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This commit is contained in:
+99
-260
@@ -1,8 +1,8 @@
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"""
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YOLOv11 Face Detector using ONNX Runtime with GPU support.
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YOLOv8 Face Detector using PyTorch with ROCm support.
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This module provides high-performance face detection using
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YOLOv11-face model with CUDA acceleration.
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YOLOv8-face model with AMD GPU (ROCm) acceleration.
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"""
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import os
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@@ -13,17 +13,17 @@ import numpy as np
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class YOLOFaceDetector:
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"""
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YOLOv11 face detector with ONNX Runtime GPU support.
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YOLOv8 face detector with PyTorch ROCm support.
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Features:
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- CUDA GPU acceleration
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- ROCm GPU acceleration for AMD GPUs
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- High accuracy face detection
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- NMS for overlapping detections
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- Automatic NMS for overlapping detections
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"""
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# Default model path relative to this file
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DEFAULT_MODEL = "yolov11n-face.onnx"
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DEFAULT_MODEL = "yolov8n-face-lindevs.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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@@ -33,9 +33,9 @@ class YOLOFaceDetector:
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):
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"""
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Initialize the YOLO face detector.
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Args:
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model_path: Path to ONNX model file. If None, uses default model.
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model_path: Path to PyTorch model file. If None, uses default model.
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conf_threshold: Confidence threshold for detections
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iou_threshold: IoU threshold for NMS
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input_size: Model input size (width, height)
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@@ -43,15 +43,17 @@ class YOLOFaceDetector:
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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._session = None
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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 session(self):
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"""Lazy-load ONNX Runtime session."""
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if self._session is None:
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import onnxruntime as ort
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def model(self):
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"""Lazy-load YOLO 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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# Determine model path
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if self._model_path is None:
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# Assuming models are in ../models relative to server/detector.py
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@@ -59,255 +61,92 @@ class YOLOFaceDetector:
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model_path = str(models_dir / self.DEFAULT_MODEL)
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else:
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model_path = self._model_path
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if not os.path.exists(model_path):
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raise FileNotFoundError(f"Model not found: {model_path}")
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# Configure providers (prefer CUDA)
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providers = []
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if 'CUDAExecutionProvider' in ort.get_available_providers():
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providers.append('CUDAExecutionProvider')
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print("[FaceMask] Using CUDA GPU for inference")
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providers.append('CPUExecutionProvider')
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# Create session
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sess_options = ort.SessionOptions()
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sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
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self._session = ort.InferenceSession(
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model_path,
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sess_options=sess_options,
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providers=providers,
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)
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# Detect device (ROCm GPU or CPU)
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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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# Load model (let Ultralytics handle device management)
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try:
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self._model = YOLO(model_path)
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# Don't call .to() - let predict() handle device assignment
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print(f"[FaceMask] Model loaded, will use 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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print(f"[FaceMask] YOLO model loaded: {model_path}")
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print(f"[FaceMask] Providers: {self._session.get_providers()}")
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return self._session
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print(f"[FaceMask] Device: {self._device}")
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return self._model
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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 faces 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 cv2
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original_height, original_width = frame.shape[:2]
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input_tensor = self._preprocess(frame)
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# print(f"[DEBUG] Input tensor shape: {input_tensor.shape}, Range: [{input_tensor.min():.3f}, {input_tensor.max():.3f}]", flush=True)
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# Run inference
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input_name = self.session.get_inputs()[0].name
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outputs = self.session.run(None, {input_name: input_tensor})
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raw_output = outputs[0]
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# print(f"[DEBUG] Raw output shape: {raw_output.shape}, Range: [{raw_output.min():.3f}, {raw_output.max():.3f}]", flush=True)
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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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# Fallback to CPU
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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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# Postprocess
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detections = self._postprocess(
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raw_output,
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original_width,
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original_height,
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)
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# print(f"[DEBUG] Detections found: {len(detections)}", flush=True)
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return detections
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def _preprocess(self, frame: np.ndarray) -> np.ndarray:
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"""Preprocess frame for YOLO input with letterbox resizing."""
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import cv2
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# Letterbox resize
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shape = frame.shape[:2] # current shape [height, width]
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new_shape = self.input_size
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# Scale ratio (new / old)
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r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
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# Compute padding
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ratio = r, r # width, height ratios
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new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
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dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
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dw /= 2 # divide padding into 2 sides
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dh /= 2
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if shape[::-1] != new_unpad: # resize
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frame = cv2.resize(frame, new_unpad, interpolation=cv2.INTER_LINEAR)
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top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
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left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
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# Add border
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frame = cv2.copyMakeBorder(frame, top, bottom, left, right, cv2.BORDER_CONSTANT, value=(114, 114, 114))
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# Store metadata for postprocessing
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self._last_letterbox_meta = {'ratio': ratio, 'dwdh': (dw, dh)}
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# Convert BGR to RGB
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rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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# Normalize to [0, 1]
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normalized = rgb.astype(np.float32) / 255.0
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# Transpose to CHW format
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transposed = np.transpose(normalized, (2, 0, 1))
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# Add batch dimension
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batched = np.expand_dims(transposed, axis=0)
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return batched
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def _postprocess(
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self,
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output: np.ndarray,
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original_width: int,
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original_height: int,
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) -> List[Tuple[int, int, int, int, float]]:
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"""
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Postprocess YOLO output to get detections.
