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https://github.com/blakeblackshear/frigate.git
synced 2026-02-07 11:45:24 +03:00
Use object grid to reduce score for objects with unexpected sizes
This commit is contained in:
Nick Mowen
parent
16dc9f4bf7
commit
21c99cc955
@ -27,6 +27,8 @@ from frigate.util.image import (
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logger = logging.getLogger(__name__)
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GRID_SIZE = 8
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MIN_OBJECT_DATA_POINTS = 5
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MIN_STD_ACCURACY = 0.5
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def get_camera_regions_grid(
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@ -36,16 +38,24 @@ def get_camera_regions_grid(
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# get grid from db if available
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try:
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regions: Regions = Regions.select().where(Regions.camera == name).get()
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grid = regions.grid
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last_update = regions.last_update
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# check if old style grid is used and rebuild
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if regions.grid[0][0].get("sizes") is not None:
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grid = []
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last_update = 0
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else:
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grid = regions.grid
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last_update = regions.last_update
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except DoesNotExist:
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grid = []
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last_update = 0
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if last_update == 0:
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for x in range(GRID_SIZE):
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row = []
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for y in range(GRID_SIZE):
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row.append({"sizes": []})
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row.append({"region": {"sizes": []}})
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grid.append(row)
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last_update = 0
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# get events for timeline entries
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events = (
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@ -86,6 +96,9 @@ def get_camera_regions_grid(
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box = t["data"]["box"]
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###
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# save region to grid
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###
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# calculate centroid position
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x = box[0] + (box[2] / 2)
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y = box[1] + (box[3] / 2)
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@ -103,24 +116,40 @@ def get_camera_regions_grid(
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1.35,
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)
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# save width of region to grid as relative
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grid[x_pos][y_pos]["sizes"].append(
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grid[x_pos][y_pos]["region"]["sizes"].append(
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(calculated_region[2] - calculated_region[0]) / width
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)
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###
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# save object size to grid
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###
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label = t["data"]["label"]
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x = box[0] + (box[2] / 2)
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y = box[1] + box[3]
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x_pos = int(x * GRID_SIZE)
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y_pos = int(y * GRID_SIZE)
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if grid[x_pos][y_pos].get(label):
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grid[x_pos][y_pos][label]["sizes"].append(box[2] * box[3])
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else:
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grid[x_pos][y_pos][label] = {"sizes": [box[2] * box[3]]}
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for x in range(GRID_SIZE):
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for y in range(GRID_SIZE):
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cell = grid[x][y]
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if len(cell["sizes"]) == 0:
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continue
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std_dev = np.std(cell["sizes"])
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mean = np.mean(cell["sizes"])
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logger.debug(f"std dev: {std_dev} mean: {mean}")
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cell["x"] = x
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cell["y"] = y
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cell["std_dev"] = std_dev
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cell["mean"] = mean
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for key in cell.keys():
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if key in ["x", "y"] or len(cell[key]["sizes"]) == 0:
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continue
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std_dev = np.std(cell[key]["sizes"])
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mean = np.mean(cell[key]["sizes"])
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logger.debug(f"{x},{y} :: {key} -> std dev: {std_dev} mean: {mean}")
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cell[key]["std_dev"] = std_dev
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cell[key]["mean"] = mean
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# update db with new grid
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region = {
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@ -172,7 +201,7 @@ def get_region_from_grid(
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grid_x = int(centroid[0] / frame_shape[1] * GRID_SIZE)
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grid_y = int(centroid[1] / frame_shape[0] * GRID_SIZE)
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cell = region_grid[grid_x][grid_y]
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cell = region_grid[grid_x][grid_y]["region"]
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# if there is no known data, get standard region for motion box
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if not cell or not cell["sizes"]:
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@ -424,17 +453,19 @@ def get_startup_regions(
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"""Get a list of regions to run on startup."""
