Commit @894e2c82a5e882304376d0010aa150fbe071dd4e

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894e2c8

config_files/cctv_list.csv (Renamed from config/cctv_list.csv)

--- config/cctv_list.csv

+++ config_files/cctv_list.csv

No changes

46ada56

894e2c8

config_files/hls_streaming_config.py (Renamed from config/hls_streaming_config.py)

--- config/hls_streaming_config.py

+++ config_files/hls_streaming_config.py

No changes

46ada56

894e2c8

config_files/yolo_config.py (Renamed from config/yolo_config.py)

--- config/yolo_config.py

+++ config_files/yolo_config.py

No changes

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894e2c8

config_files/yolo_config.txt (Renamed from config/yolo_config.txt)

--- config/yolo_config.txt

+++ config_files/yolo_config.txt

No changes

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894e2c8

hls_streaming/hls.py

--- hls_streaming/hls.py

+++ hls_streaming/hls.py


         header = {
             'Content-Type': f'image/{image_type}',
             'x-time-sent': time_sent,
-            'x-cctv-info': base64.b64encode(str(self.cctvid).encode('utf-8')).decode('ascii'),
+            'x-cctv-name': base64.b64encode(str(self.cctvid).encode('utf-8')).decode('ascii'),
             'x-cctv-latitude' : str(self.lat),
             'x-cctv-longitude' : str(self.lon),
         }

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894e2c8

inference_endpoint.py

--- inference_endpoint.py

+++ inference_endpoint.py


+import numpy as np
 from flask import Flask, request
 from flask_restx import Api, Resource, fields
 import os

 from yoloseg.inference_ import Inference, overlay_mask
 import cv2
 import time
+import base64
 
 app = Flask(__name__)
 api = Api(app, version='1.0', title='CCTV Image Upload API',

 ns = api.namespace('cctv', description='CCTV operations')
 
 model_path = 'yoloseg/weight/best.onnx'
-classes_txt_file = 'config/yolo_config.txt'
+classes_txt_file = 'config_files/yolo_config.txt'
 image_path = 'yoloseg/img3.jpg'
 
 model_input_shape = (640, 640)

         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', '')
+        cctv_info = base64.b64decode(request.headers.get('x-cctv-name', '')).decode('UTF-8')
         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)
+        image = image.read()
+        image = np.frombuffer(image, np.uint8)
+        image = cv2.imdecode(image, cv2.IMREAD_COLOR)
         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:
+        print(t2 - t1)
+
+        if len(mask_maps) != 0:
             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)

46ada56

894e2c8

streaming_process.py

--- streaming_process.py

+++ streaming_process.py


 
     cctv_ind = args.cctv_num
 
-    cctv_info = pd.read_csv("config/cctv_list.csv")
+    cctv_info = pd.read_csv("config_files/cctv_list.csv")
     cctv_info = cctv_info.iloc[cctv_ind]
 
     lat = cctv_info["coordx"]

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894e2c8

test.py

--- test.py

+++ test.py


         if 'file' not in request.files:
             ns.abort(400, 'No image part in the request')
         image = request.files['file']
-        cctv_info = base64.b64decode(request.headers.get('x-cctv-info', '')).decode('UTF-8')
+        cctv_info = base64.b64decode(request.headers.get('x-cctv-name', '')).decode('UTF-8')
         print(cctv_info)
         time_sent = request.headers.get('x-time-sent', '')
         cctv_latitude = request.headers.get('x-cctv-latitude', 'Not provided')

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894e2c8

yoloseg/inference_.py

--- yoloseg/inference_.py

+++ yoloseg/inference_.py


 import cv2
 import numpy as np
 import random
-from config import CLASS_NAME, CLASS_NUM
+from config_files.yolo_config import CLASS_NAME, CLASS_NUM
 
 class Inference:
     def __init__(self, onnx_model_path, model_input_shape, classes_txt_file, run_with_cuda):

 
     # Path to your ONNX model and classes text file
     model_path = 'yoloseg/weight/best.onnx'
-    classes_txt_file = 'yoloseg/config/yolo_config.txt'
+    classes_txt_file = 'yoloseg/config_files/yolo_config.txt'
     image_path = 'yoloseg/img3.jpg'
 
     model_input_shape = (640, 640)

 
     # Path to your ONNX model and classes text file
     model_path = 'yoloseg/weight/best.onnx'
-    classes_txt_file = 'yoloseg/config/yolo_config.txt'
+    classes_txt_file = 'yoloseg/config_files/yolo_config.txt'
 
     model_input_shape = (640, 640)
     inference_engine = Inference(

...	...	@@ -100,7 +100,7 @@
100	100	header = {
101	101	'Content-Type': f'image/{image_type}',
102	102	'x-time-sent': time_sent,
103		- 'x-cctv-info': base64.b64encode(str(self.cctvid).encode('utf-8')).decode('ascii'),
	103	+ 'x-cctv-name': base64.b64encode(str(self.cctvid).encode('utf-8')).decode('ascii'),
104	104	'x-cctv-latitude' : str(self.lat),
105	105	'x-cctv-longitude' : str(self.lon),
106	106	}

