Cherokee
322f8d9b71
- Optimization 1: Downscale frames to 640px max-side before YOLOv8-seg inference in segmenter.py, resize masks back to original resolution (~13% speedup from reduced post-processing) - Optimization 2: Store bool masks instead of composited BGR frames in MaskActor.frames (~97% memory reduction per tracked frame, from ~6MB to ~200KB per entry). Green screen now applied on-the-fly during encoding rather than during tracking. - Optimization 3: Add skip_every_n parameter to VideoActorExtractor (default 1=all, 2=every other frame, up to 30). Users can trade precision for speed. - Add per-substep timing (Detection vs Tracking) printed after frame loop
128 lines
3.9 KiB
Python
128 lines
3.9 KiB
Python
"""YOLOv8 person segmentation module."""
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import os
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import cv2
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import numpy as np
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from typing import List, Tuple
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from core.config import (
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DEFAULT_SEG_MODEL,
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DEFAULT_CONF_THRESHOLD,
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DEFAULT_IOU_THRESHOLD,
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)
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class PersonSegmenter:
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"""YOLOv8-seg based person segmenter."""
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def __init__(
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self,
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model_path: str = DEFAULT_SEG_MODEL,
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device: str = "auto",
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conf_threshold: float = DEFAULT_CONF_THRESHOLD,
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iou_threshold: float = DEFAULT_IOU_THRESHOLD,
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):
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from ultralytics import YOLO
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self.conf_threshold = conf_threshold
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self.iou_threshold = iou_threshold
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# Resolve device — same pattern as PersonDetector in detector.py
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if device == "auto":
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try:
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import torch
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device = "mps" if torch.backends.mps.is_available() else "cpu"
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if torch.cuda.is_available():
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device = "cuda"
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except ImportError:
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device = "cpu"
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self.device = device
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# Load model (download if needed — same behavior as yolov8n.pt)
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resolved_path = os.path.expanduser(model_path)
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if not os.path.exists(resolved_path):
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print(
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f"[PersonSegmenter] Downloading YOLO segmentation model: {model_path}"
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)
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self.model = YOLO(resolved_path)
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print(f"[PersonSegmenter] Loaded {model_path} on {device}")
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def detect_masks(self, frame: np.ndarray) -> List[Tuple[int, np.ndarray]]:
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"""
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Detect person segmentation masks in a single frame.
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For performance, the frame is downscaled so its longest side is
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``YOLO_INFER_SIZE`` (640 px) before inference. Masks are then
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resized back to the original frame resolution so that callers
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receive full-resolution boolean masks.
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Args:
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frame: BGR image (numpy array)
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Returns:
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list of (track_id_placeholder, mask_2d) tuples for each person detected
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track_id_placeholder is just the detection index (0, 1, 2, ...)
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mask_2d is a boolean numpy array of shape (H, W) where True = person pixel
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"""
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from core.config import YOLO_INFER_SIZE
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orig_h, orig_w = frame.shape[:2]
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# Downscale frame so longest side == YOLO_INFER_SIZE
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longest = max(orig_h, orig_w)
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if longest > YOLO_INFER_SIZE:
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scale = YOLO_INFER_SIZE / longest
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new_w = int(orig_w * scale)
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new_h = int(orig_h * scale)
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infer_frame = cv2.resize(
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frame, (new_w, new_h), interpolation=cv2.INTER_AREA
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)
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else:
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infer_frame = frame
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results = self.model(
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infer_frame,
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classes=[0], # person only
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conf=self.conf_threshold,
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iou=self.iou_threshold,
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verbose=False,
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device=self.device,
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)
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detections = []
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result = results[0]
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if result.masks is None:
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return detections
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masks = result.masks.data.cpu().numpy() # (N, mask_h, mask_w)
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for idx, mask_raw in enumerate(masks):
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# Resize mask to original frame size
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mask_img = (mask_raw * 255).astype(np.uint8)
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mask_img = cv2.resize(
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mask_img, (orig_w, orig_h), interpolation=cv2.INTER_NEAREST
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)
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mask_resized = mask_img > 127
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detections.append((idx, mask_resized))
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return detections
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def apply_mask(self, frame: np.ndarray, mask: np.ndarray) -> np.ndarray:
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"""
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Apply person mask to frame — set non-person pixels to green.
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Args:
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frame: BGR image (numpy array)
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mask: boolean array (H, W), True = person pixel
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Returns:
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BGR image where non-person pixels are set to green (0, 255, 0)
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"""
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result = frame.copy()
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result[~mask] = (0, 255, 0)
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return result
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