import os import cv2 import numpy as np import mindspore from mindspore import Tensor, load_checkpoint, load_param_into_net from mindspore.dataset.vision import py_transforms from mindspore.dataset.transforms.py_transforms import Compose from PIL import Image from main import ResNet, BasicBlock

加载标签

with open('label.txt') as f: labels = f.readlines() labels = [l.strip() for l in labels]

加载人脸检测器

face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

打开摄像头

cap = cv2.VideoCapture(0)

加载模型的函数

def load_model(ckpt_file): network = ResNet(BasicBlock, [2, 2, 2, 2], num_classes=100) params = load_checkpoint(os.path.join(ckpt_dir, ckpt_file)) load_param_into_net(network, params) return network

遍历ckpt文件夹中的所有ckpt文件

ckpt_dir = 'D:/pythonProject7/ckpt/' ckpt_files = os.listdir(ckpt_dir) ckpt_files = [f for f in ckpt_files if f.endswith('.ckpt')] ckpt_files.sort(key=lambda x: os.path.getmtime(os.path.join(ckpt_dir, x)))

for ckpt_file in ckpt_files: # 加载模型 network = load_model(ckpt_file)

while True:
    # 读取视频帧
    ret, frame = cap.read()

    # 转换为灰度图像
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # 检测人脸
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)

    for (x, y, w, h) in faces:
        # 提取人脸图像
        face = gray[y:y + h, x:x + w]
        face = cv2.resize(face, (224, 224)).astype(np.float32)
        face = cv2.cvtColor(face, cv2.COLOR_GRAY2RGB)

        # 转换为Tensor类型，并进行归一化
        transform = Compose([
            py_transforms.ToTensor(),
            py_transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
        ])
        face = transform(face)

        # 转换为Tensor类型，并增加一个维度
        face = Tensor(face)
        #face = mindspore.ops.ExpandDims()(face, 0)

        # 预测人脸所属的类别
        output = network(face)
        prediction = np.argmax(output.asnumpy())

        # 在图像上标注人脸和类别
        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 2)
        cv2.putText(frame, labels[prediction], (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)

    # 显示图像
    cv2.imshow('frame',frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# 释放摄像头并关闭窗口
cap.release()
cv2.destroyAllWindows()