314 lines
10 KiB
Python
314 lines
10 KiB
Python
"""
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YOLOv8 Pose Head Detector using PyTorch with ROCm support.
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Detects human heads from all angles (frontal, profile, rear) by using
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YOLOv8 pose estimation and extracting head bounding boxes from keypoints.
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"""
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import os
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from typing import List, Tuple, Optional
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import numpy as np
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# COCO pose keypoint indices
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_HEAD_KP = [0, 1, 2, 3, 4] # nose, left_eye, right_eye, left_ear, right_ear
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_SHOULDER_KP = [5, 6] # left_shoulder, right_shoulder
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_KP_CONF_THRESH = 0.3
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def _head_bbox_from_pose(
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kp_xy: np.ndarray,
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kp_conf: np.ndarray,
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person_x1: float,
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person_y1: float,
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person_x2: float,
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person_y2: float,
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) -> Tuple[int, int, int, int]:
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"""
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Estimate head bounding box (x, y, w, h) from COCO pose keypoints.
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Strategy:
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1. Use head keypoints (0-4: nose, eyes, ears) if visible.
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2. Fall back to shoulder keypoints (5-6) to infer head position.
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3. Last resort: use top of the person bounding box.
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"""
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person_w = max(person_x2 - person_x1, 1.0)
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# --- Step 1: head keypoints ---
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visible_head = [
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(float(kp_xy[i][0]), float(kp_xy[i][1]))
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for i in _HEAD_KP
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if float(kp_conf[i]) > _KP_CONF_THRESH
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]
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if visible_head:
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xs = [p[0] for p in visible_head]
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ys = [p[1] for p in visible_head]
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kp_x1, kp_y1 = min(xs), min(ys)
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kp_x2, kp_y2 = max(xs), max(ys)
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span = max(kp_x2 - kp_x1, kp_y2 - kp_y1, 1.0)
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cx = (kp_x1 + kp_x2) / 2.0
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cy = (kp_y1 + kp_y2) / 2.0
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# Head radius: inter-landmark span ≈ 80% of head width, so expand by ~1.25
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# Shift center upward slightly to include scalp
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r = max(span * 1.25, person_w * 0.20)
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x1 = int(cx - r)
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y1 = int(cy - r * 1.15) # extra margin above (scalp)
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x2 = int(cx + r)
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y2 = int(cy + r * 0.85) # less margin below (chin)
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return x1, y1, x2 - x1, y2 - y1
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# --- Step 2: shoulder keypoints ---
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visible_shoulder = [
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(float(kp_xy[i][0]), float(kp_xy[i][1]))
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for i in _SHOULDER_KP
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if float(kp_conf[i]) > _KP_CONF_THRESH
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]
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if visible_shoulder:
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cx = sum(p[0] for p in visible_shoulder) / len(visible_shoulder)
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cy_sh = sum(p[1] for p in visible_shoulder) / len(visible_shoulder)
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if len(visible_shoulder) == 2:
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sh_width = abs(visible_shoulder[1][0] - visible_shoulder[0][0])
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else:
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sh_width = person_w * 0.5
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r = max(sh_width * 0.5, person_w * 0.20)
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cy = cy_sh - r * 1.3 # head center is above shoulders
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x1 = int(cx - r)
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y1 = int(cy - r)
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x2 = int(cx + r)
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y2 = int(cy + r)
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return x1, y1, x2 - x1, y2 - y1
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# --- Step 3: person bbox top ---
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r = max(person_w * 0.35, 20.0)
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cx = (person_x1 + person_x2) / 2.0
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x1 = int(cx - r)
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y1 = int(person_y1)
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x2 = int(cx + r)
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y2 = int(person_y1 + r * 2.0)
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return x1, y1, x2 - x1, y2 - y1
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class YOLOPoseHeadDetector:
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"""
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Head detector using YOLOv8 pose estimation with PyTorch ROCm support.
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Extracts head bounding boxes from COCO pose keypoints (nose, eyes, ears)
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so that detection works regardless of the person's facing direction.
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"""
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# Standard Ultralytics model — auto-downloaded on first use
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DEFAULT_MODEL = os.path.join("models", "yolov8n-pose.pt")
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def __init__(
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self,
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model_path: Optional[str] = None,
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conf_threshold: float = 0.25,
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iou_threshold: float = 0.45,
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input_size: Tuple[int, int] = (640, 640),
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):
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self.conf_threshold = conf_threshold
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self.iou_threshold = iou_threshold
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self.input_size = input_size
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self._model = None
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self._model_path = model_path
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self._device = None
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@property
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def model(self):
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"""Lazy-load YOLO pose model."""
