FastDeploy+英特尔NUC+大疆无人机梦幻联动！推动智慧城市巡检应用落地开发者社区

FastDeploy+英特尔NUC+大疆无人机梦幻联动！推动智慧城市巡检应用落地

worker_processes  1;
events {
    worker_connections  1024;
rtmp {
    server {
        listen 8899;
        chunk_size 4000;
        application live {
             live on;
             allow publish all;
             allow play all;
}

import cv2
rtmpUrl = 'rtmp://192.168.31.246:8899/live'
vid = cv2.VideoCapture(rtmpUrl)
while vid.isOpened():
    ret,frame = vid.read()
    cv2.imshow('RTMP Test',frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
vid.release()
cv2.destroyAllWindows()

python PaddleSeg/export.py \
       --config ppliteseg_stdc1.yml \ #配置文件路径，根据实际情况修改
       --model_path output_ppliteseg_stdc1/best_model/model.pdparams \ 
# 动态图模型路径，根据实际情况修改
       --save_dir inference_ppliteseg_stdc1 \ # 导出目录
       --input_shape 1 3 512 1024 #模型输入尺寸

python PaddleDetection/tools/export_model.py \
       -c PaddleDetection/configs/ppyolo/ppyolo_tiny_650e_coco.yml \ # 配置文件路径，根据实际情况修改
       --output_dir=./ppyolo_inference_model \ # 保存模型的路径
       -o weights=ppyolo_tiny_650e_coco.pdparams # 动态图模型路径

import cv2
import numpy as np
import fastdeploy as fd
from PIL import Image
from collections import Counter
def FastdeployOption(device=0):
    option = fd.RuntimeOption()
    if device == 0:
        option.use_gpu()
    else:
        # 使用OpenVino推理
        option.use_openvino_backend()
        option.use_cpu()
    return option

class SegModel(object):
    def __init__(self, device=0) -> None:
        self.segModel = fd.vision.segmentation.ppseg.PaddleSegModel(
            model_file = 'inference/ppliteseg/model.pdmodel',
            params_file = 'inference/ppliteseg/model.pdiparams',
            config_file = 'inference/ppliteseg/deploy.yaml',
            runtime_option=FastdeployOption(device)
    def predict(self, img):
        segResult = self.segModel.predict(img)
        result = self.postprocess(segResult)
        visImg = fd.vision.vis_segmentation(img, segResult)
        return result, visImg
    def postprocess(self, result):
        resultShape = result.shape
        labelmap = result.label_map
        labelmapCount = dict(Counter(labelmap))
        pixelTotal = int(resultShape[0] * resultShape[1])
        # 统计建筑率和绿地率
        buildingRate, greenRate = 0, 0
        if 8 in labelmapCount:
            buildingRate = round(labelmapCount[8] / pixelTotal* 100, 3) 
        if 9 in labelmapCount:
            greenRate = round(labelmapCount[9] / pixelTotal * 100 , 3)
        return {"building": buildingRate, "green": greenRate}

class DetModel(object):
    def __init__(self, device=0) -> None:
        self.detModel = fd.vision.detection.PPYOLO(
            model_file = 'inference/ppyolo/model.pdmodel',
            params_file = 'inference/ppyolo/model.pdiparams',
            config_file = 'inference/ppyolo/infer_cfg.yml',
            runtime_option=FastdeployOption(device)
        # 阈值设置
        self.threshold = 0.3
    def predict(self, img):
        detResult = self.detModel.predict(img.copy())
        result = self.postprocess(detResult)
        visImg = fd.vision.vis_detection(img, detResult, self.threshold, 2)
        return result, visImg
    def postprocess(self, result):
        # 得到结果
        detIds = result.label_ids
        detScores = result.scores
        # 统计数量
        humanNum, CarNum = 0, 0
        for i in range(len(detIds)):