import json import threading import time import traceback from typing import Callable, Union import cv2 import numpy as np import supervision as sv from PIL import Image import inference.core.entities.requests.inference from inference.core.active_learning.middlewares import ( NullActiveLearningMiddleware, ThreadingActiveLearningMiddleware, ) from inference.core.cache import cache from inference.core.env import ( ACTIVE_LEARNING_ENABLED, API_KEY, API_KEY_ENV_NAMES, CLASS_AGNOSTIC_NMS, CONFIDENCE, ENABLE_BYTE_TRACK, ENFORCE_FPS, IOU_THRESHOLD, JSON_RESPONSE, MAX_CANDIDATES, MAX_DETECTIONS, MODEL_ID, STREAM_ID, ) from inference.core.interfaces.base import BaseInterface from inference.core.interfaces.camera.camera import WebcamStream from inference.core.logger import logger from inference.core.registries.roboflow import get_model_type from inference.models.utils import get_roboflow_model class Stream(BaseInterface): """Roboflow defined stream interface for a general-purpose inference server. Attributes: model_manager (ModelManager): The manager that handles model inference tasks. model_registry (RoboflowModelRegistry): The registry to fetch model instances. api_key (str): The API key for accessing models. class_agnostic_nms (bool): Flag for class-agnostic non-maximum suppression. confidence (float): Confidence threshold for inference. iou_threshold (float): The intersection-over-union threshold for detection. json_response (bool): Flag to toggle JSON response format. max_candidates (float): The maximum number of candidates for detection. max_detections (float): The maximum number of detections. model (str|Callable): The model to be used. stream_id (str): The ID of the stream to be used. use_bytetrack (bool): Flag to use bytetrack, Methods: init_infer: Initialize the inference with a test frame. preprocess_thread: Preprocess incoming frames for inference. inference_request_thread: Manage the inference requests. run_thread: Run the preprocessing and inference threads. """ def __init__( self, api_key: str = API_KEY, class_agnostic_nms: bool = CLASS_AGNOSTIC_NMS, confidence: float = CONFIDENCE, enforce_fps: bool = ENFORCE_FPS, iou_threshold: float = IOU_THRESHOLD, max_candidates: float = MAX_CANDIDATES, max_detections: float = MAX_DETECTIONS, model: Union[str, Callable] = MODEL_ID, source: Union[int, str] = STREAM_ID, use_bytetrack: bool = ENABLE_BYTE_TRACK, use_main_thread: bool = False, output_channel_order: str = "RGB", on_prediction: Callable = None, on_start: Callable = None, on_stop: Callable = None, ): """Initialize the stream with the given parameters. Prints the server settings and initializes the inference with a test frame. """ logger.info("Initializing server") self.frame_count = 0 self.byte_tracker = sv.ByteTrack() if use_bytetrack else None self.use_bytetrack = use_bytetrack if source == "webcam": stream_id = 0 else: stream_id = source self.stream_id = stream_id if self.stream_id is None: raise ValueError("STREAM_ID is not defined") self.model_id = model if not self.model_id: raise ValueError("MODEL_ID is not defined") self.api_key = api_key self.active_learning_middleware = NullActiveLearningMiddleware() if isinstance(model, str): self.model = get_roboflow_model(model, self.api_key) if ACTIVE_LEARNING_ENABLED: self.active_learning_middleware = ( ThreadingActiveLearningMiddleware.init( api_key=self.api_key, model_id=self.model_id, cache=cache, ) ) self.task_type = get_model_type( model_id=self.model_id, api_key=self.api_key )[0] else: self.model = model self.task_type = "unknown" self.class_agnostic_nms = class_agnostic_nms self.confidence = confidence self.iou_threshold = iou_threshold self.max_candidates = max_candidates self.max_detections = max_detections self.use_main_thread = use_main_thread self.output_channel_order = output_channel_order self.inference_request_type = ( inference.core.entities.requests.inference.ObjectDetectionInferenceRequest ) self.webcam_stream = WebcamStream( stream_id=self.stream_id, enforce_fps=enforce_fps ) logger.info( f"Streaming from device with resolution: {self.webcam_stream.width} x {self.webcam_stream.height}" ) self.on_start_callbacks = [] self.on_stop_callbacks = [ lambda: self.active_learning_middleware.stop_registration_thread() ] self.on_prediction_callbacks = [] if on_prediction: self.on_prediction_callbacks.append(on_prediction) if on_start: self.on_start_callbacks.append(on_start) if on_stop: self.on_stop_callbacks.append(on_stop) self.init_infer() self.preproc_result = None self.inference_request_obj = None self.queue_control = False self.inference_response = None self.stop = False self.frame = None self.frame_cv = None self.frame_id = None