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Refactor to simplify support for additional detector types (#3656)

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* Refactor EdgeTPU and CPU model handling to detector submodules.

* Fix selecting the correct detection device type from the config

* Remove detector type check when creating ObjectDetectProcess

* Fixes after rebasing to 0.11

* Add init file to detector folder

* Rename to detect_api

Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>

* Add unit test for LocalObjectDetector class

* Add configuration for model inputs
Support transforming detection regions to RGB or BGR.
Support specifying the input tensor shape.  The tensor shape has a standard format ["BHWC"] when handed to the detector, but can be transformed in the detector to match the model shape using the model  input_tensor config.

* Add documentation for new model config parameters

* Add input tensor transpose to LocalObjectDetector

* Change the model input tensor config to use an enumeration

* Updates for model config documentation

Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
This commit is contained in:
Nate Meyer
2022-11-03 22:23:09 -04:00
committed by GitHub
parent 1bc9efd529
commit 4383b883c0
17 changed files with 456 additions and 150 deletions
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65
benchmark.py
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@@ -3,10 +3,16 @@ from statistics import mean
import multiprocessing as mp
import numpy as np
import datetime
from frigate.edgetpu import LocalObjectDetector, EdgeTPUProcess, RemoteObjectDetector, load_labels
from frigate.config import DetectorTypeEnum
from frigate.object_detection import (
LocalObjectDetector,
ObjectDetectProcess,
RemoteObjectDetector,
load_labels,
)
my_frame = np.expand_dims(np.full((300,300,3), 1, np.uint8), axis=0)
labels = load_labels('/labelmap.txt')
my_frame = np.expand_dims(np.full((300, 300, 3), 1, np.uint8), axis=0)
labels = load_labels("/labelmap.txt")
######
# Minimal same process runner
@@ -39,20 +45,23 @@ labels = load_labels('/labelmap.txt')
def start(id, num_detections, detection_queue, event):
object_detector = RemoteObjectDetector(str(id), '/labelmap.txt', detection_queue, event)
start = datetime.datetime.now().timestamp()
object_detector = RemoteObjectDetector(
str(id), "/labelmap.txt", detection_queue, event
)
start = datetime.datetime.now().timestamp()
frame_times = []
for x in range(0, num_detections):
start_frame = datetime.datetime.now().timestamp()
detections = object_detector.detect(my_frame)
frame_times.append(datetime.datetime.now().timestamp()-start_frame)
frame_times = []
for x in range(0, num_detections):
start_frame = datetime.datetime.now().timestamp()
detections = object_detector.detect(my_frame)
frame_times.append(datetime.datetime.now().timestamp() - start_frame)
duration = datetime.datetime.now().timestamp() - start
object_detector.cleanup()
print(f"{id} - Processed for {duration:.2f} seconds.")
print(f"{id} - FPS: {object_detector.fps.eps():.2f}")
print(f"{id} - Average frame processing time: {mean(frame_times)*1000:.2f}ms")
duration = datetime.datetime.now().timestamp()-start
object_detector.cleanup()
print(f"{id} - Processed for {duration:.2f} seconds.")
print(f"{id} - FPS: {object_detector.fps.eps():.2f}")
print(f"{id} - Average frame processing time: {mean(frame_times)*1000:.2f}ms")
######
# Separate process runner
@@ -71,23 +80,29 @@ camera_processes = []
events = {}
for x in range(0, 10):
events[str(x)] = mp.Event()
events[str(x)] = mp.Event()
detection_queue = mp.Queue()
edgetpu_process_1 = EdgeTPUProcess(detection_queue, events, 'usb:0')
edgetpu_process_2 = EdgeTPUProcess(detection_queue, events, 'usb:1')
edgetpu_process_1 = ObjectDetectProcess(
detection_queue, events, DetectorTypeEnum.edgetpu, "usb:0"
)
edgetpu_process_2 = ObjectDetectProcess(
detection_queue, events, DetectorTypeEnum.edgetpu, "usb:1"
)
for x in range(0, 10):
camera_process = mp.Process(target=start, args=(x, 300, detection_queue, events[str(x)]))
camera_process.daemon = True
camera_processes.append(camera_process)
camera_process = mp.Process(
target=start, args=(x, 300, detection_queue, events[str(x)])
)
camera_process.daemon = True
camera_processes.append(camera_process)
start_time = datetime.datetime.now().timestamp()
for p in camera_processes:
p.start()
p.start()
for p in camera_processes:
p.join()
p.join()
duration = datetime.datetime.now().timestamp()-start_time
print(f"Total - Processed for {duration:.2f} seconds.")
duration = datetime.datetime.now().timestamp() - start_time
print(f"Total - Processed for {duration:.2f} seconds.")