Commit @d2813695829c67dfd6d951f9e996152980ab0278

윤영준 10-08

testing suites

@d2813695829c67dfd6d951f9e996152980ab0278

870ee36

d281369

hls_streaming/hls.py

--- hls_streaming/hls.py

+++ hls_streaming/hls.py


 from datetime import datetime
 from threading import Lock, Thread, Event
 
-DEBUG = False
+DEBUG = True
+SUPPRESS_MESSAGE = True
 
 
 class FrameCapturer:

             response = session.post(self.endpoint, headers=header, data=multipart_data)
 
         except Exception as e:
-            print(e)
-            print("Can not connect to the analyzer server. Check the endpoint address or connection.\n"
-                  f"Can not connect to : {self.endpoint}")
+            if not SUPPRESS_MESSAGE:
+                print(e)
+                print("Can not connect to the analyzer server. Check the endpoint address or connection.\n"
+                      f"Can not connect to : {self.endpoint}")
 
     def start(self):
         print(self.hls_url)

 # Example usage
 if __name__ == "__main__":
     capturer = FrameCapturer(
-        'http://cctvsec.ktict.co.kr/5545/LFkDslDT81tcSYh3G4306+mcGlLb3yShF9rx2vcPfltwUL4+I950kcBlD15uWm6K0cKCtAMlxsIptMkCDo5lGQiLlARP+SyUloz8vIMNB18=',
+        'http://cctvsec.ktict.co.kr/72957/LkVa3bIbXIBudmzokRTyNtAbqEoZCk+DHRZ8i6cs+rL6QIEM7Fv1N+wfJPjSFQOE2mi3PFCExH9gQhypA++a5DTmeAp2DUZ6xqXs22FgPvI=',
         "[국도] 테해란로", 10, 5
     )
     t1 = time.time()

870ee36

d281369

inference_endpoint.py

--- inference_endpoint.py

+++ inference_endpoint.py


 from zoneinfo import ZoneInfo
 
 from flask import Flask, request
-from flask_restx import Api, Resource, fields
+from flask_restx import Api, Resource, fields, reqparse
 import requests
 from requests_toolbelt import MultipartEncoder
 import base64

 )
 
 # 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')
-})
+upload_parser = reqparse.RequestParser()
+upload_parser.add_argument('file', location='files', type='FileStorage', required=True, help='Image file to upload')
+upload_parser.add_argument('x-cctv-name', location='headers', required=False, help='CCTV identifier')
+upload_parser.add_argument('x-time-sent', location='headers', required=False, help='Time image was sent')
+upload_parser.add_argument('x-cctv-latitude', location='headers', required=False, help='Latitude of CCTV')
+upload_parser.add_argument('x-cctv-longitude', location='headers', required=False, help='Longitude of CCTV')
+
 
 # Define the directory where images will be saved
 IMAGE_DIR = "network_test"

 
 @ns.route('/infer', )
 class ImageUpload(Resource):
-    # @ns.expect(image_upload_model, validate=True)
+    @ns.expect(upload_parser)
     def __init__(self, *args, **kargs):
         super().__init__(*args, **kargs)
         self.time_sent = None

 
 
 if __name__ == '__main__':
-    app.run(debug=False, port=12345)
+    app.run(debug=True, port=12345)

d281369

testing.html (added)

