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Unverified Commit 191c9dee authored by Evezerest's avatar Evezerest Committed by GitHub
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Merge branch 'dygraph' into dygraph

parents 3c6d5512 8def6786
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Showing with 509 additions and 612 deletions
deploy/android_demo/app/src/main/cpp/native.cpp
View file @ 191c9dee
......@@ -13,7 +13,7 @@ static paddle::lite_api::PowerMode str_to_cpu_mode(const std::string &cpu_mode);
extern "C" JNIEXPORT jlong JNICALL
Java_com_baidu_paddle_lite_demo_ocr_OCRPredictorNative_init(
JNIEnv *env, jobject thiz, jstring j_det_model_path,
jstring j_rec_model_path, jstring j_cls_model_path, jint j_thread_num,
jstring j_rec_model_path, jstring j_cls_model_path, jint j_use_opencl, jint j_thread_num,
jstring j_cpu_mode) {
std::string det_model_path = jstring_to_cpp_string(env, j_det_model_path);
std::string rec_model_path = jstring_to_cpp_string(env, j_rec_model_path);
......@@ -21,6 +21,7 @@ Java_com_baidu_paddle_lite_demo_ocr_OCRPredictorNative_init(
int thread_num = j_thread_num;
std::string cpu_mode = jstring_to_cpp_string(env, j_cpu_mode);
ppredictor::OCR_Config conf;
conf.use_opencl = j_use_opencl;
conf.thread_num = thread_num;
conf.mode = str_to_cpu_mode(cpu_mode);
ppredictor::OCR_PPredictor *orc_predictor =
......@@ -57,32 +58,31 @@ str_to_cpu_mode(const std::string &cpu_mode) {
extern "C" JNIEXPORT jfloatArray JNICALL
Java_com_baidu_paddle_lite_demo_ocr_OCRPredictorNative_forward(
JNIEnv *env, jobject thiz, jlong java_pointer, jfloatArray buf,
jfloatArray ddims, jobject original_image) {
JNIEnv *env, jobject thiz, jlong java_pointer, jobject original_image,jint j_max_size_len, jint j_run_det, jint j_run_cls, jint j_run_rec) {
LOGI("begin to run native forward");
if (java_pointer == 0) {
LOGE("JAVA pointer is NULL");
return cpp_array_to_jfloatarray(env, nullptr, 0);
}
cv::Mat origin = bitmap_to_cv_mat(env, original_image);
if (origin.size == 0) {
LOGE("origin bitmap cannot convert to CV Mat");
return cpp_array_to_jfloatarray(env, nullptr, 0);
}
int max_size_len = j_max_size_len;
int run_det = j_run_det;
int run_cls = j_run_cls;
int run_rec = j_run_rec;
ppredictor::OCR_PPredictor *ppredictor =
(ppredictor::OCR_PPredictor *)java_pointer;
std::vector<float> dims_float_arr = jfloatarray_to_float_vector(env, ddims);
std::vector<int64_t> dims_arr;
dims_arr.resize(dims_float_arr.size());
std::copy(dims_float_arr.cbegin(), dims_float_arr.cend(), dims_arr.begin());
// 这里值有点大，就不调用jfloatarray_to_float_vector了
int64_t buf_len = (int64_t)env->GetArrayLength(buf);
jfloat *buf_data = env->GetFloatArrayElements(buf, JNI_FALSE);
float *data = (jfloat *)buf_data;
std::vector<ppredictor::OCRPredictResult> results =
ppredictor->infer_ocr(dims_arr, data, buf_len, NET_OCR, origin);
ppredictor->infer_ocr(origin, max_size_len, run_det, run_cls, run_rec);
LOGI("infer_ocr finished with boxes %ld", results.size());
// 这里将std::vector<ppredictor::OCRPredictResult> 序列化成
// float数组，传输到java层再反序列化
std::vector<float> float_arr;
......@@ -90,13 +90,18 @@ Java_com_baidu_paddle_lite_demo_ocr_OCRPredictorNative_forward(
float_arr.push_back(r.points.size());
float_arr.push_back(r.word_index.size());
float_arr.push_back(r.score);
// add det point
for (const std::vector<int> &point : r.points) {
float_arr.push_back(point.at(0));
float_arr.push_back(point.at(1));
}
// add rec word idx
for (int index : r.word_index) {
float_arr.push_back(index);
}
// add cls result
float_arr.push_back(r.cls_label);
float_arr.push_back(r.cls_score);
}
return cpp_array_to_jfloatarray(env, float_arr.data(), float_arr.size());
}
......
deploy/android_demo/app/src/main/cpp/ocr_ppredictor.cpp
View file @ 191c9dee
......@@ -17,15 +17,15 @@ int OCR_PPredictor::init(const std::string &det_model_content,
const std::string &rec_model_content,
const std::string &cls_model_content) {
_det_predictor = std::unique_ptr<PPredictor>(
new PPredictor{_config.thread_num, NET_OCR, _config.mode});
new PPredictor{_config.use_opencl,_config.thread_num, NET_OCR, _config.mode});
_det_predictor->init_nb(det_model_content);
_rec_predictor = std::unique_ptr<PPredictor>(
new PPredictor{_config.thread_num, NET_OCR_INTERNAL, _config.mode});
new PPredictor{_config.use_opencl,_config.thread_num, NET_OCR_INTERNAL, _config.mode});
_rec_predictor->init_nb(rec_model_content);
_cls_predictor = std::unique_ptr<PPredictor>(
new PPredictor{_config.thread_num, NET_OCR_INTERNAL, _config.mode});
new PPredictor{_config.use_opencl,_config.thread_num, NET_OCR_INTERNAL, _config.mode});
_cls_predictor->init_nb(cls_model_content);
return RETURN_OK;
}
......@@ -34,15 +34,16 @@ int OCR_PPredictor::init_from_file(const std::string &det_model_path,
const std::string &rec_model_path,
const std::string &cls_model_path) {
_det_predictor = std::unique_ptr<PPredictor>(
new PPredictor{_config.thread_num, NET_OCR, _config.mode});
new PPredictor{_config.use_opencl, _config.thread_num, NET_OCR, _config.mode});
_det_predictor->init_from_file(det_model_path);
_rec_predictor = std::unique_ptr<PPredictor>(
new PPredictor{_config.thread_num, NET_OCR_INTERNAL, _config.mode});
new PPredictor{_config.use_opencl,_config.thread_num, NET_OCR_INTERNAL, _config.mode});
_rec_predictor->init_from_file(rec_model_path);
_cls_predictor = std::unique_ptr<PPredictor>(
new PPredictor{_config.thread_num, NET_OCR_INTERNAL, _config.mode});
new PPredictor{_config.use_opencl,_config.thread_num, NET_OCR_INTERNAL, _config.mode});
_cls_predictor->init_from_file(cls_model_path);
return RETURN_OK;
}
......@@ -77,33 +78,126 @@ visual_img(const std::vector<std::vector<std::vector<int>>> &filter_boxes,
}
std::vector<OCRPredictResult>
OCR_PPredictor::infer_ocr(const std::vector<int64_t> &dims,
const float *input_data, int input_len, int net_flag,
cv::Mat &origin) {
OCR_PPredictor::infer_ocr(cv::Mat &origin,int max_size_len, int run_det, int run_cls, int run_rec) {
LOGI("ocr cpp start *****************");
LOGI("ocr cpp det: %d, cls: %d, rec: %d", run_det, run_cls, run_rec);
std::vector<OCRPredictResult> ocr_results;
if(run_det){
infer_det(origin, max_size_len, ocr_results);
}
if(run_rec){
if(ocr_results.size()==0){
OCRPredictResult res;
ocr_results.emplace_back(std::move(res));
}
for(int i = 0; i < ocr_results.size();i++) {
infer_rec(origin, run_cls, ocr_results[i]);
}
}else if(run_cls){
ClsPredictResult cls_res = infer_cls(origin);
OCRPredictResult res;
res.cls_score = cls_res.cls_score;
res.cls_label = cls_res.cls_label;
ocr_results.push_back(res);
}
LOGI("ocr cpp end *****************");
return ocr_results;
}
cv::Mat DetResizeImg(const cv::Mat img, int max_size_len,
std::vector<float> &ratio_hw) {
int w = img.cols;
int h = img.rows;
float ratio = 1.f;
int max_wh = w >= h ? w : h;
if (max_wh > max_size_len) {
if (h > w) {
ratio = static_cast<float>(max_size_len) / static_cast<float>(h);
} else {
ratio = static_cast<float>(max_size_len) / static_cast<float>(w);
}
}
int resize_h = static_cast<int>(float(h) * ratio);
int resize_w = static_cast<int>(float(w) * ratio);
if (resize_h % 32 == 0)
resize_h = resize_h;
else if (resize_h / 32 < 1 + 1e-5)
resize_h = 32;
else
resize_h = (resize_h / 32 - 1) * 32;
if (resize_w % 32 == 0)
resize_w = resize_w;
else if (resize_w / 32 < 1 + 1e-5)
resize_w = 32;
else
resize_w = (resize_w / 32 - 1) * 32;
cv::Mat resize_img;
cv::resize(img, resize_img, cv::Size(resize_w, resize_h));
ratio_hw.push_back(static_cast<float>(resize_h) / static_cast<float>(h));
ratio_hw.push_back(static_cast<float>(resize_w) / static_cast<float>(w));
return resize_img;
