adaptive-motion-preprocessing

🎞️ Adaptive Motion Preprocessing

Turn video into motion images your neural network can read.

While something moves, each window of frames becomes one grayscale picture: older frames dim, newer ones bright, so a single image shows where the subject went and how fast. Every image has the same shape, so your model never gets a surprise.

📦 Install

pip install adaptive-motion-preprocessing

Then import amprep.

🚀 Quick start

You bring the frames (any iterable of uint8 BGR arrays) and the package does the rest:

import cv2

from amprep import AdaptiveMotionPreprocessor


def frames_from(path):
    capture = cv2.VideoCapture(path)
    try:
        if not capture.isOpened():
            raise OSError(f"cannot open video: {path}")
        while True:
            ok, frame = capture.read()
            if not ok:
                return
            yield frame
    finally:
        capture.release()


for image in AdaptiveMotionPreprocessor().process(frames_from("clip.mp4")):
    print(image.data.shape)

🎥 Try it on your webcam

See your camera and the motion images side by side, live:

git clone https://github.com/sdrfsh/adaptive-motion-preprocessing
cd adaptive-motion-preprocessing
pip install -e .
python examples/live_camera.py

Stay out of shot for a second while it learns the background, then move. Press q or Esc to quit. Add --camera 1 for an external webcam, or --threshold 0.03 if it triggers when nothing is moving.

⚙️ Settings

All optional keyword arguments of AdaptiveMotionPreprocessor(...):

💡 Good to know

• ⏱️ Frames, not seconds. 10 frames is about 0.33 s at 30 fps and 1 s at 10 fps. The package never reads the frame rate, so that math is yours.
• 🔁 A steady stream. While motion lasts you get one image every window_frames frames. A half-full window is dropped when motion stops.
• 🌱 Warm-up. The default subtractor spends its first 4 frames learning the background, so they never produce images. Change it with KNNBackgroundSubtractor(warmup_frames=...).
• 🎬 New scene? Call reset(). It forgets the background, any half-built window and the frame size. Otherwise state carries over between process() calls.
• 📐 One frame size per scene. Frames that change size mid-stream raise a ValueError. Call reset() first if the change is on purpose.

📚 Examples

• examples/live_camera.py: watch it live on your webcam, camera and motion image side by side
• examples/from_video_file.py: run it on a video file
• examples/custom_background_subtractor.py: plug in your own stage