+++ testing.html

...	...	@@ -0,0 +1,178 @@
	1	+<!DOCTYPE html>
	2	+<html lang="en">
	3	+<head>
	4	+ <meta charset="UTF-8">
	5	+ <meta name="viewport" content="width=device-width, initial-scale=1.0">
	6	+ <title>이미지 처리</title>
	7	+ <style>
	8	+ body {
	9	+ font-family: Arial, sans-serif;
	10	+ display: flex;
	11	+ flex-direction: column;
	12	+ height: 100vh;
	13	+ margin: 0;
	14	+ }
	15	+ .header {
	16	+ background-color: #1E4E90;
	17	+ color: white;
	18	+ text-align: center;
	19	+ padding: 10px 0;
	20	+ position: fixed;
	21	+ width: 100%;
	22	+ top: 0;
	23	+ z-index: 1;
	24	+ }
	25	+ .container {
	26	+ text-align: center;
	27	+ margin-top: 100px; /* .header의 높이 만큼 여백 추가 */
	28	+ }
	29	+ #preview img {
	30	+ max-width: 100%;
	31	+ height: auto;
	32	+ margin-top: 20px;
	33	+ }
	34	+ #processButton {
	35	+ background-color: #1E4E90;
	36	+ color: white;
	37	+ border: none;
	38	+ padding: 10px 20px;
	39	+ text-align: center;
	40	+ text-decoration: none;
	41	+ display: inline-block;
	42	+ font-size: 16px;
	43	+ margin-top: 20px;
	44	+ cursor: pointer;
	45	+ }
	46	+ #processButton:hover {
	47	+ background-color: #1E4E90;
	48	+ }
	49	+ #copyright {
	50	+ background-color: #f1f1f1;
	51	+ text-align: center;
	52	+ padding: 10px;
	53	+ position: fixed;
	54	+ width: 100%;
	55	+ bottom: 0;
	56	+ }
	57	+ #messageContainer {
	58	+ display: flex;
	59	+ justify-content: center;
	60	+ height: 100vh;
	61	+ }
	62	+ #messageContainer div{
	63	+ margin-top:30px;
	64	+ }
	65	+ </style>
	66	+</head>
	67	+<body>
	68	+ <div class="header">
	69	+ <h1>침수감지모델 테스트도구</h1>
	70	+ </div>
	71	+ <div class="container">
	72	+ <div id="preview"></div>
	73	+ <input type="file" id="imageInput">
	74	+ <button id="processButton">이미지 처리</button>
	75	+ <button id="clearButton">Clear</button> <!-- Clear 버튼 추가 -->
	76	+
	77	+ </div>
	78	+ <div id="messageContainer"></div>
	79	+
	80	+ <div id="copyright">
	81	+ <p>COPYRIGHT © 2024 SOLFAC. All Rights Reserved.</p>
	82	+ </div>
	83	+
	84	+ <script>
	85	+ const fileInput = document.getElementById('imageInput');
	86	+ const processButton = document.getElementById('processButton');
	87	+ const preview = document.getElementById('preview');
	88	+ const messageContainer = document.getElementById('messageContainer'); // 메시지를 담을 컨테이너 추가
	89	+
	90	+ clearButton.addEventListener('click', function() {
	91	+ // 메시지 컨테이너의 내용을 비움
	92	+ preview.innerHTML = '';
	93	+ messageContainer.innerHTML = '';
	94	+ });
	95	+ fileInput.addEventListener('change', function(event) {
	96	+ const file = event.target.files[0];
	97	+ const reader = new FileReader();
	98	+
	99	+ reader.onloadend = function() {
	100	+ // 이미지 미리보기를 생성하여 화면에 표시
	101	+ const img = new Image();
	102	+ img.src = reader.result;
	103	+ img.style.width = '1080px'; // 미리보기 이미지의 너비 조절
