将算法独立出来
This commit is contained in:
467
src/face_recognition_algorithm.py
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467
src/face_recognition_algorithm.py
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# face_recognition_algorithm.py
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import cv2
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import numpy as np
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import time
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from insightface.app import FaceAnalysis
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from typing import List, Dict, Tuple, Optional
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import os
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import glob
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class FaceRecognitionAlgorithm:
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"""
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人脸识别核心算法类
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包含人脸检测、识别、质量评估等核心算法
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"""
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def __init__(self, model_name: str = 'buffalo_l', use_gpu: bool = True, use_npu: bool = False,
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npu_device_id: int = 0):
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# 设备配置映射（NPU采用华为指定的完整参数）
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self.DEVICE_CONFIG = {
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"cpu": (['CPUExecutionProvider'], -1),
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"gpu": (['CUDAExecutionProvider'], 0),
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"npu": (
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[
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(
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"CANNExecutionProvider",
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{
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"device_id": npu_device_id,
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"arena_extend_strategy": "kNextPowerOfTwo",
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"npu_mem_limit": 16 * 1024 * 1024 * 1024,
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"op_select_impl_mode": "high_precision",
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"precision_mode": "allow_fp32_to_fp16",
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"enable_cann_graph": True,
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},
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),
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"CPUExecutionProvider"
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],
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npu_device_id
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)
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}
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# 质量阈值设置
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self.det_size = 640 # 320快速 640中等 1280慢
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# 默认配置 - blacklist
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self.list_mode = "blacklist" # "blacklist" 或 "whitelist"
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self.det_threshold = 0.5 # 人脸置信度
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self.clarity_threshold = 100.0 # 清晰度阈值，低于此值认为人脸模糊
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self.min_face_size = 20 # 最小人脸像素尺寸
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self.pitch_threshold = 90 #
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self.yaw_threshold = 90 #
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self.quality_threshold = 0.6 # 质量得分阈值
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self.similarity_threshold = 0.3
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# 根据设备类型选择配置
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if use_npu:
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device_type = "npu"
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print(f"✅ 使用NPU设备，设备ID: {npu_device_id}")
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elif use_gpu:
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device_type = "gpu"
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print("✅ 使用GPU设备")
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else:
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device_type = "cpu"
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print("✅ 使用CPU设备")
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providers, ctx_id = self.DEVICE_CONFIG[device_type]
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# 初始化人脸识别模型
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self.app = FaceAnalysis(name=model_name, providers=providers)
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self.app.prepare(
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ctx_id=ctx_id,
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det_thresh=self.det_threshold,
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det_size=(self.det_size, self.det_size)
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)
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# 名单相关变量
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self.registered_faces = {} # {name: embedding}
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print(f"✅ 人脸识别算法初始化完成 - 设备: {device_type.upper()}")
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def set_list_mode(self, mode: str):
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"""设置名单模式"""
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if mode.lower() in ["blacklist", "whitelist"]:
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self.list_mode = mode.lower()
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print(f"✅ 名单模式设置为: {self.list_mode}")
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else:
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print("❌ 无效的名单模式，请使用 'blacklist' 或 'whitelist'")
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def set_det_threshold(self, threshold: float):
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"""设置检测阈值"""
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self.det_threshold = threshold
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print(f"✅ 检测阈值设置为: {threshold}")
