aigoogle-coralcameratensorflowrtsprealtimeobject-detectionnvrmqtthomeautomationhome-automationhome-assistant
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108 lines
3.1 KiB
108 lines
3.1 KiB
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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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 = mp.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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