156 lines
6.2 KiB
Python
156 lines
6.2 KiB
Python
import cv2
|
||
import numpy as np
|
||
import insightface
|
||
from insightface.app import FaceAnalysis
|
||
from io import BytesIO
|
||
from PIL import Image
|
||
import logging
|
||
|
||
# 配置日志（便于排查）
|
||
logging.basicConfig(level=logging.INFO, format='%(asctime)s - [FaceUtil] - %(levelname)s - %(message)s')
|
||
logger = logging.getLogger(__name__)
|
||
|
||
# 全局变量存储InsightFace引擎和特征列表
|
||
_insightface_app = None
|
||
_feature_list = []
|
||
|
||
|
||
def init_insightface():
|
||
"""初始化InsightFace引擎（确保成功后再使用）"""
|
||
global _insightface_app
|
||
try:
|
||
if _insightface_app is not None:
|
||
logger.info("InsightFace引擎已初始化，无需重复执行")
|
||
return _insightface_app
|
||
|
||
logger.info("正在初始化InsightFace引擎（模型：buffalo_l）...")
|
||
# 手动指定模型下载路径（避免权限问题，可选）
|
||
app = FaceAnalysis(
|
||
name='buffalo_l',
|
||
root='~/.insightface', # 模型默认下载路径
|
||
providers=['CPUExecutionProvider'] # 强制用CPU（若有GPU可加'CUDAExecutionProvider'）
|
||
)
|
||
app.prepare(ctx_id=0, det_size=(640, 640)) # det_size越大，小人脸检测越准
|
||
logger.info("InsightFace引擎初始化完成")
|
||
_insightface_app = app
|
||
return app
|
||
except Exception as e:
|
||
logger.error(f"InsightFace初始化失败：{str(e)}", exc_info=True) # 打印详细堆栈
|
||
_insightface_app = None
|
||
return None
|
||
|
||
|
||
def add_binary_data(binary_data):
|
||
"""
|
||
接收单张图片的二进制数据、提取特征并保存
|
||
返回：(True, 特征值numpy数组) 或 (False, 错误信息字符串)
|
||
"""
|
||
global _insightface_app, _feature_list
|
||
|
||
# 1. 先检查引擎是否初始化成功
|
||
if not _insightface_app:
|
||
init_result = init_insightface() # 尝试重新初始化
|
||
if not init_result:
|
||
error_msg = "InsightFace引擎未初始化，无法检测人脸"
|
||
logger.error(error_msg)
|
||
return False, error_msg
|
||
|
||
try:
|
||
# 2. 验证二进制数据有效性
|
||
if len(binary_data) < 1024: # 过滤过小的无效图片（小于1KB）
|
||
error_msg = f"图片过小（{len(binary_data)}字节），可能不是有效图片"
|
||
logger.warning(error_msg)
|
||
return False, error_msg
|
||
|
||
# 3. 二进制数据转CV2格式（关键步骤，避免通道错误）
|
||
try:
|
||
img = Image.open(BytesIO(binary_data)).convert("RGB") # 强制转RGB
|
||
frame = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR) # InsightFace需要BGR格式
|
||
except Exception as e:
|
||
error_msg = f"图片格式转换失败：{str(e)}"
|
||
logger.error(error_msg, exc_info=True)
|
||
return False, error_msg
|
||
|
||
# 4. 检查图片尺寸（避免极端尺寸导致检测失败）
|
||
height, width = frame.shape[:2]
|
||
if height < 64 or width < 64: # 人脸检测最小建议尺寸
|
||
error_msg = f"图片尺寸过小（{width}x{height}），需至少64x64像素"
|
||
logger.warning(error_msg)
|
||
return False, error_msg
|
||
|
||
# 5. 调用InsightFace检测人脸
|
||
logger.info(f"开始检测人脸（图片尺寸：{width}x{height}，格式：BGR）")
|
||
faces = _insightface_app.get(frame)
|
||
|
||
if not faces:
|
||
error_msg = "未检测到人脸（请确保图片包含清晰正面人脸，无遮挡、不模糊）"
|
||
logger.warning(error_msg)
|
||
return False, error_msg
|
||
|
||
# 6. 提取特征并保存
|
||
current_feature = faces[0].embedding
|
||
_feature_list.append(current_feature)
|
||
logger.info(f"人脸检测成功，提取特征值（维度：{current_feature.shape[0]}），累计特征数：{len(_feature_list)}")
|
||
return True, current_feature
|
||
|
||
except Exception as e:
|
||
error_msg = f"处理图片时发生异常：{str(e)}"
|
||
logger.error(error_msg, exc_info=True)
|
||
return False, error_msg
|
||
|
||
|
||
# 以下函数保持不变（get_average_feature/clear_features/get_feature_list）
|
||
def get_average_feature(features=None):
|
||
global _feature_list
|
||
try:
|
||
if features is None:
|
||
features = _feature_list
|
||
if not isinstance(features, list) or len(features) == 0:
|
||
logger.warning("输入必须是包含至少一个特征值的列表")
|
||
return None
|
||
|
||
processed_features = []
|
||
for i, embedding in enumerate(features):
|
||
try:
|
||
if isinstance(embedding, str):
|
||
embedding_str = embedding.replace('[', '').replace(']', '').replace(',', ' ').strip()
|
||
embedding_list = [float(num) for num in embedding_str.split() if num.strip()]
|
||
embedding_np = np.array(embedding_list, dtype=np.float32)
|
||
else:
|
||
embedding_np = np.array(embedding, dtype=np.float32)
|
||
|
||
if len(embedding_np.shape) == 1:
|
||
processed_features.append(embedding_np)
|
||
logger.info(f"已添加第 {i + 1} 个特征值用于计算平均值")
|
||
else:
|
||
logger.warning(f"跳过第 {i + 1} 个特征值：不是一维数组")
|
||
except Exception as e:
|
||
logger.error(f"处理第 {i + 1} 个特征值时出错：{str(e)}")
|
||
|
||
if not processed_features:
|
||
logger.warning("没有有效的特征值用于计算平均值")
|
||
return None
|
||
|
||
dims = {feat.shape[0] for feat in processed_features}
|
||
if len(dims) > 1:
|
||
logger.error(f"特征值维度不一致：{dims}，无法计算平均值")
|
||
return None
|
||
|
||
avg_feature = np.mean(processed_features, axis=0)
|
||
logger.info(f"计算成功：{len(processed_features)} 个特征值的平均向量（维度：{avg_feature.shape[0]}）")
|
||
return avg_feature
|
||
except Exception as e:
|
||
logger.error(f"计算平均特征值出错：{str(e)}", exc_info=True)
|
||
return None
|
||
|
||
|
||
def clear_features():
|
||
global _feature_list
|
||
_feature_list = []
|
||
logger.info("已清空所有特征数据")
|
||
|
||
|
||
def get_feature_list():
|
||
global _feature_list
|
||
logger.info(f"当前特征列表长度：{len(_feature_list)}")
|
||
return _feature_list.copy() |