pympta & webcam dev tester
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parent
5da166a341
commit
ee0071284e
3 changed files with 190 additions and 155 deletions
158
app.py
158
app.py
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@ -18,6 +18,9 @@ from fastapi.websockets import WebSocketDisconnect
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from websockets.exceptions import ConnectionClosedError
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from ultralytics import YOLO
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# Import shared pipeline functions
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from siwatsystem.pympta import load_pipeline_from_zip, run_pipeline
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app = FastAPI()
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# Global dictionaries to keep track of models and streams
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@ -57,161 +60,6 @@ WORKER_TIMEOUT_MS = 10000
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streams_lock = threading.Lock()
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models_lock = threading.Lock()
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####################################################
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# Pipeline (Model)-loading helper functions
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####################################################
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def load_pipeline_node(node_config: dict, models_dir: str) -> dict:
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"""
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Recursively load a model node.
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Expects node_config to have:
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- modelId: a unique identifier
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- modelFile: the .pt file in models_dir
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- triggerClasses: list of class names that activate child branches
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- crop: boolean; if True, we crop to the bounding box for the next model
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- minConfidence: (optional) minimum confidence required to enter this branch
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- branches: list of child node configurations
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"""
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model_path = os.path.join(models_dir, node_config["modelFile"])
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if not os.path.exists(model_path):
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logging.error(f"Model file {model_path} not found.")
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raise FileNotFoundError(f"Model file {model_path} not found.")
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logging.info(f"Loading model for node {node_config['modelId']} from {model_path}")
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model = YOLO(model_path)
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if torch.cuda.is_available():
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model.to("cuda")
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node = {
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"modelId": node_config["modelId"],
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"modelFile": node_config["modelFile"],
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"triggerClasses": node_config.get("triggerClasses", []),
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"crop": node_config.get("crop", False),
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"minConfidence": node_config.get("minConfidence", None), # NEW FIELD
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"model": model,
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"branches": []
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}
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for child_config in node_config.get("branches", []):
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child_node = load_pipeline_node(child_config, models_dir)
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node["branches"].append(child_node)
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return node
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def load_pipeline_from_zip(zip_url: str, target_dir: str) -> dict:
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"""
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Download the .mpta file from zip_url, extract it to target_dir,
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and load the pipeline configuration (pipeline.json).
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Returns the model tree (root node) loaded with YOLO models.
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"""
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os.makedirs(target_dir, exist_ok=True)
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zip_path = os.path.join(target_dir, "pipeline.mpta")
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try:
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response = requests.get(zip_url, stream=True)
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if response.status_code == 200:
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with open(zip_path, "wb") as f:
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for chunk in response.iter_content(chunk_size=8192):
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f.write(chunk)
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logging.info(f"Downloaded .mpta file from {zip_url} to {zip_path}")
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else:
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logging.error(f"Failed to download .mpta file (status {response.status_code})")
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return None
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except Exception as e:
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logging.error(f"Exception downloading .mpta file from {zip_url}: {e}")
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return None
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# Extract the .mpta file
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try:
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with zipfile.ZipFile(zip_path, "r") as zip_ref:
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zip_ref.extractall(target_dir)
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logging.info(f"Extracted .mpta file to {target_dir}")
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except Exception as e:
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logging.error(f"Failed to extract .mpta file: {e}")
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return None
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finally:
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if os.path.exists(zip_path):
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os.remove(zip_path)
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# Load pipeline.json
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pipeline_json_path = os.path.join(target_dir, "pipeline.json")
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if not os.path.exists(pipeline_json_path):
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logging.error("pipeline.json not found in the .mpta file")
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return None
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try:
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with open(pipeline_json_path, "r") as f:
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pipeline_config = json.load(f)
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# Build the model tree recursively
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model_tree = load_pipeline_node(pipeline_config["pipeline"], target_dir)
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return model_tree
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except Exception as e:
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logging.error(f"Error loading pipeline.json: {e}")
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return None
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####################################################
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# Model execution function
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####################################################
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def run_pipeline(frame, node: dict):
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"""
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Run the model at the current node.
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- Select the highest-confidence detection (if any).
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- If 'crop' is True, crop to the bounding box for the next stage.
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- If the detected class matches a branch's triggerClasses, check the confidence.
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If the detection's confidence is below branch["minConfidence"] (if specified),
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do not enter the branch and return the current detection.
