mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-11-21 19:07:46 +01:00
212 lines
8.3 KiB
Python
212 lines
8.3 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 (DictFrameManager, EventsPerSecond,
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SharedMemoryFrameManager, draw_box_with_label)
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from frigate.video import (capture_frames, process_frames,
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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 = " ".join([
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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.Popen(ffprobe_cmd, stdout=sp.PIPE, shell=True)
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(output, err) = p.communicate()
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p_status = p.wait()
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info = json.loads(output)
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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 = [c['cmd'] for c in self.camera_config.ffmpeg_cmds if 'detect' in c['roles']][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 = self.camera_config.frame_shape_yuv[0] * self.camera_config.frame_shape_yuv[1]
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ffmpeg_process = start_or_restart_ffmpeg(self.ffmpeg_cmd, logger, sp.DEVNULL, frame_size)
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capture_frames(ffmpeg_process, self.camera_name, self.camera_config.frame_shape_yuv, self.frame_manager,
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self.frame_queue, fps, skipped_fps, current_frame)
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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(self.frame_shape, mask, self.camera_config.motion)
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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(self.camera_name, self.frame_queue, self.frame_shape, model_shape,
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self.frame_manager, motion_detector, object_detector, object_tracker,
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self.detected_objects_queue, process_info,
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objects_to_track, object_filters, mask, stop_event, exit_on_empty=True)
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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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camera_name, frame_time, current_tracked_objects = self.detected_objects_queue.get()
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if not debug_path is None:
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self.save_debug_frame(debug_path, frame_time, current_tracked_objects.values())
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self.camera_state.update(frame_time, current_tracked_objects)
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# for obj in self.camera_state.tracked_objects.values():
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# obj_data = obj.to_dict()
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# print(f"{frame_time}: {obj_data['id']} - {obj_data['label']} - {obj_data['score']} - {obj.score_history}")
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self.frame_manager.delete(self.camera_state.previous_frame_id)
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return {
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'object_detected': obj_detected,
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'top_score': top_computed_score
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}
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def save_debug_frame(self, debug_path, frame_time, tracked_objects):
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current_frame = cv2.cvtColor(self.frame_manager.get(f"{self.camera_name}{frame_time}", self.camera_config.frame_shape_yuv), cv2.COLOR_YUV2BGR_I420)
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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(current_frame, box[0], box[1], box[2], box[3], obj['id'], f"{int(obj['score']*100)}% {int(obj['area'])}", thickness=thickness, color=color)
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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(current_frame, region[0], region[1], region[2], region[3], 'region', "", thickness=1, color=(0,255,0))
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cv2.imwrite(f"{os.path.join(debug_path, os.path.basename(self.clip_path))}.{int(frame_time*1000000)}.jpg", current_frame)
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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': {
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'host': 'mqtt'
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},
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'cameras': {
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'camera': {
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'ffmpeg': {
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'inputs': [
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{ 'path': 'path.mp4', 'global_args': '', 'input_args': '', 'roles': ['detect'] }
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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(f"Objects were detected in {positive_count}/{len(results)}({positive_count/len(results)*100:.2f}%) clip(s).")
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if __name__ == '__main__':
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process()
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