，比例是：scale=4
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for item in coordinateList:
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lef_x = item["lef_x"]
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lef_y = item["lef_y"]
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right_x = item["right_x"]
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right_y = item["right_y"]
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# 将比例还原
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scale = 4
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lef_x = int(lef_x * scale)
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lef_y = int(lef_y * scale)
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right_x = int(right_x * scale)
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right_y = int(right_y * scale)
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x=right_x-lef_x
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y=right_y-lef_y
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# 中心坐标
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# 计算矩形框的中心点坐标
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center_x = int((lef_x + right_x) / 2)
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center_y = int((lef_y + right_y) / 2)
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# 经纬度转换
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long, lat ,focalLength,altitude= longandlat(center_x, center_y, imageMap)
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img_array = np.frombuffer(resp.content, np.uint8)
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# 将矩形框绘制到图片上
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img_copy = cv2.imdecode(img_array, cv2.IMREAD_COLOR)
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# 在图像上绘制矩形框
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cv2.rectangle(img_copy, (lef_x,lef_y), (right_x,right_y), (0, 255, 255), 10)
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# OpenCV图像对象转换为字节流格式。
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_, buffer = cv2.imencode('.jpg', img_copy)
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img_bytes = buffer.tobytes()
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# 文件名称
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unique_id = str(uuid.uuid4())
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imgName = 'python/' + unique_id + "违建图.jpg"
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# 将图片上传到阿里云OSS
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url = upload_image_to_oss(imgName, img_bytes)
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# 获取长
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w = sum(focalLength,x,altitude)
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# 获取宽
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h = sum(focalLength,y,altitude)
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# 计算矩形面积并保留两位小数
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area = round(w * h, 2)
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#截图 对比图放大
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cropped_image1=crop_image(img_copy, center_x,center_y)
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# OpenCV图像对象转换为字节流格式。
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_, buffer1 = cv2.imencode('.jpg',cropped_image1)
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img_bytes1 = buffer1.tobytes()
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# 文件名称
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unique_id1 = str(uuid.uuid4())
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imgNameOne = 'python/' + unique_id1 + "对比图违建放大图.jpg"
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# 将图片上传到阿里云OSS
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maxImages = upload_image_to_oss(imgNameOne, img_bytes1)
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# 从URL加载图像
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resp1 = requests.get(mateSourceImgUrl)
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# 创建临时文件
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temp_file1 = tempfile.NamedTemporaryFile(delete=False)
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temp_file1.write(resp1.content)
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temp_file1.close()
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# 模板图画框
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img_array2 = np.frombuffer(resp1.content, np.uint8)
