Commit @e379e608a9884498ed3ec514af26c72d735fe4ee

31b3383

e379e60

inference_endpoint.py

--- inference_endpoint.py

+++ inference_endpoint.py


 import base64
 import requests
 from requests_toolbelt import MultipartEncoder
-from config_files import API_ENDPOINT_MAIN
+# from config_files import API_ENDPOINT_MAIN
 
 app = Flask(__name__)
 api = Api(app, version='1.0', title='CCTV Image Upload API',

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

         # filename = f"{timestamp}_{self.cctv_info}.png"
 
         t1 = time.time()
-        detections, self.mask = inference_engine.run_inference(image)
-        self.mask_blob = cv2.imencode(self.mask)
-        self.mask_blob = self.mask.tobytes()
+        detections, self.mask = inference_engine.run_inference(cv2.resize(image, model_input_shape))
         t2 = time.time()
+        if len(self.mask) > 0:
+            print(self.mask.shape)
+            self.mask_blob = cv2.imencode('.png', self.mask)
+            self.mask_blob = self.mask.tobytes()
 
-        print(t2 - t1)
+
+        # print(t2 - t1)
 
         if len(self.mask) != 0:
             seg_image = overlay_mask(image, self.mask[0], color=(0, 255, 0), alpha=0.3)

         else :
             self.area_percent = np.sum(self.mask) / image.shape[0] * image.shape[1]
 
-        self.send_result()
+
+
+        # self.send_result()
         # write another post request for pushing a detection result
         return {"message": f"Image {self.mask} uploaded successfully!"}
 

e379e60

postprocess_draft_pingpong.py (added)

+++ postprocess_draft_pingpong.py

...	...	@@ -0,0 +1,78 @@
	1	+from flask import Flask, request, jsonify
	2	+
	3	+app = Flask(__name__)
	4	+
	5	+# Dictionary to store the status counts for each source
	6	+status_counts = {
	7	+ "source1": [],
	8	+ "source2": [],
	9	+ "source3": [],
	10	+ "source4": [],
	11	+ "source5": []
	12	+}
	13	+
	14	+# Dictionary to keep track of OK counts for each source
	15	+ok_counts = {
	16	+ "source1": 0,
	17	+ "source2": 0,
	18	+ "source3": 0,
	19	+ "source4": 0,
	20	+ "source5": 0
	21	+}
	22	+
	23	+# Dictionary to track if a source is in force send mode
	24	+force_send_mode = {
	25	+ "source1": False,
	26	+ "source2": False,
	27	+ "source3": False,
	28	+ "source4": False,
	29	+ "source5": False
	30	+}
	31	+
	32	+
	33	+# Function to check if there are 5 consecutive failures
	34	+def check_consecutive_failures(source, status_list):
	35	+ if len(status_list) >= 5 and status_list[-5:] == ['FAIL'] * 5:
	36	+ print(f"Source {source} has 5 consecutive FAILs!")
	37	+ force_send_mode[source] = True
	38	+ send_message(source, force_send=True)
	39	+
	40	+
	41	+# Function to send a message
	42	+def send_message(source, force_send=False):
	43	+ if force_send or ok_counts[source] >= 10:
	44	+ print(f"Sending message for {source} - Status: {'FORCE SEND' if force_send else 'OK SEND'}")
	45	+ ok_counts[source] = 0 # Reset counter after sending
	46	+
	47	+
	48	+# Route to receive data from sources
	49	[email protected]('/status', methods=['POST'])
	50	+def receive_status():
	51	+ data = request.json
	52	+ source = data.get('source')
	53	+ status = data.get('status')
	54	+
	55	+ if source in status_counts:
	56	+ status_counts[source].append(status)
	57	+ # Ensure we keep only the latest 5 statuses to check for consecutive FAILs
	58	+ if len(status_counts[source]) > 5:
	59	+ status_counts[source].pop(0)
	60	+
	61	+ if status == 'OK':
	62	+ ok_counts[source] += 1
	63	+ if force_send_mode[source] and ok_counts[source] >= 10:
	64	+ # Revert to normal mode after 10 consecutive OKs
	65	+ force_send_mode[source] = False
	66	+ ok_counts[source] = 0
	67	+ elif not force_send_mode[source]:
	68	+ send_message(source)
	69	+ else:
	70	+ ok_counts[source] = 0 # Reset counter on FAIL
	71	+
	72	+ check_consecutive_failures(source, status_counts[source])
	73	+
	74	+ return jsonify({"message": "Status received"}), 200
	75	+
	76	+
	77	+if __name__ == '__main__':
	78	+ app.run(debug=True, port=5000)

e379e60

postprocess_draft_test.py (added)

