MobileNet 是专为移动和嵌入式设备设计的卷积神经网络 (CNN)。MobileNet 通常用于图像分类。本教程演示了如何使用 MobileNet 和 TensorFlow Lite 对图像进行分类。
准备环境
下载预训练的 MobileNet 模型：
curl -Lo mobilenet_v1_1.0_224.tflite https://tfhub.dev/tensorflow/lite-model/mobilenet_v1_1.0_224/1/default/1?lite-format=tflite一个模型在 ImageNet 数据集上训练了 1001 个类。“背景”的 0 类，后面是 1000 个实际的 ImageNet 类。图像输入尺寸 224×224。
下载 ImageNet 类标签：
curl -o labels.txt https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt下载图片进行测试：
curl -o test.bmp https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/lite/examples/label_image/testdata/grace_hopper.bmp推理
类标签从文件写入数组。TensorFlow Lite 模型加载到内存中。在预处理过程中，调整图像大小，并将像素值归一化到 [-1, 1] 范围内。为了对图像进行分类，调用了一个模型。模型返回类别概率列表。最大概率映射到相关类别。
from tflite_runtime.interpreter import Interpreter
import numpy as np
import cv2
def readLabels(labelsFile):
with open(labelsFile, 'r') as file:
labels = [line.strip() for line in file.readlines()]
return labels
def preprocessImage(img):
img = cv2.resize(img, (224, 224), interpolation=cv2.INTER_AREA)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = np.expand_dims(img, axis=0)
return np.float32((1 / 127.5) * img - 1)
def main():
LABELS_FILE = 'labels.txt'
IMG_FILE = 'test.bmp'
MODEL_FILE = 'mobilenet_v1_1.0_224.tflite'
labels = readLabels(LABELS_FILE)
img = cv2.imread(IMG_FILE)
img = preprocessImage(img)
interpreter = Interpreter(MODEL_FILE)
interpreter.allocate_tensors()
inputDetails = interpreter.get_input_details()
interpreter.set_tensor(inputDetails[0]['index'], img)
interpreter.invoke()
outputDetails = interpreter.get_output_details()
outputData = interpreter.get_tensor(outputDetails[0]['index'])
outputData = np.squeeze(outputData)
idx = np.argmax(outputData)
label = labels[idx]
output = outputData[idx]
print('%.6f: %s (%s)' % (output, label, idx))
main()#include <iostream>
#include <fstream>
#include <algorithm>
#include <opencv2/opencv.hpp>
#include <tensorflow/lite/interpreter.h>
#include <tensorflow/lite/kernels/register.h>
using namespace cv;
using namespace tflite;
void readLabels(const char *labelsFile, std::vector<std::string> &labels)
{
std::string line;
std::ifstream fin(labelsFile);
while (getline(fin, line)) {
labels.push_back(line);
}
}
void preprocessImage(Mat &img)
{
resize(img, img, Size(224, 224), 0, 0, INTER_AREA);
cvtColor(img, img, COLOR_BGRA2RGB);
img.convertTo(img, CV_32FC3, 1 / 127.5f, -1);
}
int main()
{
const int NUM_CLASSES = 1001;
const char *LABELS_FILE = "labels.txt";
const char *IMG_FILE = "test.bmp";
const char *MODEL_FILE = "mobilenet_v1_1.0_224.tflite";
std::vector<std::string> labels;
readLabels(LABELS_FILE, labels);
Mat img = imread(IMG_FILE);
preprocessImage(img);
std::unique_ptr<FlatBufferModel> model = FlatBufferModel::BuildFromFile(MODEL_FILE);
ops::builtin::BuiltinOpResolver resolver;
std::unique_ptr<Interpreter> interpreter;
InterpreterBuilder(*model, resolver)(&interpreter);
interpreter->AllocateTensors();
auto *inputTensor = interpreter->typed_input_tensor<float>(0);
memcpy(inputTensor, img.data, img.total() * img.elemSize());
interpreter->Invoke();
auto *outputData = interpreter->typed_output_tensor<float>(0);
float *output = std::max_element(outputData, outputData + NUM_CLASSES);
long idx = output - outputData;
std::string label = labels[idx];
std::cout << *output << ": " << label << " (" << idx << ")" << std::endl;
return 0;
}C++ 代码是使用 CMake 构建的。
CMake is not required.cmake_minimum_required(VERSION 3.22)
project(app)
set(CMAKE_CXX_STANDARD 14)
add_executable(app main.cpp)
target_link_libraries(app tensorflow-lite opencv_core opencv_imgcodecs opencv_imgproc)在我们的例子中，返回以下输出：
0.912099: military uniform (653)0.912099: military uniform (653)图像被正确分类的概率约为 91%。
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