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https://github.com/blakeblackshear/frigate.git
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ab50d0b006
* Add isort and ruff linter Both linters are pretty common among modern python code bases. The isort tool provides stable sorting and grouping, as well as pruning of unused imports. Ruff is a modern linter, that is very fast due to being written in rust. It can detect many common issues in a python codebase. Removes the pylint dev requirement, since ruff replaces it. * treewide: fix issues detected by ruff * treewide: fix bare except clauses * .devcontainer: Set up isort * treewide: optimize imports * treewide: apply black * treewide: make regex patterns raw strings This is necessary for escape sequences to be properly recognized.
109 lines
3.1 KiB
Python
Executable File
109 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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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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