+def create_url(apiKey, roadType, cctvType, minX, maxX, minY, maxY, getType="json",

+    국가교통정보센터 api 예제 실행 코드, 더 자세한 내용은

+    :param roadType: ``str`` 도로 유형 ('ex' : 고속도로, 'its' : 국도)

+    assert roadType != "ex" or "its", 'Error! roadType should be either "ex" or "its"'

+        f"roadType={roadType}&"

+def gather_cctv_list(xmin, xmax, ymin, ymax, intervals, roadType, cctvType):

+    '''

+    :param minX: 최소 경도 영역

+    :param maxX: 최대 경도 영역

+    :param minY: 최소 위도 영역

+    :param maxY: 최대 경도 영역

+    :param intervals: api를 통해서 cctv 목록을 불러올때 위경도 격자 간격

+    :param roadType: ``str`` 도로 유형 ('ex' : 고속도로, 'its' : 국도)

+    :param cctvType: ``int`` CCTV 유형 (1. 실시간 스트리밍(HLS) / 2. 동영상 파일(m3u8) / 3. 정지 영상(JPEG))

+    :return: pandas DataFrame 형태로 반환

+            주요 컬럼은 다음과 같음

+            coordx	 coordy   cctvtype	cctvformat	 cctvname	cctvurl

+

+

+    '''

+            url = create_url(apiKey, roadType, cctvType,

+    # all_data_df.to_csv(f"result/{xmin}_{xmax}_y{ymin}_{ymax}.csv")

+import os

+import pandas as pd

+from ITS.api import gather_cctv_list

+

+df = pd.read_csv("result/129.2_129.3_y35.9_36.07.csv")

+

+name_of_original = df.loc[0]["cctvname"]

+lat = df.loc[0]["coordx"]

+lon = df.loc[0]["coordy"]

+apiKey = os.getenv("ITS_API")

+lat_lon_interval = 0.000001

+api_result = gather_cctv_list(xmin=lat-lat_lon_interval, ymin=lon-lat_lon_interval, xmax=lat+lat_lon_interval, ymax=lon+lat_lon_interval, intervals=1, roadType=1)

+

+name_of_requested = api_result.loc[0]["cctvname"]

+

+if name_of_original == name_of_requested:

+    print("success")

+    print(name_of_original)

+

+import parser

+                            cv2.imwrite(f'hls_streaming/captured_frame_/{datetime.now()}_{frame_name}', self.current_frame)

+        101, 10, 5

+from ITS.api import gather_cctv_list

+from hls_streaming.hls import FrameCapturer

+

+def refresh_hls_address(lat, lon, lat_lon_interval=0.000001):

+    api_result = gather_cctv_list(xmin=lat-lat_lon_interval, ymin=lon-lat_lon_interval, xmax=lat+lat_lon_interval, ymax=lon+lat_lon_interval, intervals=1, roadType=1)

+    return api_result.loc[0]["cctvurl"]

+

+if __name__ == "__main__":

+    import argparse

+    args = argparse.ArgumentParser

+    args.add_argument("cctv_num", help="index number of cctv, see cctv_list.csv")

+    args = args.parse_args()

+

+    cctv_ind = args.cctv_num

+from flask import Flask, request

+from flask_restx import Api, Resource, fields

+import os

+from datetime import datetime

+from yoloseg.inference_ import Inference, overlay_mask

+import cv2

+import time

+

+app = Flask(__name__)

+api = Api(app, version='1.0', title='CCTV Image Upload API',

+          description='A simple API for receiving CCTV images')

+

+# Namespace definition

+ns = api.namespace('cctv', description='CCTV operations')

+

+model_path = 'yoloseg/weight/best.onnx'

+classes_txt_file = 'config/yolo_config.txt'

+image_path = 'yoloseg/img3.jpg'

+

+model_input_shape = (640, 640)

+inference_engine = Inference(

+    onnx_model_path=model_path,

+    model_input_shape=model_input_shape,

+    classes_txt_file=classes_txt_file,

+    run_with_cuda=True

+)

+

+# Define the expected model for incoming data

+image_upload_model = api.model('ImageUpload', {

+    'image': fields.String(required=True, description='Image file', dt='File'),

+    'x-cctv-info': fields.String(required=False, description='CCTV identifier'),

+    'x-time-sent': fields.String(required=False, description='Time image was sent'),

+    'x-cctv-latitude': fields.String(required=False, description='Latitude of CCTV'),

+    'x-cctv-longitude': fields.String(required=False, description='Longitude of CCTV')

+})

+

+# Define the directory where images will be saved

+IMAGE_DIR = "network_test"

+if not os.path.exists(IMAGE_DIR):

+    os.makedirs(IMAGE_DIR)

+

[email protected]('/infer', )

+class ImageUpload(Resource):

+    # @ns.expect(image_upload_model, validate=True)

+    @ns.response(200, 'Success')

+    @ns.response(400, 'Validation Error')

+    def post(self):

+        if 'file' not in request.files:

+            ns.abort(400, 'No image part in the request')

+        image = request.files['file']

+        cctv_info = request.headers.get('x-cctv-info', '')

+        time_sent = request.headers.get('x-time-sent', '')

+        cctv_latitude = request.headers.get('x-cctv-latitude', 'Not provided')

+        cctv_longitude = request.headers.get('x-cctv-longitude', 'Not provided')

+

+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")

+        image = cv2.imdecode(image)

+        filename = f"{timestamp}_{cctv_info}.png"

+

+        t1 = time.time()

+        detections, mask_maps = inference_engine.run_inference(image)

+        t2 = time.time()

+

+        if mask_maps is not None:

+            seg_image = overlay_mask(image, mask_maps[0], color=(0, 255, 0), alpha=0.3)

+

+        if image.filename == '':

+            ns.abort(400, 'No selected image')

+

+        # Use current timestamp to avoid filename conflicts

+        # image_path = os.path.join(IMAGE_DIR, filename)

+        # image.save(image_path)

+

+        return {"message": f"Image {filename} uploaded successfully!"}

+

+    def send_result(self):

+        pass

+if __name__ == '__main__':

+    app.run(debug=True, port=12345)

+#!/bin/bash

+

+# Array to hold the process IDs

+declare -a pids

+

+# Start multiple Python processes in the background

+python hls_streaming/hls.py &

+pids+=($!)

+python hls_streaming/hls.py &

+pids+=($!)

+

+# Function to kill all processes

+cleanup() {

+    echo "Terminating all processes..."

+    for pid in "${pids[@]}"; do

+        kill -9 $pid

+    done

+}

+

+# Trap keyboard interrupt and call cleanup

+trap cleanup SIGINT

+

+# Print message

+echo "Running... Press Ctrl+C to stop."

+

+# Wait forever until Ctrl+C is pressed

+while true; do

+    sleep 1

+done

+from yoloseg.inference_ import Inference

+IMAGE_DIR = "network_test"

+    classes_txt_file = 'yoloseg/config/yolo_config.txt'

+    classes_txt_file = 'yoloseg/config/yolo_config.txt'