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deploy/cpp/src/paddlex.cpp
浏览文件 @ 39347594
......@@ -599,20 +599,18 @@ bool Model::predict(const cv::Mat& im, SegResult* result) {
inputs_.im_size_before_resize_.pop_back();
auto resize_w = before_shape[0];
auto resize_h = before_shape[1];
if (mask_label->rows != resize_h || mask_label->cols != resize_w) {
cv::resize(mask_label,
mask_label,
cv::Size(resize_h, resize_w),
0,
0,
cv::INTER_NEAREST);
cv::resize(mask_score,
mask_score,
cv::Size(resize_h, resize_w),
0,
0,
cv::INTER_NEAREST);
}
cv::resize(mask_label,
mask_label,
cv::Size(resize_h, resize_w),
0,
0,
cv::INTER_NEAREST);
cv::resize(mask_score,
mask_score,
cv::Size(resize_h, resize_w),
0,
0,
cv::INTER_LINEAR);
}
++idx;
}
......
deploy/cpp/src/transforms.cpp
浏览文件 @ 39347594
......@@ -152,10 +152,8 @@ bool Resize::Run(cv::Mat* im, ImageBlob* data) {
data->im_size_before_resize_.push_back({im->rows, im->cols});
data->reshape_order_.push_back("resize");
if (im->rows != height_ || im->cols != width_) {
cv::resize(
*im, *im, cv::Size(width_, height_), 0, 0, interpolations[interp_]);
}
cv::resize(
*im, *im, cv::Size(width_, height_), 0, 0, interpolations[interp_]);
data->new_im_size_[0] = im->rows;
data->new_im_size_[1] = im->cols;
return true;
......
examples/meter_reader/README.md
浏览文件 @ 39347594
......@@ -28,13 +28,13 @@
| 表计测试图片 | 表计检测数据集 | 指针和刻度分割数据集 |
| ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
| [meter_test](https://bj.bcebos.com/paddlex/meterreader/datasets/meter_test.tar.gz) | [meter_det](https://bj.bcebos.com/paddlex/meterreader/datasets/meter_det.tar.gz) | [meter_seg](https://bj.bcebos.com/paddlex/meterreader/datasets/meter_seg.tar.gz) |
| [meter_test](https://bj.bcebos.com/paddlex/examples/meter_reader/datasets/meter_test.tar.gz) | [meter_det](https://bj.bcebos.com/paddlex/examples/meter_reader/datasets/meter_det.tar.gz) | [meter_seg](https://bj.bcebos.com/paddlex/examples/meter_reader/datasets/meter_seg.tar.gz) |
本案例开放了预先训练好的检测模型和语义分割模型，可以使用这些模型快速体验表计读数全流程，也可以直接将这些模型部署在服务器端或jetson嵌入式设备上进行推理预测。
| 表计检测模型 | 指针和刻度分割模型 |
| ------------------------------------------------------------ | ------------------------------------------------------------ |
| [meter_det_inference_model](https://bj.bcebos.com/paddlex/meterreader/models/meter_det_inference_model.tar.gz) | [meter_seg_inference_model](https://bj.bcebos.com/paddlex/meterreader/models/meter_seg_inference_model.tar.gz) |
| [meter_det_inference_model](https://bj.bcebos.com/paddlex/examples/meter_reader/models/meter_det_inference_model.tar.gz) | [meter_seg_inference_model](https://bj.bcebos.com/paddlex/examples/meter_reader/models/meter_seg_inference_model.tar.gz) |
## <h2 id="3">快速体验表盘读数</h2>
......@@ -77,6 +77,7 @@ cd PaddleX/examples/meter_reader/
| seg_batch_size | 分割的批量大小，默认为2 |
| seg_thread_num | 分割预测的线程数，默认为cpu处理器个数 |
| use_camera | 是否使用摄像头采集图片，默认为False |
| camera_id | 摄像头设备ID，默认值为0 |
| use_erode | 是否使用图像腐蚀对分割预测图进行细分，默认为False |
| erode_kernel | 图像腐蚀操作时的卷积核大小，默认值为4 |
......@@ -122,7 +123,7 @@ python3 reader_infer.py --detector_dir /path/to/det_inference_model --segmenter_
git clone https://github.com/PaddlePaddle/PaddleX
```
2. 将`PaddleX\examples\meter_reader\deploy\cpp`下的`meter`文件夹和`CMakeList.txt`拷贝至`PaddleX\deploy\cpp`目录下，拷贝之前可以将`PaddleX\deploy\cpp`下原本的`CMakeList.txt`做好备份。