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# 将矩形框绘制到图片上
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img_copy2 = cv2.imdecode(img_array2, cv2.IMREAD_COLOR)
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# 在图像上绘制矩形框
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cv2.rectangle(img_copy2, (lef_x, lef_y), (right_x, right_y), (255, 255, 0), 10)
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# OpenCV图像对象转换为字节流格式。
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_, buffer2 = cv2.imencode('.jpg', img_copy2)
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img_bytes2 = buffer2.tobytes()
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# 文件名称
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unique_id3 = str(uuid.uuid4())
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imgName3 = 'python/' + unique_id3 + "违建图.jpg"
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# 将图片上传到阿里云OSS
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url3 = upload_image_to_oss(imgName3, img_bytes2)
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cropped_image2 = crop_image(img_copy2,center_x,center_y) # OpenCV图像对象转换为字节流格式。
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_, buffer1 = cv2.imencode('.jpg',cropped_image2)
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img_bytes2 = buffer1.tobytes()
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# 文件名称
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unique_id2 = str(uuid.uuid4())
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imgNameTwo = 'python/' + unique_id2 + "放大模板图.jpg"
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# 将图片上传到阿里云OSS
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maxMateSourceImgUrl = upload_image_to_oss(imgNameTwo, img_bytes2)
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id = re["id"]
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taskId = re["taskId"]
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map = {"lng": long, "lat": lat, "url": url, "taskImageId": id, "taskId": taskId, "images": url,
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"handleType": 1,"areaL":w,"areaW":h,"area":area,"maxMateSourceImgUrl":maxMateSourceImgUrl,
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"maxImages":maxImages,"mateSourceImgUrl":url3}
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maplist.append(map)
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return maplist
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# 根据像素坐标生成图片和经纬度，放入集合中，返回到前端
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def result(re,imgeurl,coordinateList):
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if len(coordinateList)!=0:
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print("读取")
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# 从URL加载图像
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imgeurl=str(imgeurl).replace("https", "http")
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resp = requests.get(imgeurl,stream=True)
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# 创建临时文件
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with tempfile.NamedTemporaryFile(delete=False) as temp_file:
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for chunk in resp.iter_content(chunk_size=1024):
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temp_file.write(chunk)
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print("创建")
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# 创建pyexiv2.Image对象
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# 从临时文件读取图像数据
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image = cv2.imread(temp_file.name)
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if image is not None:
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# 文件名称
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unique_id = str(uuid.uuid4())
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print("生成")
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#前端缩放了，比例是：scale=4
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for item in coordinateList:
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print(type(item))
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lef_x=item["lef_x"]
|
||
|
|
lef_y=item["lef_y"]
|
||
|
|
right_x = item["right_x"]
|
||
|
|
right_y = item["right_y"]
|
||
|
|
|
||
|
|
#将比例还原
|
||
|
|
scale=4
|
||
|
|
lef_x=lef_x*scale
|
||
|
|
lef_y=lef_y*scale
|
||
|
|
right_x=right_x*scale
|
||
|
|
right_y=right_y*scale
|
||
|
|
|
||
|
|
|
||
|
|
# # 中心坐标
|
