mirror of
https://github.com/blakeblackshear/frigate.git
synced 2025-10-27 10:52:11 +01:00
562e2627c2
* Add ability to GPU device to be automatically detected when multiple exist * Add logging info * Fix access * Fix * Formatting * Fix path of device * Use log error instead of raise * Remove log which could apply to other caess * Set default value * rework logic and support auto gpu selection for encoding gpu as well
1070 lines
32 KiB
Python
Executable File
1070 lines
32 KiB
Python
Executable File
import copy
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import datetime
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import logging
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import shlex
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import subprocess as sp
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import json
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import re
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import signal
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import traceback
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import urllib.parse
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import yaml
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from abc import ABC, abstractmethod
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from collections import Counter
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from collections.abc import Mapping
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from multiprocessing import shared_memory
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from typing import Any, AnyStr, Optional, Tuple
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import cv2
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import numpy as np
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import os
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import psutil
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import pytz
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from frigate.const import REGEX_HTTP_CAMERA_USER_PASS, REGEX_RTSP_CAMERA_USER_PASS
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logger = logging.getLogger(__name__)
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def deep_merge(dct1: dict, dct2: dict, override=False, merge_lists=False) -> dict:
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"""
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:param dct1: First dict to merge
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:param dct2: Second dict to merge
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:param override: if same key exists in both dictionaries, should override? otherwise ignore. (default=True)
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:return: The merge dictionary
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"""
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merged = copy.deepcopy(dct1)
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for k, v2 in dct2.items():
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if k in merged:
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v1 = merged[k]
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if isinstance(v1, dict) and isinstance(v2, Mapping):
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merged[k] = deep_merge(v1, v2, override)
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elif isinstance(v1, list) and isinstance(v2, list):
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if merge_lists:
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merged[k] = v1 + v2
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else:
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if override:
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merged[k] = copy.deepcopy(v2)
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else:
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merged[k] = copy.deepcopy(v2)
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return merged
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def load_config_with_no_duplicates(raw_config) -> dict:
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"""Get config ensuring duplicate keys are not allowed."""
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# https://stackoverflow.com/a/71751051
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class PreserveDuplicatesLoader(yaml.loader.Loader):
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pass
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def map_constructor(loader, node, deep=False):
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keys = [loader.construct_object(node, deep=deep) for node, _ in node.value]
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vals = [loader.construct_object(node, deep=deep) for _, node in node.value]
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key_count = Counter(keys)
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data = {}
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for key, val in zip(keys, vals):
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if key_count[key] > 1:
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raise ValueError(
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f"Config input {key} is defined multiple times for the same field, this is not allowed."
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)
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else:
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data[key] = val
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return data
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PreserveDuplicatesLoader.add_constructor(
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yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG, map_constructor
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)
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return yaml.load(raw_config, PreserveDuplicatesLoader)
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def draw_timestamp(
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frame,
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timestamp,
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timestamp_format,
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font_effect=None,
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font_thickness=2,
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font_color=(255, 255, 255),
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position="tl",
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):
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time_to_show = datetime.datetime.fromtimestamp(timestamp).strftime(timestamp_format)
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# calculate a dynamic font size
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size = cv2.getTextSize(
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time_to_show,
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cv2.FONT_HERSHEY_SIMPLEX,
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fontScale=1.0,
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thickness=font_thickness,
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)
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text_width = size[0][0]
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desired_size = max(150, 0.33 * frame.shape[1])
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font_scale = desired_size / text_width
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# calculate the actual size with the dynamic scale
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size = cv2.getTextSize(
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time_to_show,
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cv2.FONT_HERSHEY_SIMPLEX,
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fontScale=font_scale,
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thickness=font_thickness,
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)
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image_width = frame.shape[1]
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image_height = frame.shape[0]
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text_width = size[0][0]
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text_height = size[0][1]
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line_height = text_height + size[1]
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if position == "tl":
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text_offset_x = 0
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text_offset_y = 0 if 0 < line_height else 0 - (line_height + 8)
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elif position == "tr":
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text_offset_x = image_width - text_width
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text_offset_y = 0 if 0 < line_height else 0 - (line_height + 8)
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elif position == "bl":
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text_offset_x = 0