2. 将`PaddleX\examples\meter_reader\deploy\cpp`下的`meter_reader`文件夹和`CMakeList.txt`拷贝至`PaddleX\deploy\cpp`目录下，拷贝之前可以将`PaddleX\deploy\cpp`下原本的`CMakeList.txt`做好备份。
3. 按照[Windows平台部署](https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/tutorials/deploy/deploy_server/deploy_cpp/deploy_cpp_win_vs2019.md)中的Step2至Step4完成C++预测代码的编译。
......@@ -132,7 +133,7 @@ git clone https://github.com/PaddlePaddle/PaddleX
cd PaddleX\deploy\cpp\out\build\x64-Release
```
预测程序为paddle_inference\meter.exe，其主要命令参数说明如下：
预测程序为paddle_inference\meter_reader.exe，其主要命令参数说明如下：
| 参数 | 说明 |
| ---- | ---- |
......@@ -151,7 +152,7 @@ git clone https://github.com/PaddlePaddle/PaddleX
| camera_id | 摄像头设备ID，默认值为0 |
| use_erode | 是否使用图像腐蚀对分割预测图进行去噪，支持值为0或1(默认值为1) |
| erode_kernel | 图像腐蚀操作时的卷积核大小，默认值为4 |
| score_threshold | 检测模型输出结果中，预测得分低于该阈值的框将被滤除，默认值为0.5|
5. 推理预测：
......@@ -160,19 +161,19 @@ git clone https://github.com/PaddlePaddle/PaddleX
* 使用未加密的模型对单张图片做预测
```shell
.\paddlex_inference\meter.exe --det_model_dir=\path\to\det_inference_model --seg_model_dir=\path\to\seg_inference_model --image=\path\to\meter_test\20190822_168.jpg --use_gpu=1 --use_erode=1 --save_dir=output
.\paddlex_inference\meter_reader.exe --det_model_dir=\path\to\det_inference_model --seg_model_dir=\path\to\seg_inference_model --image=\path\to\meter_test\20190822_168.jpg --use_gpu=1 --use_erode=1 --save_dir=output
```
* 使用未加密的模型对图像列表做预测
```shell
.\paddlex_inference\meter.exe --det_model_dir=\path\to\det_inference_model --seg_model_dir=\path\to\seg_inference_model --image_list=\path\to\meter_test\image_list.txt --use_gpu=1 --use_erode=1 --save_dir=output
.\paddlex_inference\meter_reader.exe --det_model_dir=\path\to\det_inference_model --seg_model_dir=\path\to\seg_inference_model --image_list=\path\to\meter_test\image_list.txt --use_gpu=1 --use_erode=1 --save_dir=output
```
* 使用未加密的模型开启摄像头做预测
```shell
.\paddlex_inference\meter.exe --det_model_dir=\path\to\det_inference_model --seg_model_dir=\path\to\seg_inference_model --use_camera=1 --use_gpu=1 --use_erode=1 --save_dir=output
.\paddlex_inference\meter_reader.exe --det_model_dir=\path\to\det_inference_model --seg_model_dir=\path\to\seg_inference_model --use_camera=1 --use_gpu=1 --use_erode=1 --save_dir=output
```
* 使用加密后的模型对单张图片做预测
......@@ -180,7 +181,7 @@ git clone https://github.com/PaddlePaddle/PaddleX
如果未对模型进行加密，请参考[加密PaddleX模型](https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/tutorials/deploy/deploy_server/encryption.html#paddlex)对模型进行加密。例如加密后的检测模型所在目录为`\path\to\encrypted_det_inference_model`，密钥为`yEBLDiBOdlj+5EsNNrABhfDuQGkdcreYcHcncqwdbx0=`；加密后的分割模型所在目录为`\path\to\encrypted_seg_inference_model`，密钥为`DbVS64I9pFRo5XmQ8MNV2kSGsfEr4FKA6OH9OUhRrsY=`
```shell
.\paddlex_inference\meter.exe --det_model_dir=\path\to\encrypted_det_inference_model --seg_model_dir=\path\to\encrypted_seg_inference_model --image=\path\to\test.jpg --use_gpu=1 --use_erode=1 --save_dir=output --det_key yEBLDiBOdlj+5EsNNrABhfDuQGkdcreYcHcncqwdbx0= --seg_key DbVS64I9pFRo5XmQ8MNV2kSGsfEr4FKA6OH9OUhRrsY=
.\paddlex_inference\meter_reader.exe --det_model_dir=\path\to\encrypted_det_inference_model --seg_model_dir=\path\to\encrypted_seg_inference_model --image=\path\to\test.jpg --use_gpu=1 --use_erode=1 --save_dir=output --det_key yEBLDiBOdlj+5EsNNrABhfDuQGkdcreYcHcncqwdbx0= --seg_key DbVS64I9pFRo5XmQ8MNV2kSGsfEr4FKA6OH9OUhRrsY=
```