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# return 8 most popular regions for the camera
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all_cells = np.concatenate(region_grid).flat
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startup_cells = sorted(all_cells, key=lambda c: len(c["sizes"]), reverse=True)[0:8]
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startup_cells = sorted(
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all_cells, key=lambda c: len(c["region"]["sizes"]), reverse=True
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)[0:8]
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regions = []
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for cell in startup_cells:
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# rest of the cells are empty
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if not cell["sizes"]:
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if not cell["region"]["sizes"]:
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break
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x = frame_shape[1] / GRID_SIZE * (0.5 + cell["x"])
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y = frame_shape[0] / GRID_SIZE * (0.5 + cell["y"])
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size = cell["mean"] * frame_shape[1]
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size = cell["region"]["mean"] * frame_shape[1]
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regions.append(
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calculate_region(
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frame_shape,
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@ -544,3 +575,52 @@ def reduce_detections(
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return get_consolidated_object_detections(
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reduce_overlapping_detections(all_detections)
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)
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def validate_object_size_with_grid(
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detect_config: DetectConfig,
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label: str,
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score: float,
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box: tuple[int],
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region_grid: list[list[dict[str, any]]],
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) -> tuple[bool, float]:
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"""Validate if the object is within expected size and return new size if not."""
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# get object position in grid with bottom center
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x = box[0] + ((box[2] - box[0]) / 2)
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y = box[3]
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x_pos = int(x / detect_config.width * GRID_SIZE)
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y_pos = int(y / detect_config.height * GRID_SIZE)
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cell = region_grid[x_pos][y_pos]
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# get data specific for this object type
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object_data = cell.get(label)
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# check if enough data in grid to process
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if not object_data or len(object_data["sizes"]) < MIN_OBJECT_DATA_POINTS:
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return (False, 0.0)
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# check if data is accurate enough to process
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std_dev = object_data["std_dev"]
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mean = object_data["mean"]
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if std_dev / mean > MIN_STD_ACCURACY:
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return (False, 0.0)
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relative_area = ((box[2] - box[0]) / detect_config.width) * (
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(box[3] - box[1]) / detect_config.height
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)
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diff = abs(mean - relative_area)
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# if object area is within 1 std deviation, no changes should be made
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if diff < std_dev:
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return (False, 0.0)
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logger.debug(f"Comparing for {x_pos},{y_pos} {label} {relative_area} vs {mean} +- {std_dev}")
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# calculate how many std deviations away the area is
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factor = relative_area / std_dev
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# reduce object score by 5% for every factor
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new_score = score - factor * 0.05
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return (True, new_score)
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@ -11,7 +11,7 @@ import time
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import cv2
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from setproctitle import setproctitle
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from frigate.config import CameraConfig, DetectConfig, ModelConfig
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from frigate.config import CameraConfig, DetectConfig, FilterConfig, ModelConfig
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from frigate.const import (
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ALL_ATTRIBUTE_LABELS,
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ATTRIBUTE_LABEL_MAP,
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@ -44,6 +44,7 @@ from frigate.util.object import (
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intersects_any,
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is_object_filtered,
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reduce_detections,
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validate_object_size_with_grid,
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)
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from frigate.util.services import listen
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@ -459,10 +460,11 @@ def detect(
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detect_config: DetectConfig,
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object_detector,
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frame,
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model_config,
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model_config: ModelConfig,
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region,
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objects_to_track,
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object_filters,
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object_filters: dict[str, FilterConfig],
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region_grid: list[list[dict[str, any]]],
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):
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tensor_input = create_tensor_input(frame, model_config, region)
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@ -484,18 +486,35 @@ def detect(
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height = y_max - y_min
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area = width * height
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ratio = width / max(1, height)
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label = d[0]
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score = d[1]
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box = (x_min, y_min, x_max, y_max)
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# check if object is outside range of expected sizes
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# only check objects with scores that are > min_score
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if score >= object_filters[label].min_score:
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outside_expected_size, new_score = validate_object_size_with_grid(
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detect_config, d[0], score, box, region_grid
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)
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if outside_expected_size:
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logger.debug(f"{label} {box} has a new score {score} -> {new_score}")
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score = new_score
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det = (
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d[0],
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d[1],
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(x_min, y_min, x_max, y_max),
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label,
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score,
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box,
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area,
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ratio,
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region,
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)
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# apply object filters
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if is_object_filtered(det, objects_to_track, object_filters):
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continue
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detections.append(det)
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return detections
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@ -690,6 +709,7 @@ def process_frames(
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region,
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objects_to_track,
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object_filters,
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region_grid,
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)
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)
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