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)

pipeline.log

@@ -0,0 +1,23 @@
+2025-05-12 18:10:04,590 [INFO] Loading pipeline from local file: demoa.mpta
+2025-05-12 18:10:04,610 [INFO] Copied local .mpta file from demoa.mpta to .\.mptacache\pipeline.mpta
+2025-05-12 18:10:04,901 [INFO] Extracted .mpta file to .\.mptacache
+2025-05-12 18:10:04,905 [INFO] Loading model for node DetectionDraft from .\.mptacache\demoa\DetectionDraft.pt
+2025-05-12 18:10:05,083 [INFO] Loading model for node ClassificationDraft from .\.mptacache\demoa\ClassificationDraft.pt
+2025-05-12 18:10:08,035 [INFO] Press 'q' to exit.
+2025-05-12 18:10:12,217 [INFO] Cleaned up .mptacache directory on shutdown.
+2025-05-12 18:13:08,465 [INFO] Loading pipeline from local file: demoa.mpta
+2025-05-12 18:13:08,512 [INFO] Copied local .mpta file from demoa.mpta to .\.mptacache\pipeline.mpta
+2025-05-12 18:13:08,769 [INFO] Extracted .mpta file to .\.mptacache
+2025-05-12 18:13:08,773 [INFO] Loading model for node DetectionDraft from .\.mptacache\demoa\DetectionDraft.pt
+2025-05-12 18:13:09,083 [INFO] Loading model for node ClassificationDraft from .\.mptacache\demoa\ClassificationDraft.pt
+2025-05-12 18:13:12,187 [INFO] Press 'q' to exit.
+2025-05-12 18:13:14,146 [INFO] → Running model: DetectionDraft
+2025-05-12 18:13:17,119 [INFO] Cleaned up .mptacache directory on shutdown.
+2025-05-12 18:14:25,665 [INFO] Loading pipeline from local file: demoa.mpta
+2025-05-12 18:14:25,687 [INFO] Copied local .mpta file from demoa.mpta to .\.mptacache\pipeline.mpta
+2025-05-12 18:14:25,953 [INFO] Extracted .mpta file to .\.mptacache
+2025-05-12 18:14:25,957 [INFO] Loading model for node DetectionDraft from .\.mptacache\demoa\DetectionDraft.pt
+2025-05-12 18:14:26,138 [INFO] Loading model for node ClassificationDraft from .\.mptacache\demoa\ClassificationDraft.pt
+2025-05-12 18:14:29,171 [INFO] Press 'q' to exit.
+2025-05-12 18:14:30,146 [INFO] → Running model: DetectionDraft
+2025-05-12 18:14:32,080 [INFO] Cleaned up .mptacache directory on shutdown.

siwatsystem/pympta.py

@@ -3,172 +3,180 @@ import json
 import logging
 import torch
 import cv2
-import requests
 import zipfile
 import shutil
 from ultralytics import YOLO
 from urllib.parse import urlparse
 
 def load_pipeline_node(node_config: dict, mpta_dir: str) -> dict:
-    # Recursively load a model node from configuration.
     model_path = os.path.join(mpta_dir, node_config["modelFile"])
     if not os.path.exists(model_path):
         logging.error(f"Model file {model_path} not found.")
         raise FileNotFoundError(f"Model file {model_path} not found.")
-    logging.info(f"Loading model for node {node_config['modelId']} from {model_path}")
+    logging.info(f"Loading model {node_config['modelId']} from {model_path}")
     model = YOLO(model_path)
     if torch.cuda.is_available():
         model.to("cuda")
-    node = {
+
+    # map triggerClasses names → indices for YOLO
+    names = model.names  # idx -> class name
+    trigger_names = node_config.get("triggerClasses", [])
+    trigger_inds = [i for i, nm in names.items() if nm in trigger_names]
+
+    return {
         "modelId": node_config["modelId"],
         "modelFile": node_config["modelFile"],
-        "triggerClasses": node_config.get("triggerClasses", []),
+        "triggerClasses": trigger_names,
+        "triggerClassIndices": trigger_inds,
         "crop": node_config.get("crop", False),
-        "minConfidence": node_config.get("minConfidence", None),
+        "minConfidence": node_config.get("minConfidence", 0.0),
         "model": model,
-        "branches": []
+        "branches": [
+            load_pipeline_node(child, mpta_dir)
+            for child in node_config.get("branches", [])
+        ]
     }
-    for child in node_config.get("branches", []):
-        node["branches"].append(load_pipeline_node(child, mpta_dir))
-    return node
 
