ResNet人脸识别模型训练与实时检测（MindSpore）

本代码使用MindSpore框架训练ResNet模型进行人脸识别，并使用摄像头进行实时检测，识别数字0到33。

代码实现

from collections import defaultdict, Counter
from mindspore.train.serialization import load_checkpoint, load_param_into_net
import numpy as np
import mindspore.dataset as ds
import cv2
import mindspore.nn as nn
import os
from mindspore import context, ops, Tensor
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
from mindspore.train import Model
from mindspore.nn.metrics import Accuracy
np.random.seed(58)


class ResidualBlock(nn.Cell):
    expansion = 1
    def __init__(self, in_channels, out_channels, stride=1, downsample=None):
        super(ResidualBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride,   pad_mode='same')
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU()
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1,   pad_mode='same')
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.downsample = downsample
        self.stride = stride

    def construct(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            identity = self.downsample(x)
        out += identity
        out = self.relu(out)

        return out

class ResNet(nn.Cell):
    def __init__(self, block, layers, num_classes=34):
        super(ResNet, self).__init__()
        self.in_channels = 64

        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2,   pad_mode='valid')
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU()
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2,   pad_mode='valid')

        self.layer1 = self.make_layer(block, 64, layers[0])
        self.layer2 = self.make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self.make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self.make_layer(block, 512, layers[3], stride=2)

        self.avgpool = nn.AvgPool2d(kernel_size=3, stride=1,   pad_mode='valid')
        self.fc = nn.Dense(512 * block.expansion, num_classes)

    def make_layer(self, block, out_channels, blocks, stride=1):
        downsample = None
        if (stride != 1) or (self.in_channels != out_channels * block.expansion):
            downsample = nn.SequentialCell([
                nn.Conv2d(self.in_channels, out_channels * block.expansion, kernel_size=1, stride=stride),
                nn.BatchNorm2d(out_channels * block.expansion)
            ])
        layers = []
        layers.append(block(self.in_channels, out_channels, stride, downsample))
        self.in_channels = out_channels * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.in_channels, out_channels))
        return nn.SequentialCell(layers)

    def construct(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = ops.Reshape()(x, (ops.Shape()(x)[0], -1))
        x = self.fc(x)

        return x


class TrainDatasetGenerator:
    def __init__(self, file_path):
        self.file_path = file_path
        self.img_names = os.listdir(file_path)

    def __getitem__(self, index=0):
        data = cv2.imread(os.path.join(self.file_path, self.img_names[index]))
        label = int(self.img_names[index].split('-')[0])
        data = cv2.resize(data,(100,100))
        data = data.transpose().astype(np.float32) / 255.
        return data, label

    def __len__(self):
        return len(self.img_names)

def load_model_from_ckpt():
    context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
    # 创建ResNet模型
    network = ResNet(ResidualBlock,[2,2,2,2])
    # 加载ckpt文件中的模型参数
    param_dict = load_checkpoint('D:/pythonproject2/ckpt/checkpoint_resnet_1-20_49.ckpt')
     #将模型参数加载到模型中
    load_param_into_net(network, param_dict)
    # 返回模型
    return network

def train_resnet():
    context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
    train_dataset_generator = TrainDatasetGenerator('D:/pythonproject2/digital_mindspore/dataset')
    ds_train = ds.GeneratorDataset(train_dataset_generator, ['data', 'label'], shuffle=True)
    ds_train = ds_train.shuffle(buffer_size=10)
    ds_train = ds_train.batch(batch_size=4, drop_remainder=True)
    network = load_model_from_ckpt()
    net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
    net_opt = nn.Momentum(network.trainable_params(), learning_rate=0.001, momentum=0.9)
    #time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())
    #config_ck = CheckpointConfig(save_checkpoint_steps=10,keep_checkpoint_max=10)
    #config_ckpt_path = 'D:/pythonproject2/ckpt/'
    #ckpoint_cb = ModelCheckpoint(prefix='checkpoint_resnet', directory=config_ckpt_path, config=config_ck)

