Feat: connect with cms

app.py

@@ -13,7 +13,13 @@ import requests
 import asyncio
 import psutil
 import zipfile
+import ssl
+import urllib3
+import subprocess
+import tempfile
 from urllib.parse import urlparse
+from requests.adapters import HTTPAdapter
+from urllib3.util.ssl_ import create_urllib3_context
 from fastapi import FastAPI, WebSocket, HTTPException
 from fastapi.websockets import WebSocketDisconnect
 from fastapi.responses import Response
@@ -88,7 +94,101 @@ def download_mpta(url: str, dest_path: str) -> str:
     try:
         logger.info(f"Starting download of model from {url} to {dest_path}")
         os.makedirs(os.path.dirname(dest_path), exist_ok=True)
-        response = requests.get(url, stream=True)
+        
+        # Configure session with headers and SSL settings for compatibility
+        session = requests.Session()
+        
+        # Add headers to mimic browser request
+        headers = {
+            'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36',
+            'Accept': '*/*',
+            'Accept-Encoding': 'gzip, deflate',
+            'Connection': 'keep-alive',
+            'Cache-Control': 'no-cache',
+        }
+        
+        session.headers.update(headers)
+        
+        # Disable SSL verification warnings
+        urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
+        
+        # Try multiple approaches for SSL compatibility
+        ssl_success = False
+        response = None
+        
+        # Approach 1: Standard request with verify=False and updated TLS
+        try:
+            # Create a custom SSL context with modern settings
+            ssl_context = ssl.create_default_context()
+            ssl_context.check_hostname = False
+            ssl_context.verify_mode = ssl.CERT_NONE
+            ssl_context.set_ciphers('DEFAULT:@SECLEVEL=1')
+            
+            # Create adapter with custom SSL context
+            class SSLContextAdapter(HTTPAdapter):
+                def __init__(self, ssl_context=None):
+                    self.ssl_context = ssl_context
+                    super().__init__()
+                
+                def init_poolmanager(self, *args, **kwargs):
+                    kwargs['ssl_context'] = self.ssl_context
+                    return super().init_poolmanager(*args, **kwargs)
+            
+            session.mount('https://', SSLContextAdapter(ssl_context))
+            response = session.get(url, stream=True, verify=False, timeout=30)
+            ssl_success = True
+        except Exception as e1:
+            logger.debug(f"First SSL approach failed: {e1}")
+            
+            # Approach 2: Fallback to basic request without custom SSL
+            try:
+                response = session.get(url, stream=True, verify=False, timeout=30)
+                ssl_success = True
+            except Exception as e2:
+                logger.debug(f"Second SSL approach failed: {e2}")
+                
+                # Approach 3: Last resort - use system curl if available
+                try:
+                    with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
+                        tmp_path = tmp_file.name
+                    
+                    curl_cmd = [
+                        'curl', '-L', '-k', '--silent', '--show-error',
+                        '-H', f'User-Agent: {headers["User-Agent"]}',
+                        '-o', tmp_path, url
+                    ]
+                    result = subprocess.run(curl_cmd, capture_output=True, text=True, timeout=60)
+                    if result.returncode == 0:
+                        # Create a fake response object
+                        class FakeResponse:
+                            def __init__(self, file_path):
+                                self.status_code = 200
+                                self.headers = {'content-length': str(os.path.getsize(file_path))}
+                                self._file_path = file_path
+                            
+                            def iter_content(self, chunk_size=8192):
+                                with open(self._file_path, 'rb') as f:
+                                    while True:
+                                        chunk = f.read(chunk_size)
+                                        if not chunk:
+                                            break
+                                        yield chunk
+                                os.unlink(self._file_path)  # Clean up temp file
+                        
+                        response = FakeResponse(tmp_path)
+                        ssl_success = True
+                        logger.info("Successfully downloaded using system curl as fallback")
+                    else:
+                        logger.error(f"curl fallback failed: {result.stderr}")
+                        if os.path.exists(tmp_path):
+                            os.unlink(tmp_path)
+                except Exception as e3:
+                    logger.debug(f"curl fallback failed: {e3}")
+                    if 'tmp_path' in locals() and os.path.exists(tmp_path):
+                        os.unlink(tmp_path)
+        
+        if not ssl_success or not response:
+            raise Exception("All SSL approaches failed - unable to establish secure connection")
         if response.status_code == 200:
             file_size = int(response.headers.get('content-length', 0))
             logger.info(f"Model file size: {file_size/1024/1024:.2f} MB")
@@ -107,6 +207,13 @@ def download_mpta(url: str, dest_path: str) -> str:
     except Exception as e:
         logger.error(f"Exception downloading mpta file from {url}: {str(e)}", exc_info=True)
         return None
+    finally:
+        # Clean up session resources
+        try:
+            if 'session' in locals():
+                session.close()
+        except:
+            pass
 