+++ postprocess_draft_test.py

...	...	@@ -0,0 +1,65 @@
	1	+import requests
	2	+import time
	3	+
	4	+# Base URL for the server
	5	+BASE_URL = 'http://localhost:5000/status'
	6	+
	7	+# Function to send a POST request with status data
	8	+def send_status(source, status):
	9	+ response = requests.post(BASE_URL, json={"source": source, "status": status})
	10	+ print(f"Sent {status} for {source}, Response: {response.json()}")
	11	+
	12	+# Function to test normal and force send modes for multiple sources
	13	+def test_server():
	14	+ sources = ['source1', 'source2', 'source3', 'source4', 'source5']
	15	+
	16	+ # Scenario 1: Normal behavior with source1
	17	+ print("\nScenario 1: Normal behavior with source1")
	18	+ for _ in range(4):
	19	+ send_status('source1', 'OK')
	20	+ for _ in range(5):
	21	+ send_status('source1', 'FAIL')
	22	+ for _ in range(10):
	23	+ send_status('source1', 'OK')
	24	+ for _ in range(10):
	25	+ send_status('source1', 'OK')
	26	+
	27	+ # Scenario 2: Force send and revert with source2
	28	+ print("\nScenario 2: Force send and revert with source2")
	29	+ for _ in range(5):
	30	+ send_status('source2', 'FAIL')
	31	+ for _ in range(10):
	32	+ send_status('source2', 'OK')
	33	+ for _ in range(10):
	34	+ send_status('source2', 'OK')
	35	+
	36	+ # Scenario 3: Random mix with source3
	37	+ print("\nScenario 3: Random mix with source3")
	38	+ mix = ['OK', 'FAIL', 'OK', 'FAIL', 'OK', 'FAIL', 'OK', 'OK', 'OK', 'OK']
	39	+ for status in mix:
	40	+ send_status('source3', status)
	41	+ for _ in range(5):
	42	+ send_status('source3', 'FAIL')
	43	+ for _ in range(10):
	44	+ send_status('source3', 'OK')
	45	+
	46	+ # Scenario 4: Source4 fails and recovers
	47	+ print("\nScenario 4: Source4 fails and recovers")
	48	+ for _ in range(5):
	49	+ send_status('source4', 'FAIL')
	50	+ for _ in range(10):
	51	+ send_status('source4', 'OK')
	52	+ for _ in range(10):
	53	+ send_status('source4', 'OK')
	54	+
	55	+ # Scenario 5: Source5 normal operation
	56	+ print("\nScenario 5: Source5 normal operation")
	57	+ for _ in range(10):
	58	+ send_status('source5', 'OK')
	59	+ for _ in range(10):
	60	+ send_status('source5', 'OK')
	61	+
	62	+if __name__ == '__main__':
	63	+ # Give some time for the server to start
	64	+ time.sleep(2)
	65	+ test_server()

31b3383

e379e60

postprocessing.py

--- postprocessing.py

+++ postprocessing.py


         self.image_type = request.headers.get('Content-Type')
         self.area_percent = request.headers.get('x-area-percentage')
 
-
-
+    async def data_ping_pong(self):
+        pass

31b3383

e379e60

run_image_anal_backend.sh

--- run_image_anal_backend.sh

+++ run_image_anal_backend.sh


 python streaming_process.py --cctv_num 1 &
 pids+=($!)
 
-
+#python test.py
 python inference_endpoint.py
 #gunicorn --workers=6 inference_endpoint:app
 pids+=($!)