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text_offset_y = image_height - (line_height + 8)
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elif position == "br":
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text_offset_x = image_width - text_width
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text_offset_y = image_height - (line_height + 8)
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if font_effect == "solid":
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# make the coords of the box with a small padding of two pixels
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timestamp_box_coords = np.array(
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[
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[text_offset_x, text_offset_y],
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[text_offset_x + text_width, text_offset_y],
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[text_offset_x + text_width, text_offset_y + line_height + 8],
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[text_offset_x, text_offset_y + line_height + 8],
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]
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)
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cv2.fillPoly(
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frame,
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[timestamp_box_coords],
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# inverse color of text for background for max. contrast
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(255 - font_color[0], 255 - font_color[1], 255 - font_color[2]),
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)
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elif font_effect == "shadow":
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cv2.putText(
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frame,
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time_to_show,
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(text_offset_x + 3, text_offset_y + line_height),
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cv2.FONT_HERSHEY_SIMPLEX,
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fontScale=font_scale,
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color=(255 - font_color[0], 255 - font_color[1], 255 - font_color[2]),
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thickness=font_thickness,
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)
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cv2.putText(
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frame,
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time_to_show,
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(text_offset_x, text_offset_y + line_height - 3),
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cv2.FONT_HERSHEY_SIMPLEX,
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fontScale=font_scale,
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color=font_color,
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thickness=font_thickness,
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)
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def draw_box_with_label(
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frame,
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x_min,
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y_min,
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x_max,
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y_max,
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label,
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info,
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thickness=2,
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color=None,
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position="ul",
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):
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if color is None:
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color = (0, 0, 255)
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display_text = "{}: {}".format(label, info)
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cv2.rectangle(frame, (x_min, y_min), (x_max, y_max), color, thickness)
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font_scale = 0.5
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font = cv2.FONT_HERSHEY_SIMPLEX
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# get the width and height of the text box
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size = cv2.getTextSize(display_text, font, fontScale=font_scale, thickness=2)
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text_width = size[0][0]
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text_height = size[0][1]
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line_height = text_height + size[1]
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# set the text start position
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if position == "ul":
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text_offset_x = x_min
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text_offset_y = 0 if y_min < line_height else y_min - (line_height + 8)
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elif position == "ur":
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text_offset_x = x_max - (text_width + 8)
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text_offset_y = 0 if y_min < line_height else y_min - (line_height + 8)
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elif position == "bl":
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text_offset_x = x_min
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text_offset_y = y_max
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elif position == "br":
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text_offset_x = x_max - (text_width + 8)
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text_offset_y = y_max
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# make the coords of the box with a small padding of two pixels
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textbox_coords = (
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(text_offset_x, text_offset_y),
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(text_offset_x + text_width + 2, text_offset_y + line_height),
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)
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cv2.rectangle(frame, textbox_coords[0], textbox_coords[1], color, cv2.FILLED)
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cv2.putText(
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frame,
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display_text,
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(text_offset_x, text_offset_y + line_height - 3),
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font,
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fontScale=font_scale,
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color=(0, 0, 0),
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thickness=2,
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)
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def calculate_region(frame_shape, xmin, ymin, xmax, ymax, model_size, multiplier=2):
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# size is the longest edge and divisible by 4
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size = int((max(xmax - xmin, ymax - ymin) * multiplier) // 4 * 4)
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# dont go any smaller than the model_size
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if size < model_size:
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size = model_size
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# x_offset is midpoint of bounding box minus half the size
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x_offset = int((xmax - xmin) / 2.0 + xmin - size / 2.0)
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# if outside the image
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if x_offset < 0:
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x_offset = 0
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elif x_offset > (frame_shape[1] - size):
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x_offset = max(0, (frame_shape[1] - size))
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# y_offset is midpoint of bounding box minus half the size
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y_offset = int((ymax - ymin) / 2.0 + ymin - size / 2.0)
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# # if outside the image
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if y_offset < 0:
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y_offset = 0
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elif y_offset > (frame_shape[0] - size):
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y_offset = max(0, (frame_shape[0] - size))
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return (x_offset, y_offset, x_offset + size, y_offset + size)