||
|
|
# # 计算矩形框的中心点坐标
|
||
|
|
# center_x = int(lef_x+right_x / 2)
|
||
|
|
# center_y = int(lef_y+right_y / 2)
|
||
|
|
# # # 经纬度转换
|
||
|
|
# # long,lat=longandlat(image,center_x,center_y)
|
||
|
|
# # 将矩形框绘制到图片上
|
||
|
|
# nparr = np.frombuffer(resp.content, np.uint8)
|
||
|
|
# img_copy = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
|
||
|
|
# 在图像上绘制矩形框
|
||
|
|
cv2.rectangle(image, (int(lef_x),int(lef_y)), (int(right_x),int(right_y)), (0,0,255), 10)
|
||
|
|
# OpenCV图像对象转换为字节流格式。
|
||
|
|
_, buffer = cv2.imencode('.jpg', image)
|
||
|
|
img_bytes = buffer.tobytes()
|
||
|
|
imgName = 'python/' + unique_id + "违建图.jpg"
|
||
|
|
print("上传")
|
||
|
|
# 将图片上传到阿里云OSS
|
||
|
|
url = upload_image_to_oss(imgName, img_bytes)
|
||
|
|
id = re["id"]
|
||
|
|
taskId = re["taskId"]
|
||
|
|
map = {"id": id, "taskId": taskId,"discernImgUrl": url}
|
||
|
|
os.remove(temp_file.name)
|
||
|
|
return map
|
||
|
|
def messageData(msg):
|
||
|
|
topic = msg.topic
|
||
|
|
print("处理消息,时间：" + str(datetime.datetime.now()))
|
||
|
|
data = json.loads(msg.payload.decode())
|
||
|
|
# 获取图片路径、图片名称和MQTT消息的时间戳
|
||
|
|
if data:
|
||
|
|
if topic==appTwo:
|
||
|
|
print("通知")
|
||
|
|
if "id" in data:
|
||
|
|
print("数据情况",data)
|
||
|
|
id=data["id"]
|
||
|
|
notice = {"id":id }
|
||
|
|
on_publish(pushNoticeTopic, json.dumps(notice), 0)
|
||
|
|
if topic==weijian:
|
||
|
|
print("画违建矩形框")
|
||
|
|
if "sourceImgUrl" in data and "pixelCoordinate" in data:
|
||
|
|
image = data["sourceImgUrl"]
|
||
|
|
pixelCoordinate = data["pixelCoordinate"]
|
||
|
|
# 指向算法比对，获取图片
|
||
|
|
coordinateList = json.loads(pixelCoordinate)
|
||
|
|
print(type(coordinateList))
|
||
|
|
map = result(data, image, coordinateList)
|
||
|
|
if map is None:
|
||
|
|
print("生成图片出错，为null")
|
||
|
|
else:
|
||
|
|
url = "http://106.13.50.125:9006/ploughe-boot/wayline/waylineTaskImage/leakEdit"
|
||
|
|
# url = "http://192.168.1.20:9005/ploughe-boot/wayline/waylineTaskImage/leakEdit"
|
||
|
|
# 调取接口
|
||
|
|
response = requests.post(url, json=map)
|
||
|
|
message = response.text
|
||
|
|
print("接口返回值：", message)
|
||
|
|
print("结束时间:" + str(datetime.datetime.now()))
|
||
|
|
elif topic==longandlat_mqtt:
|
||
|
|
print("提交违建，生产预警")
|
||
|
|
# 创建线程池
|
||
|
|
with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
|
||
|
|
if data and "waylineTaskImages" in data:
|
||
|
|
waylineImgList = data["waylineTaskImages"]
|
||
|
|
# 提交任务给线程池
|
||
|
|
futurelist = [executor.submit(compute, re) for re in waylineImgList]
|
||
|
|
# 等待所有任务完成
|
||
|
|
concurrent.futures.wait(futurelist)
|
||
|
|
# 获取每个任务的处理结果
|
||
|
|
final_results = [item for sublist in
|
||
|
|
[future.result() for future in futurelist if future.result() is not None] for item
|
||
|
|
in
|
||
|
|
sublist]
|
||
|
|
print("返回的结果", final_results)
|
||
|
|
print("返回的结果数量", len(final_results))
|
||
|
|
if len(final_results) != 0:
|
||
|
|
data['waylineTaskHandles'] = final_results
|
||
|
|
print("提交数据：", data)
|
||
|
|
url = "http://106.13.50.125:9006/ploughe-boot/wayline/waylineTaskWhee/leakEdit"
|
||
|
|
# url = "http://192.168.1.20:9005/ploughe-boot/wayline/waylineTaskWhee/leakEdit"
|
||
|
|
# 调取接口
|
||
|
|
# 将list作为JSON数据
|
||
|
|
# 发送POST请求，并将data作为请求体
|
||
|
|
response = requests.post(url, json=data, headers={'Content-Type': 'application/json'})
|
||
|
|
# 获取响应结果
|
||
|
|
body = response.json()
|
||
|
|
print("接口返回值：", body)
|
||
|
|
print("结束时间:" + str(datetime.datetime.now()))
|
||
|
|
# 关闭线程池
|
||
|
|
executor.shutdown()
|
||
|
|
|
||
|
|
|
||
|
|
def on_message(client, userdata, msg):
|
||
|
|
# """一旦订阅到消息, 回调此方法"""
|
||
|
|
print("主题:" + msg.topic + " 消息:" + msg.payload.decode('utf-8')) # 客户端返回的消息,使用gb2312编码中文不会报错
|
||
|
|
# 多线程执行
|
||
|
|
threading.Thread(target=messageData, args=((msg,))).start()
|
||
|
|
|
||
|
|
|
||
|
|
def signal_handler(signal, frame):
|
||
|
|
print('接收到键盘中断信号，程序将退出')
|
||
|
|
sys.exit(0)
|
||
|
|
def on_publish(topic, payload, qos):
|
||
|
|
re=client.publish(topic, payload, qos)
|
||
|
|
print("结果："+str(re)+"时间："+str(datetime.datetime.now()))
|
||
|
|
|
||
|
|
def run():
|
||
|
|
# 创建客户端对象
|
||
|
|
|
||
|
|
client.username_pw_set("adminRQ", "yongqiang666")
|
||
|
|
# 设置连接回调函数
|
||
|
|
client.on_connect = on_connect
|
||
|
|
# 设置接收消息回调函数
|
||
|
|
client.on_message = on_message
|
||
|
|
# 连接到MQTT代理
|
||
|
|
client.connect("106.13.50.125", 1883, 60)
|
||
|
|
# 注册信号处理程序
|
||
|
|
signal.signal(signal.SIGINT, signal_handler)
|
||
|
|
# 保持连接并接收消息
|
||
|
|
client.loop_forever()
|
||
|
|
|
||
|
|
if __name__ == '__main__':
|
||
|
|
run()
|
||
|
|
|