def __init__(self, **config):
super().__init__(**config)
self._merged_labelmap = {
**load_labels(config.get("labelmap_path", "/labelmap.txt")),
**config.get("labelmap", {}),
}
cmap = plt.cm.get_cmap("tab10", len(self._merged_labelmap.keys()))
self._colormap = {}
for key, val in self._merged_labelmap.items():
self._colormap[val] = tuple(int(round(255 * c)) for c in cmap(key)[:3])
import threading
import queue
import copy
import numpy as np
from collections import Counter, defaultdict
import itertools
import pyarrow.plasma as plasma
import matplotlib.pyplot as plt
from frigate.util import draw_box_with_label, PlasmaFrameManager
from frigate.edgetpu import load_labels
from typing import Callable, Dict
from statistics import mean, median
PATH_TO_LABELS = '/labelmap.txt'
LABELS = load_labels(PATH_TO_LABELS)
cmap = plt.cm.get_cmap('tab10', len(LABELS.keys()))
COLOR_MAP = {}
for key, val in LABELS.items():
COLOR_MAP[val] = tuple(int(round(255 * c)) for c in cmap(key)[:3])
def zone_filtered(obj, object_config):
object_name = obj['label']
if object_name in object_config:
obj_settings = object_config[object_name]
# if the min area is larger than the
# detected object, don't add it to detected objects
import os
from statistics import mean
import multiprocessing as mp
import numpy as np
import datetime
from frigate.edgetpu import LocalObjectDetector, EdgeTPUProcess, RemoteObjectDetector, load_labels
my_frame = np.expand_dims(np.full((300, 300, 3), 1, np.uint8), axis=0)
labels = load_labels('/labelmap.txt')
######
# Minimal same process runner
######
# object_detector = ObjectDetector()
# tensor_input = np.expand_dims(np.full((300,300,3), 0, np.uint8), axis=0)
# start = datetime.datetime.now().timestamp()
# frame_times = []
# for x in range(0, 1000):
# start_frame = datetime.datetime.now().timestamp()
# tensor_input[:] = my_frame
# detections = object_detector.detect_raw(tensor_input)
# parsed_detections = []
# for d in detections:
# if d[1] < 0.4:
# break
# parsed_detections.append((
# labels[int(d[0])],
# float(d[1]),
