人脸识别代码：利用训练出的参数进行识别
人脸识别代码：利用训练出的参数进行识别
本代码示例展示了如何利用训练好的ResNet模型参数，进行人脸识别。代码中使用MindSpore框架，并使用摄像头进行实时人脸捕捉，将识别结果显示在图像上。
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
np.random.seed(58)
class ResidualBlock(nn.Cell): # 这个残差块可以被用于构建深度神经网络，增加网络的层数，提高网络的性能。
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('E:/program/miniconda/envs/MindSpore/ckpt1/checkpoint_resnet-10_126.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('E:/program/miniconda/envs/MindSpore/exam')
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()
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_alt.xml') # 加载检测器
# threshold = 0.4 # 设置阈值
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:
cv2.putText(img1, 'unrecognized', (10, 20), cv2.FONT_HERSHEY_COMPLEX, 1, (128, 128, 0), 2)
if not rect is None:
for (x, y, w, h) in rect:
face = img[y:y + w, x:x + h].astype(np.float32) # 数值转换
face = cv2.resize(face, (100, 100))
face = face.transpose().astype(np.float32) / 255.
face = np.expand_dims(face, axis=0) # 扩展维度，变成(batch_size, channels, height, width)
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)
# if output.asnumpy()[0][predicted_class[0]] < threshold:
# label = 'unknown'
# else:
label = subjects[predicted_class[0]]
cv2.putText(img1, label, (x, y), cv2.FONT_HERSHEY_COMPLEX, 1, (128, 128, 0), 2)
cv2.imshow('img', img1)
k = cv2.waitKey(1)
if k & 0xFF == ord('q'): # 按下q程序结束
stop = True
cv2.destroyAllWindows() # 释放窗口
if __name__ == '__main__':
train_resnet()
流程图内容：
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