}
void OCR_PPredictor::infer_det(cv::Mat &origin, int max_size_len, std::vector<OCRPredictResult> &ocr_results) {
std::vector<float> mean = {0.485f, 0.456f, 0.406f};
std::vector<float> scale = {1 / 0.229f, 1 / 0.224f, 1 / 0.225f};
PredictorInput input = _det_predictor->get_first_input();
input.set_dims(dims);
input.set_data(input_data, input_len);
std::vector<float> ratio_hw;
cv::Mat input_image = DetResizeImg(origin, max_size_len, ratio_hw);
input_image.convertTo(input_image, CV_32FC3, 1 / 255.0f);
const float *dimg = reinterpret_cast<const float *>(input_image.data);
int input_size = input_image.rows * input_image.cols;
input.set_dims({1, 3, input_image.rows, input_image.cols});
neon_mean_scale(dimg, input.get_mutable_float_data(), input_size, mean,
scale);
LOGI("ocr cpp det shape %d,%d", input_image.rows,input_image.cols);
std::vector<PredictorOutput> results = _det_predictor->infer();
PredictorOutput &res = results.at(0);
std::vector<std::vector<std::vector<int>>> filtered_box = calc_filtered_boxes(
res.get_float_data(), res.get_size(), (int)dims[2], (int)dims[3], origin);
LOGI("Filter_box size %ld", filtered_box.size());
return infer_rec(filtered_box, origin);
res.get_float_data(), res.get_size(), input_image.rows, input_image.cols, origin);
LOGI("ocr cpp det Filter_box size %ld", filtered_box.size());
for(int i = 0;i<filtered_box.size();i++){
LOGI("ocr cpp box %d,%d,%d,%d,%d,%d,%d,%d", filtered_box[i][0][0],filtered_box[i][0][1], filtered_box[i][1][0],filtered_box[i][1][1], filtered_box[i][2][0],filtered_box[i][2][1], filtered_box[i][3][0],filtered_box[i][3][1]);
OCRPredictResult res;
res.points = filtered_box[i];
ocr_results.push_back(res);
}
}
std::vector<OCRPredictResult> OCR_PPredictor::infer_rec(
const std::vector<std::vector<std::vector<int>>> &boxes,
const cv::Mat &origin_img) {
void OCR_PPredictor::infer_rec(const cv::Mat &origin_img, int run_cls, OCRPredictResult& ocr_result) {
std::vector<float> mean = {0.5f, 0.5f, 0.5f};
std::vector<float> scale = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f};
std::vector<int64_t> dims = {1, 3, 0, 0};
std::vector<OCRPredictResult> ocr_results;
PredictorInput input = _rec_predictor->get_first_input();
for (auto bp = boxes.crbegin(); bp != boxes.crend(); ++bp) {
const std::vector<std::vector<int>> &box = *bp;
cv::Mat crop_img = get_rotate_crop_image(origin_img, box);
crop_img = infer_cls(crop_img);
const std::vector<std::vector<int>> &box = ocr_result.points;
cv::Mat crop_img;
if(box.size()>0){
crop_img = get_rotate_crop_image(origin_img, box);
}
else{
crop_img = origin_img;
}
if(run_cls){
ClsPredictResult cls_res = infer_cls(crop_img);
crop_img = cls_res.img;
ocr_result.cls_score = cls_res.cls_score;
ocr_result.cls_label = cls_res.cls_label;
}
float wh_ratio = float(crop_img.cols) / float(crop_img.rows);
cv::Mat input_image = crnn_resize_img(crop_img, wh_ratio);
......@@ -122,8 +216,6 @@ std::vector<OCRPredictResult> OCR_PPredictor::infer_rec(
const float *predict_batch = results.at(0).get_float_data();
const std::vector<int64_t> predict_shape = results.at(0).get_shape();
OCRPredictResult res;
// ctc decode
int argmax_idx;
int last_index = 0;
......@@ -140,27 +232,19 @@ std::vector<OCRPredictResult> OCR_PPredictor::infer_rec(
if (argmax_idx > 0 && (!(n > 0 && argmax_idx == last_index))) {
score += max_value;
count += 1;
res.word_index.push_back(argmax_idx);
ocr_result.word_index.push_back(argmax_idx);
}
last_index = argmax_idx;
}
score /= count;
if (res.word_index.empty()) {
continue;
}
res.score = score;
res.points = box;
ocr_results.emplace_back(std::move(res));
}
LOGI("ocr_results finished %lu", ocr_results.size());
return ocr_results;
ocr_result.score = score;
LOGI("ocr cpp rec word size %ld", count);
}
cv::Mat OCR_PPredictor::infer_cls(const cv::Mat &img, float thresh) {
ClsPredictResult OCR_PPredictor::infer_cls(const cv::Mat &img, float thresh) {
std::vector<float> mean = {0.5f, 0.5f, 0.5f};
std::vector<float> scale = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f};
std::vector<int64_t> dims = {1, 3, 0, 0};
std::vector<OCRPredictResult> ocr_results;
PredictorInput input = _cls_predictor->get_first_input();
......@@ -182,7 +266,7 @@ cv::Mat OCR_PPredictor::infer_cls(const cv::Mat &img, float thresh) {
float score = 0;
int label = 0;
for (int64_t i = 0; i < results.at(0).get_size(); i++) {
LOGI("output scores [%f]", scores[i]);
LOGI("ocr cpp cls output scores [%f]", scores[i]);
if (scores[i] > score) {
score = scores[i];
label = i;
......@@ -193,7 +277,12 @@ cv::Mat OCR_PPredictor::infer_cls(const cv::Mat &img, float thresh) {
if (label % 2 == 1 && score > thresh) {
cv::rotate(srcimg, srcimg, 1);
}
return srcimg;
ClsPredictResult res;
res.cls_label = label;
res.cls_score = score;
res.img = srcimg;
LOGI("ocr cpp cls word cls %ld, %f", label, score);
return res;
}
std::vector<std::vector<std::vector<int>>>
......
deploy/android_demo/app/src/main/cpp/ocr_ppredictor.h
View file @ 191c9dee
......@@ -15,6 +15,7 @@ namespace ppredictor {
* Config
*/
struct OCR_Config {
int use_opencl = 0;
int thread_num = 4; // Thread num
paddle::lite_api::PowerMode mode =
paddle::lite_api::LITE_POWER_HIGH; // PaddleLite Mode
......@@ -27,8 +28,15 @@ struct OCRPredictResult {
std::vector<int> word_index;
std::vector<std::vector<int>> points;
float score;
float cls_score;
int cls_label=-1;
};
struct ClsPredictResult {
float cls_score;
int cls_label=-1;
cv::Mat img;
};
/**
* OCR there are 2 models
* 1. First model（det），select polygones to show where are the texts
......@@ -62,8 +70,7 @@ public:
* @return
*/
virtual std::vector<OCRPredictResult>
infer_ocr(const std::vector<int64_t> &dims, const float *input_data,
int input_len, int net_flag, cv::Mat &origin);
infer_ocr(cv::Mat &origin, int max_size_len, int run_det, int run_cls, int run_rec);
virtual NET_TYPE get_net_flag() const;
......@@ -80,16 +87,17 @@ private:
calc_filtered_boxes(const float *pred, int pred_size, int output_height,
int output_width, const cv::Mat &origin);
void
infer_det(cv::Mat &origin, int max_side_len, std::vector<OCRPredictResult>& ocr_results);
/**
* infer for second model
* infer for rec model
*
* @param boxes
* @param origin
* @return
*/
std::vector<OCRPredictResult>
infer_rec(const std::vector<std::vector<std::vector<int>>> &boxes,
const cv::Mat &origin);
void
infer_rec(const cv::Mat &origin, int run_cls, OCRPredictResult& ocr_result);
/**
* infer for cls model
......@@ -98,7 +106,7 @@ private:
* @param origin
* @return
*/
cv::Mat infer_cls(const cv::Mat &origin, float thresh = 0.9);
ClsPredictResult infer_cls(const cv::Mat &origin, float thresh = 0.9);
/**
* Postprocess or sencod model to extract text
......
deploy/android_demo/app/src/main/cpp/ppredictor.cpp
View file @ 191c9dee
......@@ -2,9 +2,9 @@
#include "common.h"
namespace ppredictor {
PPredictor::PPredictor(int thread_num, int net_flag,
PPredictor::PPredictor(int use_opencl, int thread_num, int net_flag,
paddle::lite_api::PowerMode mode)
: _thread_num(thread_num), _net_flag(net_flag), _mode(mode) {}
: _use_opencl(use_opencl), _thread_num(thread_num), _net_flag(net_flag), _mode(mode) {}
int PPredictor::init_nb(const std::string &model_content) {
paddle::lite_api::MobileConfig config;
......@@ -19,10 +19,40 @@ int PPredictor::init_from_file(const std::string &model_content) {
}
template <typename ConfigT> int PPredictor::_init(ConfigT &config) {
bool is_opencl_backend_valid = paddle::lite_api::IsOpenCLBackendValid(/*check_fp16_valid = false*/);
if (is_opencl_backend_valid) {
if (_use_opencl != 0) {
// Make sure you have write permission of the binary path.