### Linux系统的jeton嵌入式设备安全部署
......@@ -193,12 +194,12 @@ git clone https://github.com/PaddlePaddle/PaddleX
git clone https://github.com/PaddlePaddle/PaddleX
```
2. 将`PaddleX/examples/meter_readerdeploy/cpp`下的`meter`文件夹和`CMakeList.txt`拷贝至`PaddleX/deploy/cpp`目录下，拷贝之前可以将`PaddleX/deploy/cpp`下原本的`CMakeList.txt`做好备份。
2. 将`PaddleX/examples/meter_reader/deploy/cpp`下的`meter_reader`文件夹和`CMakeList.txt`拷贝至`PaddleX/deploy/cpp`目录下，拷贝之前可以将`PaddleX/deploy/cpp`下原本的`CMakeList.txt`做好备份。
3. 按照[Nvidia-Jetson开发板部署]()中的Step2至Step3完成C++预测代码的编译。
4. 编译成功后，可执行程为`build/meter/detector`，其主要命令参数说明如下：
4. 编译成功后，可执行程为`build/meter_reader/meter_reader`，其主要命令参数说明如下：
| 参数 | 说明 |
| ---- | ---- |
| det_model_dir | 表计检测模型路径 |
......@@ -216,6 +217,7 @@ git clone https://github.com/PaddlePaddle/PaddleX
| camera_id | 摄像头设备ID，默认值为0 |
| use_erode | 是否使用图像腐蚀对分割预测图进行细分，支持值为0或1(默认值为1) |
| erode_kernel | 图像腐蚀操作时的卷积核大小，默认值为4 |
| score_threshold | 检测模型输出结果中，预测得分低于该阈值的框将被滤除，默认值为0.5|
5. 推理预测：
......@@ -224,19 +226,19 @@ git clone https://github.com/PaddlePaddle/PaddleX
* 使用未加密的模型对单张图片做预测
```shell
./build/meter/meter --det_model_dir=/path/to/det_inference_model --seg_model_dir=/path/to/seg_inference_model --image=/path/to/meter_test/20190822_168.jpg --use_gpu=1 --use_erode=1 --save_dir=output
./build/meter_reader/meter_reader --det_model_dir=/path/to/det_inference_model --seg_model_dir=/path/to/seg_inference_model --image=/path/to/meter_test/20190822_168.jpg --use_gpu=1 --use_erode=1 --save_dir=output
```
* 使用未加密的模型对图像列表做预测
```shell
./build/meter/meter --det_model_dir=/path/to/det_inference_model --seg_model_dir=/path/to/seg_inference_model --image_list=/path/to/image_list.txt --use_gpu=1 --use_erode=1 --save_dir=output
./build/meter_reader/meter_reader --det_model_dir=/path/to/det_inference_model --seg_model_dir=/path/to/seg_inference_model --image_list=/path/to/image_list.txt --use_gpu=1 --use_erode=1 --save_dir=output
```
* 使用未加密的模型开启摄像头做预测
```shell
./build/meter/meter --det_model_dir=/path/to/det_inference_model --seg_model_dir=/path/to/seg_inference_model --use_camera=1 --use_gpu=1 --use_erode=1 --save_dir=output
./build/meter_reader/meter_reader --det_model_dir=/path/to/det_inference_model --seg_model_dir=/path/to/seg_inference_model --use_camera=1 --use_gpu=1 --use_erode=1 --save_dir=output
```
* 使用加密后的模型对单张图片做预测
......@@ -244,7 +246,7 @@ git clone https://github.com/PaddlePaddle/PaddleX
如果未对模型进行加密，请参考[加密PaddleX模型](https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/tutorials/deploy/deploy_server/encryption.html#paddlex)对模型进行加密。例如加密后的检测模型所在目录为`/path/to/encrypted_det_inference_model`，密钥为`yEBLDiBOdlj+5EsNNrABhfDuQGkdcreYcHcncqwdbx0=`；加密后的分割模型所在目录为`/path/to/encrypted_seg_inference_model`，密钥为`DbVS64I9pFRo5XmQ8MNV2kSGsfEr4FKA6OH9OUhRrsY=`
```shell
./build/meter/meter --det_model_dir=/path/to/encrypted_det_inference_model --seg_model_dir=/path/to/encrypted_seg_inference_model --image=/path/to/test.jpg --use_gpu=1 --use_erode=1 --save_dir=output --det_key yEBLDiBOdlj+5EsNNrABhfDuQGkdcreYcHcncqwdbx0= --seg_key DbVS64I9pFRo5XmQ8MNV2kSGsfEr4FKA6OH9OUhRrsY=
./build/meter_reader/meter_reader --det_model_dir=/path/to/encrypted_det_inference_model --seg_model_dir=/path/to/encrypted_seg_inference_model --image=/path/to/test.jpg --use_gpu=1 --use_erode=1 --save_dir=output --det_key yEBLDiBOdlj+5EsNNrABhfDuQGkdcreYcHcncqwdbx0= --seg_key DbVS64I9pFRo5XmQ8MNV2kSGsfEr4FKA6OH9OUhRrsY=
```
......
