comments typical of a gemini thinking

pipeline/core.py

@@ -7,6 +7,7 @@ from pipeline.types import Frame, Ticket
 
 logger = logging.getLogger(__name__)
 
+
 class ConsoleStore:
     def store(self, ticket: Ticket):
         logger.info(
@@ -74,7 +75,22 @@ class SmokingDetectionPipeline:
         candidates = []
         for person in people:
             # ROI: Region of Interest (e.g., upper body or hands)
-            person_crop = person.box.crop(frame.data)
+            # EXPANSTION: Expand the person box to include extended arms/surroundings
+            h_img, w_img = frame.data.shape[:2]
+            margin = 0.2  # 20% expansion
+
+            pb = person.box
+            bw = pb.x2 - pb.x1
+            bh = pb.y2 - pb.y1
+
+            # Expanded coordinates for Step 2
+            ex1 = max(0, int(pb.x1 - bw * margin))
+            ey1 = max(0, int(pb.y1 - bh * margin))
+            ex2 = min(w_img, int(pb.x2 + bw * margin))
+            ey2 = min(h_img, int(pb.y2 + bh * margin))
+
+            # Use expanded crop for cigarette detection
+            person_crop = frame.data[ey1:ey2, ex1:ex2]
 
             # If crop is too small, skip
             if (
@@ -85,15 +101,27 @@ class SmokingDetectionPipeline:
                 continue
 
             cig_candidates = self.cigarette_model.predict(person_crop)
+
+            # Filter out extremely small candidates (noise)
+            # User requested check similar to person_crop check (< 10px)
+            cig_candidates = [
+                c
+                for c in cig_candidates
+                if (c.box.x2 - c.box.x1) >= 10 and (c.box.y2 - c.box.y1) >= 10
+            ]
+
             if cig_candidates:
-                candidates.append((person, cig_candidates))
+                # Store expansion offset for later coordinate translation
+                # We store (ex1, ey1) as metadata on the person detection or passed tuple?
+                # Easiest is to pass it in the tuple: (person, cigs, offset)
+                candidates.append((person, cig_candidates, (ex1, ey1)))
 
                 # Check detections (translate to global coordinates)
                 for cig in cig_candidates:
                     # Clone or modify the cig detection to be global
                     # Assuming cig.box is relevant to person_crop
-                    # Global X = person.box.x1 + local_x
-                    # Global Y = person.box.y1 + local_y
+                    # Global X = person_crop_origin_x + local_x
+                    # Global Y = person_crop_origin_y + local_y
 
                     # Update in place might affect other logic if it assumes local,
                     # but logic below (Expansion) seems to rely on cig.box being used on raw_person_crop
@@ -104,10 +132,10 @@ class SmokingDetectionPipeline:
 
                     global_cig = Detection(
                         box=BoundingBox(
-                            x1=person.box.x1 + cig.box.x1,
-                            y1=person.box.y1 + cig.box.y1,
-                            x2=person.box.x1 + cig.box.x2,
-                            y2=person.box.y1 + cig.box.y2,
+                            x1=ex1 + cig.box.x1,
+                            y1=ey1 + cig.box.y1,
+                            x2=ex1 + cig.box.x2,
+                            y2=ey1 + cig.box.y2,
                             conf=cig.box.conf,
                         ),
                         score=cig.score,
@@ -124,7 +152,7 @@ class SmokingDetectionPipeline:
         # --- Step 3: Verification (Classifier) ---
         # "Is that small thing actually a cigarette?"
         confirmed_hits = []
-        for person, cigs in candidates:
+        for person, cigs, (ex1, ey1) in candidates:
             for cig in cigs:
                 # Crop just the object.
                 # Note: box coordinates are relative to the person crop, or global?
@@ -152,20 +180,49 @@ class SmokingDetectionPipeline:
                 # Strategy: cig detection on person_crop is easiest.
 
                 # Re-cutting the crop:
-                raw_person_crop = person.box.crop(frame.data)
+                # We need the SAME crop used for detection, or at least the same coordinate system.
+                # Since we used the expanded crop (person_crop created via ex1, ey1),
+                # cig.box is relative to that.
+
+                # We can just re-extract the crop from the frame using ex1, ey1 if we didn't save it.
+                # Or just use the global coordinates we know now.
+
+                # Let's derive the object crop from the expanded person crop logic.
 
