mirror of
https://github.com/blakeblackshear/frigate.git
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a4e6d9ed9a
When running ffprobe, use `subprocess.run` rather than `subprocess.Popen`. This simplifies the handling that is needed to run and process the outputs. Here, filename parsing is also simplified by explicitly removing the file extension with `os.path.splitext` and forcing a single split into the camera name and the formatted date.
286 lines
8.9 KiB
Python
286 lines
8.9 KiB
Python
import datetime
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import json
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import logging
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import multiprocessing as mp
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import os
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import subprocess as sp
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import sys
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from unittest import TestCase, main
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import click
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import cv2
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import numpy as np
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from frigate.config import FRIGATE_CONFIG_SCHEMA, FrigateConfig
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from frigate.edgetpu import LocalObjectDetector
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from frigate.motion import MotionDetector
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from frigate.object_processing import COLOR_MAP, CameraState
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from frigate.objects import ObjectTracker
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from frigate.util import (
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DictFrameManager,
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EventsPerSecond,
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SharedMemoryFrameManager,
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draw_box_with_label,
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)
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from frigate.video import capture_frames, process_frames, start_or_restart_ffmpeg
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logging.basicConfig()
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logging.root.setLevel(logging.DEBUG)
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logger = logging.getLogger(__name__)
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def get_frame_shape(source):
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ffprobe_cmd = [
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"ffprobe",
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"-v",
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"panic",
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"-show_error",
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"-show_streams",
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"-of",
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"json",
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source,
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]
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p = sp.run(ffprobe_cmd, capture_output=True)
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info = json.loads(p.stdout)
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video_info = [s for s in info["streams"] if s["codec_type"] == "video"][0]
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if video_info["height"] != 0 and video_info["width"] != 0:
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return (video_info["height"], video_info["width"], 3)
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# fallback to using opencv if ffprobe didnt succeed
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video = cv2.VideoCapture(source)
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ret, frame = video.read()
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frame_shape = frame.shape
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video.release()
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return frame_shape
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class ProcessClip:
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def __init__(self, clip_path, frame_shape, config: FrigateConfig):
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self.clip_path = clip_path
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self.camera_name = "camera"
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self.config = config
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self.camera_config = self.config.cameras["camera"]
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self.frame_shape = self.camera_config.frame_shape
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self.ffmpeg_cmd = [
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c["cmd"] for c in self.camera_config.ffmpeg_cmds if "detect" in c["roles"]
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][0]
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self.frame_manager = SharedMemoryFrameManager()
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self.frame_queue = mp.Queue()
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self.detected_objects_queue = mp.Queue()
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self.camera_state = CameraState(self.camera_name, config, self.frame_manager)
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def load_frames(self):
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fps = EventsPerSecond()
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skipped_fps = EventsPerSecond()
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current_frame = mp.Value("d", 0.0)
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frame_size = (
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self.camera_config.frame_shape_yuv[0]
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* self.camera_config.frame_shape_yuv[1]
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)
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ffmpeg_process = start_or_restart_ffmpeg(
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self.ffmpeg_cmd, logger, sp.DEVNULL, frame_size
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)
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capture_frames(
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ffmpeg_process,
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self.camera_name,
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self.camera_config.frame_shape_yuv,
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self.frame_manager,
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self.frame_queue,
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fps,
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skipped_fps,
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current_frame,
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)
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ffmpeg_process.wait()
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ffmpeg_process.communicate()
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def process_frames(self, objects_to_track=["person"], object_filters={}):
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mask = np.zeros((self.frame_shape[0], self.frame_shape[1], 1), np.uint8)
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mask[:] = 255
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motion_detector = MotionDetector(
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self.frame_shape, mask, self.camera_config.motion
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)
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object_detector = LocalObjectDetector(labels="/labelmap.txt")
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object_tracker = ObjectTracker(self.camera_config.detect)
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process_info = {
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"process_fps": mp.Value("d", 0.0),
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"detection_fps": mp.Value("d", 0.0),
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"detection_frame": mp.Value("d", 0.0),
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}
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stop_event = mp.Event()
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model_shape = (self.config.model.height, self.config.model.width)
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process_frames(
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self.camera_name,
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self.frame_queue,
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self.frame_shape,
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model_shape,
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self.frame_manager,
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motion_detector,
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object_detector,
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object_tracker,
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self.detected_objects_queue,
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process_info,
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objects_to_track,
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object_filters,
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mask,
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stop_event,
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exit_on_empty=True,
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)
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def top_object(self, debug_path=None):
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obj_detected = False
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top_computed_score = 0.0
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def handle_event(name, obj, frame_time):