examples/meter_reader/deploy/cpp/CMakeLists.txt
浏览文件 @ 39347594
cmake_minimum_required(VERSION 3.0)
project(PaddleX CXX C)
option(WITH_MKL "Compile meter with MKL/OpenBlas support,defaultuseMKL." ON)
option(WITH_GPU "Compile meter with GPU/CPU, default use CPU." ON)
option(WITH_STATIC_LIB "Compile meter with static/shared library, default use static." OFF)
option(WITH_TENSORRT "Compile meter with TensorRT." OFF)
option(WITH_ENCRYPTION "Compile meter with encryption tool." OFF)
option(WITH_MKL "Compile meter_reader with MKL/OpenBlas support,defaultuseMKL." ON)
option(WITH_GPU "Compile meter_reader with GPU/CPU, default use CPU." ON)
option(WITH_STATIC_LIB "Compile meter_reader with static/shared library, default use static." OFF)
option(WITH_TENSORRT "Compile meter_reader with TensorRT." OFF)
option(WITH_ENCRYPTION "Compile meter_reader with encryption tool." OFF)
SET(TENSORRT_DIR "" CACHE PATH "Location of libraries")
SET(PADDLE_DIR "" CACHE PATH "Location of libraries")
......@@ -16,7 +16,7 @@ SET(CUDA_LIB "" CACHE PATH "Location of libraries")
if (NOT WIN32)
set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/lib)
set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/lib)
set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/meter)
set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/meter_reader)
else()
set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/paddlex_inference)
set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/paddlex_inference)
......@@ -275,12 +275,12 @@ add_library(paddlex_inference SHARED src/visualize src/transforms.cpp src/paddle
ADD_DEPENDENCIES(paddlex_inference ext-yaml-cpp)
target_link_libraries(paddlex_inference ${DEPS})
add_executable(meter meter/meter.cpp meter/global.cpp meter/readvalue.cpp src/transforms.cpp src/paddlex.cpp src/visualize.cpp)
ADD_DEPENDENCIES(meter ext-yaml-cpp)
target_link_libraries(meter ${DEPS})
add_executable(meter_reader meter_reader/meter_reader.cpp meter_reader/global.cpp meter_reader/postprocess.cpp src/transforms.cpp src/paddlex.cpp src/visualize.cpp)
ADD_DEPENDENCIES(meter_reader ext-yaml-cpp)
target_link_libraries(meter_reader ${DEPS})
if (WIN32 AND WITH_MKL)
add_custom_command(TARGET meter POST_BUILD
add_custom_command(TARGET meter_reader POST_BUILD
COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_DIR}/third_party/install/mklml/lib/mklml.dll ./mklml.dll
COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_DIR}/third_party/install/mklml/lib/libiomp5md.dll ./libiomp5md.dll
COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_DIR}/third_party/install/mkldnn/lib/mkldnn.dll ./mkldnn.dll
......@@ -290,7 +290,7 @@ if (WIN32 AND WITH_MKL)
)
# for encryption
if (EXISTS "${ENCRYPTION_DIR}/lib/pmodel-decrypt.dll")
add_custom_command(TARGET meter POST_BUILD
add_custom_command(TARGET meter_reader POST_BUILD
COMMAND ${CMAKE_COMMAND} -E copy_if_different ${ENCRYPTION_DIR}/lib/pmodel-decrypt.dll ./pmodel-decrypt.dll
COMMAND ${CMAKE_COMMAND} -E copy_if_different ${ENCRYPTION_DIR}/lib/pmodel-decrypt.dll ./release/pmodel-decrypt.dll
)
......
