环境：
conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia -ypip install onnx==1.15 onnxruntime-gpu==1.17pip install insightface==0.7.3pip install opencv-pythonpip install gradio图片清洗，只留下图像中只有一张人脸的，而且人脸是全的。
import os
import shutilimport cv2
import numpy as np
from insightface.app import FaceAnalysisdef is_full_face(facedata, img_shape, threshold=0.5):"""判断是否为整张脸:param facedata: 人脸数据:param img_shape: 图片尺寸:param threshold: 阈值:return: 布尔值，True 表示整张脸，False 表示部分脸"""img_width, img_height = img_shape[1], img_shape[0]# 检查人脸关键点是否在图片内部kps = facedata['kps']if np.all(kps >= 10) and np.all(kps[:, 0] <= img_width - 10) and np.all(kps[:, 1] <= img_height - 10):keypoints_inside = Trueelse:keypoints_inside = False# 满足阈值并且关键点在图片内部return keypoints_insidedef listPathAllfiles(dirname):result = []for maindir, subdir, file_name_list in os.walk(dirname):for filename in file_name_list:apath = os.path.join(maindir, filename)result.append(apath)return result# 使用的检测模型名为 buffalo_sc
app = FaceAnalysis(name='buffalo_sc', providers=['CUDAExecutionProvider'])
app.prepare(ctx_id=0, det_size=(640, 640)) # ctx_id 小于0 表示用 CPU 预测，det_size 表示 resize 后的图片分辨率src = r"C:\Users\Administrator\Pictures\girl_no_train\mangguo_dst"
dst = r"C:\Users\Administrator\Pictures\girl_no_train\mangguo_dst2"
os.makedirs(dst, exist_ok=True)files = listPathAllfiles(src)
for file in files:img = cv2.imread(file) # 读取图片faces = app.get(img) # 得到人脸信息if len(faces) == 0:continueif len(faces) > 1:continuefor facedata in faces:if is_full_face(facedata, img.shape):# print("This is a full face.")shutil.copy(file, dst)
)