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def get_yuv_crop(frame_shape, crop):
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# crop should be (x1,y1,x2,y2)
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frame_height = frame_shape[0] // 3 * 2
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frame_width = frame_shape[1]
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# compute the width/height of the uv channels
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uv_width = frame_width // 2 # width of the uv channels
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uv_height = frame_height // 4 # height of the uv channels
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# compute the offset for upper left corner of the uv channels
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uv_x_offset = crop[0] // 2 # x offset of the uv channels
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uv_y_offset = crop[1] // 4 # y offset of the uv channels
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# compute the width/height of the uv crops
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uv_crop_width = (crop[2] - crop[0]) // 2 # width of the cropped uv channels
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uv_crop_height = (crop[3] - crop[1]) // 4 # height of the cropped uv channels
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# ensure crop dimensions are multiples of 2 and 4
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y = (crop[0], crop[1], crop[0] + uv_crop_width * 2, crop[1] + uv_crop_height * 4)
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u1 = (
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0 + uv_x_offset,
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frame_height + uv_y_offset,
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0 + uv_x_offset + uv_crop_width,
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frame_height + uv_y_offset + uv_crop_height,
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)
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u2 = (
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uv_width + uv_x_offset,
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frame_height + uv_y_offset,
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uv_width + uv_x_offset + uv_crop_width,
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frame_height + uv_y_offset + uv_crop_height,
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)
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v1 = (
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0 + uv_x_offset,
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frame_height + uv_height + uv_y_offset,
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0 + uv_x_offset + uv_crop_width,
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frame_height + uv_height + uv_y_offset + uv_crop_height,
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)
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v2 = (
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uv_width + uv_x_offset,
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frame_height + uv_height + uv_y_offset,
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uv_width + uv_x_offset + uv_crop_width,
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frame_height + uv_height + uv_y_offset + uv_crop_height,
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)
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return y, u1, u2, v1, v2
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def yuv_crop_and_resize(frame, region, height=None):
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# Crops and resizes a YUV frame while maintaining aspect ratio
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# https://stackoverflow.com/a/57022634
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height = frame.shape[0] // 3 * 2
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width = frame.shape[1]
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# get the crop box if the region extends beyond the frame
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crop_x1 = max(0, region[0])
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crop_y1 = max(0, region[1])
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# ensure these are a multiple of 4
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crop_x2 = min(width, region[2])
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crop_y2 = min(height, region[3])
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crop_box = (crop_x1, crop_y1, crop_x2, crop_y2)
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y, u1, u2, v1, v2 = get_yuv_crop(frame.shape, crop_box)
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# if the region starts outside the frame, indent the start point in the cropped frame
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y_channel_x_offset = abs(min(0, region[0]))
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y_channel_y_offset = abs(min(0, region[1]))
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uv_channel_x_offset = y_channel_x_offset // 2
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uv_channel_y_offset = y_channel_y_offset // 4
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# create the yuv region frame
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# make sure the size is a multiple of 4
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# TODO: this should be based on the size after resize now
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size = (region[3] - region[1]) // 4 * 4
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yuv_cropped_frame = np.zeros((size + size // 2, size), np.uint8)
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# fill in black
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yuv_cropped_frame[:] = 128
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yuv_cropped_frame[0:size, 0:size] = 16
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# copy the y channel
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yuv_cropped_frame[
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y_channel_y_offset : y_channel_y_offset + y[3] - y[1],
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y_channel_x_offset : y_channel_x_offset + y[2] - y[0],
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] = frame[y[1] : y[3], y[0] : y[2]]
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uv_crop_width = u1[2] - u1[0]
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uv_crop_height = u1[3] - u1[1]
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# copy u1
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yuv_cropped_frame[
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size + uv_channel_y_offset : size + uv_channel_y_offset + uv_crop_height,
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0 + uv_channel_x_offset : 0 + uv_channel_x_offset + uv_crop_width,
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] = frame[u1[1] : u1[3], u1[0] : u1[2]]
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# copy u2
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yuv_cropped_frame[
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size + uv_channel_y_offset : size + uv_channel_y_offset + uv_crop_height,
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size // 2
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+ uv_channel_x_offset : size // 2
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+ uv_channel_x_offset
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+ uv_crop_width,
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] = frame[u2[1] : u2[3], u2[0] : u2[2]]
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# copy v1
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yuv_cropped_frame[
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size
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+ size // 4
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+ uv_channel_y_offset : size
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+ size // 4
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+ uv_channel_y_offset
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+ uv_crop_height,
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0 + uv_channel_x_offset : 0 + uv_channel_x_offset + uv_crop_width,
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] = frame[v1[1] : v1[3], v1[0] : v1[2]]
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# copy v2
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yuv_cropped_frame[
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size
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+ size // 4
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+ uv_channel_y_offset : size
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+ size // 4
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+ uv_channel_y_offset
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+ uv_crop_height,
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size // 2
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+ uv_channel_x_offset : size // 2