examples/meter_reader/deploy/cpp/meter/global.cpp → examples/meter_reader/deploy/cpp/meter_reader/global.cpp
浏览文件 @ 39347594
......@@ -21,7 +21,7 @@
#include <opencv2/highgui.hpp>
#include <opencv2/core/core.hpp>
#include "meter/global.h"
#include "meter_reader/global.h"
std::vector<int> IMAGE_SHAPE = {1920, 1080};
std::vector<int> RESULT_SHAPE = {1280, 720};
......
examples/meter_reader/deploy/cpp/meter/meter.cpp → examples/meter_reader/deploy/cpp/meter_reader/meter_reader.cpp
浏览文件 @ 39347594
......@@ -26,8 +26,8 @@
#include <opencv2/highgui.hpp>
#include <opencv2/core/core.hpp>
#include "meter/global.h"
#include "meter/readvalue.h"
#include "meter_reader/global.h"
#include "meter_reader/postprocess.h"
#include "include/paddlex/paddlex.h"
#include "include/paddlex/visualize.h"
......@@ -51,16 +51,31 @@ DEFINE_string(seg_key, "", "Segmenter model key of encryption");
DEFINE_string(image, "", "Path of test image file");
DEFINE_string(image_list, "", "Path of test image list file");
DEFINE_string(save_dir, "output", "Path to save visualized image");
DEFINE_double(score_threshold, 0.5, "Detected bbox whose score is lower than this threshlod is filtered");
void predict(const cv::Mat &input_image, PaddleX::Model *det_model,
PaddleX::Model *seg_model, const std::string save_dir,
const std::string image_path, const bool use_erode,
const int erode_kernel, const int thread_num,
const int seg_batch_size) {
const int seg_batch_size, const double threshold) {
PaddleX::DetResult det_result;
det_model->predict(input_image, &det_result);
int meter_num = det_result.boxes.size();
PaddleX::DetResult filter_result;
int num_bboxes = det_result.boxes.size();
for (int i = 0; i < num_bboxes; ++i) {
double score = det_result.boxes[i].score;
if (score > threshold || score == threshold) {
PaddleX::Box box;
box.category_id = det_result.boxes[i].category_id;
box.category = det_result.boxes[i].category;
box.score = det_result.boxes[i].score;
box.coordinate = det_result.boxes[i].coordinate;
filter_result.boxes.push_back(std::move(box));
}
}
int meter_num = filter_result.boxes.size();
if (!meter_num) {
std::cout << "Don't find any meter." << std::endl;
return;
......@@ -74,10 +89,10 @@ void predict(const cv::Mat &input_image, PaddleX::Model *det_model,
int batch_thread_num = std::min(thread_num, im_vec_size - i);
#pragma omp parallel for num_threads(batch_thread_num)
for (int j = i; j < im_vec_size; ++j) {
int left = static_cast<int>(det_result.boxes[j].coordinate[0]);
int top = static_cast<int>(det_result.boxes[j].coordinate[1]);
int width = static_cast<int>(det_result.boxes[j].coordinate[2]);
int height = static_cast<int>(det_result.boxes[j].coordinate[3]);
int left = static_cast<int>(filter_result.boxes[j].coordinate[0]);
int top = static_cast<int>(filter_result.boxes[j].coordinate[1]);
int width = static_cast<int>(filter_result.boxes[j].coordinate[2]);
int height = static_cast<int>(filter_result.boxes[j].coordinate[3]);
int right = left + width - 1;
int bottom = top + height - 1;
......@@ -142,10 +157,10 @@ void predict(const cv::Mat &input_image, PaddleX::Model *det_model,
<< " -- result: " << result
<< " --" << std::endl;
int lx = static_cast<int>(det_result.boxes[i].coordinate[0]);
int ly = static_cast<int>(det_result.boxes[i].coordinate[1]);
int w = static_cast<int>(det_result.boxes[i].coordinate[2]);
int h = static_cast<int>(det_result.boxes[i].coordinate[3]);
int lx = static_cast<int>(filter_result.boxes[i].coordinate[0]);
int ly = static_cast<int>(filter_result.boxes[i].coordinate[1]);
int w = static_cast<int>(filter_result.boxes[i].coordinate[2]);
int h = static_cast<int>(filter_result.boxes[i].coordinate[3]);
cv::Rect bounding_box = cv::Rect(lx, ly, w, h) &
cv::Rect(0, 0, output_image.cols, output_image.rows);
......@@ -223,7 +238,8 @@ int main(int argc, char **argv) {
std::string ext_name = ".jpg";
predict(im, &det_model, &seg_model, FLAGS_save_dir,
