Update app.py

app.py

@@ -211,17 +211,17 @@ CONFIG = {
         "improper_tool_use": 0.3
     },
     "MIN_VIOLATION_FRAMES": 1,
-    "VIOLATION_COOLDOWN": 30.0,  # Increased cooldown period
+    "VIOLATION_COOLDOWN": 30.0,
     "WORKER_TRACKING_DURATION": 5.0,
     "MAX_PROCESSING_TIME": 60,
-    "FRAME_SKIP": 2,  # Skip more frames for faster processing
+    "FRAME_SKIP": 2,
     "BATCH_SIZE": 16,
     "PARALLEL_WORKERS": max(1, cpu_count() - 1),
     "TRACK_BUFFER": 30,
     "TRACK_THRESH": 0.3,
     "MATCH_THRESH": 0.7,
-    "SNAPSHOT_QUALITY": 95,  # Higher quality for better visibility
-    "MAX_WORKER_DISTANCE": 100  # Maximum pixel distance to consider same worker
+    "SNAPSHOT_QUALITY": 95,
+    "MAX_WORKER_DISTANCE": 100
 }
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -498,7 +498,7 @@ def push_report_to_salesforce(violations, score, pdf_path, pdf_file):
         return None, ""
 
 def process_video(video_data):
-    """Process video to detect safety violations"""
+    """Process video to detect safety violations with de-duplication"""
     try:
         os.makedirs(CONFIG["OUTPUT_DIR"], exist_ok=True)
         logger.info(f"Output directory ensured: {CONFIG['OUTPUT_DIR']}")
@@ -528,7 +528,7 @@ def process_video(video_data):
         )
 
         # Track unique violations by worker ID
-        unique_violations = {}  # {worker_id: {violation_type: first_detection_time}}
+        unique_violations = {}  # {worker_id: {violation_type: {"timestamp": float, "confidence": float, "bbox": list}}}
         snapshots = []
         start_time = time.time()
         frame_skip = CONFIG["FRAME_SKIP"]
@@ -620,7 +620,11 @@ def process_video(video_data):
                     # Check if this violation type has been recorded for this worker
                     if label not in unique_violations[worker_id]:
                         # This is a new violation type for this worker
-                        unique_violations[worker_id][label] = current_time
+                        unique_violations[worker_id][label] = {
+                            "timestamp": current_time,
+                            "confidence": round(conf, 2),
+                            "bbox": bbox
+                        }
                         
                         # Create detection object
                         detection = {
@@ -676,11 +680,13 @@ def process_video(video_data):
         # Convert tracked violations to final violation list
         violations = []
         for worker_id, worker_violations in unique_violations.items():
-            for label, detection_time in worker_violations.items():
+            for label, info in worker_violations.items():
                 violation = {
                     "worker_id": worker_id,
                     "violation": label,
-                    "timestamp": detection_time
+                    "timestamp": info["timestamp"],
+                    "confidence": info["confidence"],
+                    "bounding_box": info["bbox"]
                 }
                 violations.append(violation)