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+ uv_channel_x_offset
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+ uv_crop_width,
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] = frame[v2[1] : v2[3], v2[0] : v2[2]]
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return yuv_cropped_frame
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def yuv_to_3_channel_yuv(yuv_frame):
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height = yuv_frame.shape[0] // 3 * 2
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width = yuv_frame.shape[1]
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# flatten the image into array
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yuv_data = yuv_frame.ravel()
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# create a numpy array to hold all the 3 chanel yuv data
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all_yuv_data = np.empty((height, width, 3), dtype=np.uint8)
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y_count = height * width
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uv_count = y_count // 4
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# copy the y_channel
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all_yuv_data[:, :, 0] = yuv_data[0:y_count].reshape((height, width))
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# copy the u channel doubling each dimension
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all_yuv_data[:, :, 1] = np.repeat(
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np.reshape(
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np.repeat(yuv_data[y_count : y_count + uv_count], repeats=2, axis=0),
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(height // 2, width),
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),
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repeats=2,
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axis=0,
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)
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# copy the v channel doubling each dimension
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all_yuv_data[:, :, 2] = np.repeat(
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np.reshape(
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np.repeat(
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yuv_data[y_count + uv_count : y_count + uv_count + uv_count],
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repeats=2,
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axis=0,
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),
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(height // 2, width),
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),
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repeats=2,
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axis=0,
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)
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return all_yuv_data
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|
|
|
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def copy_yuv_to_position(
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destination_frame,
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destination_offset,
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destination_shape,
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source_frame=None,
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source_channel_dim=None,
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):
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# get the coordinates of the channels for this position in the layout
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y, u1, u2, v1, v2 = get_yuv_crop(
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destination_frame.shape,
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(
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destination_offset[1],
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destination_offset[0],
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destination_offset[1] + destination_shape[1],
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destination_offset[0] + destination_shape[0],
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),
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)
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# clear y
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destination_frame[
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y[1] : y[3],
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y[0] : y[2],
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] = 16
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# clear u1
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destination_frame[u1[1] : u1[3], u1[0] : u1[2]] = 128
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# clear u2
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destination_frame[u2[1] : u2[3], u2[0] : u2[2]] = 128
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# clear v1
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destination_frame[v1[1] : v1[3], v1[0] : v1[2]] = 128
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# clear v2
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destination_frame[v2[1] : v2[3], v2[0] : v2[2]] = 128
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if not source_frame is None:
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# calculate the resized frame, maintaining the aspect ratio
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source_aspect_ratio = source_frame.shape[1] / (source_frame.shape[0] // 3 * 2)
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dest_aspect_ratio = destination_shape[1] / destination_shape[0]
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if source_aspect_ratio <= dest_aspect_ratio:
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y_resize_height = int(destination_shape[0] // 4 * 4)
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y_resize_width = int((y_resize_height * source_aspect_ratio) // 4 * 4)
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else:
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y_resize_width = int(destination_shape[1] // 4 * 4)
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y_resize_height = int((y_resize_width / source_aspect_ratio) // 4 * 4)
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uv_resize_width = int(y_resize_width // 2)
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uv_resize_height = int(y_resize_height // 4)
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y_y_offset = int((destination_shape[0] - y_resize_height) / 4 // 4 * 4)
|
|
y_x_offset = int((destination_shape[1] - y_resize_width) / 2 // 4 * 4)
|
|
|
|
uv_y_offset = y_y_offset // 4
|
|
uv_x_offset = y_x_offset // 2
|
|
|
|
interpolation = cv2.INTER_LINEAR
|
|
# resize/copy y channel
|
|
destination_frame[
|
|
y[1] + y_y_offset : y[1] + y_y_offset + y_resize_height,
|
|
y[0] + y_x_offset : y[0] + y_x_offset + y_resize_width,
|
|
] = cv2.resize(
|
|
source_frame[
|
|
source_channel_dim["y"][1] : source_channel_dim["y"][3],
|
|
source_channel_dim["y"][0] : source_channel_dim["y"][2],
|
|
],
|
|
dsize=(y_resize_width, y_resize_height),
|
|
interpolation=interpolation,
|
|
)
|
|
|
|
# resize/copy u1
|
|
destination_frame[
|
|
u1[1] + uv_y_offset : u1[1] + uv_y_offset + uv_resize_height,
|
|
u1[0] + uv_x_offset : u1[0] + uv_x_offset + uv_resize_width,
|
|
] = cv2.resize(
|
|
source_frame[
|
|
source_channel_dim["u1"][1] : source_channel_dim["u1"][3],
|
|
source_channel_dim["u1"][0] : source_channel_dim["u1"][2],
|
|
],
|
|
dsize=(uv_resize_width, uv_resize_height),
|
|
interpolation=interpolation,
|
|
)
|
|
# resize/copy u2
|
|
destination_frame[
|
|
u2[1] + uv_y_offset : u2[1] + uv_y_offset + uv_resize_height,
|
|
u2[0] + uv_x_offset : u2[0] + uv_x_offset + uv_resize_width,
|
|
] = cv2.resize(
|
|
source_frame[
|
|
source_channel_dim["u2"][1] : source_channel_dim["u2"][3],
|
|
source_channel_dim["u2"][0] : source_channel_dim["u2"][2],
|
|
],
|
|
dsize=(uv_resize_width, uv_resize_height),
|
|
interpolation=interpolation,
|
|
)
|
|
# resize/copy v1
|
|
destination_frame[
|
|
v1[1] + uv_y_offset : v1[1] + uv_y_offset + uv_resize_height,
|
|
v1[0] + uv_x_offset : v1[0] + uv_x_offset + uv_resize_width,
|
|
] = cv2.resize(
|
|
source_frame[
|
|
source_channel_dim["v1"][1] : source_channel_dim["v1"][3],
|
|
source_channel_dim["v1"][0] : source_channel_dim["v1"][2],
|
|
],
|
|
dsize=(uv_resize_width, uv_resize_height),
|
|
interpolation=interpolation,
|
|
)
|
|
# resize/copy v2
|
|
destination_frame[
|
|
v2[1] + uv_y_offset : v2[1] + uv_y_offset + uv_resize_height,
|
|
v2[0] + uv_x_offset : v2[0] + uv_x_offset + uv_resize_width,
|
|
] = cv2.resize(
|
|
source_frame[
|
|
source_channel_dim["v2"][1] : source_channel_dim["v2"][3],
|
|
source_channel_dim["v2"][0] : source_channel_dim["v2"][2],
|
|
],
|
|
dsize=(uv_resize_width, uv_resize_height),
|
|
interpolation=interpolation,
|
|
)
|
|
|
|
|
|
def yuv_region_2_yuv(frame, region):
|
|
try:
|
|
# TODO: does this copy the numpy array?
|
|
yuv_cropped_frame = yuv_crop_and_resize(frame, region)
|
|
return yuv_to_3_channel_yuv(yuv_cropped_frame)
|
|
except:
|
|
print(f"frame.shape: {frame.shape}")
|
|
print(f"region: {region}")
|
|
raise
|
|
|
|
|
|
def yuv_region_2_rgb(frame, region):
|
|
try:
|
|
# TODO: does this copy the numpy array?
|
|
yuv_cropped_frame = yuv_crop_and_resize(frame, region)
|
|
return cv2.cvtColor(yuv_cropped_frame, cv2.COLOR_YUV2RGB_I420)
|
|
except:
|
|
print(f"frame.shape: {frame.shape}")
|
|
print(f"region: {region}")
|
|
raise
|
|
|
|
|
|
def yuv_region_2_bgr(frame, region):
|
|
try:
|
|
yuv_cropped_frame = yuv_crop_and_resize(frame, region)
|
|
return cv2.cvtColor(yuv_cropped_frame, cv2.COLOR_YUV2BGR_I420)
|
|
except:
|
|
print(f"frame.shape: {frame.shape}")
|
|
print(f"region: {region}")
|
|
raise
|
|
|
|
|
|
def intersection(box_a, box_b):
|
|
return (
|
|
max(box_a[0], box_b[0]),
|
|
max(box_a[1], box_b[1]),
|
|
min(box_a[2], box_b[2]),
|
|
min(box_a[3], box_b[3]),
|
|
)
|
|
|
|
|
|
def area(box):
|
|
return (box[2] - box[0] + 1) * (box[3] - box[1] + 1)
|
|
|
|
|
|
def intersection_over_union(box_a, box_b):
|
|
# determine the (x, y)-coordinates of the intersection rectangle
|
|
intersect = intersection(box_a, box_b)
|
|
|
|
# compute the area of intersection rectangle
|
|
inter_area = max(0, intersect[2] - intersect[0] + 1) * max(
|
|
0, intersect[3] - intersect[1] + 1
|
|
)
|
|
|
|
if inter_area == 0:
|
|
return 0.0
|
|
|
|
# compute the area of both the prediction and ground-truth
|
|
# rectangles
|
|
box_a_area = (box_a[2] - box_a[0] + 1) * (box_a[3] - box_a[1] + 1)
|
|
box_b_area = (box_b[2] - box_b[0] + 1) * (box_b[3] - box_b[1] + 1)
|
|
|
|
# compute the intersection over union by taking the intersection
|
|
# area and dividing it by the sum of prediction + ground-truth
|
|
# areas - the interesection area
|
|
iou = inter_area / float(box_a_area + box_b_area - inter_area)
|
|
|
|
# return the intersection over union value
|
|
return iou
|
|
|
|
|
|
def clipped(obj, frame_shape):
|
|
# if the object is within 5 pixels of the region border, and the region is not on the edge
|
|
# consider the object to be clipped
|
|
box = obj[2]
|
|
region = obj[5]
|
|
if (
|
|
(region[0] > 5 and box[0] - region[0] <= 5)
|
|
or (region[1] > 5 and box[1] - region[1] <= 5)
|
|
or (frame_shape[1] - region[2] > 5 and region[2] - box[2] <= 5)
|
|
or (frame_shape[0] - region[3] > 5 and region[3] - box[3] <= 5)
|
|
):
|
|
return True
|
|
else:
|
|
return False
|
|
|
|
|
|
def restart_frigate():
|
|
proc = psutil.Process(1)
|
|
# if this is running via s6, sigterm pid 1
|
|
if proc.name() == "s6-svscan":
|
|
proc.terminate()
|
|
# otherwise, just try and exit frigate
|
|
else:
|
|
os.kill(os.getpid(), signal.SIGTERM)
|
|
|
|
|
|
class EventsPerSecond:
|
|
def __init__(self, max_events=1000):
|
|
self._start = None
|
|
self._max_events = max_events
|
|
self._timestamps = []
|
|
|
|
def start(self):
|
|
self._start = datetime.datetime.now().timestamp()
|
|
|
|
def update(self):
|
|
if self._start is None:
|
|
self.start()
|
|
self._timestamps.append(datetime.datetime.now().timestamp())
|
|
# truncate the list when it goes 100 over the max_size
|
|
if len(self._timestamps) > self._max_events + 100:
|
|
self._timestamps = self._timestamps[(1 - self._max_events) :]
|
|
|
|
def eps(self, last_n_seconds=10):
|
|
if self._start is None:
|
|
self.start()
|
|
# compute the (approximate) events in the last n seconds
|
|
now = datetime.datetime.now().timestamp()
|
|
seconds = min(now - self._start, last_n_seconds)
|
|
# avoid divide by zero
|
|
if seconds == 0:
|
|
seconds = 1
|
|
return (
|
|
len([t for t in self._timestamps if t > (now - last_n_seconds)]) / seconds
|
|
)
|
|
|
|
|
|
def print_stack(sig, frame):
|
|
traceback.print_stack(frame)
|
|
|
|
|
|
def listen():
|
|
signal.signal(signal.SIGUSR1, print_stack)
|
|
|
|
|
|
def create_mask(frame_shape, mask):
|
|
mask_img = np.zeros(frame_shape, np.uint8)
|
|
mask_img[:] = 255
|
|
|
|
if isinstance(mask, list):
|
|
for m in mask:
|
|
add_mask(m, mask_img)
|
|
|
|
elif isinstance(mask, str):
|
|
add_mask(mask, mask_img)
|
|
|
|
return mask_img
|
|
|
|
|
|
def add_mask(mask, mask_img):
|
|
points = mask.split(",")
|
|
contour = np.array(
|
|
[[int(points[i]), int(points[i + 1])] for i in range(0, len(points), 2)]
|
|
)
|
|
cv2.fillPoly(mask_img, pts=[contour], color=(0))
|
|
|
|
|
|
def load_labels(path, encoding="utf-8"):
|
|
"""Loads labels from file (with or without index numbers).