    model = Model(network, net_loss, net_opt, metrics={'Accuracy': Accuracy()})
    #epoch_size = 20
    #print('============== Starting Training =============')
    #model.train(epoch_size, ds_train, callbacks=[time_cb, ckpoint_cb, LossMonitor()])
    face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_alt.xml')  # 加载检测器
    # 训练阶段

    cap = cv2.VideoCapture(0)
    stop = False
    while not stop:
        success, img = cap.read()
        subjects = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17',
                    '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33']
        # 生成图像的副本，这样就能保留原始图像
        img1 = img.copy()
        # 检测人脸
        # 将测试图像转换为灰度图像，因为opencv人脸检测器需要灰度图像
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        # 检测多尺度图像，返回值是一张脸部区域信息的列表（x,y,宽,高）
        rect = face_cascade.detectMultiScale(img, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30), 
                                             flags=cv2.CASCADE_SCALE_IMAGE)
        # 如果未检测到面部
        if len(rect) == 0:
            txt = 'no face!'
            cv2.putText(img1, txt, (10, 20), cv2.FONT_HERSHEY_COMPLEX, 1, (128, 128, 0), 2)
        if not rect is None:
            for (x, y, w, h) in rect:
                face = gray[y:y + w, x:x + h].astype(np.float32)  # 数值转换
                face = Tensor(face.transpose().astype(np.float32) / 255.)
                face = Tensor(face)
                cv2.rectangle(img1, (x, y), (x + w, y + h), (0, 255, 0), 2)  # 画出矩形框
                output =network(face)
                predicted_class = np.argmax(output.asnumpy(),axis=1)
                label = subjects[predicted_class[0]]
                #if min_d < 200000000000:
                cv2.putText(img1, label, (x, y), cv2.FONT_HERSHEY_COMPLEX, 1, (128, 128, 0), 2)
                #else:
                 #   label = 'unknown'
                  #  cv2.putText(img1, label, (x, y), cv2.FONT_HERSHEY_COMPLEX, 1, (128, 128, 0), 2)
        cv2.imshow('img', img1)
        if (cv2.waitKey(1) & 0xFF == ord('q')):  # 按下q程序结束
                stop = True
                cv2.destroyAllWindows()  # 释放窗口

if __name__ == '__main__':
    train_resnet()

# 报错解决

出现报错'cv2.error: OpenCV(4.7.0) D:\a\opencv-python\opencv-python\opencv\modules\imgproc\src\resize.cpp:4062: error: (-215:Assertion failed) !ssize.empty() in function 'cv::resize''

这个报错是因为resize函数的输入参数中有一个尺寸为0，导致无法进行resize操作。建议检查输入的图像数据是否为空或者尺寸为0，或者检查resize函数的输入参数是否正确。

总结

本代码实现了一个使用MindSpore框架训练ResNet模型进行人脸识别，并使用摄像头进行实时检测的示例。在使用过程中需要注意代码中路径的配置，以及输入图像数据的大小和尺寸。

注意事项

代码中的路径需要根据实际情况进行修改。
在使用摄像头进行实时检测时，需要注意摄像头的分辨率和帧率。
如果出现报错，建议仔细检查代码，并根据报错信息进行排查。

参考资料

代码解析

导入必要的库
定义ResNet模型
- ResidualBlock类：定义残差块，是ResNet模型的基本构成单元。
- ResNet类：定义ResNet模型，包含卷积层、池化层、全连接层等。
定义训练数据集生成器
- TrainDatasetGenerator类：生成训练数据集，包含读取图像数据和标签的功能。
加载预训练模型
- load_model_from_ckpt函数：加载预训练模型，并返回模型实例。
训练ResNet模型
- train_resnet函数：训练ResNet模型，包括定义网络、优化器、损失函数、评价指标等。
实时人脸检测
- 使用OpenCV加载摄像头并进行实时检测，检测到人脸后将人脸图像输入模型进行识别。

代码改进

可以添加其他预处理步骤，例如图像增强、数据归一化等，以提高模型的性能。
可以使用其他优化算法，例如Adam、RMSprop等，以加速训练过程。
可以将模型部署到其他平台，例如移动端、云端等，以实现更广泛的应用。