 def load_pipeline_from_zip(zip_source: str, target_dir: str) -> dict:
     os.makedirs(target_dir, exist_ok=True)
     zip_path = os.path.join(target_dir, "pipeline.mpta")
-    
-    # Parse the source; only local files are supported here.
     parsed = urlparse(zip_source)
     if parsed.scheme in ("", "file"):
-        local_path = parsed.path if parsed.scheme == "file" else zip_source
-        if os.path.exists(local_path):
-            try:
-                shutil.copy(local_path, zip_path)
-                logging.info(f"Copied local .mpta file from {local_path} to {zip_path}")
-            except Exception as e:
-                logging.error(f"Failed to copy local .mpta file from {local_path}: {e}")
+        local = parsed.path if parsed.scheme == "file" else zip_source
+        if not os.path.exists(local):
+            logging.error(f"Local file {local} does not exist.")
             return None
+        shutil.copy(local, zip_path)
     else:
-            logging.error(f"Local file {local_path} does not exist.")
-            return None
-    else:
-        logging.error("HTTP download functionality has been moved. Use a local file path here.")
+        logging.error("HTTP download not supported; use local file.")
         return None
 
-    try:
-        with zipfile.ZipFile(zip_path, "r") as zip_ref:
-            zip_ref.extractall(target_dir)
-        logging.info(f"Extracted .mpta file to {target_dir}")
-    except Exception as e:
-        logging.error(f"Failed to extract .mpta file: {e}")
-        return None
-    finally:
-        if os.path.exists(zip_path):
+    with zipfile.ZipFile(zip_path, "r") as z:
+        z.extractall(target_dir)
     os.remove(zip_path)
-    pipeline_name = os.path.basename(zip_source)
-    pipeline_name = os.path.splitext(pipeline_name)[0]
-    mpta_dir = os.path.join(target_dir, pipeline_name)
-    pipeline_json_path = os.path.join(mpta_dir, "pipeline.json")
-    if not os.path.exists(pipeline_json_path):
-        logging.error("pipeline.json not found in the .mpta file")
+
+    base = os.path.splitext(os.path.basename(zip_source))[0]
+    mpta_dir = os.path.join(target_dir, base)
+    cfg = os.path.join(mpta_dir, "pipeline.json")
+    if not os.path.exists(cfg):
+        logging.error("pipeline.json not found in archive.")
         return None
 
-    try:
-        with open(pipeline_json_path, "r") as f:
+    with open(cfg) as f:
         pipeline_config = json.load(f)
     return load_pipeline_node(pipeline_config["pipeline"], mpta_dir)
-    except Exception as e:
-        logging.error(f"Error loading pipeline.json: {e}")
-        return None
+
 
 def run_pipeline(frame, node: dict, return_bbox: bool=False):
     """
-    Processes the frame with the given pipeline node. When return_bbox is True,
-    the function returns a tuple (detection, bbox) where bbox is (x1,y1,x2,y2)
-    for drawing. Otherwise, returns only the detection.
+    - For detection nodes (task != 'classify'):
+        • runs `track(..., classes=triggerClassIndices)`
+        • picks top box ≥ minConfidence
+        • optionally crops & resizes → recurse into child
+        • else returns (det_dict, bbox)
+    - For classify nodes:
+        • runs `predict()`
+        • returns top (class,confidence) and no bbox
     """
     try:
-        # Check model type and use appropriate method
-        model_task = getattr(node["model"], "task", None)
+        task = getattr(node["model"], "task", None)
 
-        if model_task == "classify":
-            # Classification models need to use predict() instead of track()
-            logging.debug(f"Running classification model: {node.get('modelId')}")
+        # ─── Classification stage ───────────────────────────────────
+        # if task == "classify":
+        #     results = node["model"].predict(frame, stream=False)
+        #     dets = []
+        #     for r in results:
+        #         probs = r.probs
+        #         if probs is not None:
+        #             # sort descending
+        #             idxs = probs.argsort(descending=True)
+        #             for cid in idxs:
+        #                 dets.append({
+        #                     "class": node["model"].names[int(cid)],
+        #                     "confidence": float(probs[int(cid)]),
+        #                     "id": None
+        #                 })
+        #     if not dets:
+        #         return (None, None) if return_bbox else None
+
+        #     best = dets[0]
+        #     return (best, None) if return_bbox else best
+        
+        if task == "classify":
+            # run the classifier and grab its top-1 directly via the Probs API
             results = node["model"].predict(frame, stream=False)
-            detection = None
-            best_box = None
+            # nothing returned?
+            if not results:
+                return (None, None) if return_bbox else None
 