|
|
Args:
|
|
path: path to label file.
|
|
encoding: label file encoding.
|
|
Returns:
|
|
Dictionary mapping indices to labels.
|
|
"""
|
|
with open(path, "r", encoding=encoding) as f:
|
|
labels = {index: "unknown" for index in range(91)}
|
|
lines = f.readlines()
|
|
if not lines:
|
|
return {}
|
|
|
|
if lines[0].split(" ", maxsplit=1)[0].isdigit():
|
|
pairs = [line.split(" ", maxsplit=1) for line in lines]
|
|
labels.update({int(index): label.strip() for index, label in pairs})
|
|
else:
|
|
labels.update({index: line.strip() for index, line in enumerate(lines)})
|
|
return labels
|
|
|
|
|
|
def clean_camera_user_pass(line: str) -> str:
|
|
"""Removes user and password from line."""
|
|
if line.startswith("rtsp://"):
|
|
return re.sub(REGEX_RTSP_CAMERA_USER_PASS, "://*:*@", line)
|
|
else:
|
|
return re.sub(REGEX_HTTP_CAMERA_USER_PASS, "user=*&password=*", line)
|
|
|
|
|
|
def escape_special_characters(path: str) -> str:
|
|
"""Cleans reserved characters to encodings for ffmpeg."""
|
|
try:
|
|
found = re.search(REGEX_RTSP_CAMERA_USER_PASS, path).group(0)[3:-1]
|
|
pw = found[(found.index(":") + 1) :]
|
|
return path.replace(pw, urllib.parse.quote_plus(pw))
|
|
except AttributeError:
|
|
# path does not have user:pass
|
|
return path
|
|
|
|
|
|
def get_cgroups_version() -> str:
|
|
"""Determine what version of cgroups is enabled"""
|
|
|
|
stat_command = ["stat", "-fc", "%T", "/sys/fs/cgroup"]
|
|
|
|
p = sp.run(
|
|
stat_command,
|
|
encoding="ascii",
|
|
capture_output=True,
|
|
)
|
|
|
|
if p.returncode == 0:
|
|
value: str = p.stdout.strip().lower()
|
|
|
|
if value == "cgroup2fs":
|
|
return "cgroup2"
|
|
elif value == "tmpfs":
|
|
return "cgroup"
|
|
else:
|
|
logger.debug(
|
|
f"Could not determine cgroups version: unhandled filesystem {value}"
|
|
)
|
|
else:
|
|
logger.debug(f"Could not determine cgroups version: {p.stderr}")
|
|
|
|
return "unknown"
|
|
|
|
|
|
def get_docker_memlimit_bytes() -> int:
|
|
"""Get mem limit in bytes set in docker if present. Returns -1 if no limit detected"""
|
|
|
|
# check running a supported cgroups version
|
|
if get_cgroups_version() == "cgroup2":
|
|
|
|
memlimit_command = ["cat", "/sys/fs/cgroup/memory.max"]
|
|
|
|
p = sp.run(
|
|
memlimit_command,
|
|
encoding="ascii",
|
|
capture_output=True,
|
|
)
|
|
|
|
if p.returncode == 0:
|
|
value: str = p.stdout.strip()
|
|
|
|
if value.isnumeric():
|
|
return int(value)
|
|
elif value.lower() == "max":
|
|
return -1
|
|
else:
|
|
logger.debug(f"Unable to get docker memlimit: {p.stderr}")
|
|
|
|
return -1
|
|
|
|
|
|
def get_cpu_stats() -> dict[str, dict]:
|
|
"""Get cpu usages for each process id"""
|
|
usages = {}
|
|
# -n=2 runs to ensure extraneous values are not included
|
|
top_command = ["top", "-b", "-n", "2"]
|
|
|
|
docker_memlimit = get_docker_memlimit_bytes() / 1024
|
|
|
|
p = sp.run(
|
|
top_command,
|
|
encoding="ascii",
|
|
capture_output=True,
|
|
)
|
|
|
|
if p.returncode != 0:
|
|
logger.error(p.stderr)
|
|
return usages
|
|
else:
|
|
lines = p.stdout.split("\n")
|
|
|
|
for line in lines:
|
|
stats = list(filter(lambda a: a != "", line.strip().split(" ")))
|
|
try:
|
|
|
|
if docker_memlimit > 0:
|
|
mem_res = int(stats[5])
|
|
mem_pct = str(
|
|
round((float(mem_res) / float(docker_memlimit)) * 100, 1)
|
|
)
|
|
else:
|
|
mem_pct = stats[9]
|
|
|
|
usages[stats[0]] = {
|
|
"cpu": stats[8],
|
|
"mem": mem_pct,
|
|
}
|
|
except:
|
|
continue
|
|
|
|
return usages
|
|
|
|
|
|
def get_amd_gpu_stats() -> dict[str, str]:
|
|
"""Get stats using radeontop."""