// We strongly recommend each model has a unique binary name.
const std::string bin_path = "/data/local/tmp/";
const std::string bin_name = "lite_opencl_kernel.bin";
config.set_opencl_binary_path_name(bin_path, bin_name);
// opencl tune option
// CL_TUNE_NONE: 0
// CL_TUNE_RAPID: 1
// CL_TUNE_NORMAL: 2
// CL_TUNE_EXHAUSTIVE: 3
const std::string tuned_path = "/data/local/tmp/";
const std::string tuned_name = "lite_opencl_tuned.bin";
config.set_opencl_tune(paddle::lite_api::CL_TUNE_NORMAL, tuned_path, tuned_name);
// opencl precision option
// CL_PRECISION_AUTO: 0, first fp16 if valid, default
// CL_PRECISION_FP32: 1, force fp32
// CL_PRECISION_FP16: 2, force fp16
config.set_opencl_precision(paddle::lite_api::CL_PRECISION_FP32);
LOGI("ocr cpp device: running on gpu.");
}
} else {
LOGI("ocr cpp device: running on cpu.");
// you can give backup cpu nb model instead
// config.set_model_from_file(cpu_nb_model_dir);
}
config.set_threads(_thread_num);
config.set_power_mode(_mode);
_predictor = paddle::lite_api::CreatePaddlePredictor(config);
LOGI("paddle instance created");
LOGI("ocr cpp paddle instance created");
return RETURN_OK;
}
......@@ -43,18 +73,18 @@ std::vector<PredictorInput> PPredictor::get_inputs(int num) {
PredictorInput PPredictor::get_first_input() { return get_input(0); }
std::vector<PredictorOutput> PPredictor::infer() {
LOGI("infer Run start %d", _net_flag);
LOGI("ocr cpp infer Run start %d", _net_flag);
std::vector<PredictorOutput> results;
if (!_is_input_get) {
return results;
}
_predictor->Run();
LOGI("infer Run end");
LOGI("ocr cpp infer Run end");
for (int i = 0; i < _predictor->GetOutputNames().size(); i++) {
std::unique_ptr<const paddle::lite_api::Tensor> output_tensor =
_predictor->GetOutput(i);
LOGI("output tensor[%d] size %ld", i, product(output_tensor->shape()));
LOGI("ocr cpp output tensor[%d] size %ld", i, product(output_tensor->shape()));
PredictorOutput result{std::move(output_tensor), i, _net_flag};
results.emplace_back(std::move(result));
}
......
deploy/android_demo/app/src/main/cpp/ppredictor.h
View file @ 191c9dee
......@@ -22,7 +22,7 @@ public:
class PPredictor : public PPredictor_Interface {
public:
PPredictor(
int thread_num, int net_flag = 0,
int use_opencl, int thread_num, int net_flag = 0,
paddle::lite_api::PowerMode mode = paddle::lite_api::LITE_POWER_HIGH);
virtual ~PPredictor() {}
......@@ -54,6 +54,7 @@ protected:
template <typename ConfigT> int _init(ConfigT &config);
private:
int _use_opencl;
int _thread_num;
paddle::lite_api::PowerMode _mode;
std::shared_ptr<paddle::lite_api::PaddlePredictor> _predictor;
......
deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/MainActivity.java
View file @ 191c9dee
......@@ -13,6 +13,7 @@ import android.graphics.BitmapFactory;
import android.graphics.drawable.BitmapDrawable;
import android.media.ExifInterface;
import android.content.res.AssetManager;
import android.media.FaceDetector;
import android.net.Uri;
import android.os.Bundle;
import android.os.Environment;
......@@ -27,7 +28,9 @@ import android.view.Menu;
import android.view.MenuInflater;
import android.view.MenuItem;
import android.view.View;
import android.widget.CheckBox;
import android.widget.ImageView;
import android.widget.Spinner;
import android.widget.TextView;
import android.widget.Toast;
......@@ -68,23 +71,24 @@ public class MainActivity extends AppCompatActivity {
protected ImageView ivInputImage;
protected TextView tvOutputResult;
protected TextView tvInferenceTime;
protected CheckBox cbOpencl;
protected Spinner spRunMode;
// Model settings of object detection
// Model settings of ocr
protected String modelPath = "";
protected String labelPath = "";
protected String imagePath = "";
protected int cpuThreadNum = 1;
protected String cpuPowerMode = "";
protected String inputColorFormat = "";
protected long[] inputShape = new long[]{};
protected float[] inputMean = new float[]{};
protected float[] inputStd = new float[]{};
protected int detLongSize = 960;
protected float scoreThreshold = 0.1f;
private String currentPhotoPath;
private AssetManager assetManager =null;
private AssetManager assetManager = null;
protected Predictor predictor = new Predictor();
private Bitmap cur_predict_image = null;
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
......@@ -98,10 +102,12 @@ public class MainActivity extends AppCompatActivity {
// Setup the UI components
tvInputSetting = findViewById(R.id.tv_input_setting);
cbOpencl = findViewById(R.id.cb_opencl);
tvStatus = findViewById(R.id.tv_model_img_status);
ivInputImage = findViewById(R.id.iv_input_image);
tvInferenceTime = findViewById(R.id.tv_inference_time);
tvOutputResult = findViewById(R.id.tv_output_result);
spRunMode = findViewById(R.id.sp_run_mode);
tvInputSetting.setMovementMethod(ScrollingMovementMethod.getInstance());
tvOutputResult.setMovementMethod(ScrollingMovementMethod.getInstance());
......@@ -111,26 +117,26 @@ public class MainActivity extends AppCompatActivity {
public void handleMessage(Message msg) {
switch (msg.what) {
case RESPONSE_LOAD_MODEL_SUCCESSED:
if(pbLoadModel!=null && pbLoadModel.isShowing()){
if (pbLoadModel != null && pbLoadModel.isShowing()) {
pbLoadModel.dismiss();
}
onLoadModelSuccessed();
break;
case RESPONSE_LOAD_MODEL_FAILED:
if(pbLoadModel!=null && pbLoadModel.isShowing()){
if (pbLoadModel != null && pbLoadModel.isShowing()) {
pbLoadModel.dismiss();
}
Toast.makeText(MainActivity.this, "Load model failed!", Toast.LENGTH_SHORT).show();
onLoadModelFailed();
break;
case RESPONSE_RUN_MODEL_SUCCESSED:
if(pbRunModel!=null && pbRunModel.isShowing()){
if (pbRunModel != null && pbRunModel.isShowing()) {
pbRunModel.dismiss();
}
onRunModelSuccessed();
break;
case RESPONSE_RUN_MODEL_FAILED:
if(pbRunModel!=null && pbRunModel.isShowing()){
if (pbRunModel != null && pbRunModel.isShowing()) {
pbRunModel.dismiss();
}
Toast.makeText(MainActivity.this, "Run model failed!", Toast.LENGTH_SHORT).show();
......@@ -175,71 +181,47 @@ public class MainActivity extends AppCompatActivity {
super.onResume();
SharedPreferences sharedPreferences = PreferenceManager.getDefaultSharedPreferences(this);
boolean settingsChanged = false;
boolean model_settingsChanged = false;
String model_path = sharedPreferences.getString(getString(R.string.MODEL_PATH_KEY),
getString(R.string.MODEL_PATH_DEFAULT));
String label_path = sharedPreferences.getString(getString(R.string.LABEL_PATH_KEY),
getString(R.string.LABEL_PATH_DEFAULT));
String image_path = sharedPreferences.getString(getString(R.string.IMAGE_PATH_KEY),
getString(R.string.IMAGE_PATH_DEFAULT));
settingsChanged |= !model_path.equalsIgnoreCase(modelPath);
model_settingsChanged |= !model_path.equalsIgnoreCase(modelPath);
settingsChanged |= !label_path.equalsIgnoreCase(labelPath);
settingsChanged |= !image_path.equalsIgnoreCase(imagePath);
int cpu_thread_num = Integer.parseInt(sharedPreferences.getString(getString(R.string.CPU_THREAD_NUM_KEY),
getString(R.string.CPU_THREAD_NUM_DEFAULT)));
settingsChanged |= cpu_thread_num != cpuThreadNum;
model_settingsChanged |= cpu_thread_num != cpuThreadNum;
String cpu_power_mode =
sharedPreferences.getString(getString(R.string.CPU_POWER_MODE_KEY),
getString(R.string.CPU_POWER_MODE_DEFAULT));
settingsChanged |= !cpu_power_mode.equalsIgnoreCase(cpuPowerMode);
String input_color_format =
sharedPreferences.getString(getString(R.string.INPUT_COLOR_FORMAT_KEY),
getString(R.string.INPUT_COLOR_FORMAT_DEFAULT));
settingsChanged |= !input_color_format.equalsIgnoreCase(inputColorFormat);
long[] input_shape =
Utils.parseLongsFromString(sharedPreferences.getString(getString(R.string.INPUT_SHAPE_KEY),
getString(R.string.INPUT_SHAPE_DEFAULT)), ",");
float[] input_mean =
Utils.parseFloatsFromString(sharedPreferences.getString(getString(R.string.INPUT_MEAN_KEY),
getString(R.string.INPUT_MEAN_DEFAULT)), ",");
float[] input_std =
Utils.parseFloatsFromString(sharedPreferences.getString(getString(R.string.INPUT_STD_KEY)
, getString(R.string.INPUT_STD_DEFAULT)), ",");
settingsChanged |= input_shape.length != inputShape.length;
settingsChanged |= input_mean.length != inputMean.length;