 # Add helper to fetch snapshot image from HTTP/HTTPS URL
 def fetch_snapshot(url: str):
@@ -240,16 +347,14 @@ async def detect(websocket: WebSocket):
             logger.debug(f"Processing frame for camera {camera_id} with model {stream['modelId']}")
             start_time = time.time()
             
-            # Extract display identifier for session ID lookup
+            # Extract display identifier for pipeline context
             subscription_parts = stream["subscriptionIdentifier"].split(';')
             display_identifier = subscription_parts[0] if subscription_parts else None
-            session_id = session_ids.get(display_identifier) if display_identifier else None
             
-            # Create context for pipeline execution
+            # Create context for pipeline execution (session_id will be generated by pipeline)
             pipeline_context = {
                 "camera_id": camera_id,
-                "display_id": display_identifier,
+                "display_id": display_identifier
-                "session_id": session_id
             }
             
             detection_result = run_pipeline(cropped_frame, model_tree, context=pipeline_context)
@@ -259,46 +364,24 @@ async def detect(websocket: WebSocket):
             # Log the raw detection result for debugging
             logger.debug(f"Raw detection result for camera {camera_id}:\n{json.dumps(detection_result, indent=2, default=str)}")
             
-            # Direct class result (no detections/classifications structure)
+            # Extract session_id from pipeline result (generated during database record creation)
-            if detection_result and isinstance(detection_result, dict) and "class" in detection_result and "confidence" in detection_result:
+            session_id = None
-                highest_confidence_detection = {
+            if detection_result and isinstance(detection_result, dict):
-                    "class": detection_result.get("class", "none"),
+                # Check if pipeline generated a session_id (happens when Car+Frontal detected together)
-                    "confidence": detection_result.get("confidence", 1.0),
+                if "session_id" in detection_result:
-                    "box": [0, 0, 0, 0]  # Empty bounding box for classifications
+                    session_id = detection_result["session_id"]
-                }
+                    logger.debug(f"Extracted session_id from pipeline result: {session_id}")
-            # Handle case when no detections found or result is empty
-            elif not detection_result or not detection_result.get("detections"):
-                # Check if we have classification results
-                if detection_result and detection_result.get("classifications"):
-                    # Get the highest confidence classification
-                    classifications = detection_result.get("classifications", [])
-                    highest_confidence_class = max(classifications, key=lambda x: x.get("confidence", 0)) if classifications else None
             
-                    if highest_confidence_class:
+            # Process detection result - run_pipeline returns the primary detection directly
-                        highest_confidence_detection = {
+            if detection_result and isinstance(detection_result, dict) and "class" in detection_result:
-                            "class": highest_confidence_class.get("class", "none"),
+                highest_confidence_detection = detection_result
-                            "confidence": highest_confidence_class.get("confidence", 1.0),
-                            "box": [0, 0, 0, 0]  # Empty bounding box for classifications
-                        }
             else:
+                # No detection found
                 highest_confidence_detection = {
                     "class": "none",
                     "confidence": 1.0,
-                            "box": [0, 0, 0, 0]
+                    "bbox": [0, 0, 0, 0],
-                        }
+                    "branch_results": {}
-                else:
-                    highest_confidence_detection = {
-                        "class": "none",
-                        "confidence": 1.0,
-                        "box": [0, 0, 0, 0]
-                    }
-            else:
-                # Find detection with highest confidence
-                detections = detection_result.get("detections", [])
-                highest_confidence_detection = max(detections, key=lambda x: x.get("confidence", 0)) if detections else {
-                    "class": "none",
-                    "confidence": 1.0,
-                    "box": [0, 0, 0, 0]
                 }
             