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def set_similarity_threshold(self, threshold: float):
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"""设置相似度阈值"""
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self.similarity_threshold = threshold
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print(f"✅ 相似度阈值设置为: {threshold}")
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def set_pitch_threshold(self, threshold: float):
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"""设置俯仰角阈值"""
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self.pitch_threshold = threshold
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print(f"✅ 俯仰角阈值设置为: {threshold}")
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def set_yaw_threshold(self, threshold: float):
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"""设置偏航角阈值"""
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self.yaw_threshold = threshold
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print(f"✅ 偏航角阈值设置为: {threshold}")
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def set_det_size(self, size: int):
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"""设置检测尺寸"""
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self.det_size = size
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print(f"✅ 检测尺寸设置为: {size}")
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def load_registered_faces(self, register_dir: str):
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"""
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从目录加载注册的人脸图片
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文件名（去掉后缀）即为人的名字
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"""
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if not os.path.exists(register_dir):
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print(f"❌ 注册目录不存在: {register_dir}")
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return False
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# 支持的图片格式
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image_extensions = ['*.jpg', '*.jpeg', '*.png', '*.bmp']
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image_files = []
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for ext in image_extensions:
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image_files.extend(glob.glob(os.path.join(register_dir, ext)))
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image_files.extend(glob.glob(os.path.join(register_dir, ext.upper())))
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if not image_files:
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print(f"❌ 在目录 {register_dir} 中未找到图片文件")
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return False
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loaded_count = 0
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for image_path in image_files:
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# 获取文件名（不含扩展名）作为人名
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person_name = os.path.splitext(os.path.basename(image_path))[0]
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# 读取图片并提取人脸特征
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img = cv2.imread(image_path)
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if img is None:
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print(f"❌ 无法读取图片: {image_path}")
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continue
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faces = self.app.get(img)
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if not faces:
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print(f"❌ 图片中未检测到人脸: {image_path}")
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continue
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# 使用第一张检测到的人脸
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self.registered_faces[person_name] = faces[0].embedding
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loaded_count += 1
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print(f"✅ 加载注册人脸: {person_name}")
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print(f"🎉 成功加载 {loaded_count} 张注册人脸")
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return loaded_count > 0
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def find_best_match(self, embedding: np.ndarray) -> Tuple[Optional[str], float]:
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"""
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在注册人脸中查找最佳匹配
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返回: (匹配的人名, 相似度)
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"""
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if not self.registered_faces:
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return None, 0.0
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best_similarity = 0.0
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best_name = None
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# 归一化查询嵌入
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query_emb = embedding / np.linalg.norm(embedding)
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for name, registered_embedding in self.registered_faces.items():
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# 归一化注册嵌入
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reg_emb = registered_embedding / np.linalg.norm(registered_embedding)
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# 计算余弦相似度
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similarity = float(np.dot(query_emb, reg_emb))
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if similarity > best_similarity:
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best_similarity = similarity
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best_name = name
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return best_name, best_similarity
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def calculate_clarity(self, face_region: np.ndarray) -> float:
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"""
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计算人脸区域的清晰度/模糊度
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使用拉普拉斯方差方法：值越高表示图像越清晰
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"""
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if len(face_region.shape) == 3:
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gray = cv2.cvtColor(face_region, cv2.COLOR_BGR2GRAY)
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else:
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gray = face_region
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# 计算拉普拉斯算子的方差
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laplacian_var = cv2.Laplacian(gray, cv2.CV_64F).var()
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return laplacian_var
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def is_face_quality_acceptable(self, face, frame: np.ndarray) -> Tuple[bool, Dict]:
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"""
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综合判断人脸质量是否可接受
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返回: (是否可接受, 质量指标字典)
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"""
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quality_metrics = {}
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is_acceptable = True
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# 1. 检测置信度
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quality_metrics['det_score'] = float(face.det_score)
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# 2. 人脸姿态角度
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if hasattr(face, 'pose') and face.pose is not None:
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pitch, yaw, roll = face.pose
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quality_metrics['pitch'] = float(pitch)
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quality_metrics['yaw'] = float(yaw)
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quality_metrics['roll'] = float(roll)
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else:
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quality_metrics['pitch'] = 100.0
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quality_metrics['yaw'] = 100.0
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quality_metrics['roll'] = 100.0
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# 3. 人脸边界框信息
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bbox = face.bbox
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x1, y1, x2, y2 = bbox.astype(int)
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width = x2 - x1
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height = y2 - y1
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quality_metrics['bbox_width'] = width
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quality_metrics['bbox_height'] = height
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quality_metrics['bbox_area'] = width * height
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quality_metrics['aspect_ratio'] = width / height if height > 0 else 0
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# 4. 图像清晰度检测
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# 提取人脸区域
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h, w = frame.shape[:2]
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x1_clip = max(0, x1)
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y1_clip = max(0, y1)
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x2_clip = min(w, x2)
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y2_clip = min(h, y2)
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if x2_clip > x1_clip and y2_clip > y1_clip:
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face_region = frame[y1_clip:y2_clip, x1_clip:x2_clip]
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clarity_score = self.calculate_clarity(face_region)
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quality_metrics['clarity_score'] = clarity_score
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else:
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quality_metrics['clarity_score'] = 0.0
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# 5. 综合质量评分
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base_score = quality_metrics['det_score']
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# 清晰度惩罚
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clarity_penalty = 0.0
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if quality_metrics['clarity_score'] < self.clarity_threshold:
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clarity_penalty = 0.3 # 清晰度不足严重惩罚
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is_acceptable = False
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# 姿态惩罚
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pose_penalty = 0.0
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if abs(quality_metrics['yaw']) > self.yaw_threshold:
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pose_penalty += 0.2
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is_acceptable = False
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if abs(quality_metrics['pitch']) > self.pitch_threshold:
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pose_penalty += 0.2
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is_acceptable = False
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# 尺寸惩罚
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size_penalty = 0.0
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if min(width, height) < self.min_face_size:
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is_acceptable = False
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size_penalty = 0.2
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quality_metrics['quality_score'] = max(0.1, base_score - clarity_penalty - pose_penalty - size_penalty)
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return is_acceptable, quality_metrics
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def process_frame(self, frame: np.ndarray) -> Tuple[np.ndarray, List[Dict]]:
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"""
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处理单帧图像
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返回: (原始帧, 识别结果列表)
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"""
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start_time = time.time()
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# 人脸检测和识别
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faces = self.app.get(frame)
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results = []
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for face in faces:
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# 检查人脸质量是否可接受
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is_acceptable, quality_metrics = self.is_face_quality_acceptable(face, frame)
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# 查找最佳匹配
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best_name, similarity = self.find_best_match(face.embedding)
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# 根据名单模式判断是否匹配
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if self.list_mode == "blacklist":
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# 黑名单模式：在黑名单中即为匹配（需要关注）
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is_match = best_name is not None and similarity >= self.similarity_threshold
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else: # whitelist
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# 白名单模式：在白名单中即为匹配（允许通过）
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is_match = best_name is not None and similarity >= self.similarity_threshold
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result = {
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'bbox': face.bbox.astype(int).tolist(),
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'similarity': similarity,
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'best_match': best_name,
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'is_match': is_match,
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'det_score': float(face.det_score),
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'quality_metrics': quality_metrics,
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'is_acceptable': is_acceptable # 新增：是否可接受标志
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}
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results.append(result)
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processing_time = (time.time() - start_time) * 1000
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return frame, results, processing_time
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def _draw_detection(self, frame: np.ndarray, result: Dict) -> np.ndarray:
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"""在帧上绘制检测结果和质量信息"""
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bbox = result['bbox']
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similarity = result['similarity']
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is_match = result['is_match']
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is_acceptable = result['is_acceptable']
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quality_metrics = result['quality_metrics']
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best_match = result['best_match']
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# 选择颜色
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if not is_acceptable:
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color = (128, 128, 128) # 灰色 - 质量不可接受
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else:
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# 选择颜色 - 根据名单模式
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if self.list_mode == "blacklist":
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# 黑名单模式：匹配（在黑名单中）显示红色，不匹配显示绿色
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color = (0, 0, 255) if is_match else (0, 255, 0) # 红色-黑名单, 绿色-正常
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else: # whitelist
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# 白名单模式：匹配（在白名单中）显示绿色，不匹配显示红色
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color = (0, 255, 0) if is_match else (0, 0, 255) # 绿色-白名单, 红色-陌生人
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# 绘制人脸框
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x1, y1, x2, y2 = bbox
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cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
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# 准备显示文本 - 只保留关键信息
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text_lines = []
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# 第一行：匹配状态
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if not is_acceptable:
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text_lines.append("LOW QUALITY")
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else:
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status = f"MATCH: {best_match}: {similarity:.3f}" if is_match else f"NO MATCH: {similarity:.3f}"
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text_lines.append(status)
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# 第二行：质量得分（根据阈值显示颜色）
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text_lines.append(f"Quality: {quality_metrics['quality_score']:.3f}")
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# 第三行：检测得分
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text_lines.append(f"DetScore: {quality_metrics['det_score']:.3f}")
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# 第四行：清晰度
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text_lines.append(f"Clarity: {quality_metrics['clarity_score']:.1f}")
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# 第五行：姿态角度
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text_lines.append(f"Pitch: {quality_metrics['pitch']:.1f}°")
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text_lines.append(f"Yaw: {quality_metrics['yaw']:.1f}°")
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# text_lines.append(f"Roll: {quality_metrics['roll']:.1f}°")
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text_lines.append(f"Width: {quality_metrics['bbox_width']:.1f}")
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text_lines.append(f"Height: {quality_metrics['bbox_height']:.1f}")
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# 计算文本区域大小
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max_text_width = 0
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total_text_height = 0
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line_heights = []
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for line in text_lines:
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(text_width, text_height), baseline = cv2.getTextSize(line, cv2.FONT_HERSHEY_SIMPLEX, 0.4, 1)
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max_text_width = max(max_text_width, text_width)
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line_heights.append(text_height + baseline)