settingsChanged |= input_std.length != inputStd.length;
if (!settingsChanged) {
for (int i = 0; i < input_shape.length; i++) {
settingsChanged |= input_shape[i] != inputShape[i];
}
for (int i = 0; i < input_mean.length; i++) {
settingsChanged |= input_mean[i] != inputMean[i];
}
for (int i = 0; i < input_std.length; i++) {
settingsChanged |= input_std[i] != inputStd[i];
}
}
model_settingsChanged |= !cpu_power_mode.equalsIgnoreCase(cpuPowerMode);
int det_long_size = Integer.parseInt(sharedPreferences.getString(getString(R.string.DET_LONG_SIZE_KEY),
getString(R.string.DET_LONG_SIZE_DEFAULT)));
settingsChanged |= det_long_size != detLongSize;
float score_threshold =
Float.parseFloat(sharedPreferences.getString(getString(R.string.SCORE_THRESHOLD_KEY),
getString(R.string.SCORE_THRESHOLD_DEFAULT)));
settingsChanged |= scoreThreshold != score_threshold;
if (settingsChanged) {
modelPath = model_path;
labelPath = label_path;
imagePath = image_path;
detLongSize = det_long_size;
scoreThreshold = score_threshold;
set_img();
}
if (model_settingsChanged) {
modelPath = model_path;
cpuThreadNum = cpu_thread_num;
cpuPowerMode = cpu_power_mode;
inputColorFormat = input_color_format;
inputShape = input_shape;
inputMean = input_mean;
inputStd = input_std;
scoreThreshold = score_threshold;
// Update UI
tvInputSetting.setText("Model: " + modelPath.substring(modelPath.lastIndexOf("/") + 1) + "\n" + "CPU" +
" Thread Num: " + Integer.toString(cpuThreadNum) + "\n" + "CPU Power Mode: " + cpuPowerMode);
tvInputSetting.setText("Model: " + modelPath.substring(modelPath.lastIndexOf("/") + 1) + "\nOPENCL: " + cbOpencl.isChecked() + "\nCPU Thread Num: " + cpuThreadNum + "\nCPU Power Mode: " + cpuPowerMode);
tvInputSetting.scrollTo(0, 0);
// Reload model if configure has been changed
// loadModel();
set_img();
loadModel();
}
}
......@@ -254,20 +236,28 @@ public class MainActivity extends AppCompatActivity {
}
public boolean onLoadModel() {
return predictor.init(MainActivity.this, modelPath, labelPath, cpuThreadNum,
if (predictor.isLoaded()) {
predictor.releaseModel();
}
return predictor.init(MainActivity.this, modelPath, labelPath, cbOpencl.isChecked() ? 1 : 0, cpuThreadNum,
cpuPowerMode,
inputColorFormat,
inputShape, inputMean,
inputStd, scoreThreshold);
detLongSize, scoreThreshold);
}
public boolean onRunModel() {
return predictor.isLoaded() && predictor.runModel();
String run_mode = spRunMode.getSelectedItem().toString();
int run_det = run_mode.contains("检测") ? 1 : 0;
int run_cls = run_mode.contains("分类") ? 1 : 0;
int run_rec = run_mode.contains("识别") ? 1 : 0;
return predictor.isLoaded() && predictor.runModel(run_det, run_cls, run_rec);
}
public void onLoadModelSuccessed() {
// Load test image from path and run model
tvInputSetting.setText("Model: " + modelPath.substring(modelPath.lastIndexOf("/") + 1) + "\nOPENCL: " + cbOpencl.isChecked() + "\nCPU Thread Num: " + cpuThreadNum + "\nCPU Power Mode: " + cpuPowerMode);
tvInputSetting.scrollTo(0, 0);
tvStatus.setText("STATUS: load model successed");
}
public void onLoadModelFailed() {
......@@ -290,20 +280,13 @@ public class MainActivity extends AppCompatActivity {
tvStatus.setText("STATUS: run model failed");
}
public void onImageChanged(Bitmap image) {
// Rerun model if users pick test image from gallery or camera
if (image != null && predictor.isLoaded()) {
predictor.setInputImage(image);
runModel();
}
}
public void set_img() {
// Load test image from path and run model
try {
assetManager= getAssets();
InputStream in=assetManager.open(imagePath);
Bitmap bmp=BitmapFactory.decodeStream(in);
assetManager = getAssets();
InputStream in = assetManager.open(imagePath);
Bitmap bmp = BitmapFactory.decodeStream(in);
cur_predict_image = bmp;
ivInputImage.setImageBitmap(bmp);
} catch (IOException e) {
Toast.makeText(MainActivity.this, "Load image failed!", Toast.LENGTH_SHORT).show();
......@@ -430,7 +413,7 @@ public class MainActivity extends AppCompatActivity {
Cursor cursor = managedQuery(uri, proj, null, null, null);
cursor.moveToFirst();
if (image != null) {
// onImageChanged(image);
cur_predict_image = image;
ivInputImage.setImageBitmap(image);
}
} catch (IOException e) {
......@@ -451,7 +434,7 @@ public class MainActivity extends AppCompatActivity {
Bitmap image = BitmapFactory.decodeFile(currentPhotoPath);
image = Utils.rotateBitmap(image, orientation);
if (image != null) {
// onImageChanged(image);
cur_predict_image = image;
ivInputImage.setImageBitmap(image);
}
} else {
......@@ -464,28 +447,28 @@ public class MainActivity extends AppCompatActivity {
}
}
public void btn_load_model_click(View view) {
if (predictor.isLoaded()){
tvStatus.setText("STATUS: model has been loaded");
}else{
public void btn_reset_img_click(View view) {
ivInputImage.setImageBitmap(cur_predict_image);
}
public void cb_opencl_click(View view) {
tvStatus.setText("STATUS: load model ......");
loadModel();
}
}
public void btn_run_model_click(View view) {
Bitmap image =((BitmapDrawable)ivInputImage.getDrawable()).getBitmap();
if(image == null) {
Bitmap image = ((BitmapDrawable) ivInputImage.getDrawable()).getBitmap();
if (image == null) {
tvStatus.setText("STATUS: image is not exists");
}
else if (!predictor.isLoaded()){
} else if (!predictor.isLoaded()) {
tvStatus.setText("STATUS: model is not loaded");
}else{
} else {
tvStatus.setText("STATUS: run model ...... ");
predictor.setInputImage(image);
runModel();
}
}
public void btn_choice_img_click(View view) {
if (requestAllPermissions()) {
openGallery();
......@@ -506,4 +489,32 @@ public class MainActivity extends AppCompatActivity {
worker.quit();
super.onDestroy();
}
public int get_run_mode() {
String run_mode = spRunMode.getSelectedItem().toString();
int mode;
switch (run_mode) {
case "检测+分类+识别":
mode = 1;
break;
case "检测+识别":
mode = 2;
break;
case "识别+分类":
mode = 3;
break;
case "检测":
mode = 4;
break;
case "识别":
mode = 5;
break;
case "分类":
mode = 6;
break;
default:
mode = 1;
}
return mode;
}
}
deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/MiniActivity.java deleted 100644 → 0
View file @ 3c6d5512
package com.baidu.paddle.lite.demo.ocr;
import android.graphics.Bitmap;
import android.graphics.BitmapFactory;
import android.os.Build;
import android.os.Bundle;
import android.os.Handler;
import android.os.HandlerThread;
import android.os.Message;
import android.util.Log;
import android.view.View;
import android.widget.Button;
import android.widget.ImageView;
import android.widget.TextView;
import android.widget.Toast;
import androidx.appcompat.app.AppCompatActivity;
import java.io.IOException;
import java.io.InputStream;
public class MiniActivity extends AppCompatActivity {
public static final int REQUEST_LOAD_MODEL = 0;
public static final int REQUEST_RUN_MODEL = 1;
public static final int REQUEST_UNLOAD_MODEL = 2;
public static final int RESPONSE_LOAD_MODEL_SUCCESSED = 0;
public static final int RESPONSE_LOAD_MODEL_FAILED = 1;
public static final int RESPONSE_RUN_MODEL_SUCCESSED = 2;
public static final int RESPONSE_RUN_MODEL_FAILED = 3;
private static final String TAG = "MiniActivity";
protected Handler receiver = null; // Receive messages from worker thread
protected Handler sender = null; // Send command to worker thread
protected HandlerThread worker = null; // Worker thread to load&run model
protected volatile Predictor predictor = null;
private String assetModelDirPath = "models/ocr_v2_for_cpu";
private String assetlabelFilePath = "labels/ppocr_keys_v1.txt";
private Button button;
private ImageView imageView; // image result
private TextView textView; // text result
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_mini);
Log.i(TAG, "SHOW in Logcat");
// Prepare the worker thread for mode loading and inference
worker = new HandlerThread("Predictor Worker");
worker.start();
sender = new Handler(worker.getLooper()) {
public void handleMessage(Message msg) {
switch (msg.what) {
case REQUEST_LOAD_MODEL:
// Load model and reload test image
if (!onLoadModel()) {
runOnUiThread(new Runnable() {
@Override
public void run() {
Toast.makeText(MiniActivity.this, "Load model failed!", Toast.LENGTH_SHORT).show();