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if self._model is None:
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from ultralytics import YOLO
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import torch
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# Use provided path or let Ultralytics auto-download the default
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if self._model_path is not None:
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if not os.path.exists(self._model_path):
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raise FileNotFoundError(f"Model not found: {self._model_path}")
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model_path = self._model_path
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else:
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model_path = self.DEFAULT_MODEL
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os.makedirs(os.path.dirname(model_path), exist_ok=True)
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if torch.cuda.is_available():
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self._device = 'cuda'
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device_name = torch.cuda.get_device_name(0)
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print(f"[FaceMask] Using ROCm GPU for inference: {device_name}")
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else:
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self._device = 'cpu'
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print("[FaceMask] Using CPU for inference (ROCm GPU not available)")
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try:
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self._model = YOLO(model_path)
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print(f"[FaceMask] Pose model loaded: {model_path}")
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print(f"[FaceMask] Device: {self._device}")
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except Exception as e:
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print(f"[FaceMask] Error loading model: {e}")
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import traceback
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traceback.print_exc()
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raise
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return self._model
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def _results_to_detections(self, result) -> List[Tuple[int, int, int, int, float]]:
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"""Convert a single YOLO pose result to (x, y, w, h, conf) tuples."""
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detections = []
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if result.boxes is None or result.keypoints is None:
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return detections
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boxes = result.boxes
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keypoints = result.keypoints
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for i, box in enumerate(boxes):
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conf = float(box.conf[0].cpu().numpy())
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x1, y1, x2, y2 = box.xyxy[0].cpu().numpy()
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# Extract keypoints for this person
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kp_data = keypoints.data[i].cpu().numpy() # shape (17, 3): x, y, conf
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kp_xy = kp_data[:, :2]
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kp_conf = kp_data[:, 2]
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hx, hy, hw, hh = _head_bbox_from_pose(
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kp_xy, kp_conf,
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float(x1), float(y1), float(x2), float(y2),
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)
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detections.append((hx, hy, hw, hh, conf))
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return detections
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def detect(self, frame: np.ndarray) -> List[Tuple[int, int, int, int, float]]:
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"""
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Detect heads in a frame.
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Args:
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frame: BGR image as numpy array (H, W, C)
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Returns:
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List of detections as (x, y, width, height, confidence)
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"""
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import torch
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print(f"[FaceMask] Inference device: {self._device}, CUDA available: {torch.cuda.is_available()}")
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try:
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results = self.model.predict(
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frame,
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conf=self.conf_threshold,
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iou=self.iou_threshold,
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imgsz=self.input_size[0],
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verbose=False,
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device=self._device,
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)
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except Exception as e:
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print(f"[FaceMask] ERROR during inference: {e}")
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import traceback
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traceback.print_exc()
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print("[FaceMask] Falling back to CPU inference...")
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self._device = 'cpu'
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results = self.model.predict(
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frame,
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conf=self.conf_threshold,
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iou=self.iou_threshold,
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imgsz=self.input_size[0],
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verbose=False,
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device='cpu',
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)
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if results:
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return self._results_to_detections(results[0])
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return []
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def detect_batch(self, frames: List[np.ndarray]) -> List[List[Tuple[int, int, int, int, float]]]:
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"""
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Detect heads in multiple frames at once (batch processing).
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Args:
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frames: List of BGR images as numpy arrays (H, W, C)
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Returns:
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List of detection lists, one per frame.
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Each detection: (x, y, width, height, confidence)
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"""
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if not frames:
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return []
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try:
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results = self.model.predict(
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frames,
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conf=self.conf_threshold,
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iou=self.iou_threshold,
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imgsz=self.input_size[0],
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verbose=False,
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device=self._device,
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)
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except Exception as e:
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print(f"[FaceMask] ERROR during batch inference: {e}")
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import traceback
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traceback.print_exc()
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print("[FaceMask] Falling back to CPU inference...")
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self._device = 'cpu'
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results = self.model.predict(
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frames,
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conf=self.conf_threshold,
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iou=self.iou_threshold,
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imgsz=self.input_size[0],
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verbose=False,
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device='cpu',
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)
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return [self._results_to_detections(r) for r in results]
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def generate_mask(
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self,
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frame_shape: Tuple[int, int, int],
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detections: List[Tuple[int, int, int, int, float]],
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mask_scale: float = 1.5,
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feather_radius: int = 20,
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) -> np.ndarray:
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"""
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Generate a mask image from head detections.
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Args:
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frame_shape: Shape of the original frame (height, width, channels)
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detections: List of head detections (x, y, w, h, conf)
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mask_scale: Scale factor for mask region
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feather_radius: Radius for edge feathering
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Returns:
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Grayscale mask image (white = blur, black = keep)
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"""
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import cv2
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height, width = frame_shape[:2]
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mask = np.zeros((height, width), dtype=np.uint8)
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for (x, y, w, h, conf) in detections:
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center_x = x + w // 2
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center_y = y + h // 2
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scaled_w = int(w * mask_scale)
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scaled_h = int(h * mask_scale)
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cv2.ellipse(
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mask,
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(center_x, center_y),
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(scaled_w // 2, scaled_h // 2),
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0, 0, 360,
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255, -1,
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)
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if feather_radius > 0 and len(detections) > 0:
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kernel_size = feather_radius * 2 + 1
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mask = cv2.GaussianBlur(mask, (kernel_size, kernel_size), 0)
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return mask
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# Singleton instance
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_detector: Optional[YOLOPoseHeadDetector] = None
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def get_detector(**kwargs) -> YOLOPoseHeadDetector:
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"""Get or create the global YOLO pose head detector instance."""
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global _detector
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if _detector is None:
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_detector = YOLOPoseHeadDetector(**kwargs)
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return _detector
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