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total_text_height += text_height + baseline + 2
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# 绘制文本背景
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bg_x1 = x1
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bg_y1 = y1 - total_text_height - 10
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bg_x2 = x1 + max_text_width + 10
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bg_y2 = y1
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# 如果背景超出图像顶部，调整到框下方
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if bg_y1 < 0:
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bg_y1 = y2
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bg_y2 = y2 + total_text_height + 10
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# 绘制半透明背景
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overlay = frame.copy()
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cv2.rectangle(overlay, (bg_x1, bg_y1), (bg_x2, bg_y2), (0, 0, 0), -1)
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alpha = 0.6
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cv2.addWeighted(overlay, alpha, frame, 1 - alpha, 0, frame)
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# 绘制文本
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current_y = bg_y1 + 15
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for i, line in enumerate(text_lines):
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# 根据内容选择颜色 - 按照你的要求简化颜色规则
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if i == 0: # 状态行
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if not is_acceptable:
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text_color = (128, 128, 128) # 灰色 - 质量差
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elif is_match:
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text_color = (0, 255, 0) # 绿色 - 匹配
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else:
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text_color = (0, 0, 255) # 红色 - 不匹配
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elif i == 3: # 清晰度行
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# 清晰度：低于阈值红色，大于等于阈值绿色
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if quality_metrics['clarity_score'] >= self.clarity_threshold:
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text_color = (255, 255, 255)
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else:
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text_color = (0, 0, 255)
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elif i == 4: # pitch
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if abs(quality_metrics['pitch']) > self.pitch_threshold:
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text_color = (0, 0, 255) # 红色
|
||||
else:
|
||||
text_color = (255, 255, 255)
|
||||
elif i == 5: # yaw
|
||||
if abs(quality_metrics['yaw']) > self.yaw_threshold:
|
||||
text_color = (0, 0, 255) # 红色
|
||||
else:
|
||||
text_color = (255, 255, 255)
|
||||
elif i == 6: # 宽度
|
||||
if quality_metrics['bbox_width'] < self.min_face_size:
|
||||
text_color = (0, 0, 255) # 红色
|
||||
else:
|
||||
text_color = (255, 255, 255)
|
||||
elif i == 7: # 高度
|
||||
if quality_metrics['bbox_height'] < self.min_face_size:
|
||||
text_color = (0, 0, 255) # 红色
|
||||
else:
|
||||
text_color = (255, 255, 255)
|
||||
else:
|
||||
text_color = (255, 255, 255) # 白色 - 其他信息
|
||||
|
||||
cv2.putText(frame, line, (x1 + 5, current_y),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 0.4, text_color, 1)
|
||||
current_y += line_heights[i]
|
||||
|
||||
return frame
|
||||
|
||||
def draw_detections(self, frame: np.ndarray, results: List[Dict]) -> np.ndarray:
|
||||
"""绘制所有检测结果"""
|
||||
for result in results:
|
||||
frame = self._draw_detection(frame, result)
|
||||
return frame
|
||||
|
||||
def set_quality_thresholds(self, clarity_threshold: float = None,
|
||||
quality_threshold: float = None,
|
||||
min_face_size: int = None):
|
||||
"""设置质量阈值"""
|
||||
if clarity_threshold is not None:
|
||||
self.clarity_threshold = clarity_threshold
|
||||
if quality_threshold is not None:
|
||||
self.quality_threshold = quality_threshold
|
||||
if min_face_size is not None:
|
||||
self.min_face_size = min_face_size
|
||||
print(
|
||||
f"✅ 质量阈值更新 - 清晰度: {self.clarity_threshold}, 质量得分: {self.quality_threshold}, 最小尺寸: {self.min_face_size}")
|
||||
|
||||
def get_registered_face_count(self) -> int:
|
||||
"""获取已注册人脸数量"""
|
||||
return len(self.registered_faces)
|
||||
|
||||
def get_list_mode(self) -> str:
|
||||
"""获取当前名单模式"""
|
||||
return self.list_mode
|
||||
275
src/video_face_recognition_cann_3.py
Normal file
275
src/video_face_recognition_cann_3.py
Normal file
@@ -0,0 +1,275 @@
|
||||
# video_face_recognition_cann.py
|
||||
import cv2
|
||||
import numpy as np
|
||||
import time
|
||||
import os
|
||||
from face_recognition_algorithm import FaceRecognitionAlgorithm
|
||||
|
||||
|
||||
def process_video_file(algorithm: FaceRecognitionAlgorithm, video_path: str, output_path: str = None,
|
||||
skip_frames: int = 0, show_preview: bool = True):
|
||||
"""
|
||||
处理视频文件
|
||||
|
||||
Args:
|
||||
algorithm: FaceRecognitionAlgorithm实例
|
||||
video_path: 输入视频路径
|
||||
output_path: 输出视频路径
|
||||
skip_frames: 跳帧数，用于提高处理速度
|
||||
show_preview: 是否显示实时预览
|
||||
"""
|
||||
if not os.path.exists(video_path):
|
||||
print(f"❌ 视频文件不存在: {video_path}")
|
||||
return
|
||||
|
||||
# 打开视频文件
|
||||
cap = cv2.VideoCapture(video_path)
|
||||
if not cap.isOpened():
|
||||
print(f"❌ 无法打开视频文件: {video_path}")
|
||||
return
|
||||
|
||||
# 获取视频信息
|
||||
fps = cap.get(cv2.CAP_PROP_FPS)
|
||||
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
|
||||
|
||||
print(f"📹 视频信息: {width}x{height}, {fps:.1f}FPS, 总帧数: {total_frames}")
|
||||
print(f"🎯 当前模式: {algorithm.get_list_mode()}, 注册人脸数: {algorithm.get_registered_face_count()}")
|
||||
|
||||
# 设置输出视频
|
||||
if output_path:
|
||||
# 确保输出目录存在
|
||||
os.makedirs(os.path.dirname(output_path), exist_ok=True)
|
||||
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
|
||||
out = cv2.VideoWriter(output_path, fourcc, fps / (skip_frames + 1), (width, height))
|
||||
else:
|
||||
out = None
|
||||
|
||||
# 性能统计
|
||||
frame_count = 0
|
||||
processed_frames = 0
|
||||
processing_times = []
|
||||
start_time = time.time()
|
||||
|
||||
print("🚀 开始处理视频...")
