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https://github.com/blakeblackshear/frigate.git
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c0bd3b362c
* Subclass Process for audio_process * Introduce custom mp.Process subclass In preparation to switch the multiprocessing startup method away from "fork", we cannot rely on os.fork cloning the log state at fork time. Instead, we have to set up logging before we run the business logic of each process. * Make camera_metrics into a class * Make ptz_metrics into a class * Fixed PtzMotionEstimator.ptz_metrics type annotation * Removed pointless variables * Do not start audio processor when no audio cameras are configured
110 lines
3.1 KiB
Python
Executable File
110 lines
3.1 KiB
Python
Executable File
import datetime
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import multiprocessing as mp
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from statistics import mean
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import numpy as np
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import frigate.util as util
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from frigate.config import DetectorTypeEnum
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from frigate.object_detection import (
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ObjectDetectProcess,
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RemoteObjectDetector,
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load_labels,
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)
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my_frame = np.expand_dims(np.full((300, 300, 3), 1, np.uint8), axis=0)
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labels = load_labels("/labelmap.txt")
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######
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# Minimal same process runner
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######
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# object_detector = LocalObjectDetector()
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# tensor_input = np.expand_dims(np.full((300,300,3), 0, np.uint8), axis=0)
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# start = datetime.datetime.now().timestamp()
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# frame_times = []
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# for x in range(0, 1000):
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# start_frame = datetime.datetime.now().timestamp()
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# tensor_input[:] = my_frame
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# detections = object_detector.detect_raw(tensor_input)
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# parsed_detections = []
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# for d in detections:
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# if d[1] < 0.4:
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# break
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# parsed_detections.append((
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# labels[int(d[0])],
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# float(d[1]),
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# (d[2], d[3], d[4], d[5])
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# ))
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# frame_times.append(datetime.datetime.now().timestamp()-start_frame)
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# duration = datetime.datetime.now().timestamp()-start
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# print(f"Processed for {duration:.2f} seconds.")
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# print(f"Average frame processing time: {mean(frame_times)*1000:.2f}ms")
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def start(id, num_detections, detection_queue, event):
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object_detector = RemoteObjectDetector(
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str(id), "/labelmap.txt", detection_queue, event
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)
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start = datetime.datetime.now().timestamp()
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frame_times = []
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for x in range(0, num_detections):
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start_frame = datetime.datetime.now().timestamp()
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object_detector.detect(my_frame)
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frame_times.append(datetime.datetime.now().timestamp() - start_frame)
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duration = datetime.datetime.now().timestamp() - start
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object_detector.cleanup()
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print(f"{id} - Processed for {duration:.2f} seconds.")
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print(f"{id} - FPS: {object_detector.fps.eps():.2f}")
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print(f"{id} - Average frame processing time: {mean(frame_times)*1000:.2f}ms")
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######
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# Separate process runner
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######
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# event = mp.Event()
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# detection_queue = mp.Queue()
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# edgetpu_process = EdgeTPUProcess(detection_queue, {'1': event}, 'usb:0')
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# start(1, 1000, edgetpu_process.detection_queue, event)
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# print(f"Average raw inference speed: {edgetpu_process.avg_inference_speed.value*1000:.2f}ms")
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####
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# Multiple camera processes
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####
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camera_processes = []
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events = {}
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for x in range(0, 10):
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events[str(x)] = mp.Event()
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detection_queue = mp.Queue()
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edgetpu_process_1 = ObjectDetectProcess(
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detection_queue, events, DetectorTypeEnum.edgetpu, "usb:0"
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)
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edgetpu_process_2 = ObjectDetectProcess(
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detection_queue, events, DetectorTypeEnum.edgetpu, "usb:1"
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)
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for x in range(0, 10):
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camera_process = util.Process(
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target=start, args=(x, 300, detection_queue, events[str(x)])
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)
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camera_process.daemon = True
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camera_processes.append(camera_process)
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start_time = datetime.datetime.now().timestamp()
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for p in camera_processes:
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p.start()
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for p in camera_processes:
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p.join()
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duration = datetime.datetime.now().timestamp() - start_time
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print(f"Total - Processed for {duration:.2f} seconds.")
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