}
});
}
break;
case REQUEST_RUN_MODEL:
// Run model if model is loaded
final boolean isSuccessed = onRunModel();
runOnUiThread(new Runnable() {
@Override
public void run() {
if (isSuccessed){
onRunModelSuccessed();
}else{
Toast.makeText(MiniActivity.this, "Run model failed!", Toast.LENGTH_SHORT).show();
}
}
});
break;
}
}
};
sender.sendEmptyMessage(REQUEST_LOAD_MODEL); // corresponding to REQUEST_LOAD_MODEL， to call onLoadModel()
imageView = findViewById(R.id.imageView);
textView = findViewById(R.id.sample_text);
button = findViewById(R.id.button);
button.setOnClickListener(new View.OnClickListener() {
@Override
public void onClick(View v) {
sender.sendEmptyMessage(REQUEST_RUN_MODEL);
}
});
}
@Override
protected void onDestroy() {
onUnloadModel();
if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.JELLY_BEAN_MR2) {
worker.quitSafely();
} else {
worker.quit();
}
super.onDestroy();
}
/**
* call in onCreate, model init
*
* @return
*/
private boolean onLoadModel() {
if (predictor == null) {
predictor = new Predictor();
}
return predictor.init(this, assetModelDirPath, assetlabelFilePath);
}
/**
* init engine
* call in onCreate
*
* @return
*/
private boolean onRunModel() {
try {
String assetImagePath = "images/0.jpg";
InputStream imageStream = getAssets().open(assetImagePath);
Bitmap image = BitmapFactory.decodeStream(imageStream);
// Input is Bitmap
predictor.setInputImage(image);
return predictor.isLoaded() && predictor.runModel();
} catch (IOException e) {
e.printStackTrace();
return false;
}
}
private void onRunModelSuccessed() {
Log.i(TAG, "onRunModelSuccessed");
textView.setText(predictor.outputResult);
imageView.setImageBitmap(predictor.outputImage);
}
private void onUnloadModel() {
if (predictor != null) {
predictor.releaseModel();
}
}
}
deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/OCRPredictorNative.java
View file @ 191c9dee
......@@ -29,22 +29,22 @@ public class OCRPredictorNative {
public OCRPredictorNative(Config config) {
this.config = config;
loadLibrary();
nativePointer = init(config.detModelFilename, config.recModelFilename,config.clsModelFilename,
nativePointer = init(config.detModelFilename, config.recModelFilename, config.clsModelFilename, config.useOpencl,
config.cpuThreadNum, config.cpuPower);
Log.i("OCRPredictorNative", "load success " + nativePointer);
}
public ArrayList<OcrResultModel> runImage(float[] inputData, int width, int height, int channels, Bitmap originalImage) {
Log.i("OCRPredictorNative", "begin to run image " + inputData.length + " " + width + " " + height);
float[] dims = new float[]{1, channels, height, width};
float[] rawResults = forward(nativePointer, inputData, dims, originalImage);
public ArrayList<OcrResultModel> runImage(Bitmap originalImage, int max_size_len, int run_det, int run_cls, int run_rec) {
Log.i("OCRPredictorNative", "begin to run image ");
float[] rawResults = forward(nativePointer, originalImage, max_size_len, run_det, run_cls, run_rec);
ArrayList<OcrResultModel> results = postprocess(rawResults);
return results;
}
public static class Config {
public int useOpencl;
public int cpuThreadNum;
public String cpuPower;
public String detModelFilename;
......@@ -53,16 +53,16 @@ public class OCRPredictorNative {
}
public void destory(){
public void destory() {
if (nativePointer > 0) {
release(nativePointer);
nativePointer = 0;
}
}
protected native long init(String detModelPath, String recModelPath,String clsModelPath, int threadNum, String cpuMode);
protected native long init(String detModelPath, String recModelPath, String clsModelPath, int useOpencl, int threadNum, String cpuMode);
protected native float[] forward(long pointer, float[] buf, float[] ddims, Bitmap originalImage);
protected native float[] forward(long pointer, Bitmap originalImage,int max_size_len, int run_det, int run_cls, int run_rec);
protected native void release(long pointer);
......@@ -73,9 +73,9 @@ public class OCRPredictorNative {
while (begin < raw.length) {
int point_num = Math.round(raw[begin]);
int word_num = Math.round(raw[begin + 1]);
OcrResultModel model = parse(raw, begin + 2, point_num, word_num);
begin += 2 + 1 + point_num * 2 + word_num;
results.add(model);
OcrResultModel res = parse(raw, begin + 2, point_num, word_num);
begin += 2 + 1 + point_num * 2 + word_num + 2;
results.add(res);
}
return results;
......@@ -83,19 +83,22 @@ public class OCRPredictorNative {
private OcrResultModel parse(float[] raw, int begin, int pointNum, int wordNum) {
int current = begin;
OcrResultModel model = new OcrResultModel();
model.setConfidence(raw[current]);
OcrResultModel res = new OcrResultModel();
res.setConfidence(raw[current]);
current++;
for (int i = 0; i < pointNum; i++) {
model.addPoints(Math.round(raw[current + i * 2]), Math.round(raw[current + i * 2 + 1]));
res.addPoints(Math.round(raw[current + i * 2]), Math.round(raw[current + i * 2 + 1]));
}
current += (pointNum * 2);
for (int i = 0; i < wordNum; i++) {
int index = Math.round(raw[current + i]);
model.addWordIndex(index);
res.addWordIndex(index);
}
current += wordNum;
res.setClsIdx(raw[current]);
res.setClsConfidence(raw[current + 1]);
Log.i("OCRPredictorNative", "word finished " + wordNum);
return model;
return res;
}
......
deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/OcrResultModel.java
View file @ 191c9dee
......@@ -10,6 +10,9 @@ public class OcrResultModel {
private List<Integer> wordIndex;
private String label;
private float confidence;
private float cls_idx;
private String cls_label;
private float cls_confidence;
public OcrResultModel() {
super();
......@@ -49,4 +52,28 @@ public class OcrResultModel {
public void setConfidence(float confidence) {
this.confidence = confidence;
}
public float getClsIdx() {
return cls_idx;
}
public void setClsIdx(float idx) {
this.cls_idx = idx;
}
public String getClsLabel() {
return cls_label;
}
public void setClsLabel(String label) {
this.cls_label = label;
}
public float getClsConfidence() {
return cls_confidence;
}
public void setClsConfidence(float confidence) {
this.cls_confidence = confidence;
}
}
deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/Predictor.java
View file @ 191c9dee
......@@ -31,23 +31,19 @@ public class Predictor {
protected float inferenceTime = 0;
// Only for object detection
protected Vector<String> wordLabels = new Vector<String>();
protected String inputColorFormat = "BGR";
protected long[] inputShape = new long[]{1, 3, 960};
protected float[] inputMean = new float[]{0.485f, 0.456f, 0.406f};
protected float[] inputStd = new float[]{1.0f / 0.229f, 1.0f / 0.224f, 1.0f / 0.225f};
protected int detLongSize = 960;
protected float scoreThreshold = 0.1f;
protected Bitmap inputImage = null;
protected Bitmap outputImage = null;
protected volatile String outputResult = "";
protected float preprocessTime = 0;
protected float postprocessTime = 0;
public Predictor() {
}
public boolean init(Context appCtx, String modelPath, String labelPath) {
isLoaded = loadModel(appCtx, modelPath, cpuThreadNum, cpuPowerMode);
public boolean init(Context appCtx, String modelPath, String labelPath, int useOpencl, int cpuThreadNum, String cpuPowerMode) {
isLoaded = loadModel(appCtx, modelPath, useOpencl, cpuThreadNum, cpuPowerMode);
if (!isLoaded) {
return false;
}
......@@ -56,49 +52,18 @@ public class Predictor {
}
public boolean init(Context appCtx, String modelPath, String labelPath, int cpuThreadNum, String cpuPowerMode,
String inputColorFormat,
long[] inputShape, float[] inputMean,
float[] inputStd, float scoreThreshold) {
if (inputShape.length != 3) {
Log.e(TAG, "Size of input shape should be: 3");
return false;
}
if (inputMean.length != inputShape[1]) {
Log.e(TAG, "Size of input mean should be: " + Long.toString(inputShape[1]));
return false;
}
if (inputStd.length != inputShape[1]) {
Log.e(TAG, "Size of input std should be: " + Long.toString(inputShape[1]));
return false;
}
if (inputShape[0] != 1) {
Log.e(TAG, "Only one batch is supported in the image classification demo, you can use any batch size in " +
"your Apps!");
return false;
}
if (inputShape[1] != 1 && inputShape[1] != 3) {