|
||||
|
||||
while True:
|
||||
ret, frame = cap.read()
|
||||
if not ret:
|
||||
break
|
||||
|
||||
# 跳帧处理
|
||||
if skip_frames > 0 and frame_count % (skip_frames + 1) != 0:
|
||||
frame_count += 1
|
||||
continue
|
||||
|
||||
# 处理当前帧 - 获取结果
|
||||
processed_frame, results, processing_time = algorithm.process_frame(frame)
|
||||
print(f"face process time : {processing_time}")
|
||||
|
||||
# 在帧上绘制检测结果
|
||||
processed_frame = algorithm.draw_detections(processed_frame, results)
|
||||
|
||||
# 记录处理时间
|
||||
processing_times.append(processing_time)
|
||||
|
||||
# 写入输出视频
|
||||
if out:
|
||||
out.write(processed_frame)
|
||||
|
||||
# 显示预览
|
||||
if show_preview:
|
||||
# 添加性能信息
|
||||
fps_text = f"Frame: {frame_count}/{total_frames} | Faces: {len(results)} | Mode: {algorithm.get_list_mode()}"
|
||||
cv2.putText(processed_frame, fps_text, (10, 30),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2)
|
||||
|
||||
# 添加名单统计
|
||||
match_count = sum(1 for r in results if r['is_match'])
|
||||
list_text = f"Match: {match_count}/{len(results)}"
|
||||
cv2.putText(processed_frame, list_text, (10, 60),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2)
|
||||
|
||||
cv2.imshow('Video Face Recognition', processed_frame)
|
||||
if cv2.waitKey(1) & 0xFF == ord('q'):
|
||||
break
|
||||
|
||||
frame_count += 1
|
||||
processed_frames += 1
|
||||
|
||||
# 进度显示
|
||||
if frame_count % 30 == 0:
|
||||
progress = (frame_count / total_frames) * 100
|
||||
print(f"📊 处理进度: {progress:.1f}% ({frame_count}/{total_frames})")
|
||||
|
||||
# 清理资源
|
||||
cap.release()
|
||||
if out:
|
||||
out.release()
|
||||
if show_preview:
|
||||
cv2.destroyAllWindows()
|
||||
|
||||
# 性能统计
|
||||
total_time = time.time() - start_time
|
||||
avg_processing_time = np.mean(processing_times) if processing_times else 0
|
||||
|
||||
print(f"\n🎉 视频处理完成!")
|
||||
print(f"📊 性能统计:")
|
||||
print(f" 总处理帧数: {processed_frames}")
|
||||
print(f" 总耗时: {total_time:.1f}秒")
|
||||
print(f" 平均每帧: {avg_processing_time:.1f}ms")
|
||||
print(f" 实际FPS: {processed_frames / total_time:.1f}")
|
||||
if output_path:
|
||||
print(f" 输出视频: {output_path}")
|
||||
|
||||
|
||||
def process_webcam(algorithm: FaceRecognitionAlgorithm, camera_id: int = 0, output_path: str = None):
|
||||
"""
|
||||
处理摄像头实时视频流
|
||||
"""
|
||||
cap = cv2.VideoCapture(camera_id)
|
||||
if not cap.isOpened():
|
||||
print(f"❌ 无法打开摄像头 {camera_id}")
|
||||
return
|
||||
|
||||
# 设置摄像头分辨率（可选）
|
||||
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
|
||||
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)
|
||||
|
||||
# 设置输出视频
|
||||
if output_path:
|
||||
# 确保输出目录存在
|
||||
os.makedirs(os.path.dirname(output_path), exist_ok=True)
|
||||
fps = cap.get(cv2.CAP_PROP_FPS)
|
||||
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
|
||||
out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
|
||||
else:
|
||||
out = None
|
||||
|
||||
# 性能统计
|
||||
processing_times = []
|
||||
|
||||
print(f"🎥 开始摄像头实时识别 - 模式: {algorithm.get_list_mode()} (按 'q' 退出)...")