logger.info("Server initialized with settings:") logger.info(f"Stream ID: {self.stream_id}") logger.info(f"Model ID: {self.model_id}") logger.info(f"Enforce FPS: {enforce_fps}") logger.info(f"Confidence: {self.confidence}") logger.info(f"Class Agnostic NMS: {self.class_agnostic_nms}") logger.info(f"IOU Threshold: {self.iou_threshold}") logger.info(f"Max Candidates: {self.max_candidates}") logger.info(f"Max Detections: {self.max_detections}") self.run_thread() def on_start(self, callback): self.on_start_callbacks.append(callback) unsubscribe = lambda: self.on_start_callbacks.remove(callback) return unsubscribe def on_stop(self, callback): self.on_stop_callbacks.append(callback) unsubscribe = lambda: self.on_stop_callbacks.remove(callback) return unsubscribe def on_prediction(self, callback): self.on_prediction_callbacks.append(callback) unsubscribe = lambda: self.on_prediction_callbacks.remove(callback) return unsubscribe def init_infer(self): """Initialize the inference with a test frame. Creates a test frame and runs it through the entire inference process to ensure everything is working. """ frame = Image.new("RGB", (640, 640), color="black") self.model.infer( frame, confidence=self.confidence, iou_threshold=self.iou_threshold ) self.active_learning_middleware.start_registration_thread() def preprocess_thread(self): """Preprocess incoming frames for inference. Reads frames from the webcam stream, converts them into the proper format, and preprocesses them for inference. """ webcam_stream = self.webcam_stream webcam_stream.start() # processing frames in input stream try: while True: if webcam_stream.stopped is True or self.stop: break else: self.frame_cv, frame_id = webcam_stream.read_opencv() if frame_id > 0 and frame_id != self.frame_id: self.frame_id = frame_id self.frame = cv2.cvtColor(self.frame_cv, cv2.COLOR_BGR2RGB) self.preproc_result = self.model.preprocess(self.frame_cv) self.img_in, self.img_dims = self.preproc_result self.queue_control = True except Exception as e: traceback.print_exc() logger.error(e) def inference_request_thread(self): """Manage the inference requests. Processes preprocessed frames for inference, post-processes the predictions, and sends the results to registered callbacks. """ last_print = time.perf_counter() print_ind = 0 while True: if self.webcam_stream.stopped is True or self.stop: while len(self.on_stop_callbacks) > 0: # run each onStop callback only once from this thread cb = self.on_stop_callbacks.pop() cb() break if self.queue_control: while len(self.on_start_callbacks) > 0: # run each onStart callback only once from this thread cb = self.on_start_callbacks.pop() cb() self.queue_control = False frame_id = self.frame_id inference_input = np.copy(self.frame_cv) start = time.perf_counter() predictions = self.model.predict( self.img_in, ) predictions = self.model.postprocess( predictions, self.img_dims, class_agnostic_nms=self.class_agnostic_nms, confidence=self.confidence, iou_threshold=self.iou_threshold, max_candidates=self.max_candidates, max_detections=self.max_detections, )[0] self.active_learning_middleware.register( inference_input=inference_input, prediction=predictions.dict(by_alias=True, exclude_none=True), prediction_type=self.task_type, ) if self.use_bytetrack: detections = sv.Detections.from_roboflow( predictions.dict(by_alias=True, exclude_none=True) ) detections = self.byte_tracker.update_with_detections(detections) if detections.tracker_id is None: detections.tracker_id = np.array([], dtype=int) for pred, detect in zip(predictions.predictions, detections): pred.tracker_id = int(detect[4]) predictions.frame_id = frame_id predictions = predictions.dict(by_alias=True, exclude_none=True) self.inference_response = predictions self.frame_count += 1 for cb in self.on_prediction_callbacks: if self.output_channel_order == "BGR": cb(predictions, self.frame_cv) else: cb(predictions, np.asarray(self.frame)) current = time.perf_counter() self.webcam_stream.max_fps = 1 / (current - start) logger.debug(f"FPS: {self.webcam_stream.max_fps:.2f}") if time.perf_counter() - last_print > 1: print_ind = (print_ind + 1) % 4 last_print = time.perf_counter() def run_thread(self): """Run the preprocessing and inference threads. Starts the preprocessing and inference threads, and handles graceful shutdown on KeyboardInterrupt. """ preprocess_thread = threading.Thread(target=self.preprocess_thread) preprocess_thread.start() if self.use_main_thread: self.inference_request_thread() else: # start a thread that looks for the predictions # and call the callbacks inference_request_thread = threading.Thread( target=self.inference_request_thread ) inference_request_thread.start()