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https://github.com/blakeblackshear/frigate.git
synced 2024-12-19 19:06:16 +01:00
parent
9e987fdebc
commit
80627e4989
@ -13,8 +13,7 @@ from pydantic import BaseModel, Extra, Field, validator
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from pydantic.fields import PrivateAttr
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from frigate.const import BASE_DIR, CACHE_DIR, YAML_EXT
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from frigate.edgetpu import load_labels
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from frigate.util import create_mask, deep_merge
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from frigate.util import create_mask, deep_merge, load_labels
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logger = logging.getLogger(__name__)
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@ -640,7 +639,7 @@ class ModelConfig(FrigateBaseModel):
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return self._merged_labelmap
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@property
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def colormap(self) -> Dict[int, tuple[int, int, int]]:
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def colormap(self) -> Dict[int, Tuple[int, int, int]]:
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return self._colormap
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def __init__(self, **config):
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@ -13,31 +13,11 @@ import tflite_runtime.interpreter as tflite
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from setproctitle import setproctitle
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from tflite_runtime.interpreter import load_delegate
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from frigate.util import EventsPerSecond, SharedMemoryFrameManager, listen
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from frigate.util import EventsPerSecond, SharedMemoryFrameManager, listen, load_labels
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logger = logging.getLogger(__name__)
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def load_labels(path, encoding="utf-8"):
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"""Loads labels from file (with or without index numbers).
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Args:
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path: path to label file.
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encoding: label file encoding.
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Returns:
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Dictionary mapping indices to labels.
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"""
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with open(path, "r", encoding=encoding) as f:
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lines = f.readlines()
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if not lines:
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return {}
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if lines[0].split(" ", maxsplit=1)[0].isdigit():
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pairs = [line.split(" ", maxsplit=1) for line in lines]
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return {int(index): label.strip() for index, label in pairs}
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else:
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return {index: line.strip() for index, line in enumerate(lines)}
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class ObjectDetector(ABC):
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@abstractmethod
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def detect(self, tensor_input, threshold=0.4):
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@ -359,9 +359,10 @@ def best(camera_name, label):
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crop = bool(request.args.get("crop", 0, type=int))
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if crop:
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box = best_object.get("box", (0, 0, 300, 300))
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box_size = 300
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box = best_object.get("box", (0, 0, box_size, box_size))
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region = calculate_region(
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best_frame.shape, box[0], box[1], box[2], box[3], 1.1
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best_frame.shape, box[0], box[1], box[2], box[3], box_size, multiplier=1.1
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)
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best_frame = best_frame[region[1] : region[3], region[0] : region[2]]
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@ -107,7 +107,7 @@ def create_mqtt_client(config: FrigateConfig, camera_metrics):
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+ str(rc)
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)
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logger.info("MQTT connected")
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logger.debug("MQTT connected")
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client.subscribe(f"{mqtt_config.topic_prefix}/#")
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client.publish(mqtt_config.topic_prefix + "/available", "online", retain=True)
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@ -18,12 +18,12 @@ import numpy as np
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from frigate.config import CameraConfig, SnapshotsConfig, RecordConfig, FrigateConfig
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from frigate.const import CACHE_DIR, CLIPS_DIR, RECORD_DIR
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from frigate.edgetpu import load_labels
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from frigate.util import (
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SharedMemoryFrameManager,
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calculate_region,
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draw_box_with_label,
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draw_timestamp,
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load_labels,
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)
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logger = logging.getLogger(__name__)
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@ -264,8 +264,9 @@ class TrackedObject:
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if crop:
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box = self.thumbnail_data["box"]
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box_size = 300
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region = calculate_region(
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best_frame.shape, box[0], box[1], box[2], box[3], 1.1
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best_frame.shape, box[0], box[1], box[2], box[3], box_size, multiplier=1.1
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)
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best_frame = best_frame[region[1] : region[3], region[0] : region[2]]
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@ -189,12 +189,12 @@ def draw_box_with_label(
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)
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def calculate_region(frame_shape, xmin, ymin, xmax, ymax, multiplier=2):
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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 300
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if size < 300:
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size = 300
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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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@ -601,6 +601,24 @@ def add_mask(mask, mask_img):
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)
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cv2.fillPoly(mask_img, pts=[contour], color=(0))
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def load_labels(path, encoding="utf-8"):
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"""Loads labels from file (with or without index numbers).
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Args:
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path: path to label file.
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encoding: label file encoding.
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Returns:
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Dictionary mapping indices to labels.
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"""
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with open(path, "r", encoding=encoding) as f:
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lines = f.readlines()
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if not lines:
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return {}
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if lines[0].split(" ", maxsplit=1)[0].isdigit():
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pairs = [line.split(" ", maxsplit=1) for line in lines]
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return {int(index): label.strip() for index, label in pairs}
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else:
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return {index: line.strip() for index, line in enumerate(lines)}
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class FrameManager(ABC):
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@abstractmethod
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@ -529,15 +529,16 @@ def process_frames(
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# combine motion boxes with known locations of existing objects
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combined_boxes = reduce_boxes(motion_boxes + tracked_object_boxes)
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region_min_size = max(model_shape[0], model_shape[1])
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# compute regions
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regions = [
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calculate_region(frame_shape, a[0], a[1], a[2], a[3], 1.2)
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calculate_region(frame_shape, a[0], a[1], a[2], a[3], region_min_size, multiplier=1.2)
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for a in combined_boxes
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]
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# consolidate regions with heavy overlap
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regions = [
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calculate_region(frame_shape, a[0], a[1], a[2], a[3], 1.0)
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calculate_region(frame_shape, a[0], a[1], a[2], a[3], region_min_size, multiplier=1.0)
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for a in reduce_boxes(regions, 0.4)
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]
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@ -596,7 +597,7 @@ def process_frames(
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box = obj[2]
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# calculate a new region that will hopefully get the entire object
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region = calculate_region(
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frame_shape, box[0], box[1], box[2], box[3]
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frame_shape, box[0], box[1], box[2], box[3], region_min_size
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)
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regions.append(region)
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