mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-11-26 19:06:11 +01:00
create tracked object class and save thumbnails
This commit is contained in:
Blake Blackshear
parent
24b703a875
commit
8874a55b0f
@ -88,7 +88,7 @@ class FrigateApp():
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self.detectors[name] = EdgeTPUProcess(name, self.detection_queue, out_events=self.detection_out_events, tf_device=detector.device)
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def start_detected_frames_processor(self):
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self.detected_frames_processor = TrackedObjectProcessor(self.config.cameras, self.mqtt_client, self.config.mqtt.topic_prefix,
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self.detected_frames_processor = TrackedObjectProcessor(self.config, self.mqtt_client, self.config.mqtt.topic_prefix,
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self.detected_frames_queue, self.event_queue, self.stop_event)
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self.detected_frames_processor.start()
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@ -91,16 +91,14 @@ OBJECTS_SCHEMA = vol.Schema(vol.All(filters_for_all_tracked_objects,
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}
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))
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DEFAULT_CAMERA_MQTT = {
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'crop_to_region': True
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}
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DEFAULT_CAMERA_SAVE_CLIPS = {
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'enabled': False
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}
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DEFAULT_CAMERA_SNAPSHOTS = {
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'show_timestamp': True,
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'draw_zones': False,
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'draw_bounding_boxes': True
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'draw_bounding_boxes': True,
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'crop_to_region': True
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}
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CAMERA_FFMPEG_SCHEMA = vol.Schema(
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@ -122,10 +120,6 @@ CAMERAS_SCHEMA = vol.Schema(
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'fps': int,
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'mask': str,
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vol.Optional('best_image_timeout', default=60): int,
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vol.Optional('mqtt', default=DEFAULT_CAMERA_MQTT): {
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vol.Optional('crop_to_region', default=True): bool,
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'snapshot_height': int
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},
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vol.Optional('zones', default={}): {
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str: {
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vol.Required('coordinates'): vol.Any(str, [str]),
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@ -140,7 +134,9 @@ CAMERAS_SCHEMA = vol.Schema(
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vol.Optional('snapshots', default=DEFAULT_CAMERA_SNAPSHOTS): {
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vol.Optional('show_timestamp', default=True): bool,
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vol.Optional('draw_zones', default=False): bool,
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vol.Optional('draw_bounding_boxes', default=True): bool
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vol.Optional('draw_bounding_boxes', default=True): bool,
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vol.Optional('crop_to_region', default=True): bool,
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'height': int
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},
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'objects': OBJECTS_SCHEMA
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}
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@ -296,6 +292,8 @@ class CameraSnapshotsConfig():
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self._show_timestamp = config['show_timestamp']
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self._draw_zones = config['draw_zones']
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self._draw_bounding_boxes = config['draw_bounding_boxes']
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self._crop_to_region = config['crop_to_region']
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self._height = config.get('height')
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@property
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def show_timestamp(self):
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@ -309,6 +307,14 @@ class CameraSnapshotsConfig():
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def draw_bounding_boxes(self):
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return self._draw_bounding_boxes
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@property
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def crop_to_region(self):
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return self._crop_to_region
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@property
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def height(self):
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return self._height
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class CameraSaveClipsConfig():
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def __init__(self, config):
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self._enabled = config['enabled']
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@ -327,19 +333,6 @@ class CameraSaveClipsConfig():
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def objects(self):
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return self._objects
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class CameraMqttConfig():
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def __init__(self, config):
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self._crop_to_region = config['crop_to_region']
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self._snapshot_height = config.get('snapshot_height')
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@property
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def crop_to_region(self):
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return self._crop_to_region
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@property
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def snapshot_height(self):
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return self._snapshot_height
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class ZoneConfig():
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def __init__(self, name, config):