std::to_string(imgs) + ext_name, FLAGS_use_erode,
FLAGS_erode_kernel, FLAGS_thread_num, FLAGS_seg_batch_size);
FLAGS_erode_kernel, FLAGS_thread_num,
FLAGS_seg_batch_size, FLAGS_score_threshold);
imgs++;
auto imread_duration = duration_cast<microseconds>(imread_end - start);
total_imread_time_s += static_cast<double>(imread_duration.count()) *
......@@ -254,7 +270,8 @@ int main(int argc, char **argv) {
predict(im, &det_model, &seg_model, FLAGS_save_dir,
image_path, FLAGS_use_erode, FLAGS_erode_kernel,
FLAGS_thread_num, FLAGS_seg_batch_size);
FLAGS_thread_num, FLAGS_seg_batch_size,
FLAGS_score_threshold);
auto imread_duration = duration_cast<microseconds>(imread_end - start);
total_imread_time_s += static_cast<double>(imread_duration.count()) *
......@@ -274,7 +291,8 @@ int main(int argc, char **argv) {
predict(im, &det_model, &seg_model, FLAGS_save_dir,
FLAGS_image, FLAGS_use_erode, FLAGS_erode_kernel,
FLAGS_thread_num, FLAGS_seg_batch_size);
FLAGS_thread_num, FLAGS_seg_batch_size,
FLAGS_score_threshold);
auto imread_duration = duration_cast<microseconds>(imread_end - start);
total_imread_time_s += static_cast<double>(imread_duration.count()) *
......
examples/meter_reader/deploy/cpp/meter/readvalue.cpp → examples/meter_reader/deploy/cpp/meter_reader/postprocess.cpp
浏览文件 @ 39347594
......@@ -24,8 +24,8 @@
#include <opencv2/highgui.hpp>
#include <opencv2/core/core.hpp>
#include "meter/global.h"
#include "meter/readvalue.h"
#include "meter_reader/global.h"
#include "meter_reader/postprocess.h"
using namespace std::chrono; // NOLINT
......
examples/meter_reader/deploy/python/reader_deploy.py
浏览文件 @ 39347594
......@@ -70,6 +70,12 @@ def parse_args():
dest='use_camera',
help='Whether use camera or not',
action='store_true')
parser.add_argument(
'--camera_id',
dest='camera_id',
type=int,
help='The camera id',
default=0)
parser.add_argument(
'--use_erode',
dest='use_erode',
......@@ -188,9 +194,9 @@ class MeterReader:
thread_num=seg_thread_num)
if use_erode:
kernel = np.ones((erode_kernel, erode_kernel), np.uint8)
for i in range(len(seg_results)):
result[i]['label_map'] = cv2.erode(
result[i]['label_map'], kernel)
for i in range(len(result)):
result[i]['label_map'] = cv2.erode(result[i]['label_map'],
kernel)
seg_results.extend(result)
results = list()
......@@ -330,7 +336,7 @@ def infer(args):
args.erode_kernel, args.score_threshold,
args.seg_batch_size, args.seg_thread_num)
elif args.with_camera:
cap_video = cv2.VideoCapture(0)
cap_video = cv2.VideoCapture(args.camera_id)
if not cap_video.isOpened():
raise Exception(
"Error opening video stream, please make sure the camera is working"
......
examples/meter_reader/reader_infer.py
浏览文件 @ 39347594
......@@ -70,6 +70,12 @@ def parse_args():
dest='use_camera',
help='Whether use camera or not',
action='store_true')
parser.add_argument(
'--camera_id',
dest='camera_id',
type=int,
help='The camera id',
default=0)
parser.add_argument(
'--use_erode',
dest='use_erode',
......@@ -189,8 +195,8 @@ class MeterReader:
if use_erode:
kernel = np.ones((erode_kernel, erode_kernel), np.uint8)
for i in range(len(result)):
result[i]['label_map'] = cv2.erode(
result[i]['label_map'], kernel)
result[i]['label_map'] = cv2.erode(result[i]['label_map'],
kernel)
seg_results.extend(result)
results = list()
......@@ -322,7 +328,7 @@ def infer(args):
continue
im_file = osp.join(args.image_dir, im_file)
image_lists.append(im_file)
meter_reader = MeterReader(args.detector_dir, args.segmenter_dir)
if len(image_lists) > 0:
for im_file in image_lists:
......@@ -330,7 +336,7 @@ def infer(args):
args.erode_kernel, args.score_threshold,
args.seg_batch_size, args.seg_thread_num)
elif args.with_camera:
cap_video = cv2.VideoCapture(0)
cap_video = cv2.VideoCapture(args.camera_id)
if not cap_video.isOpened():
raise Exception(
"Error opening video stream, please make sure the camera is working"
......