                 # --- Expansion Logic ---
                 # Expand the box to give context
-                h, w = raw_person_crop.shape[:2]
-                pad_x = int((cig.box.x2 - cig.box.x1) * 0.5)  # Add 50% width padding
-                pad_y = int((cig.box.y2 - cig.box.y1) * 0.5)  # Add 50% height padding
+                # Here we are expanding the CIGARETTE box within the PERSON crop?
+                # The user asked to expand the hitbox (of the human) stage two too.
+                # We did that in Step 2.
 
-                x1 = max(0, cig.box.x1 - pad_x)
-                y1 = max(0, cig.box.y1 - pad_y)
-                x2 = min(w, cig.box.x2 + pad_x)
-                y2 = min(h, cig.box.y2 + pad_y)
+                # Now we need to prepare the crop for VERIFICATION (Step 3).
+                # We take the cig box (relative to expanded person crop) and add context?
 
-                obj_crop = raw_person_crop[y1:y2, x1:x2]
+                cig_w = cig.box.x2 - cig.box.x1
+                cig_h = cig.box.y2 - cig.box.y1
+
+                pad_x = int(cig_w * 0.5)  # Add 50% width padding
+                pad_y = int(cig_h * 0.5)  # Add 50% height padding
+
+                # Coordinates relative to the expanded person crop
+                cx1 = max(0, cig.box.x1 - pad_x)
+                cy1 = max(0, cig.box.y1 - pad_y)
+                # We don't know the exact W/H of the expanded crop easily here without recomputing or checking boundaries
+                # But we can map to Global and crop from frame directly to be safe.
+
+                global_cx1 = ex1 + cx1
+                global_cy1 = ey1 + cy1
+                global_cx2 = ex1 + cig.box.x2 + pad_x
+                global_cy2 = ey1 + cig.box.y2 + pad_y
+
+                h_img, w_img = frame.data.shape[:2]
+
+                final_x1 = max(0, global_cx1)
+                final_y1 = max(0, global_cy1)
+                final_x2 = min(w_img, global_cx2)
+                final_y2 = min(h_img, global_cy2)
+
+                obj_crop = frame.data[final_y1:final_y2, final_x1:final_x2]
 
                 if obj_crop.size == 0:
                     continue
@@ -180,10 +237,10 @@ class SmokingDetectionPipeline:
 
                     verified_det = Detection(
                         box=BoundingBox(
-                            x1=person.box.x1 + cig.box.x1,
-                            y1=person.box.y1 + cig.box.y1,
-                            x2=person.box.x1 + cig.box.x2,
-                            y2=person.box.y1 + cig.box.y2,
+                            x1=ex1 + cig.box.x1,
+                            y1=ey1 + cig.box.y1,
+                            x2=ex1 + cig.box.x2,
+                            y2=ey1 + cig.box.y2,
                             conf=score,
                         ),
                         score=score,

pipeline/outputs.py

@@ -6,11 +6,43 @@ from typing import List, Optional
 import cv2
 import numpy as np
 
-from pipeline.types import Detection, Frame
+from pipeline.types import Detection, Frame, Ticket
 
 logger = logging.getLogger(__name__)
 
 
+class FileAlertStore:
+    """
+    Persists actionable alerts (Tickets) to a JSONL file.
+    Use this for integrating with backend systems or audit logs.
+    """
+
+    def __init__(self, path: str):
+        self.path = path
+        self.file = open(path, "a")  # Append mode
+        logger.info(f"Initialized FileAlertStore writing to {path}")
+
+    def store(self, ticket: Ticket):
+        record = {
+            "type": "alert",
+            "frame_id": ticket.frame_id,
+            "confidence": ticket.confidence,
+            "stage": ticket.stage_confirmed,
+            "metadata": ticket.metadata,
+        }
+        # Note: We skip serializing 'evidence' (raw arrays) to JSON
+        # In a real app, you'd save the image to disk and link the path here.
+
+        self.file.write(json.dumps(record) + "\n")
+        self.file.flush()
+        logger.info(
+            f"[ALERT SAVED] {ticket.confidence:.2f} confirmed at {ticket.stage_confirmed}"
+        )
+
+    def close(self):
+        self.file.close()
+
+
 class VideoFileStore:
     """
     Writes processed frames to a video file with visualization overlays.