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Returns the final detection result (dict) or None.
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"""
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try:
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results = node["model"].track(frame, stream=False, persist=True)
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detection = None
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max_conf = -1
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best_box = None
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for r in results:
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for box in r.boxes:
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box_cpu = box.cpu()
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conf = float(box_cpu.conf[0])
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if conf > max_conf and hasattr(box, "id") and box.id is not None:
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max_conf = conf
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detection = {
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"class": node["model"].names[int(box_cpu.cls[0])],
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"confidence": conf,
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"id": box.id.item(),
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}
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best_box = box_cpu
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# If there's a detection and crop is True, crop frame to bounding box
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if detection and node.get("crop", False) and best_box is not None:
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coords = best_box.xyxy[0] # [x1, y1, x2, y2]
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x1, y1, x2, y2 = map(int, coords)
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h, w = frame.shape[:2]
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x1 = max(0, x1)
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y1 = max(0, y1)
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x2 = min(w, x2)
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y2 = min(h, y2)
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if x2 > x1 and y2 > y1:
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frame = frame[y1:y2, x1:x2] # crop the frame
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if detection is not None:
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# Check if any branch should be entered based on trigger classes
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for branch in node["branches"]:
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if detection["class"] in branch.get("triggerClasses", []):
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# Check for a minimum confidence threshold for this branch
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min_conf = branch.get("minConfidence")
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if min_conf is not None and detection["confidence"] < min_conf:
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logging.debug(
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f"Detection confidence {detection['confidence']} below threshold "
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f"{min_conf} for branch {branch['modelId']}. Ending pipeline at current node."
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)
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return detection
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branch_detection = run_pipeline(frame, branch)
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if branch_detection is not None:
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return branch_detection
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return detection
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return None
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except Exception as e:
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logging.error(f"Error running pipeline on node {node.get('modelId')}: {e}")
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return None
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####################################################
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# Detection and frame processing functions
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####################################################
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52
pipeline_webcam.py
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52
pipeline_webcam.py
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@ -0,0 +1,52 @@
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import argparse
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import os
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import cv2
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import time
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import logging
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from siwatsystem.pympta import load_pipeline_from_zip, run_pipeline
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logging.basicConfig(level=logging.DEBUG, format="%(asctime)s [%(levelname)s] %(message)s")
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def main(mpta_url: str, video_source: str):
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extraction_dir = os.path.join("models", "webcam_pipeline")
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logging.info(f"Loading pipeline from {mpta_url}")
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model_tree = load_pipeline_from_zip(mpta_url, extraction_dir)
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if model_tree is None:
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logging.error("Failed to load pipeline.")
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return
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cap = cv2.VideoCapture(video_source)
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if not cap.isOpened():
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logging.error(f"Cannot open video source {video_source}")
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return
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logging.info("Press 'q' to exit.")
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while True:
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ret, frame = cap.read()
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if not ret:
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logging.error("Failed to capture frame.")
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break
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detection, bbox = run_pipeline(frame, model_tree, return_bbox=True)
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if bbox:
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x1, y1, x2, y2 = bbox
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cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
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label = detection["class"] if detection else "Detection"
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cv2.putText(frame, label, (x1, y1 - 10),
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cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36, 255, 12), 2)
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cv2.imshow("Pipeline Webcam", frame)
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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cap.release()
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cv2.destroyAllWindows()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Run pipeline webcam utility.")
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parser.add_argument("--mpta-url", type=str, required=True, help="URL to the pipeline mpta (ZIP) file.")
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parser.add_argument("--video", type=str, default="0", help="Video source (default webcam index 0).")
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args = parser.parse_args()
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video_source = int(args.video) if args.video.isdigit() else args.video
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main(args.mpta_url, video_source)
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135
siwatsystem/pympta.py
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135
siwatsystem/pympta.py
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@ -0,0 +1,135 @@
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import os
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import json
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import logging
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import torch
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import cv2
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import requests
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import zipfile
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from ultralytics import YOLO
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def load_pipeline_node(node_config: dict, models_dir: str) -> dict:
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# Recursively load a model node from configuration.
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model_path = os.path.join(models_dir, node_config["modelFile"])
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if not os.path.exists(model_path):
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logging.error(f"Model file {model_path} not found.")