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self._coordinates = config['coordinates']
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@ -391,7 +384,6 @@ class CameraConfig():
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self._fps = config.get('fps')
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self._mask = self._create_mask(config.get('mask'))
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self._best_image_timeout = config['best_image_timeout']
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self._mqtt = CameraMqttConfig(config['mqtt'])
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self._zones = { name: ZoneConfig(name, z) for name, z in config['zones'].items() }
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self._save_clips = CameraSaveClipsConfig(config['save_clips'])
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self._snapshots = CameraSnapshotsConfig(config['snapshots'])
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@ -4,6 +4,7 @@ import hashlib
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import itertools
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import json
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import logging
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import os
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import queue
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import threading
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import time
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@ -15,7 +16,7 @@ import cv2
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import matplotlib.pyplot as plt
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import numpy as np
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from frigate.config import CameraConfig
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from frigate.config import FrigateConfig, CameraConfig
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from frigate.edgetpu import load_labels
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from frigate.util import SharedMemoryFrameManager, draw_box_with_label
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@ -30,28 +31,6 @@ COLOR_MAP = {}
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for key, val in LABELS.items():
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COLOR_MAP[val] = tuple(int(round(255 * c)) for c in cmap(key)[:3])
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def zone_filtered(obj, object_config):
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object_name = obj['label']
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if object_name in object_config:
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obj_settings = object_config[object_name]
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# if the min area is larger than the
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# detected object, don't add it to detected objects
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if obj_settings.min_area > obj['area']:
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return True
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# if the detected object is larger than the
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# max area, don't add it to detected objects
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if obj_settings.max_area < obj['area']:
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return True
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# if the score is lower than the threshold, skip
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if obj_settings.threshold > obj['computed_score']:
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return True
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return False
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def on_edge(box, frame_shape):
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if (
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box[0] == 0 or
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@ -80,19 +59,182 @@ def is_better_thumbnail(current_thumb, new_obj, frame_shape) -> bool:
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return False
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class TrackedObject():
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def __init__(self, camera, camera_config: CameraConfig, thumbnail_frames, obj_data):
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self.obj_data = obj_data
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self.camera = camera
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self.camera_config = camera_config
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self.thumbnail_frames = thumbnail_frames
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self.current_zones = []
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self.entered_zones = set()
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self._false_positive = True
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self.top_score = self.computed_score = 0.0
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self.thumbnail_data = {
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'frame_time': obj_data['frame_time'],
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'box': obj_data['box'],
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'area': obj_data['area'],
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'region': obj_data['region'],
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'score': obj_data['score']
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}
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self.frame = None
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self._snapshot_jpg_time = 0
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self._snapshot_jpg = None
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# start the score history
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self.score_history = [self.obj_data['score']]
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def false_positive(self):
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# once a true positive, always a true positive
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if not self._false_positive:
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return False
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threshold = self.camera_config.objects.filters[self.obj_data['label']].threshold
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if self.computed_score < threshold:
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return True
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return False
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def compute_score(self):
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scores = self.score_history[:]
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# pad with zeros if you dont have at least 3 scores
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if len(scores) < 3:
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scores += [0.0]*(3 - len(scores))
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return median(scores)
