+            try :

+                response = requests.get(url)

+            except :

+                df_temp = pd.DataFrame([x,x_next,y,y_next], columns=["x_min", "x_max", "y_min", "y_max"])

+                df_temp.to_csv(f"ITS/list/error/{x}_{x_next}_{y}_{y_next}.csv")

+                continue

+            print(response)

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

+    df = gather_cctv_list(125, 130, 33, 39, 2, "ex", 1)

+import requests

+import pandas as pd

+import numpy as np

+import os

+import json

+import time

+from dotenv import load_dotenv

+import cv2

+

+

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

+               baseurl="https://openapi.its.go.kr:9443/cctvInfo"):

+    '''

+    국가교통정보센터 api 예제 실행 코드, 더 자세한 내용은

+    https://www.its.go.kr/opendata/openApiEx?service=cctv 참고

+    :param apiKey: ``str`` 국가교통정보센터에서 발급받은 api 키

+    :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))

+    :param minX: 최소 경도 영역

+    :param maxX: 최대 경도 영역

+    :param minY: 최소 위도 영역

+    :param maxY: 최대 경도 영역

+    :param getType: 출력 결과 형식 ("xml" or "json")

+    :return: api 요청 url

+    '''

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

+    assert cctvType != 1 or 2 or 3, 'Error! cctvType should be one of 1, 2, 3!'

+    assert getType != "json" or "xml", 'Error! gettype should be either "json" or "xml"!'

+    return (

+        f"{baseurl}?"

+        f"apiKey={apiKey}&"

+        f"type={roadType}&"

+        f"cctvType={cctvType}&"

+        f"minX={minX}&maxX={maxX}&minY={minY}&maxY={maxY}&"

+        f"getType={getType}"

+    )

+

+

+def gather_cctv_list(xmin, xmax, ymin, ymax, intervals, roadType, cctvType, depth=0, max_depth=5):

+    '''

+    :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))

+    :param depth: ``int`` 재귀적으로 불러온 함수의 깊이 (절대로 직접 넣지 말것.)

+    :param max_depth: ``int`` 재귀 함수 깊이 제한

+    :return: pandas DataFrame 형태로 반환

+            주요 컬럼은 다음과 같음

+            coordx	 coordy   cctvtype	cctvformat	 cctvname	cctvurl

+    '''

+    dotenv = load_dotenv()

+    apiKey = os.getenv("ITS_API")

+    x_values = np.linspace(xmin, xmax, intervals + 1)

+    y_values = np.linspace(ymin, ymax, intervals + 1)

+    all_data_df = pd.DataFrame()

+

+    if depth > max_depth:

+        return all_data_df  # Avoids excessive recursion depth

+

+    for i in range(len(x_values) - 1):

+        for j in range(len(y_values) - 1):

+            x = x_values[i]

+            y = y_values[j]

+            x_next = x_values[i + 1]

+            y_next = y_values[j + 1]

+

+            url = create_url(apiKey, roadType, cctvType, x, x_next, y, y_next)

+            try:

+                response = requests.get(url)

+                response_json = json.loads(response.text)

+                if response_json['response']['datacount'] == 0:

+                    continue

+                new_df = pd.json_normalize(response_json['response']['data'])

+                all_data_df = pd.concat([all_data_df, new_df], ignore_index=True)

+                print(f"x: {i}, y: {j}, d: {depth}")

+            except Exception as e:

+                # Splitting the area into four if an exception occurs (likely due to too many entries)

+                if depth < max_depth:

+                    mid_x = (x + x_next) / 2

+                    mid_y = (y + y_next) / 2

+                    all_data_df = pd.concat([

+                        all_data_df,

+                        gather_cctv_list(x, mid_x, y, mid_y, intervals, roadType, cctvType, depth + 1, max_depth),

+                        gather_cctv_list(mid_x, x_next, y, mid_y, intervals, roadType, cctvType, depth + 1, max_depth),

+                        gather_cctv_list(x, mid_x, mid_y, y_next, intervals, roadType, cctvType, depth + 1, max_depth),

+                        gather_cctv_list(mid_x, x_next, mid_y, y_next, intervals, roadType, cctvType, depth + 1, max_depth)

+                    ], ignore_index=True)

+            time.sleep(1)  # To prevent hitting the API rate limit

+    return all_data_df

+

+

+if __name__ == "__main__":

+    list2= [

+        [129.37762, 36.318485, 'its'],

+        [129.381293, 36.339985, 'its'],

+        [129.3459, 36.11983, 'its'],

+        [129.200930555555, 35.772125, 'its'],

+        [129.347244, 36.286135, 'its'],

+        [128.03166, 36.011665, 'its'],

+        [128.840556, 35.384722, 'ex']

+    ]

+    delta = 0.01

+    all_data_df = pd.DataFrame()

+    for i in list2:

+        x = i[0]

+        y = i[1]

+        ty = i[2]

