说明
C# PaddleInference OCR 验证码识别
自己训练的模型，只针对测试图片类型，准确率99%
效果
项目
VS2022+.net4.8+OpenCvSharp4+Sdcb.PaddleInference
测试图片
代码
using OpenCvSharp;
using Sdcb.PaddleInference.Native;
using Sdcb.PaddleInference;
using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.Data;
using System.Drawing;
using System.Globalization;
using System.IO;
using System.Linq;
using System.Runtime.InteropServices;
using System.Text;
using System.Windows.Forms;
namespace PaddleInference_OCR_验证码识别
{
public partial class Form1 : Form
{
public Form1()
{
InitializeComponent();
}
Bitmap bmp;
string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
string img = "";
string startupPath = "";
OcrShape recShape = new OcrShape(3, 320, 48);
PaddlePredictor rec_predictor;
public IReadOnlyList<string> Labels;
DateTime dt1 = DateTime.Now;
DateTime dt2 = DateTime.Now;
private void button1_Click(object sender, EventArgs e)
{
OpenFileDialog ofd = new OpenFileDialog();
ofd.Filter = fileFilter;
if (ofd.ShowDialog() != DialogResult.OK) return;
pictureBox1.Image = null;
img = ofd.FileName;
bmp = new Bitmap(img);
pictureBox1.Image = new Bitmap(img);
textBox1.Text = "";
}
private unsafe void Form1_Load(object sender, EventArgs e)
{
string startupPath = Application.StartupPath;
IntPtr det_ptr = PaddleNative.PD_ConfigCreate();
Encoding PaddleEncoding = Environment.OSVersion.Platform == PlatformID.Win32NT ? Encoding.GetEncoding(CultureInfo.CurrentCulture.TextInfo.ANSICodePage) : Encoding.UTF8;
//识别模型
IntPtr rec_ptr = PaddleNative.PD_ConfigCreate();
String rec_programPath = startupPath + "\\model\\inference.pdmodel";
String rec_paramsPath = startupPath + "\\model\\inference.pdiparams";
byte[] rec_programBytes = PaddleEncoding.GetBytes(rec_programPath);
byte[] rec_paramsBytes = PaddleEncoding.GetBytes(rec_paramsPath);
fixed (byte* rec_programPtr = rec_programBytes)
fixed (byte* rec_paramsPtr = rec_paramsBytes)
{
PaddleNative.PD_ConfigSetModel(rec_ptr, (IntPtr)rec_programPtr, (IntPtr)rec_paramsPtr);
}
rec_predictor = new PaddlePredictor(PaddleNative.PD_PredictorCreate(rec_ptr));
//Labels
String labelsPath = startupPath + "\\ppocr_keys.txt";
Stream Steam = new FileStream(labelsPath, FileMode.Open, FileAccess.Read, FileShare.Read);
StreamReader reader = new StreamReader(Steam);
List<string> tempList = new List<string>();
while (!reader.EndOfStream)
{
tempList.Add(reader.ReadLine());
}
reader.Dispose();
Steam.Dispose();
Labels = tempList;
}
private void button3_Click(object sender, EventArgs e)
{
if (pictureBox1.Image == null)
{
return;
}
dt1 = DateTime.Now;
var src = OpenCvSharp.Extensions.BitmapConverter.ToMat(bmp);
int modelHeight = recShape.Height;
int maxWidth = (int)Math.Ceiling(1.0 * src.Width / src.Height * modelHeight);
Mat channel3 = new Mat();
if (src.Channels() == 4)
{
channel3 = src.CvtColor(ColorConversionCodes.RGBA2BGR);
}
else if (src.Channels() == 3)
{
channel3 = src.Clone();
}
else if (src.Channels() == 1)
{
channel3 = src.CvtColor(ColorConversionCodes.GRAY2RGB);
}
else
{
throw new Exception("Unexpect src channel: {" + src.Channels() + "}, allow: (1/3/4)");
}
Mat resized = ResizePadding(channel3, modelHeight, maxWidth);
Mat normalize = Normalize(resized);
using (PaddleTensor input = rec_predictor.GetInputTensor(rec_predictor.InputNames[0]))
{
int channel = normalize.Channels();
input.Shape = new[] { 1, channel, modelHeight, maxWidth };
float[] data = ExtractMat(normalize, channel, modelHeight, maxWidth);
input.SetData(data);
}
normalize.Dispose();
resized.Dispose();
if (!rec_predictor.Run())
{
throw new Exception($"PaddlePredictor(Recognizer) run failed.");
}
using (PaddleTensor output = rec_predictor.GetOutputTensor(rec_predictor.OutputNames[0]))
{
float[] data = output.GetData<float>();
int[] shape = output.Shape;
GCHandle dataHandle = default;
try