|
||||
print(f"📋 注册人脸数: {algorithm.get_registered_face_count()}")
|
||||
|
||||
while True:
|
||||
ret, frame = cap.read()
|
||||
if not ret:
|
||||
print("❌ 无法读取摄像头帧")
|
||||
break
|
||||
|
||||
# 处理当前帧 - 获取结果
|
||||
processed_frame, results, processing_time = algorithm.process_frame(frame)
|
||||
print(f"face process time : {processing_time}")
|
||||
# 在帧上绘制检测结果
|
||||
processed_frame = algorithm.draw_detections(processed_frame, results)
|
||||
|
||||
# 记录处理时间
|
||||
processing_times.append(processing_time)
|
||||
|
||||
# 添加实时信息
|
||||
current_fps = 1000 / processing_times[-1] if processing_times else 0
|
||||
info_text = f"FPS: {current_fps:.1f} | Faces: {len(results)} | Mode: {algorithm.get_list_mode()}"
|
||||
cv2.putText(processed_frame, info_text, (10, 30),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2)
|
||||
|
||||
# 添加名单统计
|
||||
match_count = sum(1 for r in results if r['is_match'])
|
||||
list_text = f"Match: {match_count}/{len(results)}"
|
||||
cv2.putText(processed_frame, list_text, (10, 60),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2)
|
||||
|
||||
# 写入输出
|
||||
if out:
|
||||
out.write(processed_frame)
|
||||
|
||||
# 显示预览
|
||||
cv2.imshow('Real-time Face Recognition', processed_frame)
|
||||
|
||||
# 按'q'退出
|
||||
if cv2.waitKey(1) & 0xFF == ord('q'):
|
||||
break
|
||||
|
||||
# 清理资源
|
||||
cap.release()
|
||||
if out:
|
||||
out.release()
|
||||
cv2.destroyAllWindows()
|
||||
|
||||
print("✅ 摄像头处理结束")
|
||||
|
||||
|
||||
# 使用示例
|
||||
def main():
|
||||
# 直接创建人脸识别算法实例
|
||||
# 使用NPU：
|
||||
# algorithm = FaceRecognitionAlgorithm(use_gpu=False, use_npu=True, npu_device_id=0)
|
||||
# 使用GPU：
|
||||
algorithm = FaceRecognitionAlgorithm(use_gpu=True, use_npu=False)
|
||||
# 使用CPU：
|
||||
# algorithm = FaceRecognitionAlgorithm(use_gpu=False, use_npu=False)
|
||||
# algorithm = FaceRecognitionAlgorithm(use_gpu=True, use_npu=False) # 默认使用GPU
|
||||
|
||||
# 设置名单模式
|
||||
# algorithm.set_list_mode("blacklist") # 黑名单模式
|
||||
# algorithm.set_list_mode("whitelist") # 白名单模式
|
||||
|
||||
# 加载注册人脸
|
||||
register_dir = "test_data/register" # 注册图片目录
|
||||
if os.path.exists(register_dir):
|
||||
algorithm.load_registered_faces(register_dir)
|
||||
else:
|
||||
print(f"⚠️ 注册目录不存在: {register_dir}")
|
||||
#
|
||||
# # 设置质量阈值（可根据实际情况调整）
|
||||
# algorithm.set_quality_thresholds(
|
||||
# clarity_threshold=1000.0, # 清晰度阈值
|
||||
# quality_threshold=0.6, # 质量得分阈值
|
||||
# min_face_size=30
|
||||
# )
|
||||
|
||||
# # 选择处理模式
|
||||
# print("请选择处理模式:")
|
||||
# print("1. 处理视频文件")
|
||||
# print("2. 实时摄像头")
|
||||
#
|
||||
# choice = input("请输入选择 (1 或 2): ").strip()
|
||||
|
||||
choice = "1"
|
||||
|
||||
if choice == "1":
|
||||
# 处理视频文件
|
||||
video_path = "test_data/video/video_2.mp4"
|
||||
output_path = "test_data/output_video/video_2_white_9_gpu.mp4"
|
||||
# output_path = "test_data/output_video/video_2_black_2.mp4"
|
||||
|
||||
# 性能优化：跳帧处理
|
||||
skip_frames = 2
|
||||
|
||||
process_video_file(
|
||||
algorithm=algorithm,
|
||||
video_path=video_path,
|
||||
output_path=output_path,
|
||||
skip_frames=skip_frames,
|
||||
show_preview=False
|
||||
)
|
||||
|
||||
elif choice == "2":
|
||||
# 实时摄像头
|
||||
output_path = "webcam_recording.mp4"
|
||||
|
||||
process_webcam(
|
||||
algorithm=algorithm,
|
||||
camera_id=0,
|
||||
output_path=output_path
|
||||
)
|
||||
|
||||
else:
|
||||
print("❌ 无效选择")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user