31b3383

e379e60

yoloseg/inference_.py

--- yoloseg/inference_.py

+++ yoloseg/inference_.py


 
             # Place the mask in the corresponding location on a full-sized mask image_binary
             full_mask = np.zeros((image_shape[0], image_shape[1]), dtype=np.uint8)
-            print(final_mask.shape)
-            print(full_mask[y1:y2, x1:x2].shape)
+            # print("---------")
+            # print(f"x1 : {x1}, y1 : {y1}, w: {w}, h: {h}")
+            # print(f"x2: {x2}, y2 : {y2}")
+            # print(final_mask.shape)
+            # print(full_mask[y1:y2, x1:x2].shape)
             full_mask[y1:y2, x1:x2] = final_mask
 
             # Combine the mask with the masks of other detections
             full_masks[idx] = full_mask
-
-        return full_masks
+        all_mask = full_masks.sum(axis=0)
+        return all_mask
 
     def load_classes_from_file(self):
         with open(self.classes_path, 'r') as f:

...	...	@@ -139,14 +139,17 @@
139	139
140	140	# Place the mask in the corresponding location on a full-sized mask image_binary
141	141	full_mask = np.zeros((image_shape[0], image_shape[1]), dtype=np.uint8)
142		- print(final_mask.shape)
143		- print(full_mask[y1:y2, x1:x2].shape)
	142	+ # print("---------")
	143	+ # print(f"x1 : {x1}, y1 : {y1}, w: {w}, h: {h}")
	144	+ # print(f"x2: {x2}, y2 : {y2}")
	145	+ # print(final_mask.shape)
	146	+ # print(full_mask[y1:y2, x1:x2].shape)
144	147	full_mask[y1:y2, x1:x2] = final_mask
145	148
146	149	# Combine the mask with the masks of other detections
147	150	full_masks[idx] = full_mask
148		-
149		- return full_masks
	151	+ all_mask = full_masks.sum(axis=0)
	152	+ return all_mask
150	153
151	154	def load_classes_from_file(self):
152	155	with open(self.classes_path, 'r') as f:

...	...	@@ -9,7 +9,7 @@
9	9	import base64
10	10	import requests
11	11	from requests_toolbelt import MultipartEncoder
12		-from config_files import API_ENDPOINT_MAIN
	12	+# from config_files import API_ENDPOINT_MAIN
13	13
14	14	app = Flask(__name__)
15	15	api = Api(app, version='1.0', title='CCTV Image Upload API',
...	...	@@ -47,8 +47,8 @@
47	47	@ns.route('/infer', )
48	48	class ImageUpload(Resource):
49	49	# @ns.expect(image_upload_model, validate=True)
50		- def __init__(self):
51		- super().__init__(api)
	50	+ def __init__(self, args, *kargs):
	51	+ super().__init__(args, *kargs)
52	52	self.time_sent = None
53	53	self.cctv_latitude = None
54	54	self.cctv_longitude = None
...	...	@@ -78,12 +78,15 @@
78	78	# filename = f"{timestamp}_{self.cctv_info}.png"
79	79
80	80	t1 = time.time()
81		- detections, self.mask = inference_engine.run_inference(image)
82		- self.mask_blob = cv2.imencode(self.mask)
83		- self.mask_blob = self.mask.tobytes()
	81	+ detections, self.mask = inference_engine.run_inference(cv2.resize(image, model_input_shape))
84	82	t2 = time.time()
	83	+ if len(self.mask) > 0:
	84	+ print(self.mask.shape)
	85	+ self.mask_blob = cv2.imencode('.png', self.mask)
	86	+ self.mask_blob = self.mask.tobytes()
85	87
86		- print(t2 - t1)
	88	+
	89	+ # print(t2 - t1)
87	90
88	91	if len(self.mask) != 0:
89	92	seg_image = overlay_mask(image, self.mask[0], color=(0, 255, 0), alpha=0.3)
...	...	@@ -91,7 +94,9 @@
91	94	else :
92	95	self.area_percent = np.sum(self.mask) / image.shape[0] * image.shape[1]
93	96
94		- self.send_result()
	97	+
	98	+
	99	+ # self.send_result()
95	100	# write another post request for pushing a detection result
96	101	return {"message": f"Image {self.mask} uploaded successfully!"}
97	102

...	...	@@ -40,5 +40,5 @@
40	40	self.image_type = request.headers.get('Content-Type')
41	41	self.area_percent = request.headers.get('x-area-percentage')
42	42
43		-
44		-
	43	+ async def data_ping_pong(self):
	44	+ pass

...	...	@@ -11,7 +11,7 @@
11	11	python streaming_process.py --cctv_num 1 &
12	12	pids+=($!)
13	13
14		-
	14	+#python test.py
15	15	python inference_endpoint.py
16	16	#gunicorn --workers=6 inference_endpoint:app
17	17	pids+=($!)

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