func2stream

func2stream is a Python library that simplifies the construction of data processing pipelines, particularly for computationally intensive tasks such as real-time video processing and computer vision applications.

Inspired by Gstreamer's pipeline architecture, func2stream provides a clean and minimally invasive way to integrate your existing code into an efficient and scalable processing workflow.

✅ Latest Updates

Better VideoIO performance with Gstreamer:
- Moore Threads's VAAPI decoding is now enabled by default.
- Intel's iHD VAAPI decoding is also enabled by default.
- For more details, refer to func2stream/utils.py.
"No-Fuss" Context Management, now with ContextContainer and init_ctx:
- This enhancement makes it easier to manage and pass around state within your pipeline functions by encapsulating the state within closures.
- To use ContextContainer feature:
  - JUST simply place some global initialization code in a function
  - decorate it with @init_ctx
  - use return locals() at the end of the function.
  - Call your_ctx_name = your_init_func() to get the ContextContainer object.
  - Use like a class instance to access all the variables by using your_ctx_name.var_name.

For example, you can migrate your code to clean up global variables and class instances by simply using init_ctx:

Expand the full code example for migrating single, messy dashcam POC code to multiple dashcams

import torch, cv2, numpy as np, time
from collections import deque
from func2stream import Pipeline, init_ctx, from_ctx
from VehicleOBDInterface import create_obd_interface # a mockup class for OBD data source

obd_datasorce = create_obd_interface(subscribe=['steering_angle,brake,throttle,speed'])

@init_ctx
def redlight_recognizer_dashcam(camera_id=0):
    # The variables and functions you have might originate from initial test code for a single dashcam.
    # For demonstration, suppose you want to extend this code to manage multiple dashcams.
    # without func2stream, you may:
    #   - use class instance to manage them, but got a class like a cthulhu(overkill)
    #   - use global variables, but got a global hell, and lock hell
    # func2stream provides a better way to manage them:
    #   - use `@init_ctx` to encapsulate them in a closure, and 'ContextContainer' will manage explicitly
    #   - use `return locals()` at the end, and all the variables will be managed by 'ContextContainer'

    # some global constants form your single dashcam POC code
    THRESHOLD = 0.5
    MODEL_DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
    CAMERA_ID = camera_id

    # some debug varaibles can be modified somehow
    redlight_break_counter = 0
    frame_snap_shot = None

    # init a DNN model, or pass it as a parameter(recommended, but for simplicity, we init it here)
    # model is a classifer model, and we want to use it to recognize redlight
    model = torch.hub.load('pytorch/vision:v0.9.0', 'resnet18', pretrained=True)
    model.eval().to(MODEL_DEVICE)

    snd_buffer = deque(maxlen=10)

    vout = cv2.VideoWriter(f'dashcam{i}.mp4', cv2.VideoWriter_fourcc(*'mp4v'), 30, (1920, 1080))

    @from_ctx(get=['frame'], set=['tensor'])
    def process_frame(frame):
        # Do some preprocessing
        frame = cv2.resize(frame, (224, 224))
        return torch.tensor(frame)

    @from_ctx(get=['tensor'])
    def infer(tensor):
        # Do some inference
        ret = model(tensor)
        snd_buffer.append(ret)

    @from_ctx(get=['frame'])
    def dbg_draw_and_save(frame):
        caption = f'Green Light' if snd_buffer[-1].argmax() == 0 else 'Red Light'
        cv2.putText(frame, f'{caption}', (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 1,
                    (0, 255, 0) if snd_buffer[-1].argmax() == 0 else (0, 0, 255), 2)

        if frame_snap_shot is None:   # ugly debug code, just demo how to modify the varaibles from outside
          frame_snap_shot = frame.copy()
          cv2.imwrite(f'debug_{camera_id}.jpg', frame_snap_shot)
        cv2.imshow(f'Dashcam {camera_id}', frame)
        cv2.waitKey(1)
        vout.write(frame)

    # Add all the variables and functions to ContextContainer
    # ...

    # ----------------------
    # Understanding `locals()`
    # ----------------------
    # The `locals()` function serves as a hint for the `ContextContainer` to gather variables and functions within its scope.
    # Here’s a deeper explanation:

    # In the function scope, all local variables are stored in a dictionary returned by `locals()`.
    # The dictionary provided by `locals()` offers a read-only snapshot of the interpreter's current state.