	104	+ preview.innerHTML = ''; // 이전 미리보기 삭제
	105	+ preview.appendChild(img); // 새로운 미리보기 추가
	106	+
	107	+ // 파일 정보를 기반으로 FormData를 생성하여 서버에 전송할 준비
	108	+ const formData = new FormData();
	109	+ formData.append('file', file); // 이미지 파일을 FormData에 추가
	110	+
	111	+ const LAT = "36.123"; // Replace with the actual latitude value
	112	+ const LON = "123.123"; // Replace with the actual longitude value
	113	+ const FILENAME = file.name; // Use the selected file's name as the filename
	114	+ const FILE_TYPE = file.type; // Use the selected file's type as the file type
	115	+
	116	+ // 여기서 formData와 FILENAME, FILE_TYPE을 이용하여 서버에 요청을 보내면 됩니다.
	117	+ // ...
	118	+ };
	119	+
	120	+ if (file) {
	121	+ reader.readAsDataURL(file);
	122	+ }
	123	+ });
	124	+
	125	+ processButton.addEventListener('click', async function() {
	126	+ const file = fileInput.files[0];
	127	+
	128	+ if (file) {
	129	+
	130	+ const LAT = "36.123"; // Replace with the actual latitude value
	131	+ const LON = "123.123"; // Replace with the actual longitude value
	132	+ const FILENAME = file.name; // Use the selected file's name as the filename
	133	+ const FILE_TYPE = file.type; // Use the selected file's type as the file type
	134	+ const formData = new FormData();
	135	+ formData.append("data", JSON.stringify({ gps_x: LAT, gps_y: LON, filename: FILENAME, file_type: FILE_TYPE }));
	136	+ const previewBlob = await fetch(preview.querySelector('img').src).then(res => res.blob());
	137	+ formData.append('file', previewBlob, FILENAME); // Blob 객체와 원래 파일 이름을 전송
	138	+
	139	+
	140	+
	141	+ try {
	142	+ const URL = "http://127.0.0.1:12345/cctv/infer"; // Replace with the actual server URL
	143	+ const response = await fetch(URL, {
	144	+ method: "POST",
	145	+ body: formData
	146	+ });
	147	+
	148	+ if (response.ok) {
	149	+ const data = await response.json();
	150	+ console.log(data); // 서버에서 받은 응답 처리
	151	+
	152	+ // data 객체에서 rain이 true인 경우 메시지 표시
	153	+ if (data.rain === true) {
	154	+ const message = document.createElement('div');
	155	+ message.textContent = '비 사진 감지되었습니다.';
	156	+ messageContainer.innerHTML = ''; // 기존 메시지 삭제
	157	+ messageContainer.appendChild(message); // 새로운 메시지 추가
	158	+ } else {
	159	+ const message = document.createElement('div');
	160	+ message.textContent = '일반 사진 감지되었습니다.';
	161	+ messageContainer.innerHTML = ''; // 기존 메시지 삭제
	162	+ messageContainer.appendChild(message); // 새로운 메시지 추가
	163	+ }
	164	+ } else {
	165	+ console.error("Error occurred while sending the request:", response.statusText);
	166	+ }
	167	+ } catch (error) {
	168	+ console.error("Error occurred:", error);
	169	+ }
	170	+ } else {
	171	+ console.error("No file selected.");
	172	+ }
	173	+ });
	174	+
	175	+
	176	+ </script>
	177	+</body>
	178	+</html>(파일 끝에 줄바꿈 문자 없음)

