new logic

debug.py

@@ -0,0 +1,143 @@
+import argparse
+import os
+import cv2
+import time
+import logging
+import shutil
+import threading  # added threading
+import yaml  # for silencing YOLO
+
+from siwatsystem.pympta import load_pipeline_from_zip, run_pipeline
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
+
+# Silence YOLO logging
+os.environ["YOLO_VERBOSE"] = "False"
+for logger_name in ["ultralytics", "ultralytics.hub", "ultralytics.yolo.utils"]:
+    logging.getLogger(logger_name).setLevel(logging.WARNING)
+
+# Global variables for frame sharing
+global_frame = None
+global_ret = False
+capture_running = False
+
+def video_capture_loop(cap):
+    global global_frame, global_ret, capture_running
+    while capture_running:
+        global_ret, global_frame = cap.read()
+        time.sleep(0.01)  # slight delay to reduce CPU usage
+
+def clear_cache(cache_dir: str):
+    if os.path.exists(cache_dir):
+        shutil.rmtree(cache_dir)
+
+def log_pipeline_flow(frame, model_tree, level=0):
+    """
+    Wrapper around run_pipeline that logs the model flow and detection results.
+    Returns the same output as the original run_pipeline function.
+    """
+    indent = "  " * level
+    model_id = model_tree.get("modelId", "unknown")
+    logging.info(f"{indent}→ Running model: {model_id}")
+    
+    detection, bbox = run_pipeline(frame, model_tree, return_bbox=True)
+    
+    if detection:
+        confidence = detection.get("confidence", 0) * 100
+        class_name = detection.get("class", "unknown")
+        object_id = detection.get("id", "N/A")
+        
+        logging.info(f"{indent}✓ Detected: {class_name} (ID: {object_id}, confidence: {confidence:.1f}%)")
+        
+        # Check if any branches were triggered
+        triggered = False
+        for branch in model_tree.get("branches", []):
+            trigger_classes = branch.get("triggerClasses", [])
+            min_conf = branch.get("minConfidence", 0)
+            
+            if class_name in trigger_classes and detection.get("confidence", 0) >= min_conf:
+                triggered = True
+                if branch.get("crop", False) and bbox:
+                    x1, y1, x2, y2 = bbox
+                    cropped_frame = frame[y1:y2, x1:x2]
+                    logging.info(f"{indent}  ⌊ Triggering branch with cropped region {x1},{y1} to {x2},{y2}")
+                    branch_result = log_pipeline_flow(cropped_frame, branch, level + 1)
+                else:
+                    logging.info(f"{indent}  ⌊ Triggering branch with full frame")
+                    branch_result = log_pipeline_flow(frame, branch, level + 1)
+                
+                if branch_result[0]:  # If branch detection successful, return it
+                    return branch_result
+        
+        if not triggered and model_tree.get("branches"):
+            logging.info(f"{indent}  ⌊ No branches triggered")
+    else:
+        logging.info(f"{indent}✗ No detection for {model_id}")
+    
+    return detection, bbox
+
+def main(mpta_file: str, video_source: str):
+    global capture_running
+    CACHE_DIR = os.path.join(".", ".mptacache")
+    clear_cache(CACHE_DIR)
+    logging.info(f"Loading pipeline from local file: {mpta_file}")
+    model_tree = load_pipeline_from_zip(mpta_file, CACHE_DIR)
+    if model_tree is None:
+        logging.error("Failed to load pipeline.")
+        return
+
+    cap = cv2.VideoCapture(video_source)
+    if not cap.isOpened():
+        logging.error(f"Cannot open video source {video_source}")
+        return
+
+    # Start video capture in a separate thread
+    capture_running = True
+    capture_thread = threading.Thread(target=video_capture_loop, args=(cap,))
+    capture_thread.start()
+
+    logging.info("Press 'q' to exit.")
+    try:
+        while True:
+            # Use the global frame and ret updated by the thread
+            if not global_ret or global_frame is None:
+                continue  # wait until a frame is available
+            
+            frame = global_frame.copy()  # local copy to work with
+
+            # Replace run_pipeline with our logging version
+            detection, bbox = log_pipeline_flow(frame, model_tree)
+
+            # Stop if "honda" is detected
+            if detection and detection.get("class", "").lower() == "toyota":
+                logging.info("Detected 'toyota'. Stopping pipeline.")
+                break
+
+            if bbox:
+                x1, y1, x2, y2 = bbox
+                cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
+                label = detection["class"] if detection else "Detection"
+                cv2.putText(frame, label, (x1, y1 - 10),
+                            cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36, 255, 12), 2)
+
+            cv2.imshow("Pipeline Webcam", frame)
+            if cv2.waitKey(1) & 0xFF == ord('q'):
+                break
+
+    finally:
+        # Stop capture thread and cleanup
+        capture_running = False
+        capture_thread.join()
+        cap.release()
+        cv2.destroyAllWindows()
+        clear_cache(CACHE_DIR)
+        logging.info("Cleaned up .mptacache directory on shutdown.")
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Run pipeline webcam utility.")
+    parser.add_argument("--mpta-file", type=str, required=True, help="Path to the local pipeline mpta (ZIP) file.")
+    parser.add_argument("--video", type=str, default="0", help="Video source (default webcam index 0).")
+    args = parser.parse_args()
+    video_source = int(args.video) if args.video.isdigit() else args.video
+    main(args.mpta_file, video_source)