|
|
radeontop_command = ["radeontop", "-d", "-", "-l", "1"]
|
|
|
|
p = sp.run(
|
|
radeontop_command,
|
|
encoding="ascii",
|
|
capture_output=True,
|
|
)
|
|
|
|
if p.returncode != 0:
|
|
logger.error(f"Unable to poll radeon GPU stats: {p.stderr}")
|
|
return None
|
|
else:
|
|
usages = p.stdout.split(",")
|
|
results: dict[str, str] = {}
|
|
|
|
for hw in usages:
|
|
if "gpu" in hw:
|
|
results["gpu"] = f"{hw.strip().split(' ')[1].replace('%', '')} %"
|
|
elif "vram" in hw:
|
|
results["mem"] = f"{hw.strip().split(' ')[1].replace('%', '')} %"
|
|
|
|
return results
|
|
|
|
|
|
def get_intel_gpu_stats() -> dict[str, str]:
|
|
"""Get stats using intel_gpu_top."""
|
|
intel_gpu_top_command = [
|
|
"timeout",
|
|
"0.5s",
|
|
"intel_gpu_top",
|
|
"-J",
|
|
"-o",
|
|
"-",
|
|
"-s",
|
|
"1",
|
|
]
|
|
|
|
p = sp.run(
|
|
intel_gpu_top_command,
|
|
encoding="ascii",
|
|
capture_output=True,
|
|
)
|
|
|
|
# timeout has a non-zero returncode when timeout is reached
|
|
if p.returncode != 124:
|
|
logger.error(f"Unable to poll intel GPU stats: {p.stderr}")
|
|
return None
|
|
else:
|
|
reading = "".join(p.stdout.split())
|
|
results: dict[str, str] = {}
|
|
|
|
# render is used for qsv
|
|
render = []
|
|
for result in re.findall('"Render/3D/0":{[a-z":\d.,%]+}', reading):
|
|
packet = json.loads(result[14:])
|
|
single = packet.get("busy", 0.0)
|
|
render.append(float(single))
|
|
|
|
if render:
|
|
render_avg = sum(render) / len(render)
|
|
else:
|
|
render_avg = 1
|
|
|
|
# video is used for vaapi
|
|
video = []
|
|
for result in re.findall('"Video/\d":{[a-z":\d.,%]+}', reading):
|
|
packet = json.loads(result[10:])
|
|
single = packet.get("busy", 0.0)
|
|
video.append(float(single))
|
|
|
|
if video:
|
|
video_avg = sum(video) / len(video)
|
|
else:
|
|
video_avg = 1
|
|
|
|
results["gpu"] = f"{round((video_avg + render_avg) / 2, 2)} %"
|
|
results["mem"] = "- %"
|
|
return results
|
|
|
|
|
|
def get_nvidia_gpu_stats() -> dict[str, str]:
|
|
"""Get stats using nvidia-smi."""