Log.e(TAG, "Only one/three channels are supported in the image classification demo, you can use any " +
"channel size in your Apps!");
return false;
}
if (!inputColorFormat.equalsIgnoreCase("BGR")) {
Log.e(TAG, "Only BGR color format is supported.");
return false;
}
boolean isLoaded = init(appCtx, modelPath, labelPath);
public boolean init(Context appCtx, String modelPath, String labelPath, int useOpencl, int cpuThreadNum, String cpuPowerMode,
int detLongSize, float scoreThreshold) {
boolean isLoaded = init(appCtx, modelPath, labelPath, useOpencl, cpuThreadNum, cpuPowerMode);
if (!isLoaded) {
return false;
}
this.inputColorFormat = inputColorFormat;
this.inputShape = inputShape;
this.inputMean = inputMean;
this.inputStd = inputStd;
this.detLongSize = detLongSize;
this.scoreThreshold = scoreThreshold;
return true;
}
protected boolean loadModel(Context appCtx, String modelPath, int cpuThreadNum, String cpuPowerMode) {
protected boolean loadModel(Context appCtx, String modelPath, int useOpencl, int cpuThreadNum, String cpuPowerMode) {
// Release model if exists
releaseModel();
......@@ -118,12 +83,13 @@ public class Predictor {
}
OCRPredictorNative.Config config = new OCRPredictorNative.Config();
config.useOpencl = useOpencl;
config.cpuThreadNum = cpuThreadNum;
config.detModelFilename = realPath + File.separator + "ch_ppocr_mobile_v2.0_det_opt.nb";
config.recModelFilename = realPath + File.separator + "ch_ppocr_mobile_v2.0_rec_opt.nb";
config.clsModelFilename = realPath + File.separator + "ch_ppocr_mobile_v2.0_cls_opt.nb";
Log.e("Predictor", "model path" + config.detModelFilename + " ; " + config.recModelFilename + ";" + config.clsModelFilename);
config.cpuPower = cpuPowerMode;
config.detModelFilename = realPath + File.separator + "det_db.nb";
config.recModelFilename = realPath + File.separator + "rec_crnn.nb";
config.clsModelFilename = realPath + File.separator + "cls.nb";
Log.i("Predictor", "model path" + config.detModelFilename + " ; " + config.recModelFilename + ";" + config.clsModelFilename);
paddlePredictor = new OCRPredictorNative(config);
this.cpuThreadNum = cpuThreadNum;
......@@ -170,82 +136,29 @@ public class Predictor {
}
public boolean runModel() {
public boolean runModel(int run_det, int run_cls, int run_rec) {
if (inputImage == null || !isLoaded()) {
return false;
}
// Pre-process image, and feed input tensor with pre-processed data
Bitmap scaleImage = Utils.resizeWithStep(inputImage, Long.valueOf(inputShape[2]).intValue(), 32);
Date start = new Date();
int channels = (int) inputShape[1];
int width = scaleImage.getWidth();
int height = scaleImage.getHeight();
float[] inputData = new float[channels * width * height];
if (channels == 3) {
int[] channelIdx = null;
if (inputColorFormat.equalsIgnoreCase("RGB")) {
channelIdx = new int[]{0, 1, 2};
} else if (inputColorFormat.equalsIgnoreCase("BGR")) {
channelIdx = new int[]{2, 1, 0};
} else {
Log.i(TAG, "Unknown color format " + inputColorFormat + ", only RGB and BGR color format is " +
"supported!");
return false;
}
int[] channelStride = new int[]{width * height, width * height * 2};
int[] pixels=new int[width*height];
scaleImage.getPixels(pixels,0,scaleImage.getWidth(),0,0,scaleImage.getWidth(),scaleImage.getHeight());
for (int i = 0; i < pixels.length; i++) {
int color = pixels[i];
float[] rgb = new float[]{(float) red(color) / 255.0f, (float) green(color) / 255.0f,
(float) blue(color) / 255.0f};
inputData[i] = (rgb[channelIdx[0]] - inputMean[0]) / inputStd[0];
inputData[i + channelStride[0]] = (rgb[channelIdx[1]] - inputMean[1]) / inputStd[1];
inputData[i+ channelStride[1]] = (rgb[channelIdx[2]] - inputMean[2]) / inputStd[2];
}
} else if (channels == 1) {
int[] pixels=new int[width*height];
scaleImage.getPixels(pixels,0,scaleImage.getWidth(),0,0,scaleImage.getWidth(),scaleImage.getHeight());
for (int i = 0; i < pixels.length; i++) {
int color = pixels[i];
float gray = (float) (red(color) + green(color) + blue(color)) / 3.0f / 255.0f;
inputData[i] = (gray - inputMean[0]) / inputStd[0];
}
} else {
Log.i(TAG, "Unsupported channel size " + Integer.toString(channels) + ", only channel 1 and 3 is " +
"supported!");
return false;
}
float[] pixels = inputData;
Log.i(TAG, "pixels " + pixels[0] + " " + pixels[1] + " " + pixels[2] + " " + pixels[3]
+ " " + pixels[pixels.length / 2] + " " + pixels[pixels.length / 2 + 1] + " " + pixels[pixels.length - 2] + " " + pixels[pixels.length - 1]);
Date end = new Date();
preprocessTime = (float) (end.getTime() - start.getTime());
// Warm up
for (int i = 0; i < warmupIterNum; i++) {
paddlePredictor.runImage(inputData, width, height, channels, inputImage);
paddlePredictor.runImage(inputImage, detLongSize, run_det, run_cls, run_rec);
}
warmupIterNum = 0; // do not need warm
// Run inference
start = new Date();
ArrayList<OcrResultModel> results = paddlePredictor.runImage(inputData, width, height, channels, inputImage);
end = new Date();
Date start = new Date();
ArrayList<OcrResultModel> results = paddlePredictor.runImage(inputImage, detLongSize, run_det, run_cls, run_rec);
Date end = new Date();
inferenceTime = (end.getTime() - start.getTime()) / (float) inferIterNum;
results = postprocess(results);
Log.i(TAG, "[stat] Preprocess Time: " + preprocessTime
+ " ; Inference Time: " + inferenceTime + " ;Box Size " + results.size());
Log.i(TAG, "[stat] Inference Time: " + inferenceTime + " ;Box Size " + results.size());
drawResults(results);
return true;
}
public boolean isLoaded() {
return paddlePredictor != null && isLoaded;
}
......@@ -282,10 +195,6 @@ public class Predictor {
return outputResult;
}
public float preprocessTime() {
return preprocessTime;
}
public float postprocessTime() {
return postprocessTime;
}
......@@ -310,6 +219,7 @@ public class Predictor {
}
}
r.setLabel(word.toString());
r.setClsLabel(r.getClsIdx() == 1 ? "180" : "0");
}
return results;
}
......@@ -319,14 +229,22 @@ public class Predictor {
for (int i = 0; i < results.size(); i++) {
OcrResultModel result = results.get(i);
StringBuilder sb = new StringBuilder("");
sb.append(result.getLabel());
sb.append(" ").append(result.getConfidence());
sb.append("; Points: ");
if(result.getPoints().size()>0){
sb.append("Det: ");
for (Point p : result.getPoints()) {
sb.append("(").append(p.x).append(",").append(p.y).append(") ");
}
}
if(result.getLabel().length() > 0){
sb.append("\n Rec: ").append(result.getLabel());
sb.append(",").append(result.getConfidence());
}
if(result.getClsIdx()!=-1){
sb.append(" Cls: ").append(result.getClsLabel());
sb.append(",").append(result.getClsConfidence());
}
Log.i(TAG, sb.toString()); // show LOG in Logcat panel
outputResultSb.append(i + 1).append(": ").append(result.getLabel()).append("\n");
outputResultSb.append(i + 1).append(": ").append(sb.toString()).append("\n");
}
outputResult = outputResultSb.toString();
outputImage = inputImage;
......@@ -344,6 +262,9 @@ public class Predictor {
for (OcrResultModel result : results) {
Path path = new Path();
List<Point> points = result.getPoints();
if(points.size()==0){
continue;
}
path.moveTo(points.get(0).x, points.get(0).y);
for (int i = points.size() - 1; i >= 0; i--) {
Point p = points.get(i);
......
deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/SettingsActivity.java
View file @ 191c9dee
......@@ -20,16 +20,13 @@ public class SettingsActivity extends AppCompatPreferenceActivity implements Sha
ListPreference etImagePath = null;
ListPreference lpCPUThreadNum = null;
ListPreference lpCPUPowerMode = null;
ListPreference lpInputColorFormat = null;
EditTextPreference etInputShape = null;
EditTextPreference etInputMean = null;
EditTextPreference etInputStd = null;
EditTextPreference etDetLongSize = null;
EditTextPreference etScoreThreshold = null;
List<String> preInstalledModelPaths = null;
List<String> preInstalledLabelPaths = null;
List<String> preInstalledImagePaths = null;
List<String> preInstalledInputShapes = null;
List<String> preInstalledDetLongSizes = null;
List<String> preInstalledCPUThreadNums = null;
List<String> preInstalledCPUPowerModes = null;
List<String> preInstalledInputColorFormats = null;