    # The `ContextContainer` leverages the keys from this dictionary to reconstruct its attributes, allowing them to be modified.
    # This approach enhances both the security and flexibility in managing data, based on collective best practices.

    # While you are free to manipulate the contents of objects such as deque, or interact with the model, it is generally advised
    # against swapping, deleting, or monkey patching instance members of a class. The same principle applies to the `ContextContainer`:
    # it should be initialized and utilized as intended, but modifications beyond typical use are discouraged to maintain stability
    # and predictability.
    return locals() 

# 4 dashcams instance can be created in one line
redlight_recognizer_dashcams = [redlight_recognizer_dashcam(i) for _ in range(4)] 

# build a pipeline for each dashcam, and start it
# use like a class instance to access all the variables by using `rrd.var_name`

uri_formatter = "rtsp://localhost:8554/dash/{}"
dashcam_pipelines = [Pipeline([
    VideoSource(uri_formatter.format(i),use_umat=False),
    build_ctx(key="frame"),
    rrd.process_frame, 
    rrd.infer,
    rrd.dbg_draw_and_save
]).start() for i,rrd in enumerate(redlight_recognizer_dashcams)]

# Now, you can access all the variables by using `rrd.var_name` in the pipeline functions.
# For example, `rrd.snd_buffer` will be accessible and you can aggregate the results from multiple dashcams.
while True:
    # Read data from OBD
    steering_angle, brake, throttle, speed = obd_datasorce.read()

    # Aggregate the results from multiple dashcams
    rets = [rrd.snd_buffer[-1] for rrd in redlight_recognizer_dashcams]
    # Do some aggregation
    status = sum(rets) > 2
    if status:
        print('AUTOHOLD WILL BE ACTIVATED')
        # for example, you can increase the counter
        for rrd in redlight_recognizer_dashcams:
            rrd.redlight_break_counter += 1

    elif status <= 1 and steering_angle <-10:
        print('UNPROTECTED LEFT TURN ACTION DETECTED')
        # for example, you can clear the buffer
        # modify the data inside the instance, not the instance itself
        for rrd in redlight_recognizer_dashcams:
            rrd.snd_buffer.clear()
        # and add some padding
        for rr in dashcam_pipelines:
            for i in range(10): rr.snd_buffer.append(0)

    if brake > 0.5 and throttle > 0.5 and speed <10:
        print('PANIC DEBUGGING')
        time.sleep(1) # for debugging
        for rrd in redlight_recognizer_dashcams:
            rrd.frame_snap_shot = None
        break

🚧 Current Status and Roadmap

func2stream is currently in early development and is being actively worked on. While it shows promise, it is not yet recommended for production use. The following areas are being prioritized for improvement:

Key Features

1. Easy Integration with Existing Code

func2stream allows you to use your existing functions without modifying their internal logic. You can focus on your project's requirements while func2stream handles the underlying asynchronous Pipeline orchestration.

2. Intuitive Pipeline Construction

Building pipelines with func2stream is straightforward thanks to its Element abstraction and automatic Pipeline assembly.

Functions can be easily transformed into Elements, promoting modularity and reusability in Pipeline design.
Simply define the sequence of functions or Elements in your Pipeline, and func2stream will efficiently link them together.
Pipelines can be treated as Elements, allowing for the creation of complex, nested structures.

3. MapReduce Processing

func2stream incorporates the Map-Reduce paradigm, enabling easy parallelization of processing tasks for improved performance.

Define concurrent Elements using the MapReduce([fn1,fn2,fn3...]) syntax, and func2stream will handle parallel execution and result aggregation.
The MapReduce functionality itself is an Element, seamlessly integrating parallel processing into your Pipeline.

4. Managed Context

func2stream introduces a managed context mechanism to streamline data flow through the pipeline, reducing the need for manual data management between Elements.

The managed context handles data passing between Elements, making data usage across functions more intuitive and convenient.
Decorators allow you to specify which variables an Element reads from and writes to the context.
The centralized data store ensures that variables accessed and modified by different asynchronous steps remain independent, preventing unintended interactions and maintaining data integrity.

BICHENG/func2stream