examples/meter_reader/train_detection.py
浏览文件 @ 39347594
......@@ -10,14 +10,24 @@ meter_det_dataset = 'https://bj.bcebos.com/paddlex/meterreader/datasets/meter_de
pdx.utils.download_and_decompress(meter_det_dataset, path='./')
# 定义训练和验证时的transforms
# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/det_transforms.html#composedyolotransforms
train_transforms = transforms.ComposedYOLOv3Transforms(
mode='train', shape=[608, 608])
eval_transforms = transforms.ComposedYOLOv3Transforms(
mode='eval', shape=[608, 608])
train_transforms = transforms.Compose([
transforms.MixupImage(mixup_epoch=250),
transforms.RandomDistort(),
transforms.RandomExpand(),
transforms.RandomCrop(),
transforms.Resize(
target_size=608, interp='RANDOM'),
transforms.RandomHorizontalFlip(),
transforms.Normalize(),
])
eval_transforms = transforms.Compose([
transforms.Resize(
target_size=608, interp='CUBIC'),
transforms.Normalize(),
])
# 定义训练和验证所用的数据集
# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/datasets/detection.html#vocdetection
train_dataset = pdx.datasets.CocoDetection(
data_dir='meter_det/train/',
ann_file='meter_det/annotations/instance_train.json',
......
paddlex/cv/datasets/__init__.py
浏览文件 @ 39347594
......@@ -19,4 +19,4 @@ from .seg_dataset import SegDataset
from .easydata_cls import EasyDataCls
from .easydata_det import EasyDataDet
from .easydata_seg import EasyDataSeg
from .dataset import GenerateMiniBatch
from .dataset import generate_minibatch
paddlex/cv/datasets/dataset.py
浏览文件 @ 39347594
......@@ -114,7 +114,7 @@ def multithread_reader(mapper,
while not isinstance(sample, EndSignal):
batch_data.append(sample)
if len(batch_data) == batch_size:
batch_data = GenerateMiniBatch(batch_data)
batch_data = generate_minibatch(batch_data)
yield batch_data
batch_data = []
sample = out_queue.get()
......@@ -126,11 +126,11 @@ def multithread_reader(mapper,
else:
batch_data.append(sample)
if len(batch_data) == batch_size:
batch_data = GenerateMiniBatch(batch_data)
batch_data = generate_minibatch(batch_data)
yield batch_data
batch_data = []
if not drop_last and len(batch_data) != 0:
batch_data = GenerateMiniBatch(batch_data)
batch_data = generate_minibatch(batch_data)
yield batch_data
batch_data = []
......@@ -187,49 +187,62 @@ def multiprocess_reader(mapper,
else:
batch_data.append(sample)
if len(batch_data) == batch_size:
batch_data = GenerateMiniBatch(batch_data)
batch_data = generate_minibatch(batch_data)
yield batch_data
batch_data = []
if len(batch_data) != 0 and not drop_last:
batch_data = GenerateMiniBatch(batch_data)
batch_data = generate_minibatch(batch_data)
yield batch_data
batch_data = []
return queue_reader
def GenerateMiniBatch(batch_data):
def generate_minibatch(batch_data, label_padding_value=255):
# if batch_size is 1, do not pad the image
if len(batch_data) == 1:
return batch_data
width = [data[0].shape[2] for data in batch_data]
height = [data[0].shape[1] for data in batch_data]
# if the sizes of images in a mini-batch are equal,
# do not pad the image
if len(set(width)) == 1 and len(set(height)) == 1:
return batch_data
max_shape = np.array([data[0].shape for data in batch_data]).max(axis=0)
padding_batch = []
for data in batch_data:
# pad the image to a same size
im_c, im_h, im_w = data[0].shape[:]
padding_im = np.zeros(
(im_c, max_shape[1], max_shape[2]), dtype=np.float32)
padding_im[:, :im_h, :im_w] = data[0]
if len(data) > 1:
if isinstance(data[1], np.ndarray):
# padding the image and label of segmentation
# during the training and evaluating phase
padding_label = np.zeros(
(1, max_shape[1], max_shape[2])).astype('int64')
(1, max_shape[1], max_shape[2]
)).astype('int64') + label_padding_value
_, label_h, label_w = data[1].shape
padding_label[:, :label_h, :label_w] = data[1]
padding_batch.append((padding_im, padding_label))
elif len(data[1]) == 0 or isinstance(
data[1][0],
tuple) and data[1][0][0] in ['resize', 'padding']:
# padding the image and insert 'padding' into `im_info`
# of segmentation during the infering phase
if len(data[1]) == 0 or 'padding' not in [
data[1][i][0] for i in range(len(data[1]))
]:
data[1].append(('padding', [im_h, im_w]))
padding_batch.append((padding_im, ) + tuple(data[1:]))
else:
# padding the image of detection, or
# padding the image of classification during the trainging
# and evaluating phase
padding_batch.append((padding_im, ) + tuple(data[1:]))
else:
# padding the image of classification during the infering phase
padding_batch.append((padding_im))
return padding_batch
......