......@@ -50,7 +47,7 @@ public class SettingsActivity extends AppCompatPreferenceActivity implements Sha
preInstalledModelPaths = new ArrayList<String>();
preInstalledLabelPaths = new ArrayList<String>();
preInstalledImagePaths = new ArrayList<String>();
preInstalledInputShapes = new ArrayList<String>();
preInstalledDetLongSizes = new ArrayList<String>();
preInstalledCPUThreadNums = new ArrayList<String>();
preInstalledCPUPowerModes = new ArrayList<String>();
preInstalledInputColorFormats = new ArrayList<String>();
......@@ -63,10 +60,7 @@ public class SettingsActivity extends AppCompatPreferenceActivity implements Sha
preInstalledImagePaths.add(getString(R.string.IMAGE_PATH_DEFAULT));
preInstalledCPUThreadNums.add(getString(R.string.CPU_THREAD_NUM_DEFAULT));
preInstalledCPUPowerModes.add(getString(R.string.CPU_POWER_MODE_DEFAULT));
preInstalledInputColorFormats.add(getString(R.string.INPUT_COLOR_FORMAT_DEFAULT));
preInstalledInputShapes.add(getString(R.string.INPUT_SHAPE_DEFAULT));
preInstalledInputMeans.add(getString(R.string.INPUT_MEAN_DEFAULT));
preInstalledInputStds.add(getString(R.string.INPUT_STD_DEFAULT));
preInstalledDetLongSizes.add(getString(R.string.DET_LONG_SIZE_DEFAULT));
preInstalledScoreThresholds.add(getString(R.string.SCORE_THRESHOLD_DEFAULT));
// Setup UI components
......@@ -89,11 +83,7 @@ public class SettingsActivity extends AppCompatPreferenceActivity implements Sha
(ListPreference) findPreference(getString(R.string.CPU_THREAD_NUM_KEY));
lpCPUPowerMode =
(ListPreference) findPreference(getString(R.string.CPU_POWER_MODE_KEY));
lpInputColorFormat =
(ListPreference) findPreference(getString(R.string.INPUT_COLOR_FORMAT_KEY));
etInputShape = (EditTextPreference) findPreference(getString(R.string.INPUT_SHAPE_KEY));
etInputMean = (EditTextPreference) findPreference(getString(R.string.INPUT_MEAN_KEY));
etInputStd = (EditTextPreference) findPreference(getString(R.string.INPUT_STD_KEY));
etDetLongSize = (EditTextPreference) findPreference(getString(R.string.DET_LONG_SIZE_KEY));
etScoreThreshold = (EditTextPreference) findPreference(getString(R.string.SCORE_THRESHOLD_KEY));
}
......@@ -112,11 +102,7 @@ public class SettingsActivity extends AppCompatPreferenceActivity implements Sha
editor.putString(getString(R.string.IMAGE_PATH_KEY), preInstalledImagePaths.get(modelIdx));
editor.putString(getString(R.string.CPU_THREAD_NUM_KEY), preInstalledCPUThreadNums.get(modelIdx));
editor.putString(getString(R.string.CPU_POWER_MODE_KEY), preInstalledCPUPowerModes.get(modelIdx));
editor.putString(getString(R.string.INPUT_COLOR_FORMAT_KEY),
preInstalledInputColorFormats.get(modelIdx));
editor.putString(getString(R.string.INPUT_SHAPE_KEY), preInstalledInputShapes.get(modelIdx));
editor.putString(getString(R.string.INPUT_MEAN_KEY), preInstalledInputMeans.get(modelIdx));
editor.putString(getString(R.string.INPUT_STD_KEY), preInstalledInputStds.get(modelIdx));
editor.putString(getString(R.string.DET_LONG_SIZE_KEY), preInstalledDetLongSizes.get(modelIdx));
editor.putString(getString(R.string.SCORE_THRESHOLD_KEY),
preInstalledScoreThresholds.get(modelIdx));
editor.apply();
......@@ -129,10 +115,7 @@ public class SettingsActivity extends AppCompatPreferenceActivity implements Sha
etImagePath.setEnabled(enableCustomSettings);
lpCPUThreadNum.setEnabled(enableCustomSettings);
lpCPUPowerMode.setEnabled(enableCustomSettings);
lpInputColorFormat.setEnabled(enableCustomSettings);
etInputShape.setEnabled(enableCustomSettings);
etInputMean.setEnabled(enableCustomSettings);
etInputStd.setEnabled(enableCustomSettings);
etDetLongSize.setEnabled(enableCustomSettings);
etScoreThreshold.setEnabled(enableCustomSettings);
modelPath = sharedPreferences.getString(getString(R.string.MODEL_PATH_KEY),
getString(R.string.MODEL_PATH_DEFAULT));
......@@ -144,14 +127,8 @@ public class SettingsActivity extends AppCompatPreferenceActivity implements Sha
getString(R.string.CPU_THREAD_NUM_DEFAULT));
String cpuPowerMode = sharedPreferences.getString(getString(R.string.CPU_POWER_MODE_KEY),
getString(R.string.CPU_POWER_MODE_DEFAULT));
String inputColorFormat = sharedPreferences.getString(getString(R.string.INPUT_COLOR_FORMAT_KEY),
getString(R.string.INPUT_COLOR_FORMAT_DEFAULT));
String inputShape = sharedPreferences.getString(getString(R.string.INPUT_SHAPE_KEY),
getString(R.string.INPUT_SHAPE_DEFAULT));
String inputMean = sharedPreferences.getString(getString(R.string.INPUT_MEAN_KEY),
getString(R.string.INPUT_MEAN_DEFAULT));
String inputStd = sharedPreferences.getString(getString(R.string.INPUT_STD_KEY),
getString(R.string.INPUT_STD_DEFAULT));
String detLongSize = sharedPreferences.getString(getString(R.string.DET_LONG_SIZE_KEY),
getString(R.string.DET_LONG_SIZE_DEFAULT));
String scoreThreshold = sharedPreferences.getString(getString(R.string.SCORE_THRESHOLD_KEY),
getString(R.string.SCORE_THRESHOLD_DEFAULT));
etModelPath.setSummary(modelPath);
......@@ -164,14 +141,8 @@ public class SettingsActivity extends AppCompatPreferenceActivity implements Sha
lpCPUThreadNum.setSummary(cpuThreadNum);
lpCPUPowerMode.setValue(cpuPowerMode);
lpCPUPowerMode.setSummary(cpuPowerMode);
lpInputColorFormat.setValue(inputColorFormat);
lpInputColorFormat.setSummary(inputColorFormat);
etInputShape.setSummary(inputShape);
etInputShape.setText(inputShape);
etInputMean.setSummary(inputMean);
etInputMean.setText(inputMean);
etInputStd.setSummary(inputStd);
etInputStd.setText(inputStd);
etDetLongSize.setSummary(detLongSize);
etDetLongSize.setText(detLongSize);
etScoreThreshold.setText(scoreThreshold);
etScoreThreshold.setSummary(scoreThreshold);
}
......
deploy/android_demo/app/src/main/res/layout/activity_main.xml
View file @ 191c9dee
......@@ -23,13 +23,7 @@
android:layout_height="wrap_content"
android:orientation="horizontal">
<Button
android:id="@+id/btn_load_model"
android:layout_width="0dp"
android:layout_height="wrap_content"
android:layout_weight="1"
android:onClick="btn_load_model_click"
android:text="加载模型" />
<Button
android:id="@+id/btn_run_model"
android:layout_width="0dp"
......@@ -52,7 +46,45 @@
android:onClick="btn_choice_img_click"
android:text="选取图片" />
<Button
android:id="@+id/btn_reset_img"
android:layout_width="0dp"
android:layout_height="wrap_content"
android:layout_weight="1"
android:onClick="btn_reset_img_click"
android:text="清空绘图" />
</LinearLayout>
<LinearLayout
android:id="@+id/run_mode_layout"
android:layout_width="fill_parent"
android:layout_height="wrap_content"
android:orientation="horizontal">
<CheckBox
android:id="@+id/cb_opencl"
android:layout_width="0dp"
android:layout_weight="1"
android:layout_height="wrap_content"
android:text="开启OPENCL"
android:onClick="cb_opencl_click"
android:visibility="gone"/>
<TextView
android:layout_width="0dp"
android:layout_weight="0.5"
android:layout_height="wrap_content"
android:text="运行模式:"/>
<Spinner
android:id="@+id/sp_run_mode"
android:layout_width="0dp"
android:layout_weight="1.5"
android:layout_height="wrap_content"
android:entries="@array/run_Model"
/>
</LinearLayout>
<TextView
android:id="@+id/tv_input_setting"
android:layout_width="wrap_content"
......@@ -60,7 +92,7 @@
android:scrollbars="vertical"
android:layout_marginLeft="12dp"
android:layout_marginRight="12dp"
android:layout_marginTop="10dp"
android:layout_marginTop="5dp"
android:layout_marginBottom="5dp"
android:lineSpacingExtra="4dp"
android:singleLine="false"
......
deploy/android_demo/app/src/main/res/layout/activity_mini.xml deleted 100644 → 0
View file @ 3c6d5512
<?xml version="1.0" encoding="utf-8"?>
<!-- for MiniActivity Use Only -->
<androidx.constraintlayout.widget.ConstraintLayout xmlns:android="http://schemas.android.com/apk/res/android"
xmlns:app="http://schemas.android.com/apk/res-auto"
xmlns:tools="http://schemas.android.com/tools"
android:layout_width="match_parent"
android:layout_height="match_parent"
app:layout_constraintLeft_toLeftOf="parent"
app:layout_constraintLeft_toRightOf="parent"
tools:context=".MainActivity">
<TextView
android:id="@+id/sample_text"
android:layout_width="0dp"
android:layout_height="wrap_content"
android:text="Hello World!"