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def update(self, current_frame_time, obj_data):
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self.obj_data.update(obj_data)
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# if the object is not in the current frame, add a 0.0 to the score history
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if self.obj_data['frame_time'] != current_frame_time:
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self.score_history.append(0.0)
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else:
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self.score_history.append(self.obj_data['score'])
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# only keep the last 10 scores
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if len(self.score_history) > 10:
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self.score_history = self.score_history[-10:]
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# calculate if this is a false positive
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self.computed_score = self.compute_score()
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if self.computed_score > self.top_score:
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self.top_score = self.computed_score
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self._false_positive = self.false_positive()
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# determine if this frame is a better thumbnail
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if is_better_thumbnail(self.thumbnail_data, self.obj_data, self.camera_config.frame_shape):
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self.thumbnail_data = {
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'frame_time': self.obj_data['frame_time'],
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'box': self.obj_data['box'],
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'area': self.obj_data['area'],
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'region': self.obj_data['region'],
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'score': self.obj_data['score']
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}
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# check zones
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current_zones = []
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bottom_center = (self.obj_data['centroid'][0], self.obj_data['box'][3])
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# check each zone
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for name, zone in self.camera_config.zones.items():
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contour = zone.contour
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# check if the object is in the zone
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if (cv2.pointPolygonTest(contour, bottom_center, False) >= 0):
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# if the object passed the filters once, dont apply again
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if name in self.current_zones or not zone_filtered(self, zone.filters):
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current_zones.append(name)
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self.entered_zones.add(name)
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self.current_zones = current_zones
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def to_dict(self):
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return {
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'id': self.obj_data['id'],
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'camera': self.camera,
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'frame_time': self.obj_data['frame_time'],
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'label': self.obj_data['label'],
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'top_score': self.top_score,
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'false_positive': self._false_positive,
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'start_time': self.obj_data['start_time'],
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'end_time': self.obj_data.get('end_time', None),
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'score': self.obj_data['score'],
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'box': self.obj_data['box'],
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'area': self.obj_data['area'],
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'region': self.obj_data['region'],
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'current_zones': self.current_zones.copy(),
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'entered_zones': list(self.entered_zones).copy()
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}
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@property
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def snapshot_jpg(self):
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if self._snapshot_jpg_time == self.thumbnail_data['frame_time']:
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return self._snapshot_jpg
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# TODO: crop first to avoid converting the entire frame?
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snapshot_config = self.camera_config.snapshots
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best_frame = cv2.cvtColor(self.thumbnail_frames[self.thumbnail_data['frame_time']], cv2.COLOR_YUV2BGR_I420)
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if snapshot_config.draw_bounding_boxes:
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thickness = 2
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color = COLOR_MAP[self.obj_data['label']]
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box = self.thumbnail_data['box']
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draw_box_with_label(best_frame, box[0], box[1], box[2], box[3], self.obj_data['label'],
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f"{int(self.thumbnail_data['score']*100)}% {int(self.thumbnail_data['area'])}", thickness=thickness, color=color)
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if snapshot_config.crop_to_region:
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region = self.thumbnail_data['region']
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best_frame = best_frame[region[1]:region[3], region[0]:region[2]]
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if snapshot_config.height:
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height = snapshot_config.height
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width = int(height*best_frame.shape[1]/best_frame.shape[0])
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best_frame = cv2.resize(best_frame, dsize=(width, height), interpolation=cv2.INTER_AREA)
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if snapshot_config.show_timestamp:
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time_to_show = datetime.datetime.fromtimestamp(self.thumbnail_data['frame_time']).strftime("%m/%d/%Y %H:%M:%S")