870ee36

d281369

yoloseg/inference_gpu_.py

--- yoloseg/inference_gpu_.py

+++ yoloseg/inference_gpu_.py

...	...	@@ -312,7 +312,103 @@
312	312	# cv2.destroyAllWindows()
313	313
314	314
	315	+def process_video(video_path, output_dir, model_input_shape=(480,480)):
	316	+ import os
	317	+ import av
	318	+ import csv
	319	+
	320	+
	321	+ model_path = 'yoloseg/weight/best.onnx'
	322	+ classes_txt_file = 'config_files/yolo_config.txt'
	323	+
	324	+ model_input_shape = (480, 480)
	325	+ inference_engine = Inference(
	326	+ onnx_model_path=model_path,
	327	+ model_input_shape=model_input_shape,
	328	+ classes_txt_file=classes_txt_file,
	329	+ run_with_cuda=True
	330	+ )
	331	+
	332	+ # Open video using PyAV
	333	+ container = av.open(video_path)
	334	+
	335	+ frame_times = [] # List to store inference time per frame
	336	+ frame_count = 0
	337	+
	338	+ # Create output directory if it doesn't exist
	339	+ if not os.path.exists(output_dir):
	340	+ os.makedirs(output_dir)
	341	+
	342	+ # Get frame rate from video to control display speed
	343	+ video_fps = container.streams.video[0].average_rate
	344	+ if video_fps is None:
	345	+ video_fps = 25 # Default to 25 FPS if unavailable
	346	+
	347	+ # Decode video frame by frame
	348	+ for frame in container.decode(video=0):
	349	+ frame_count += 1
	350	+
	351	+
	352	+ # Convert PyAV frame to numpy array (OpenCV format)
	353	+ img = frame.to_ndarray(format='bgr24')
	354	+
	355	+ t1 = time.time()
	356	+
	357	+ # Resize frame to match model input shape
	358	+ resized_frame = cv2.resize(img, model_input_shape)
	359	+
	360	+ # Run inference
	361	+ detections, mask_maps = inference_engine.run_inference(resized_frame)
	362	+
	363	+
	364	+ # Display detections
	365	+ for detection in detections:
	366	+ x, y, w, h = detection['box']
	367	+ class_name = detection['class_name']
	368	+ confidence = detection['confidence']
	369	+ cv2.rectangle(resized_frame, (x, y), (x+w, y+h), detection['color'], 2)
	370	+ label = f"{class_name}: {confidence:.2f}"
	371	+ cv2.putText(resized_frame, label, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, detection['color'], 2)
	372	+
	373	+
	374	+ if len(mask_maps) != 0:
	375	+ for i in range(mask_maps.shape[2]): # Iterate over each mask
	376	+ resized_frame = overlay_mask(resized_frame, mask_maps[:, :, i], color=(0, 255, 0), alpha=0.3)
	377	+
	378	+ # Show the processed frame
	379	+ # cv2.imshow('Processed Frame', resized_frame)
	380	+ # # Save the processed frame
	381	+ # output_frame_path = os.path.join(output_dir, f"frame_{frame_count}.jpg")
	382	+ # cv2.imwrite(output_frame_path, resized_frame)
	383	+
	384	+ t2 = time.time()
	385	+ frame_time = t2 - t1
	386	+ frame_times.append(frame_time)
	387	+ print(f"Frame {frame_count} inference time: {frame_time:.4f} seconds")
	388	+
	389	+ # Adjust frame display rate based on FPS
	390	+ if cv2.waitKey(int(1000 / video_fps)) & 0xFF == ord('q'):
	391	+ break
	392	+
	393	+ # Close OpenCV windows
	394	+ cv2.destroyAllWindows()
	395	+
	396	+ # Calculate and save inference times to CSV
	397	+ avg_inference_time = sum(frame_times) / len(frame_times) if frame_times else 0
	398	+ output_csv_path = os.path.join(output_dir, "inference.csv")
	399	+
	400	+ with open(output_csv_path, mode='w', newline='') as csv_file:
	401	+ writer = csv.writer(csv_file)
	402	+ writer.writerow(["Frame", "Inference Time (seconds)"])
	403	+ for i, time_val in enumerate(frame_times):
	404	+ writer.writerow([i + 1, time_val])
	405	+ writer.writerow(["Average", avg_inference_time])
	406	+
	407	+ print(f"Average inference time: {avg_inference_time:.4f} seconds")
	408	+ print(f"Inference times saved to {output_csv_path}")
315	409
316	410
317	411	if __name__ == "__main__":
318		- test() (파일 끝에 줄바꿈 문자 없음)
	412	+ # test()
	413	+
	414	+ process_video("/home/juni/사진/flood/test_video.mp4", "./") (파일 끝에 줄바꿈 문자 없음)