{
dataHandle = GCHandle.Alloc(data, GCHandleType.Pinned);
IntPtr dataPtr = dataHandle.AddrOfPinnedObject();
int labelCount = shape[2];
int charCount = shape[1];
StringBuilder sbInfo = new StringBuilder();
for (int i = 0; i < shape[0]; i++)
{
StringBuilder sb = new StringBuilder();
int lastIndex = 0;
float score = 0;
for (int n = 0; n < charCount; ++n)
{
Mat mat = new Mat(1, labelCount, MatType.CV_32FC1, dataPtr + (n + i * charCount) * labelCount * sizeof(float));
int[] maxIdx = new int[2];
mat.MinMaxIdx(out double _, out double maxVal, new int[0], maxIdx);
if (maxIdx[1] > 0 && (!(n > 0 && maxIdx[1] == lastIndex)))
{
score += (float)maxVal;
sb.Append(GetLabelByIndex(maxIdx[1]));
}
lastIndex = maxIdx[1];
mat.Dispose();
}
sbInfo.AppendLine("Text:" + sb.ToString());
sbInfo.AppendLine("Score:" + score / sb.Length);
}
dt2 = DateTime.Now;
sbInfo.AppendLine("-----------------------------------\n");
sbInfo.AppendLine(DateTime.Now.ToString("yyyy-MM-dd HH:mm:ss.fff"));
sbInfo.AppendLine("耗时:" + (dt2 - dt1).TotalMilliseconds + "ms\n");
textBox1.Text = sbInfo.ToString();
}
finally
{
dataHandle.Free();
}
}
}
string GetLabelByIndex(int x)
{
if (x > 0 && x <= Labels.Count)
{
return Labels[x - 1];
}
else if (x == Labels.Count + 1)
{
return "";
}
else
{
throw new Exception("Unable to GetLabelByIndex: index {" + x + "} out of range {" + Labels.Count + "}, OCR model or labels not matched?");
}
}
private Mat ResizePadding(Mat src, int height, int targetWidth)
{
OpenCvSharp.Size size = src.Size();
float whRatio = 1.0f * size.Width / size.Height;
int width = (int)Math.Ceiling(height * whRatio);
if (width == targetWidth)
{
return src.Resize(new OpenCvSharp.Size(width, height));
}
else
{
Mat resized = src.Resize(new OpenCvSharp.Size(width, height));
return resized.CopyMakeBorder(0, 0, 0, targetWidth - width, BorderTypes.Constant, Scalar.Gray);
}
}
private Mat Normalize(Mat src)
{
Mat normalized = new Mat();
src.ConvertTo(normalized, MatType.CV_32FC3, 1.0 / 255);
Mat[] bgr = normalized.Split();
float[] scales = new[] { 1 / 0.229f, 1 / 0.224f, 1 / 0.225f };
float[] means = new[] { 0.485f, 0.456f, 0.406f };
for (int i = 0; i < bgr.Length; ++i)
{
bgr[i].ConvertTo(bgr[i], MatType.CV_32FC1, 1.0 * scales[i], (0.0 - means[i]) * scales[i]);
}
normalized.Dispose();
Mat dest = new Mat();
Cv2.Merge(bgr, dest);
foreach (Mat channel in bgr)
{
channel.Dispose();
}
return dest;
}
private float[] ExtractMat(Mat mat, int channel, int height, int width)
{
float[] result = new float[1 * channel * width * height];
GCHandle resultHandle = GCHandle.Alloc(result, GCHandleType.Pinned);
IntPtr resultPtr = resultHandle.AddrOfPinnedObject();
try
{
Mat src = mat.Clone();
if (src.Channels() != channel)
{
throw new Exception($"src channel={src.Channels()}, expected {channel}");
}
for (int c = 0; c < channel; ++c)
{
Mat dest = new Mat(height, width, MatType.CV_32FC1, resultPtr + c * height * width * sizeof(float));
Cv2.ExtractChannel(src, dest, c);
dest.Dispose();
}
return result;
}
finally
{
resultHandle.Free();
}
}
private float[] ExtractMat(Mat[] srcs, int channel, int height, int width)
{
float[] result = new float[srcs.Length * channel * width * height];
GCHandle resultHandle = GCHandle.Alloc(result, GCHandleType.Pinned);
IntPtr resultPtr = resultHandle.AddrOfPinnedObject();
try
{
for (int i = 0; i < srcs.Length; ++i)
{
Mat src = srcs[i];
if (src.Channels() != channel)
{
throw new Exception($"src[{i}] channel={src.Channels()}, expected {channel}");
}
for (int c = 0; c < channel; ++c)
{
Mat dest = new Mat(height, width, MatType.CV_32FC1, resultPtr + (c + i * channel) * height * width * sizeof(float));
Cv2.ExtractChannel(src, dest, c);
dest.Dispose();
}
}
return result;
}
finally
{
resultHandle.Free();
}
}
}
}
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