mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-10-15 20:07:23 +02:00
88 lines
2.7 KiB
Python
Executable File
88 lines
2.7 KiB
Python
Executable File
import os
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from statistics import mean
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import multiprocessing as mp
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import numpy as np
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import datetime
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from frigate.edgetpu import LocalObjectDetector, EdgeTPUProcess, RemoteObjectDetector, load_labels
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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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######
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# Separate process runner
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######
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def start(id, num_detections, detection_queue, event):
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object_detector = RemoteObjectDetector(str(id), '/labelmap.txt', detection_queue, event)
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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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detections = 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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print(f"{id} - Processed for {duration:.2f} seconds.")
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print(f"{id} - Average frame processing time: {mean(frame_times)*1000:.2f}ms")
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event = mp.Event()
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edgetpu_process = EdgeTPUProcess({'1': event})
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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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# pipes = {}
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# for x in range(0, 10):
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# pipes[x] = mp.Pipe(duplex=False)
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# edgetpu_process = EdgeTPUProcess({str(key): value[1] for (key, value) in pipes.items()})
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# for x in range(0, 10):
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# camera_process = mp.Process(target=start, args=(x, 100, edgetpu_process.detection_queue, pipes[x][0]))
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# camera_process.daemon = True
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# camera_processes.append(camera_process)
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# start = 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
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# print(f"Total - Processed for {duration:.2f} seconds.") |