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size = cv2.getTextSize(time_to_show, cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, thickness=2)
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text_width = size[0][0]
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desired_size = max(200, 0.33*best_frame.shape[1])
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font_scale = desired_size/text_width
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cv2.putText(best_frame, time_to_show, (5, best_frame.shape[0]-7), cv2.FONT_HERSHEY_SIMPLEX,
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fontScale=font_scale, color=(255, 255, 255), thickness=2)
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ret, jpg = cv2.imencode('.jpg', best_frame)
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if ret:
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self._snapshot_jpg = jpg.tobytes()
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return self._snapshot_jpg
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def zone_filtered(obj: TrackedObject, object_config):
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object_name = obj.obj_data['label']
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if object_name in object_config:
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obj_settings = object_config[object_name]
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# if the min area is larger than the
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# detected object, don't add it to detected objects
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if obj_settings.min_area > obj.obj_data['area']:
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return True
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# if the detected object is larger than the
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# max area, don't add it to detected objects
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if obj_settings.max_area < obj.obj_data['area']:
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return True
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# if the score is lower than the threshold, skip
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if obj_settings.threshold > obj.computed_score:
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return True
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return False
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# Maintains the state of a camera
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class CameraState():
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def __init__(self, name, config, frame_manager):
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self.name = name
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self.config = config
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self.camera_config = config.cameras[name]
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self.frame_manager = frame_manager
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self.best_objects = {}
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self.object_status = defaultdict(lambda: 'OFF')
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self.tracked_objects = {}
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self.tracked_objects: Dict[str, TrackedObject] = {}
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self.thumbnail_frames = {}
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self.zone_objects = defaultdict(lambda: [])
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self._current_frame = np.zeros(self.config.frame_shape_yuv, np.uint8)
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self._current_frame = np.zeros(self.camera_config.frame_shape_yuv, np.uint8)
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self.current_frame_lock = threading.Lock()
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self.current_frame_time = 0.0
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self.previous_frame_id = None
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@ -102,7 +244,7 @@ class CameraState():
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with self.current_frame_lock:
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frame_copy = np.copy(self._current_frame)
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frame_time = self.current_frame_time
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tracked_objects = copy.deepcopy(self.tracked_objects)
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tracked_objects = {k: v.to_dict() for k,v in self.tracked_objects.items()}
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frame_copy = cv2.cvtColor(frame_copy, cv2.COLOR_YUV2BGR_I420)
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# draw on the frame
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@ -123,42 +265,25 @@ class CameraState():
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region = obj['region']
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cv2.rectangle(frame_copy, (region[0], region[1]), (region[2], region[3]), (0,255,0), 1)
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if self.config.snapshots.show_timestamp:
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if self.camera_config.snapshots.show_timestamp:
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time_to_show = datetime.datetime.fromtimestamp(frame_time).strftime("%m/%d/%Y %H:%M:%S")
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cv2.putText(frame_copy, time_to_show, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.8, color=(255, 255, 255), thickness=2)
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if self.config.snapshots.draw_zones:
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for name, zone in self.config.zones.items():
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thickness = 8 if any([name in obj['zones'] for obj in tracked_objects.values()]) else 2
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if self.camera_config.snapshots.draw_zones:
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for name, zone in self.camera_config.zones.items():
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thickness = 8 if any([name in obj['current_zones'] for obj in tracked_objects.values()]) else 2
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cv2.drawContours(frame_copy, [zone.contour], -1, zone.color, thickness)
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return frame_copy
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def false_positive(self, obj):
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# once a true positive, always a true positive
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if not obj.get('false_positive', True):
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return False
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threshold = self.config.objects.filters[obj['label']].threshold
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if obj['computed_score'] < threshold:
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return True
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return False