app:layout_constraintLeft_toLeftOf="parent"
app:layout_constraintRight_toRightOf="parent"
app:layout_constraintTop_toBottomOf="@id/imageView"
android:scrollbars="vertical"
/>
<ImageView
android:id="@+id/imageView"
android:layout_width="wrap_content"
android:layout_height="wrap_content"
android:paddingTop="20dp"
android:paddingBottom="20dp"
app:layout_constraintBottom_toTopOf="@id/imageView"
app:layout_constraintLeft_toLeftOf="parent"
app:layout_constraintRight_toRightOf="parent"
app:layout_constraintTop_toTopOf="parent"
tools:srcCompat="@tools:sample/avatars" />
<Button
android:id="@+id/button"
android:layout_width="wrap_content"
android:layout_height="wrap_content"
android:layout_marginBottom="4dp"
android:text="Button"
app:layout_constraintBottom_toBottomOf="parent"
app:layout_constraintLeft_toLeftOf="parent"
app:layout_constraintRight_toRightOf="parent"
tools:layout_editor_absoluteX="161dp" />
</androidx.constraintlayout.widget.ConstraintLayout>
\ No newline at end of file
deploy/android_demo/app/src/main/res/values/arrays.xml
View file @ 191c9dee
<?xml version="1.0" encoding="utf-8"?>
<resources>
<string-array name="image_name_entries">
<item>0.jpg</item>
<item>90.jpg</item>
<item>180.jpg</item>
<item>270.jpg</item>
<item>det_0.jpg</item>
<item>det_90.jpg</item>
<item>det_180.jpg</item>
<item>det_270.jpg</item>
<item>rec_0.jpg</item>
<item>rec_0_180.jpg</item>
<item>rec_1.jpg</item>
<item>rec_1_180.jpg</item>
</string-array>
<string-array name="image_name_values">
<item>images/0.jpg</item>
<item>images/90.jpg</item>
<item>images/180.jpg</item>
<item>images/270.jpg</item>
<item>images/det_0.jpg</item>
<item>images/det_90.jpg</item>
<item>images/det_180.jpg</item>
<item>images/det_270.jpg</item>
<item>images/rec_0.jpg</item>
<item>images/rec_0_180.jpg</item>
<item>images/rec_1.jpg</item>
<item>images/rec_1_180.jpg</item>
</string-array>
<string-array name="cpu_thread_num_entries">
<item>1 threads</item>
......@@ -48,4 +56,12 @@
<item>BGR</item>
<item>RGB</item>
</string-array>
<string-array name="run_Model">
<item>检测+分类+识别</item>
<item>检测+识别</item>
<item>分类+识别</item>
<item>检测</item>
<item>识别</item>
<item>分类</item>
</string-array>
</resources>
\ No newline at end of file
deploy/android_demo/app/src/main/res/values/strings.xml
View file @ 191c9dee
<resources>
<string name="app_name">OCR Chinese</string>
<string name="app_name">PaddleOCR</string>
<string name="CHOOSE_PRE_INSTALLED_MODEL_KEY">CHOOSE_PRE_INSTALLED_MODEL_KEY</string>
<string name="ENABLE_CUSTOM_SETTINGS_KEY">ENABLE_CUSTOM_SETTINGS_KEY</string>
<string name="MODEL_PATH_KEY">MODEL_PATH_KEY</string>
......@@ -7,20 +7,14 @@
<string name="IMAGE_PATH_KEY">IMAGE_PATH_KEY</string>
<string name="CPU_THREAD_NUM_KEY">CPU_THREAD_NUM_KEY</string>
<string name="CPU_POWER_MODE_KEY">CPU_POWER_MODE_KEY</string>
<string name="INPUT_COLOR_FORMAT_KEY">INPUT_COLOR_FORMAT_KEY</string>
<string name="INPUT_SHAPE_KEY">INPUT_SHAPE_KEY</string>
<string name="INPUT_MEAN_KEY">INPUT_MEAN_KEY</string>
<string name="INPUT_STD_KEY">INPUT_STD_KEY</string>
<string name="DET_LONG_SIZE_KEY">DET_LONG_SIZE_KEY</string>
<string name="SCORE_THRESHOLD_KEY">SCORE_THRESHOLD_KEY</string>
<string name="MODEL_PATH_DEFAULT">models/ocr_v2_for_cpu</string>
<string name="MODEL_PATH_DEFAULT">models/ch_PP-OCRv2</string>
<string name="LABEL_PATH_DEFAULT">labels/ppocr_keys_v1.txt</string>
<string name="IMAGE_PATH_DEFAULT">images/0.jpg</string>
<string name="IMAGE_PATH_DEFAULT">images/det_0.jpg</string>
<string name="CPU_THREAD_NUM_DEFAULT">4</string>
<string name="CPU_POWER_MODE_DEFAULT">LITE_POWER_HIGH</string>
<string name="INPUT_COLOR_FORMAT_DEFAULT">BGR</string>
<string name="INPUT_SHAPE_DEFAULT">1,3,960</string>
<string name="INPUT_MEAN_DEFAULT">0.485, 0.456, 0.406</string>
<string name="INPUT_STD_DEFAULT">0.229,0.224,0.225</string>
<string name="DET_LONG_SIZE_DEFAULT">960</string>
<string name="SCORE_THRESHOLD_DEFAULT">0.1</string>
</resources>
deploy/android_demo/app/src/main/res/xml/settings.xml
View file @ 191c9dee
......@@ -47,26 +47,10 @@
android:entryValues="@array/cpu_power_mode_values"/>
</PreferenceCategory>
<PreferenceCategory android:title="Input Settings">
<ListPreference
android:defaultValue="@string/INPUT_COLOR_FORMAT_DEFAULT"
android:key="@string/INPUT_COLOR_FORMAT_KEY"
android:negativeButtonText="@null"
android:positiveButtonText="@null"
android:title="Input Color Format: BGR or RGB"
android:entries="@array/input_color_format_entries"
android:entryValues="@array/input_color_format_values"/>
<EditTextPreference
android:key="@string/INPUT_SHAPE_KEY"
android:defaultValue="@string/INPUT_SHAPE_DEFAULT"
android:title="Input Shape: (1,1,max_width_height) or (1,3,max_width_height)" />
<EditTextPreference
android:key="@string/INPUT_MEAN_KEY"
android:defaultValue="@string/INPUT_MEAN_DEFAULT"
android:title="Input Mean: (channel/255-mean)/std" />
<EditTextPreference
android:key="@string/INPUT_STD_KEY"
android:defaultValue="@string/INPUT_STD_DEFAULT"
android:title="Input Std: (channel/255-mean)/std" />
android:key="@string/DET_LONG_SIZE_KEY"
android:defaultValue="@string/DET_LONG_SIZE_DEFAULT"
android:title="det long size" />
</PreferenceCategory>
<PreferenceCategory android:title="Output Settings">
<EditTextPreference
......
deploy/cpp_infer/include/ocr_det.h
View file @ 191c9dee
......@@ -45,8 +45,9 @@ public:
const double &det_db_thresh,
const double &det_db_box_thresh,
const double &det_db_unclip_ratio,
const bool &use_polygon_score, const bool &visualize,
const bool &use_tensorrt, const std::string &precision) {
const bool &use_polygon_score, const bool &use_dilation,
const bool &visualize, const bool &use_tensorrt,
const std::string &precision) {
this->use_gpu_ = use_gpu;
this->gpu_id_ = gpu_id;
this->gpu_mem_ = gpu_mem;
......@@ -59,6 +60,7 @@ public:
this->det_db_box_thresh_ = det_db_box_thresh;
this->det_db_unclip_ratio_ = det_db_unclip_ratio;
this->use_polygon_score_ = use_polygon_score;
this->use_dilation_ = use_dilation;
this->visualize_ = visualize;
this->use_tensorrt_ = use_tensorrt;
......@@ -71,7 +73,8 @@ public:
void LoadModel(const std::string &model_dir);
// Run predictor
void Run(cv::Mat &img, std::vector<std::vector<std::vector<int>>> &boxes, std::vector<double> *times);
void Run(cv::Mat &img, std::vector<std::vector<std::vector<int>>> &boxes,
std::vector<double> *times);
private:
std::shared_ptr<Predictor> predictor_;
......@@ -88,6 +91,7 @@ private:
double det_db_box_thresh_ = 0.5;
double det_db_unclip_ratio_ = 2.0;
bool use_polygon_score_ = false;
bool use_dilation_ = false;
bool visualize_ = true;
bool use_tensorrt_ = false;
......
deploy/cpp_infer/readme.md
View file @ 191c9dee
......@@ -4,16 +4,20 @@
C++在性能计算上优于python，因此，在大多数CPU、GPU部署场景，多采用C++的部署方式，本节将介绍如何在Linux\Windows (CPU\GPU)环境下配置C++环境并完成
PaddleOCR模型部署。
* [1. 准备环境](#1)
+ [1.0 运行准备](#10)
+ [1.1 编译opencv库](#11)
+ [1.2 下载或者编译Paddle预测库](#12)
- [1.2.1 直接下载安装](#121)
- [1.2.2 预测库源码编译](#122)
* [2 开始运行](#2)
+ [2.1 将模型导出为inference model](#21)
+ [2.2 编译PaddleOCR C++预测demo](#22)
+ [2.3运行demo](#23)
- [服务器端C++预测](#服务器端c预测)
- [1. 准备环境](#1-准备环境)
- [1.0 运行准备](#10-运行准备)
- [1.1 编译opencv库](#11-编译opencv库)
- [1.2 下载或者编译Paddle预测库](#12-下载或者编译paddle预测库)
- [1.2.1 直接下载安装](#121-直接下载安装)
- [1.2.2 预测库源码编译](#122-预测库源码编译)
- [2 开始运行](#2-开始运行)
- [2.1 将模型导出为inference model](#21-将模型导出为inference-model)
- [2.2 编译PaddleOCR C++预测demo](#22-编译paddleocr-c预测demo)
- [2.3 运行demo](#23-运行demo)
- [1. 只调用检测：](#1-只调用检测)
- [2. 只调用识别：](#2-只调用识别)
- [3. 调用串联：](#3-调用串联)
<a name="1"></a>
......@@ -103,7 +107,7 @@ opencv3/
#### 1.2.1 直接下载安装
* [Paddle预测库官网](https://paddle-inference.readthedocs.io/en/latest/user_guides/download_lib.html) 上提供了不同cuda版本的Linux预测库，可以在官网查看并选择合适的预测库版本（*建议选择paddle版本>=2.0.1版本的预测库* ）。
* [Paddle预测库官网](https://paddleinference.paddlepaddle.org.cn/user_guides/download_lib.html#linux) 上提供了不同cuda版本的Linux预测库，可以在官网查看并选择合适的预测库版本（*建议选择paddle版本>=2.0.1版本的预测库* ）。
* 下载之后使用下面的方法解压。
......@@ -249,7 +253,7 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
|gpu_id|int|0|GPU id，使用GPU时有效|
|gpu_mem|int|4000|申请的GPU内存|
|cpu_math_library_num_threads|int|10|CPU预测时的线程数，在机器核数充足的情况下，该值越大，预测速度越快|
|use_mkldnn|bool|true|是否使用mkldnn库|
|enable_mkldnn|bool|true|是否使用mkldnn库|
- 检测模型相关
......
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