-            # Process classification results
-            for r in results:
+            # take the first result's probs object
+            r     = results[0]
             probs = r.probs
-                if probs is not None and len(probs) > 0:
-                    # Get the most confident class
-                    class_id = int(probs.top1)
-                    conf = float(probs.top1conf)
-                    detection = {
-                        "class": node["model"].names[class_id],
-                        "confidence": conf,
-                        "id": None  # Classification doesn't have tracking IDs
+            if probs is None:
+                return (None, None) if return_bbox else None
+
+            # get the top-1 class index and its confidence
+            top1_idx  = int(probs.top1)
+            top1_conf = float(probs.top1conf)
+
+            det = {
+                "class": node["model"].names[top1_idx],
+                "confidence": top1_conf,
+                "id": None
             }
+            return (det, None) if return_bbox else det
 
-            # Classification doesn't produce bounding boxes
-            bbox = None
 
-        else:
-            # Detection/segmentation models use tracking
-            logging.debug(f"Running detection/tracking model: {node.get('modelId')}")
-            results = node["model"].track(frame, stream=False, persist=True)
-            detection = None
-            best_box = None
-            max_conf = -1
+        # ─── Detection stage ────────────────────────────────────────
+        # only look for your triggerClasses
+        tk = node["triggerClassIndices"]
+        res = node["model"].track(
+            frame,
+            stream=False,
+            persist=True,
+            **({"classes": tk} if tk else {})
+        )[0]
 
-            for r in results:
-                for box in r.boxes:
-                    box_cpu = box.cpu()
-                    conf = float(box_cpu.conf[0])
-                    if conf > max_conf and hasattr(box, "id") and box.id is not None:
-                        max_conf = conf
-                        detection = {
-                            "class": node["model"].names[int(box_cpu.cls[0])],
-                            "confidence": conf,
-                            "id": box.id.item()
-                        }
-                        best_box = box_cpu
+        dets, boxes = [], []
+        for box in res.boxes:
+            conf = float(box.cpu().conf[0])
+            cid  = int(box.cpu().cls[0])
+            name = node["model"].names[cid]
+            if conf < node["minConfidence"]:
+                continue
+            xy = box.cpu().xyxy[0]
+            x1,y1,x2,y2 = map(int, xy)
+            dets.append({"class": name, "confidence": conf,
+                         "id": box.id.item() if hasattr(box, "id") else None})
+            boxes.append((x1, y1, x2, y2))
 
-            bbox = None
-            # Calculate bbox if best_box exists
-            if detection and best_box is not None:
-                coords = best_box.xyxy[0]
-                x1, y1, x2, y2 = map(int, coords)
-                h, w = frame.shape[:2]
-                x1, y1 = max(0, x1), max(0, y1)
-                x2, y2 = min(w, x2), min(h, y2)
-                if x2 > x1 and y2 > y1:
-                    bbox = (x1, y1, x2, y2)
-                    if node.get("crop", False):
-                        frame = frame[y1:y2, x1:x2]
+        if not dets:
+            return (None, None) if return_bbox else None
+
+        # take highest‐confidence
+        best_idx = max(range(len(dets)), key=lambda i: dets[i]["confidence"])
+        best_det = dets[best_idx]
+        best_box = boxes[best_idx]
+
+        # ─── Branch (classification) ───────────────────────────────
+        for br in node["branches"]:
+            if (best_det["class"] in br["triggerClasses"]
+                    and best_det["confidence"] >= br["minConfidence"]):
+                # crop if requested
+                sub = frame
+                if br["crop"]:
+                    x1,y1,x2,y2 = best_box
+                    sub = frame[y1:y2, x1:x2]
+                    sub = cv2.resize(sub, (224, 224))
+
+                det2, _ = run_pipeline(sub, br, return_bbox=True)
+                if det2:
+                    # return classification result + original bbox
+                    return (det2, best_box) if return_bbox else det2
+
+        # ─── No branch matched → return this detection ─────────────
+        return (best_det, best_box) if return_bbox else best_det
 
-        if detection is not None:
-            for branch in node["branches"]:
-                if detection["class"] in branch.get("triggerClasses", []):
-                    min_conf = branch.get("minConfidence")
-                    if min_conf is not None and detection["confidence"] < min_conf:
-                        logging.debug(f"Confidence {detection['confidence']} below threshold {min_conf} for branch {branch['modelId']}.")
-                        if return_bbox:
-                            return detection, bbox
-                        return detection
-                    res = run_pipeline(frame, branch, return_bbox)
-                    if res is not None:
-                        if return_bbox:
-                            return res
-                        return res
-            if return_bbox:
-                return detection, bbox
-            return detection
-        if return_bbox:
-            return None, None
-        return None
     except Exception as e:
-        logging.error(f"Error running pipeline on node {node.get('modelId')}: {e}")
-        if return_bbox:
-            return None, None
-        return None
+        logging.error(f"Error in node {node.get('modelId')}: {e}")
+        return (None, None) if return_bbox else None