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

加载模型

network = ResNet(BasicBlock, [2, 2, 2, 2], num_classes=100) model_path = 'D:/pythonProject7/ckpt/checkpoint_resnet_6-10_25.ckpt' params = load_checkpoint(model_path) load_param_into_net(network, params)

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)

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()

如何修改使用一个文件夹中的ckpt文件内容：如果想要使用一个文件夹中的ckpt文件，可以将model_path的路径修改为文件夹路径，然后在代码中获取该文件夹下最新的ckpt文件进行加载。

示例代码如下：

import os

获取ckpt文件夹中最新的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))) latest_ckpt_file = os.path.join(ckpt_dir, ckpt_files[-1])

加载模型

network = ResNet(BasicBlock, [2, 2, 2, 2], num_classes=100) params = load_checkpoint(latest_ckpt_file) load_param_into_net(network, params)