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"""
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# Output shape: [1, num_detections, 5+] where 5 = x_center, y_center, w, h, conf
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# Handle different output formats
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if output.shape[1] < output.shape[2]:
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# Format: [1, 5+, num_detections] - transpose
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output = np.transpose(output[0], (1, 0))
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else:
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output = output[0]
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# Debug confidence stats
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# if output.shape[1] >= 5:
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# max_conf = output[:, 4].max()
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# print(f"[DEBUG] Max confidence in raw output: {max_conf:.4f}", flush=True)
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# Filter by confidence
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confidences = output[:, 4]
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mask = confidences > self.conf_threshold
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filtered = output[mask]
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if len(filtered) == 0:
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return []
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# Get letterbox metadata
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if hasattr(self, '_last_letterbox_meta') and self._last_letterbox_meta:
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ratio = self._last_letterbox_meta['ratio']
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dw, dh = self._last_letterbox_meta['dwdh']
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# Extract coordinates
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x_center = filtered[:, 0]
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y_center = filtered[:, 1]
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width = filtered[:, 2]
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height = filtered[:, 3]
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confidences = filtered[:, 4]
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# Convert center to corner
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x1 = x_center - width / 2
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y1 = y_center - height / 2
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x2 = x_center + width / 2
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y2 = y_center + height / 2
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# Adjust for letterbox padding
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x1 -= dw
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y1 -= dh
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x2 -= dw
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y2 -= dh
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# Adjust for resizing
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x1 /= ratio[0]
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y1 /= ratio[1]
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x2 /= ratio[0]
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y2 /= ratio[1]
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# Clip to image bounds
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x1 = np.clip(x1, 0, original_width)
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y1 = np.clip(y1, 0, original_height)
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x2 = np.clip(x2, 0, original_width)
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y2 = np.clip(y2, 0, original_height)
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# Convert back to x, y, w, h
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final_x = x1
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final_y = y1
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final_w = x2 - x1
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final_h = y2 - y1
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else:
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# Fallback for non-letterbox (legacy)
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scale_x = original_width / self.input_size[0]
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scale_y = original_height / self.input_size[1]
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x_center = filtered[:, 0] * scale_x
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y_center = filtered[:, 1] * scale_y
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width = filtered[:, 2] * scale_x
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height = filtered[:, 3] * scale_y
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confidences = filtered[:, 4]
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final_x = x_center - width / 2
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final_y = y_center - height / 2
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final_w = width
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final_h = height
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# Apply NMS
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boxes = np.stack([final_x, final_y, final_w, final_h], axis=1)
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indices = self._nms(boxes, confidences, self.iou_threshold)
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# Format output
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# Extract detections
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detections = []
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for i in indices:
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x = int(final_x[i])
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y = int(final_y[i])
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w = int(final_w[i])
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h = int(final_h[i])
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conf = float(confidences[i])
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detections.append((x, y, w, h, conf))
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if len(results) > 0 and results[0].boxes is not None:
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boxes = results[0].boxes
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for box in boxes:
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# Get coordinates in xyxy format
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x1, y1, x2, y2 = box.xyxy[0].cpu().numpy()
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conf = float(box.conf[0].cpu().numpy())
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# Convert to x, y, width, height
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x = int(x1)
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y = int(y1)
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w = int(x2 - x1)
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h = int(y2 - y1)
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detections.append((x, y, w, h, conf))
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return detections
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def _nms(
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self,
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boxes: np.ndarray,
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scores: np.ndarray,
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iou_threshold: float,
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) -> List[int]:
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"""Non-Maximum Suppression."""
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x1 = boxes[:, 0]
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y1 = boxes[:, 1]
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x2 = x1 + boxes[:, 2]
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y2 = y1 + boxes[:, 3]
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areas = boxes[:, 2] * boxes[:, 3]
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order = scores.argsort()[::-1]
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keep = []
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while len(order) > 0:
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i = order[0]
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keep.append(i)
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if len(order) == 1:
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break
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xx1 = np.maximum(x1[i], x1[order[1:]])
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yy1 = np.maximum(y1[i], y1[order[1:]])
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xx2 = np.minimum(x2[i], x2[order[1:]])
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yy2 = np.minimum(y2[i], y2[order[1:]])
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w = np.maximum(0, xx2 - xx1)
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h = np.maximum(0, yy2 - yy1)
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inter = w * h
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iou = inter / (areas[i] + areas[order[1:]] - inter)
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inds = np.where(iou <= iou_threshold)[0]
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order = order[inds + 1]
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return keep
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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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@@ -317,29 +156,29 @@ class YOLOFaceDetector:
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) -> np.ndarray:
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"""
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Generate a mask image from face 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 face 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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# Scale the bounding box
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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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# Draw ellipse for natural face shape
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cv2.ellipse(
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mask,
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@@ -350,12 +189,12 @@ class YOLOFaceDetector:
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255, # color (white)
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-1, # filled
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)
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# Apply Gaussian blur for feathering
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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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+2
-2
@@ -74,8 +74,8 @@ def process_video_task(task_id: str, req: GenerateRequest):
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conf_threshold=req.conf_threshold,
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iou_threshold=req.iou_threshold
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)
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# Ensure session is loaded
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_ = detector.session
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# Ensure model is loaded
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_ = detector.model
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# Open video
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cap = cv2.VideoCapture(req.video_path)
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