...	...	@@ -1,3 +1,4 @@
	1	+import numpy as np
1	2	from flask import Flask, request
2	3	from flask_restx import Api, Resource, fields
3	4	import os
...	...	@@ -5,6 +6,7 @@
5	6	from yoloseg.inference_ import Inference, overlay_mask
6	7	import cv2
7	8	import time
	9	+import base64
8	10
9	11	app = Flask(__name__)
10	12	api = Api(app, version='1.0', title='CCTV Image Upload API',
...	...	@@ -14,7 +16,7 @@
14	16	ns = api.namespace('cctv', description='CCTV operations')
15	17
16	18	model_path = 'yoloseg/weight/best.onnx'
17		-classes_txt_file = 'config/yolo_config.txt'
	19	+classes_txt_file = 'config_files/yolo_config.txt'
18	20	image_path = 'yoloseg/img3.jpg'
19	21
20	22	model_input_shape = (640, 640)
...	...	@@ -48,32 +50,27 @@
48	50	if 'file' not in request.files:
49	51	ns.abort(400, 'No image part in the request')
50	52	image = request.files['file']
51		- cctv_info = request.headers.get('x-cctv-info', '')
	53	+ cctv_info = base64.b64decode(request.headers.get('x-cctv-name', '')).decode('UTF-8')
52	54	time_sent = request.headers.get('x-time-sent', '')
53	55	cctv_latitude = request.headers.get('x-cctv-latitude', 'Not provided')
54	56	cctv_longitude = request.headers.get('x-cctv-longitude', 'Not provided')
55	57
56	58	timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
57		- image = cv2.imdecode(image)
	59	+ image = image.read()
	60	+ image = np.frombuffer(image, np.uint8)
	61	+ image = cv2.imdecode(image, cv2.IMREAD_COLOR)
58	62	filename = f"{timestamp}_{cctv_info}.png"
59	63
60	64	t1 = time.time()
61	65	detections, mask_maps = inference_engine.run_inference(image)
62	66	t2 = time.time()
63	67
64		- if mask_maps is not None:
	68	+ print(t2 - t1)
	69	+
	70	+ if len(mask_maps) != 0:
65	71	seg_image = overlay_mask(image, mask_maps[0], color=(0, 255, 0), alpha=0.3)
66		-
67		- if image.filename == '':
68		- ns.abort(400, 'No selected image')
69		-
70		- # Use current timestamp to avoid filename conflicts
71		- # image_path = os.path.join(IMAGE_DIR, filename)
72		- # image.save(image_path)
73	72
74	73	return {"message": f"Image {filename} uploaded successfully!"}
75	74
76		- def send_result(self):
77		- pass
78	75	if __name__ == '__main__':
79	76	app.run(debug=True, port=12345)

...	...	@@ -25,7 +25,7 @@
25	25
26	26	cctv_ind = args.cctv_num
27	27
28		- cctv_info = pd.read_csv("config/cctv_list.csv")
	28	+ cctv_info = pd.read_csv("config_files/cctv_list.csv")
29	29	cctv_info = cctv_info.iloc[cctv_ind]
30	30
31	31	lat = cctv_info["coordx"]

...	...	@@ -35,7 +35,7 @@
35	35	if 'file' not in request.files:
36	36	ns.abort(400, 'No image part in the request')
37	37	image = request.files['file']
38		- cctv_info = base64.b64decode(request.headers.get('x-cctv-info', '')).decode('UTF-8')
	38	+ cctv_info = base64.b64decode(request.headers.get('x-cctv-name', '')).decode('UTF-8')
39	39	print(cctv_info)
40	40	time_sent = request.headers.get('x-time-sent', '')
41	41	cctv_latitude = request.headers.get('x-cctv-latitude', 'Not provided')

...	...	@@ -1,7 +1,7 @@
1	1	import cv2
2	2	import numpy as np
3	3	import random
4		-from config import CLASS_NAME, CLASS_NUM
	4	+from config_files.yolo_config import CLASS_NAME, CLASS_NUM
5	5
6	6	class Inference:
7	7	def __init__(self, onnx_model_path, model_input_shape, classes_txt_file, run_with_cuda):
...	...	@@ -188,7 +188,7 @@
188	188
189	189	# Path to your ONNX model and classes text file
190	190	model_path = 'yoloseg/weight/best.onnx'
191		- classes_txt_file = 'yoloseg/config/yolo_config.txt'
	191	+ classes_txt_file = 'yoloseg/config_files/yolo_config.txt'
192	192	image_path = 'yoloseg/img3.jpg'
193	193
194	194	model_input_shape = (640, 640)
...	...	@@ -241,7 +241,7 @@
241	241
242	242	# Path to your ONNX model and classes text file
243	243	model_path = 'yoloseg/weight/best.onnx'
244		- classes_txt_file = 'yoloseg/config/yolo_config.txt'
	244	+ classes_txt_file = 'yoloseg/config_files/yolo_config.txt'
245	245
246	246	model_input_shape = (640, 640)
247	247	inference_engine = Inference(

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