+        df = gather_cctv_list(x-delta,x+delta, y-delta, y+delta, 1, ty, 1)

+        all_data_df = pd.concat((all_data_df,df), ignore_index=True)

+    # df = gather_cctv_list(127, 130, 33, 39, 8, "its", 1)

+    all_data_df.to_csv("cctv_data.csv")(파일 끝에 줄바꿈 문자 없음)

+,128.2671,35.91457,,,1,HLS,[국도33호선] 대흥교차로,http://cctvsec.ktict.co.kr/72593/mTZfGWopuLSKGZsBABx2whdCW4FLmBl6wi4dCvp0MxMpKF6c6ydtf40G8dVXQcAGlDug1DIE/E4MVUyoa3UuD18aYxP6ZCMD5zISEiDxXTQ=

+,129.378721,36.310334,,,1,HLS,[국도 7호선] 영덕 구계휴게소,http://cctvsec.ktict.co.kr/4459/kAgktFah0VAtMosKMfrJ13d7CBLAa5+M1xhiLgsypRh5NDDIqY0Y7Z2+hkF88PZUhL1uFJTYshn/Afc4FCS67iWpGfxUOoNU8b0ul3flW8c=

+,129.37762,36.318485,,,1,HLS,"[국도7호선] 영덕 영덕구계항부근(마을주민,해안가)",http://cctvsec.ktict.co.kr/72957/LkVa3bIbXIBudmzokRTyNtAbqEoZCk+DHRZ8i6cs+rL6QIEM7Fv1N+wfJPjSFQOEmn+Sklz5M3zqnjyiuo6lcPORJsjpyig6wn11ZeH+h44=

+,129.381293,36.339985,,,1,HLS,[국도 7호선] 오션뷰C.C 앞 삼거리,http://cctvsec.ktict.co.kr/4475/0lZW6kXydguhfnjHZgPeVWi/UKIYrPrUGPfVDyXcGXynS/XFEaXHTYGgmLHdj6QwF1AcHSd6JRZnW+/Ecw30OL/L63URGEp3X/DJqKRrzR0=

+,129.3459,36.11983,,,1,HLS,[국도7호선] 곡강교,http://cctvsec.ktict.co.kr/71249/YZj8q/z+rpoz8wHUZGst7oMp01rLXrsqhKDnbcKcndono98hK4v5LXE3UumA35GOUC3vxTXsvCBdemtPt60aNJsGUbr+NNiMFUZXDoNutF8=

+,129.193728,35.762614,,,1,HLS,[국도7호선] 부지교차로,http://cctvsec.ktict.co.kr/72311/kdgcZLCWMjfPL2B06+sReFWMMB+6gW6i1XePhxb0jT/S3jhoQGFbl4nM8fnNunVxbfcv+gtwwve5/Alut8ExIXo8I9h3kV5tNjcqr2ai8d4=

+,129.200930555555,35.772125,,,1,HLS,[국도 35호선] 용장교,http://cctvsec.ktict.co.kr/5569/7Wk7xI0iDot4My7wDolq5X3jnW9Pgsp7eqT4fmbF1G1dWh248yZuO0r6DZGPJc0lFupmPdL6nCMDyuYljTRHmm7FxLfEMZ9OOKcycSLyHIk=

+,128.03166,36.011665,,,1,HLS,[국도3호선] 방산교,http://cctvsec.ktict.co.kr/72618/tJq3wZnEySy1HdQLbvzJhD6i+QrqP0HIopbzDMD/h21V/QmMVP26YJUOZsU9WnMsSrPMHfzgTP54irzJjPv+4kCcFJuqvRM63JNFmolU2tE=

+                        'seg_image' : (

+            normal_send_interval=10,

+                # self.mask = request.files['mask'].read()

+                self.seg_image = request.files['seg_image']

+sleep 1

+sleep 1

+python streaming_process.py --cctv_num 2 &

+pids+=($!)

+sleep 1

+python streaming_process.py --cctv_num 3 &

+pids+=($!)

+sleep 1

+python streaming_process.py --cctv_num 4 &

+pids+=($!)

+sleep 1

+python streaming_process.py --cctv_num 5 &

+pids+=($!)

+sleep 1

+python streaming_process.py --cctv_num 6 &

+pids+=($!)

+from setuptools import setup

+

+setup(

+    name='flood_server',

+    version='0.9',

+    packages=[''],

+    install_requires=[

+        'opencv-python',

+        'pandas',

+        'numpy',

+        'python-dotenv',

+        'flask',

+        'flask-restx',

+        'pyav',

+        'requests-toolbelt',

+        'onnxrunntime-gpu',

+    ],

+    url='',

+    license='',

+    author='takensoft-Yoon-Young-Joon',

+    author_email='[email protected]',

+    description='',

+)

+    def refresh_hls_address(lat, lon, lat_lon_interval=0.0001):

+from config_files.yolo_config import CLASS_NUM