|
|
nvidia_smi_command = [
|
|
"nvidia-smi",
|
|
"--query-gpu=gpu_name,utilization.gpu,memory.used,memory.total",
|
|
"--format=csv",
|
|
]
|
|
|
|
if (
|
|
"CUDA_VISIBLE_DEVICES" in os.environ
|
|
and os.environ["CUDA_VISIBLE_DEVICES"].isdigit()
|
|
):
|
|
nvidia_smi_command.extend(["--id", os.environ["CUDA_VISIBLE_DEVICES"]])
|
|
elif (
|
|
"NVIDIA_VISIBLE_DEVICES" in os.environ
|
|
and os.environ["NVIDIA_VISIBLE_DEVICES"].isdigit()
|
|
):
|
|
nvidia_smi_command.extend(["--id", os.environ["NVIDIA_VISIBLE_DEVICES"]])
|
|
|
|
p = sp.run(
|
|
nvidia_smi_command,
|
|
encoding="ascii",
|
|
capture_output=True,
|
|
)
|
|
|
|
if p.returncode != 0:
|
|
logger.error(f"Unable to poll nvidia GPU stats: {p.stderr}")
|
|
return None
|
|
else:
|
|
usages = p.stdout.split("\n")[1].strip().split(",")
|
|
memory_percent = f"{round(float(usages[2].replace(' MiB', '').strip()) / float(usages[3].replace(' MiB', '').strip()) * 100, 1)} %"
|
|
results: dict[str, str] = {
|
|
"name": usages[0],
|
|
"gpu": usages[1].strip(),
|
|
"mem": memory_percent,
|
|
}
|
|
|
|
return results
|
|
|
|
|
|
def ffprobe_stream(path: str) -> sp.CompletedProcess:
|
|
"""Run ffprobe on stream."""
|
|
clean_path = escape_special_characters(path)
|
|
ffprobe_cmd = [
|
|
"ffprobe",
|
|
"-timeout",
|
|
"1000000",
|
|
"-print_format",
|
|
"json",
|
|
"-show_entries",
|
|
"stream=codec_long_name,width,height,bit_rate,duration,display_aspect_ratio,avg_frame_rate",
|
|
"-loglevel",
|
|
"quiet",
|
|
clean_path,
|
|
]
|
|
return sp.run(ffprobe_cmd, capture_output=True)
|
|
|
|
|
|
def vainfo_hwaccel(device_name: Optional[str] = None) -> sp.CompletedProcess:
|
|
"""Run vainfo."""
|
|
ffprobe_cmd = (
|
|
["vainfo"]
|
|
if not device_name
|
|
else ["vainfo", "--display", "drm", "--device", f"/dev/dri/{device_name}"]
|
|
)
|
|
return sp.run(ffprobe_cmd, capture_output=True)
|
|
|
|
|
|
def get_ffmpeg_arg_list(arg: Any) -> list:
|
|
"""Use arg if list or convert to list format."""
|
|
return arg if isinstance(arg, list) else shlex.split(arg)
|
|
|
|
|
|
class FrameManager(ABC):
|
|
@abstractmethod
|
|
def create(self, name, size) -> AnyStr:
|
|
pass
|
|
|
|
@abstractmethod
|
|
def get(self, name, timeout_ms=0):
|
|
pass
|
|
|
|
@abstractmethod
|
|
def close(self, name):
|
|
pass
|
|
|
|
@abstractmethod
|
|
def delete(self, name):
|
|
pass
|
|
|
|
|
|
class DictFrameManager(FrameManager):
|
|
def __init__(self):
|
|
self.frames = {}
|
|
|
|
def create(self, name, size) -> AnyStr:
|
|
mem = bytearray(size)
|
|
self.frames[name] = mem
|
|
return mem
|
|
|
|
def get(self, name, shape):
|
|
mem = self.frames[name]
|
|
return np.ndarray(shape, dtype=np.uint8, buffer=mem)
|
|
|
|
def close(self, name):
|
|
pass
|
|
|
|
def delete(self, name):
|
|
del self.frames[name]
|
|
|
|
|
|
class SharedMemoryFrameManager(FrameManager):
|
|
def __init__(self):
|
|
self.shm_store = {}
|
|
|
|
def create(self, name, size) -> AnyStr:
|
|
shm = shared_memory.SharedMemory(name=name, create=True, size=size)
|
|
self.shm_store[name] = shm
|
|
return shm.buf
|
|
|
|
def get(self, name, shape):
|
|
if name in self.shm_store:
|
|
shm = self.shm_store[name]
|
|
else:
|
|
shm = shared_memory.SharedMemory(name=name)
|
|
self.shm_store[name] = shm
|
|
return np.ndarray(shape, dtype=np.uint8, buffer=shm.buf)
|
|
|
|
def close(self, name):
|
|
if name in self.shm_store:
|
|
self.shm_store[name].close()
|
|
del self.shm_store[name]
|
|
|
|
def delete(self, name):
|
|
if name in self.shm_store:
|
|
self.shm_store[name].close()
|
|
self.shm_store[name].unlink()
|
|
del self.shm_store[name]
|
|
|
|
|
|
def get_tz_modifiers(tz_name: str) -> Tuple[str, str]:
|
|
seconds_offset = (
|
|
datetime.datetime.now(pytz.timezone(tz_name)).utcoffset().total_seconds()
|
|
)
|
|
hours_offset = int(seconds_offset / 60 / 60)
|
|
minutes_offset = int(seconds_offset / 60 - hours_offset * 60)
|
|
hour_modifier = f"{hours_offset} hour"
|
|
minute_modifier = f"{minutes_offset} minute"
|
|
return hour_modifier, minute_modifier
|