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raise FileNotFoundError(f"Model file {model_path} not found.")
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logging.info(f"Loading model for node {node_config['modelId']} from {model_path}")
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model = YOLO(model_path)
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if torch.cuda.is_available():
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model.to("cuda")
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node = {
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"modelId": node_config["modelId"],
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"modelFile": node_config["modelFile"],
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"triggerClasses": node_config.get("triggerClasses", []),
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"crop": node_config.get("crop", False),
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"minConfidence": node_config.get("minConfidence", None),
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"model": model,
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"branches": []
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}
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for child in node_config.get("branches", []):
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node["branches"].append(load_pipeline_node(child, models_dir))
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return node
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def load_pipeline_from_zip(zip_url: str, target_dir: str) -> dict:
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# Download, extract, and load a pipeline configuration from a zip (.mpta) file.
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os.makedirs(target_dir, exist_ok=True)
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zip_path = os.path.join(target_dir, "pipeline.mpta")
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try:
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response = requests.get(zip_url, stream=True)
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if response.status_code == 200:
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with open(zip_path, "wb") as f:
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for chunk in response.iter_content(chunk_size=8192):
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f.write(chunk)
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logging.info(f"Downloaded .mpta file from {zip_url} to {zip_path}")
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else:
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logging.error(f"Failed to download .mpta file (status {response.status_code})")
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return None
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except Exception as e:
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logging.error(f"Exception downloading .mpta file from {zip_url}: {e}")
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return None
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try:
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with zipfile.ZipFile(zip_path, "r") as zip_ref:
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zip_ref.extractall(target_dir)
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logging.info(f"Extracted .mpta file to {target_dir}")
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except Exception as e:
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logging.error(f"Failed to extract .mpta file: {e}")
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return None
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finally:
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if os.path.exists(zip_path):
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os.remove(zip_path)
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pipeline_json_path = os.path.join(target_dir, "pipeline.json")
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if not os.path.exists(pipeline_json_path):
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logging.error("pipeline.json not found in the .mpta file")
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return None
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try:
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with open(pipeline_json_path, "r") as f:
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pipeline_config = json.load(f)
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return load_pipeline_node(pipeline_config["pipeline"], target_dir)
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except Exception as e:
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logging.error(f"Error loading pipeline.json: {e}")
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return None
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def run_pipeline(frame, node: dict, return_bbox: bool = False):
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"""
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Processes the frame with the given pipeline node. When return_bbox is True,
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the function returns a tuple (detection, bbox) where bbox is (x1,y1,x2,y2)
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for drawing. Otherwise, returns only the detection.
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"""
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try:
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results = node["model"].track(frame, stream=False, persist=True)
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detection = None
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best_box = None
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max_conf = -1
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for r in results:
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for box in r.boxes:
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box_cpu = box.cpu()
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conf = float(box_cpu.conf[0])
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if conf > max_conf and hasattr(box, "id") and box.id is not None:
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max_conf = conf
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detection = {
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"class": node["model"].names[int(box_cpu.cls[0])],
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"confidence": conf,
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"id": box.id.item()
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}
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best_box = box_cpu
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bbox = None
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if detection and node.get("crop", False) and best_box is not None:
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coords = best_box.xyxy[0]
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x1, y1, x2, y2 = map(int, coords)
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h, w = frame.shape[:2]
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x1, y1 = max(0, x1), max(0, y1)
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x2, y2 = min(w, x2), min(h, y2)
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if x2 > x1 and y2 > y1:
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bbox = (x1, y1, x2, y2)
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frame = frame[y1:y2, x1:x2]
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if detection is not None:
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for branch in node["branches"]:
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if detection["class"] in branch.get("triggerClasses", []):
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min_conf = branch.get("minConfidence")
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if min_conf is not None and detection["confidence"] < min_conf:
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logging.debug(f"Confidence {detection['confidence']} below threshold {min_conf} for branch {branch['modelId']}.")
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if return_bbox:
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return detection, bbox
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return detection
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res = run_pipeline(frame, branch, return_bbox)
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if res is not None:
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if return_bbox:
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return res
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return res
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if return_bbox:
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return detection, bbox
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return detection
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if return_bbox:
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return None, None
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return None
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except Exception as e:
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logging.error(f"Error running pipeline on node {node.get('modelId')}: {e}")
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if return_bbox:
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return None, None
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return None
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