             # Convert detection format to match backend expectations exactly as in worker.md section 4.2
@@ -311,9 +394,7 @@ async def detect(websocket: WebSocket):
                 "licensePlateConfidence": None
             }
             
-            # Handle different detection result formats
+            # Extract and process branch results from parallel classification
-            if isinstance(highest_confidence_detection, dict):
-                # Extract and flatten branch results from parallel classification
             branch_results = highest_confidence_detection.get("branch_results", {})
             if branch_results:
                 logger.debug(f"Processing branch results: {branch_results}")
@@ -321,6 +402,8 @@ async def detect(websocket: WebSocket):
                 # Transform branch results into backend-expected detection attributes
                 for branch_id, branch_data in branch_results.items():
                     if isinstance(branch_data, dict):
+                        logger.debug(f"Processing branch {branch_id}: {branch_data}")
+                        
                         # Map common classification fields to backend-expected names
                         if "brand" in branch_data:
                             detection_dict["carBrand"] = branch_data["brand"]
@@ -335,7 +418,9 @@ async def detect(websocket: WebSocket):
                             elif "bodytype" in branch_id.lower() or "body" in branch_id.lower():
                                 detection_dict["bodyType"] = class_name
                 
-                    logger.info(f"Detection payload: {detection_dict}")
+                logger.info(f"Detection payload after branch processing: {detection_dict}")
+            else:
+                logger.debug("No branch results found in detection result")
             
             detection_data = {
                 "type": "imageDetection",
@@ -348,12 +433,14 @@ async def detect(websocket: WebSocket):
                 }
             }
             
-            # Add session ID if available
+            # Add session ID if available (generated by pipeline when Car+Frontal detected)
             if session_id is not None:
                 detection_data["sessionId"] = session_id
+                logger.debug(f"Added session_id to WebSocket response: {session_id}")
             
-            if highest_confidence_detection["class"] != "none":
+            if highest_confidence_detection.get("class") != "none":
-                logger.info(f"Camera {camera_id}: Detected {highest_confidence_detection['class']} with confidence {highest_confidence_detection['confidence']:.2f} using model {stream['modelName']}")
+                confidence = highest_confidence_detection.get("confidence", 0.0)
+                logger.info(f"Camera {camera_id}: Detected {highest_confidence_detection['class']} with confidence {confidence:.2f} using model {stream['modelName']}")
                 
                 # Log session ID if available
                 if session_id:
@@ -361,6 +448,7 @@ async def detect(websocket: WebSocket):
             
             await websocket.send_json(detection_data)
             logger.debug(f"Sent detection data to client for camera {camera_id}")
+            logger.debug(f"Sent this detection data: {detection_data}")
             return persistent_data
         except Exception as e:
             logger.error(f"Error in handle_detection for camera {camera_id}: {str(e)}", exc_info=True)

siwatsystem/pympta.py

@@ -786,9 +786,11 @@ def run_pipeline(frame, node: dict, return_bbox: bool=False, context=None):
         primary_detection = max(all_detections, key=lambda x: x["confidence"])
         primary_bbox = primary_detection["bbox"]
         
-        # Add branch results to primary detection for compatibility
+        # Add branch results and session_id to primary detection for compatibility
         if "branch_results" in detection_result:
             primary_detection["branch_results"] = detection_result["branch_results"]
+        if "session_id" in detection_result:
+            primary_detection["session_id"] = detection_result["session_id"]
         
         return (primary_detection, primary_bbox) if return_bbox else primary_detection