MxPrepost Package

mxprepost provides a unified, high-performance interface for YOLO pre-processing and post-processing, designed for seamless integration with MemryX MXA applications across YOLOv8–YOLOv11 tasks such as detection, segmentation, and pose estimation.

It handles tasks like:

• Pre-processing: Frame preparation (e.g., resizing, normalization).
• Post-processing: Model output decoding, Non-Maximum Suppression (NMS), class filtering, and result annotation.

It is intended to be used inside the MXA input/output callbacks (via mxapi.MxAccl). Under the hood, optimized C++ bindings accelerate compute-intensive operations such as NMS and output decoding.

---

General Usage Pattern (MXA Integration)

1. Create mxapi.MxAccl

2. Connect input/output callbacks

3. Initialize mxprepost.MxPrepost with the accelerator

4. Call:

   • preprocess(frame) in input callback
   • postprocess(mxa_output, ori_height, ori_width) or postprocess(mxa_output, ori_frame) in output callback
   • draw(frame, result) for visualization

import cv2
import mxprepost
from memryx import mxapi

class App:
    def __init__(self, dfp_path, video_path):
        # Open video source
        self.cap = cv2.VideoCapture(video_path)

        # Store original frame dimensions
        self.ori_width = int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))
        self.ori_height = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))

        # Create MXA accelerator
        self.accl = mxapi.MxAccl(dfp_path, [0], [False, False], False)

		#---------------------------------------------------------------
		#-------------------------- MxPrepost --------------------------
		#---------------------------------------------------------------
        # Initialize MxPrepost
        self.pp = mxprepost.MxPrepost(
            accl=self.accl,
            task="yolov8-det"
        )

        # Connect callbacks
        self.accl.connect_stream(self.in_callback, self.out_callback, stream_id=0)

    def in_callback(self, stream_id):
        ret, frame = self.cap.read()
        if not ret:
            return None
		#---------------------------------------------------------------
		#-------------------------- MxPrepost --------------------------
		#---------------------------------------------------------------
        return self.pp.preprocess(frame)

    def out_callback(self, mxa_output, stream_id):

		#---------------------------------------------------------------
		#-------------------------- MxPrepost --------------------------
		#---------------------------------------------------------------
        # Postprocess using original dimensions
        result = self.pp.postprocess(
            mxa_output,
            self.ori_height,
            self.ori_width
        )

        # Access outputs (depending on task)
		# ---------------------------
        # Detection (Bounding Boxes)
        # ---------------------------
        if result.boxes is not None:
            for box in result.boxes:
                print("xyxy:", box.xyxy)        # [x1, y1, x2, y2]
                print("xywh:", box.xywh)        # [x_center, y_center, w, h]
                print("conf:", box.conf)        # confidence score
                print("cls_id:", box.cls_id)    # class index
                print("cls_name:", box.cls_name)# class label string
                print("----")

        # ---------------------------
        # Segmentation (Masks)
        # ---------------------------
        if result.masks is not None:
            for mask in result.masks:
                # mask is a polygon (list of Point2f), not a binary HxW array
                pts = [(p.x, p.y) for p in mask.xys]
                print("mask cls_id:", int(mask.cls_id))
                print("num polygon points:", len(pts))
                if pts:
                    print("first 5 points:", pts[:5])
                print("----")

        # ---------------------------
        # Pose Estimation (Keypoints)
        # ---------------------------
        if result.keypoints is not None:
            for det_id, kps in enumerate(result.keypoints):
                print(f"detection {det_id} keypoints:", len(kps))
                # each kp has kp.xy (Point2f) and kp.conf
                for kp_id, kp in enumerate(kps):
                    print(f"  kp[{kp_id}] = (x={kp.xy.x:.2f}, y={kp.xy.y:.2f}, conf={kp.conf:.3f})")
                print("----")

        # Optional: draw detections
        # annotated = self.pp.draw(frame, result)

---

Configuration

prepost = mxprepost.MxPrepost(
    accl=accl,                    # [required] mxapi.MxAccl instance
    task="yolov8-det",            # [required] Expected format: yolov<n>-[det|seg|pose]
    conf=0.3,                     # [optional] Default 0.3
    iou=0.4,                      # [optional] Default 0.4
    classmap_path="/path/to/classmap.txt"  # [optional] Path to a .txt file containing custom class names (one per line). Defaults to COCO dataset.
    valid_classes=[0],          # [optional] List of class IDs to return. All other detections will be ignored (e.g., [0] for person only in COCO dataset).
    model_id=0                  # [optional] model_id in the .dfp file. Default 0
    # class_agnostic=True,      # [optional] Default is False, set to True if your model is class-agnostic
)

Notes

• task examples:

  • yolov8-det
  • yolov11-det
  • yolov11-pose

• model_id is required only if multiple models are compiled into the same DFP.

---

Python Quick Start

1️⃣ Build and Install

git clone --recurse-submodules git@github.com:memryx/MxPrepost.git
cd MxPrepost

# activate your virtualenv with MemryX SDK 2.2 
source ~/.mx/bin/activate
cd pymodule
mkdir build && cd build
cmake ..
make -j

---

2️⃣ Prepare Python Sample

cd MxPrepost/samples/python

# link mxprepost module in same directory as you targeted python code.
ln -sfv ../../pymodule/build/mxprepost.cpython-*.so .

Copy the .so file to the same path as your python code if working in another directory.

---

Notes

• Prefer postprocess(ofmaps, result, ori_h, ori_w) when you already know original frame size.
• Use postprocess(ofmaps, result, original_frame) if you have the original cv::Mat available in the output callback.

Compatibility

⚠️ This library does not support true python Accl API:

• SyncAccl
• AsyncAccl
• MultistreamAsyncAccl

Only mxapi.MxAccl is supported.

---

Supported Models

• ✅ YOLOv7 (det only)
• ✅ YOLOv8 (det / pose)
• ✅ YOLOv9 (det only)
• ✅ YOLOv10 (det only)
• ✅ YOLOv11 (det / pose)
• ✅ Custom detection datasets