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...	...	@@ -9,7 +9,8 @@
9	9	from datetime import datetime
10	10	from threading import Lock, Thread, Event
11	11
12		-DEBUG = False
	12	+DEBUG = True
	13	+SUPPRESS_MESSAGE = True
13	14
14	15
15	16	class FrameCapturer:
...	...	@@ -125,9 +126,10 @@
125	126	response = session.post(self.endpoint, headers=header, data=multipart_data)
126	127
127	128	except Exception as e:
128		- print(e)
129		- print("Can not connect to the analyzer server. Check the endpoint address or connection.\n"
130		- f"Can not connect to : {self.endpoint}")
	129	+ if not SUPPRESS_MESSAGE:
	130	+ print(e)
	131	+ print("Can not connect to the analyzer server. Check the endpoint address or connection.\n"
	132	+ f"Can not connect to : {self.endpoint}")
131	133
132	134	def start(self):
133	135	print(self.hls_url)
...	...	@@ -146,7 +148,7 @@
146	148	# Example usage
147	149	if __name__ == "__main__":
148	150	capturer = FrameCapturer(
149		- 'http://cctvsec.ktict.co.kr/5545/LFkDslDT81tcSYh3G4306+mcGlLb3yShF9rx2vcPfltwUL4+I950kcBlD15uWm6K0cKCtAMlxsIptMkCDo5lGQiLlARP+SyUloz8vIMNB18=',
	151	+ 'http://cctvsec.ktict.co.kr/72957/LkVa3bIbXIBudmzokRTyNtAbqEoZCk+DHRZ8i6cs+rL6QIEM7Fv1N+wfJPjSFQOE2mi3PFCExH9gQhypA++a5DTmeAp2DUZ6xqXs22FgPvI=',
150	152	"[국도] 테해란로", 10, 5
151	153	)
152	154	t1 = time.time()

...	...	@@ -8,7 +8,7 @@
8	8	from zoneinfo import ZoneInfo
9	9
10	10	from flask import Flask, request
11		-from flask_restx import Api, Resource, fields
	11	+from flask_restx import Api, Resource, fields, reqparse
12	12	import requests
13	13	from requests_toolbelt import MultipartEncoder
14	14	import base64
...	...	@@ -38,13 +38,13 @@
38	38	)
39	39
40	40	# Define the expected model for incoming data
41		-image_upload_model = api.model('ImageUpload', {
42		- 'image': fields.String(required=True, description='Image file', dt='File'),
43		- 'x-cctv-info': fields.String(required=False, description='CCTV identifier'),
44		- 'x-time-sent': fields.String(required=False, description='Time image was sent'),
45		- 'x-cctv-latitude': fields.String(required=False, description='Latitude of CCTV'),
46		- 'x-cctv-longitude': fields.String(required=False, description='Longitude of CCTV')
47		-})
	41	+upload_parser = reqparse.RequestParser()
	42	+upload_parser.add_argument('file', location='files', type='FileStorage', required=True, help='Image file to upload')
	43	+upload_parser.add_argument('x-cctv-name', location='headers', required=False, help='CCTV identifier')
	44	+upload_parser.add_argument('x-time-sent', location='headers', required=False, help='Time image was sent')
	45	+upload_parser.add_argument('x-cctv-latitude', location='headers', required=False, help='Latitude of CCTV')
	46	+upload_parser.add_argument('x-cctv-longitude', location='headers', required=False, help='Longitude of CCTV')
	47	+
48	48
49	49	# Define the directory where images will be saved
50	50	IMAGE_DIR = "network_test"
...	...	@@ -53,7 +53,7 @@
53	53
54	54	@ns.route('/infer', )
55	55	class ImageUpload(Resource):
56		- # @ns.expect(image_upload_model, validate=True)
	56	+ @ns.expect(upload_parser)
57	57	def __init__(self, args, *kargs):
58	58	super().__init__(args, *kargs)
59	59	self.time_sent = None
...	...	@@ -185,4 +185,4 @@
185	185
186	186
187	187	if __name__ == '__main__':
188		- app.run(debug=False, port=12345)
	188	+ app.run(debug=True, port=12345)

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