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def compute_score(self, obj):
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scores = obj['score_history'][:]
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# pad with zeros if you dont have at least 3 scores
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if len(scores) < 3:
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scores += [0.0]*(3 - len(scores))
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return median(scores)
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def on(self, event_type: str, callback: Callable[[Dict], None]):
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self.callbacks[event_type].append(callback)
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def update(self, frame_time, tracked_objects):
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self.current_frame_time = frame_time
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# get the new frame and delete the old frame
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# get the new frame
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frame_id = f"{self.name}{frame_time}"
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current_frame = self.frame_manager.get(frame_id, self.config.frame_shape_yuv)
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current_frame = self.frame_manager.get(frame_id, self.camera_config.frame_shape_yuv)
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current_ids = tracked_objects.keys()
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previous_ids = self.tracked_objects.keys()
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@ -167,59 +292,20 @@ class CameraState():
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updated_ids = list(set(current_ids).intersection(previous_ids))
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for id in new_ids:
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new_obj = self.tracked_objects[id] = tracked_objects[id]
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new_obj['zones'] = []
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new_obj['entered_zones'] = set()
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new_obj['thumbnail'] = {
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'frame': new_obj['frame_time'],
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'box': new_obj['box'],
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'area': new_obj['area'],
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'region': new_obj['region'],
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'score': new_obj['score']
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}
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# start the score history
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new_obj['score_history'] = [self.tracked_objects[id]['score']]
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# calculate if this is a false positive
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new_obj['computed_score'] = self.compute_score(self.tracked_objects[id])
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new_obj['top_score'] = self.tracked_objects[id]['computed_score']
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new_obj['false_positive'] = self.false_positive(self.tracked_objects[id])
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new_obj = self.tracked_objects[id] = TrackedObject(self.name, self.camera_config, self.thumbnail_frames, tracked_objects[id])
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# call event handlers
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for c in self.callbacks['start']:
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c(self.name, new_obj)
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for id in updated_ids:
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self.tracked_objects[id].update(tracked_objects[id])
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updated_obj = self.tracked_objects[id]
|
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updated_obj.update(frame_time, tracked_objects[id])
|
||||
|
||||
# if the object is not in the current frame, add a 0.0 to the score history
|
||||
if updated_obj['frame_time'] != self.current_frame_time:
|
||||
updated_obj['score_history'].append(0.0)
|
||||
else:
|
||||
updated_obj['score_history'].append(updated_obj['score'])
|
||||
# only keep the last 10 scores
|
||||
if len(updated_obj['score_history']) > 10:
|
||||
updated_obj['score_history'] = updated_obj['score_history'][-10:]
|
||||
|
||||
# calculate if this is a false positive
|
||||
computed_score = self.compute_score(updated_obj)
|
||||
updated_obj['computed_score'] = computed_score
|
||||
if computed_score > updated_obj['top_score']:
|
||||
updated_obj['top_score'] = computed_score
|
||||
updated_obj['false_positive'] = self.false_positive(updated_obj)
|
||||
|
||||
# determine if this frame is a better thumbnail
|
||||
if is_better_thumbnail(updated_obj['thumbnail'], updated_obj, self.config.frame_shape):
|
||||
updated_obj['thumbnail'] = {
|
||||
'frame': updated_obj['frame_time'],
|
||||
'box': updated_obj['box'],
|
||||
'area': updated_obj['area'],
|
||||
'region': updated_obj['region'],
|
||||
'score': updated_obj['score']
|
||||
}
|
||||
if (not updated_obj._false_positive
|
||||
and updated_obj.thumbnail_data['frame_time'] == frame_time
|
||||
and frame_time not in self.thumbnail_frames):
|
||||
self.thumbnail_frames[frame_time] = np.copy(current_frame)
|
||||
|
||||
# call event handlers
|
||||
for c in self.callbacks['update']:
|
||||
@ -227,53 +313,35 @@ class CameraState():
|
||||
|
||||
for id in removed_ids:
|
||||
# publish events to mqtt
|
||||
self.tracked_objects[id]['end_time'] = frame_time
|
||||
removed_obj = self.tracked_objects[id]
|
||||
removed_obj.obj_data['end_time'] = frame_time
|
||||
for c in self.callbacks['end']:
|
||||
c(self.name, self.tracked_objects[id])
|
||||
c(self.name, removed_obj)
|
||||
del self.tracked_objects[id]
|
||||
|
||||
# check to see if the objects are in any zones
|
||||
for obj in self.tracked_objects.values():
|
||||
current_zones = []
|
||||
bottom_center = (obj['centroid'][0], obj['box'][3])
|
||||
# check each zone
|
||||
for name, zone in self.config.zones.items():
|
||||
contour = zone.contour
|
||||
# check if the object is in the zone
|
||||
if (cv2.pointPolygonTest(contour, bottom_center, False) >= 0):
|
||||
# if the object passed the filters once, dont apply again
|
||||
if name in obj.get('zones', []) or not zone_filtered(obj, zone.filters):
|
||||
current_zones.append(name)
|
||||
obj['entered_zones'].add(name)
|
||||
|
||||
|
||||
obj['zones'] = current_zones
|
||||
|
||||
# update frame storage for thumbnails based on thumbnails for all tracked objects
|
||||
current_thumb_frames = set([obj['thumbnail']['frame'] for obj in self.tracked_objects.values()])
|
||||
if self.current_frame_time in current_thumb_frames:
|
||||
self.thumbnail_frames[self.current_frame_time] = np.copy(current_frame)
|
||||
thumb_frames_to_delete = [t for t in self.thumbnail_frames.keys() if not t in current_thumb_frames]
|
||||
for t in thumb_frames_to_delete: del self.thumbnail_frames[t]