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nonlocal obj_detected
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nonlocal top_computed_score
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if obj.computed_score > top_computed_score:
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top_computed_score = obj.computed_score
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if not obj.false_positive:
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obj_detected = True
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self.camera_state.on("new", handle_event)
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self.camera_state.on("update", handle_event)
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while not self.detected_objects_queue.empty():
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(
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camera_name,
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frame_time,
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current_tracked_objects,
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motion_boxes,
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regions,
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) = self.detected_objects_queue.get()
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if not debug_path is None:
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self.save_debug_frame(
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debug_path, frame_time, current_tracked_objects.values()
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)
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self.camera_state.update(
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frame_time, current_tracked_objects, motion_boxes, regions
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)
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self.frame_manager.delete(self.camera_state.previous_frame_id)
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return {"object_detected": obj_detected, "top_score": top_computed_score}
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def save_debug_frame(self, debug_path, frame_time, tracked_objects):
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current_frame = cv2.cvtColor(
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self.frame_manager.get(
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f"{self.camera_name}{frame_time}", self.camera_config.frame_shape_yuv
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),
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cv2.COLOR_YUV2BGR_I420,
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)
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# draw the bounding boxes on the frame
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for obj in tracked_objects:
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thickness = 2
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color = (0, 0, 175)
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if obj["frame_time"] != frame_time:
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thickness = 1
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color = (255, 0, 0)
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else:
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color = (255, 255, 0)
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# draw the bounding boxes on the frame
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box = obj["box"]
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draw_box_with_label(
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current_frame,
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box[0],
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box[1],
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box[2],
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box[3],
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obj["id"],
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f"{int(obj['score']*100)}% {int(obj['area'])}",
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thickness=thickness,
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color=color,
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)
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# draw the regions on the frame
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region = obj["region"]
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draw_box_with_label(
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current_frame,
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region[0],
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region[1],
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region[2],
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region[3],
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"region",
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"",
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thickness=1,
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color=(0, 255, 0),
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)
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cv2.imwrite(
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f"{os.path.join(debug_path, os.path.basename(self.clip_path))}.{int(frame_time*1000000)}.jpg",
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current_frame,
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)
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@click.command()
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@click.option("-p", "--path", required=True, help="Path to clip or directory to test.")
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@click.option("-l", "--label", default="person", help="Label name to detect.")
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@click.option("-t", "--threshold", default=0.85, help="Threshold value for objects.")
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@click.option("-s", "--scores", default=None, help="File to save csv of top scores")
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@click.option("--debug-path", default=None, help="Path to output frames for debugging.")
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def process(path, label, threshold, scores, debug_path):
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clips = []
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if os.path.isdir(path):
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files = os.listdir(path)
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files.sort()
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clips = [os.path.join(path, file) for file in files]
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elif os.path.isfile(path):
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clips.append(path)
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json_config = {
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"mqtt": {"host": "mqtt"},
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"cameras": {
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"camera": {
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"ffmpeg": {
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"inputs": [
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{
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"path": "path.mp4",
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"global_args": "",
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"input_args": "",
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"roles": ["detect"],
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}
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]
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},
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"height": 1920,
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"width": 1080,
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}
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},
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}
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results = []
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for c in clips:
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logger.info(c)
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frame_shape = get_frame_shape(c)
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json_config["cameras"]["camera"]["height"] = frame_shape[0]
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json_config["cameras"]["camera"]["width"] = frame_shape[1]
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json_config["cameras"]["camera"]["ffmpeg"]["inputs"][0]["path"] = c
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config = FrigateConfig(config=FRIGATE_CONFIG_SCHEMA(json_config))
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process_clip = ProcessClip(c, frame_shape, config)
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process_clip.load_frames()
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process_clip.process_frames(objects_to_track=[label])
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results.append((c, process_clip.top_object(debug_path)))
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if not scores is None:
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with open(scores, "w") as writer:
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for result in results:
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writer.write(f"{result[0]},{result[1]['top_score']}\n")
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positive_count = sum(1 for result in results if result[1]["object_detected"])
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print(
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f"Objects were detected in {positive_count}/{len(results)}({positive_count/len(results)*100:.2f}%) clip(s)."
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)
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if __name__ == "__main__":
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process()
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