|
||||
|
||||
# TODO: can i switch to looking this up and only changing when an event ends?
|
||||
# maybe make an api endpoint that pulls the thumbnail from the file system?
|
||||
# maintain best objects
|
||||
for obj in self.tracked_objects.values():
|
||||
object_type = obj['label']
|
||||
# if the object wasn't seen on the current frame, skip it
|
||||
if obj['frame_time'] != self.current_frame_time or obj['false_positive']:
|
||||
object_type = obj.obj_data['label']
|
||||
# if the object's thumbnail is not from the current frame
|
||||
if obj.thumbnail_data['frame_time'] != self.current_frame_time or obj.false_positive:
|
||||
continue
|
||||
obj_copy = copy.deepcopy(obj)
|
||||
if object_type in self.best_objects:
|
||||
current_best = self.best_objects[object_type]
|
||||
now = datetime.datetime.now().timestamp()
|
||||
# if the object is a higher score than the current best score
|
||||
# or the current object is older than desired, use the new object
|
||||
if obj_copy['score'] > current_best['score'] or (now - current_best['frame_time']) > self.config.best_image_timeout:
|
||||
if is_better_thumbnail(current_best['thumbnail'], obj.thumbnail, self.camera_config.frame_shape) or (now - current_best['frame_time']) > self.config.best_image_timeout:
|
||||
obj_copy = copy.deepcopy(obj.obj_data)
|
||||
obj_copy['thumbnail'] = copy.deepcopy(obj.thumbnail_data)
|
||||
obj_copy['frame'] = np.copy(current_frame)
|
||||
self.best_objects[object_type] = obj_copy
|
||||
for c in self.callbacks['snapshot']:
|
||||
c(self.name, self.best_objects[object_type])
|
||||
else:
|
||||
obj_copy = copy.deepcopy(obj)
|
||||
obj_copy['thumbnail'] = copy.deepcopy(obj.thumbnail_data)
|
||||
obj_copy['frame'] = np.copy(current_frame)
|
||||
self.best_objects[object_type] = obj_copy
|
||||
for c in self.callbacks['snapshot']:
|
||||
@ -282,8 +350,8 @@ class CameraState():
|
||||
# update overall camera state for each object type
|
||||
obj_counter = Counter()
|
||||
for obj in self.tracked_objects.values():
|
||||
if not obj['false_positive']:
|
||||
obj_counter[obj['label']] += 1
|
||||
if not obj.false_positive:
|
||||
obj_counter[obj.obj_data['label']] += 1
|
||||
|
||||
# report on detected objects
|
||||
for obj_name, count in obj_counter.items():
|
||||
@ -302,6 +370,12 @@ class CameraState():
|
||||
for c in self.callbacks['snapshot']:
|
||||
c(self.name, self.best_objects[obj_name])
|
||||
|
||||
# cleanup thumbnail frame cache
|
||||
current_thumb_frames = set([obj.thumbnail_data['frame_time'] for obj in self.tracked_objects.values() if not obj._false_positive])
|
||||
thumb_frames_to_delete = [t for t in self.thumbnail_frames.keys() if not t in current_thumb_frames]
|
||||
for t in thumb_frames_to_delete:
|
||||
del self.thumbnail_frames[t]
|
||||
|
||||
with self.current_frame_lock:
|
||||
self._current_frame = current_frame
|
||||
if not self.previous_frame_id is None:
|
||||
@ -309,10 +383,10 @@ class CameraState():
|
||||
self.previous_frame_id = frame_id
|
||||
|
||||
class TrackedObjectProcessor(threading.Thread):
|
||||
def __init__(self, camera_config: Dict[str, CameraConfig], client, topic_prefix, tracked_objects_queue, event_queue, stop_event):
|
||||
def __init__(self, config: FrigateConfig, client, topic_prefix, tracked_objects_queue, event_queue, stop_event):
|
||||
threading.Thread.__init__(self)
|
||||
self.name = "detected_frames_processor"
|
||||
self.camera_config = camera_config
|
||||
self.config = config
|
||||
self.client = client
|
||||
self.topic_prefix = topic_prefix
|
||||
self.tracked_objects_queue = tracked_objects_queue
|
||||
@ -321,76 +395,29 @@ class TrackedObjectProcessor(threading.Thread):
|
||||
self.camera_states: Dict[str, CameraState] = {}
|
||||
self.frame_manager = SharedMemoryFrameManager()
|
||||
|
||||
def start(camera, obj):
|
||||
# publish events to mqtt
|
||||
event_data = {
|
||||
'id': obj['id'],
|
||||
'label': obj['label'],
|
||||
'camera': camera,
|
||||
'start_time': obj['start_time'],
|
||||
'top_score': obj['top_score'],
|
||||
'false_positive': obj['false_positive'],
|
||||
'zones': list(obj['entered_zones'])
|
||||
}
|
||||
self.client.publish(f"{self.topic_prefix}/{camera}/events/start", json.dumps(event_data), retain=False)
|
||||
self.event_queue.put(('start', camera, obj))
|
||||
def start(camera, obj: TrackedObject):
|
||||
self.client.publish(f"{self.topic_prefix}/{camera}/events/start", json.dumps(obj.to_dict()), retain=False)
|
||||
self.event_queue.put(('start', camera, obj.to_dict()))
|
||||
|
||||
def update(camera, obj):
|
||||
def update(camera, obj: TrackedObject):
|
||||
pass
|
||||
|
||||
def end(camera, obj):
|
||||
event_data = {
|
||||
'id': obj['id'],
|
||||
'label': obj['label'],
|
||||
'camera': camera,
|
||||
'start_time': obj['start_time'],
|
||||
'end_time': obj['end_time'],
|
||||
'top_score': obj['top_score'],
|
||||
'false_positive': obj['false_positive'],
|
||||
'zones': list(obj['entered_zones'])
|
||||
}
|
||||
self.client.publish(f"{self.topic_prefix}/{camera}/events/end", json.dumps(event_data), retain=False)
|
||||
self.event_queue.put(('end', camera, obj))
|
||||
def end(camera, obj: TrackedObject):
|
||||
self.client.publish(f"{self.topic_prefix}/{camera}/events/end", json.dumps(obj.to_dict()), retain=False)
|
||||
if self.config.cameras[camera].save_clips.enabled:
|
||||
thumbnail_file_name = f"{camera}-{obj.obj_data['id']}.jpg"
|
||||
with open(os.path.join(self.config.save_clips.clips_dir, thumbnail_file_name), 'wb') as f:
|
||||
f.write(obj.snapshot_jpg)
|
||||
self.event_queue.put(('end', camera, obj.to_dict()))
|
||||
|
||||
def snapshot(camera, obj):
|
||||
if not 'frame' in obj:
|
||||
return
|
||||
|
||||
best_frame = cv2.cvtColor(obj['frame'], cv2.COLOR_YUV2BGR_I420)
|
||||
if self.camera_config[camera].snapshots.draw_bounding_boxes:
|
||||
thickness = 2
|
||||
color = COLOR_MAP[obj['label']]
|
||||
box = obj['box']
|
||||
draw_box_with_label(best_frame, box[0], box[1], box[2], box[3], obj['label'], f"{int(obj['score']*100)}% {int(obj['area'])}", thickness=thickness, color=color)
|
||||
|
||||
mqtt_config = self.camera_config[camera].mqtt
|
||||
if mqtt_config.crop_to_region:
|
||||
region = obj['region']
|
||||
best_frame = best_frame[region[1]:region[3], region[0]:region[2]]
|
||||
if mqtt_config.snapshot_height:
|
||||
height = mqtt_config.snapshot_height
|
||||
width = int(height*best_frame.shape[1]/best_frame.shape[0])
|
||||
best_frame = cv2.resize(best_frame, dsize=(width, height), interpolation=cv2.INTER_AREA)
|
||||
|
||||
if self.camera_config[camera].snapshots.show_timestamp:
|
||||
time_to_show = datetime.datetime.fromtimestamp(obj['frame_time']).strftime("%m/%d/%Y %H:%M:%S")
|
||||
size = cv2.getTextSize(time_to_show, cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, thickness=2)
|
||||
text_width = size[0][0]
|
||||
text_height = size[0][1]
|
||||
desired_size = max(200, 0.33*best_frame.shape[1])
|
||||
font_scale = desired_size/text_width
|
||||
cv2.putText(best_frame, time_to_show, (5, best_frame.shape[0]-7), cv2.FONT_HERSHEY_SIMPLEX, fontScale=font_scale, color=(255, 255, 255), thickness=2)
|
||||
|
||||
ret, jpg = cv2.imencode('.jpg', best_frame)
|
||||
if ret:
|
||||
jpg_bytes = jpg.tobytes()
|
||||
self.client.publish(f"{self.topic_prefix}/{camera}/{obj['label']}/snapshot", jpg_bytes, retain=True)
|
||||
def snapshot(camera, obj: TrackedObject):
|
||||
self.client.publish(f"{self.topic_prefix}/{camera}/{obj['label']}/snapshot", obj.snapshot_jpg, retain=True)
|
||||
|
||||
def object_status(camera, object_name, status):
|
||||
self.client.publish(f"{self.topic_prefix}/{camera}/{object_name}", status, retain=False)
|
||||
|
||||
for camera in self.camera_config.keys():
|
||||
camera_state = CameraState(camera, self.camera_config[camera], self.frame_manager)
|
||||
for camera in self.config.cameras.keys():
|
||||
camera_state = CameraState(camera, self.config, self.frame_manager)
|
||||
camera_state.on('start', start)
|
||||
camera_state.on('update', update)
|
||||
camera_state.on('end', end)
|
||||
@ -398,14 +425,6 @@ class TrackedObjectProcessor(threading.Thread):
|
||||
camera_state.on('object_status', object_status)
|
||||
self.camera_states[camera] = camera_state
|
||||
|
||||
self.camera_data = defaultdict(lambda: {
|
||||
'best_objects': {},
|
||||
'object_status': defaultdict(lambda: defaultdict(lambda: 'OFF')),
|
||||
'tracked_objects': {},
|
||||
'current_frame': np.zeros((720,1280,3), np.uint8),
|
||||
'current_frame_time': 0.0,
|
||||
'object_id': None
|
||||
})
|
||||
# {
|
||||
# 'zone_name': {
|
||||
# 'person': ['camera_1', 'camera_2']
|
||||
@ -439,9 +458,9 @@ class TrackedObjectProcessor(threading.Thread):
|
||||
camera_state.update(frame_time, current_tracked_objects)
|
||||
|
||||
# update zone status for each label
|
||||
for zone in camera_state.config.zones.keys():
|
||||
for zone in self.config.cameras[camera].zones.keys():
|
||||
# get labels for current camera and all labels in current zone
|
||||
labels_for_camera = set([obj['label'] for obj in camera_state.tracked_objects.values() if zone in obj['zones'] and not obj['false_positive']])
|
||||
labels_for_camera = set([obj.obj_data['label'] for obj in camera_state.tracked_objects.values() if zone in obj.current_zones and not obj._false_positive])
|
||||
labels_to_check = labels_for_camera | set(self.zone_data[zone].keys())
|
||||
# for each label in zone
|
||||
for label in labels_to_check:
|
||||
|
||||
Loading…
Reference in New Issue
Block a user