paddlex/cv/models/classifier.py
浏览文件 @ 39347594
......@@ -23,7 +23,7 @@ import paddlex.utils.logging as logging
from paddlex.utils import seconds_to_hms
import paddlex
from paddlex.cv.transforms import arrange_transforms
from paddlex.cv.datasets import GenerateMiniBatch
from paddlex.cv.datasets import generate_minibatch
from collections import OrderedDict
from .base import BaseAPI
......@@ -289,7 +289,7 @@ class BaseClassifier(BaseAPI):
batch_data = pool.map(transforms, images)
pool.close()
pool.join()
padding_batch = GenerateMiniBatch(batch_data)
padding_batch = generate_minibatch(batch_data)
im = np.array([data[0] for data in padding_batch])
return im
......
paddlex/cv/models/deeplabv3p.py
浏览文件 @ 39347594
......@@ -23,7 +23,7 @@ import paddle.fluid as fluid
import paddlex.utils.logging as logging
import paddlex
from paddlex.cv.transforms import arrange_transforms
from paddlex.cv.datasets import GenerateMiniBatch
from paddlex.cv.datasets import generate_minibatch
from collections import OrderedDict
from .base import BaseAPI
from .utils.seg_eval import ConfusionMatrix
......@@ -340,16 +340,7 @@ class DeepLabv3p(BaseAPI):
for step, data in tqdm.tqdm(
enumerate(data_generator()), total=total_steps):
images = np.array([d[0] for d in data])
_, _, im_h, im_w = images.shape
labels = list()
for d in data:
padding_label = np.zeros(
(1, im_h, im_w)).astype('int64') + self.ignore_index
_, label_h, label_w = d[1].shape
padding_label[:, :label_h, :label_w] = d[1]
labels.append(padding_label)
labels = np.array(labels)
labels = np.array([d[1] for d in data])
num_samples = images.shape[0]
if num_samples < batch_size:
......@@ -398,7 +389,7 @@ class DeepLabv3p(BaseAPI):
batch_data = pool.map(transforms, images)
pool.close()
pool.join()
padding_batch = GenerateMiniBatch(batch_data)
padding_batch = generate_minibatch(batch_data)
im = np.array(
[data[0] for data in padding_batch],
dtype=padding_batch[0][0].dtype)
......
paddlex/cv/models/faster_rcnn.py
浏览文件 @ 39347594
......@@ -23,7 +23,7 @@ import paddlex
import os.path as osp
import copy
from paddlex.cv.transforms import arrange_transforms
from paddlex.cv.datasets import GenerateMiniBatch
from paddlex.cv.datasets import generate_minibatch
from .base import BaseAPI
from collections import OrderedDict
from .utils.detection_eval import eval_results, bbox2out
......@@ -109,24 +109,6 @@ class FasterRCNN(BaseAPI):
freeze_at=2)
return backbone
def _generate_mini_batch(self, batch_data):
if len(batch_data) == 1:
return batch_data
width = [data[0].shape[2] for data in batch_data]
height = [data[0].shape[1] for data in batch_data]
if len(set(width)) == 1 and len(set(height)) == 1:
return batch_data
max_shape = np.array([data[0].shape for data in batch_data]).max(
axis=0)
padding_batch = []
for data in batch_data:
im_c, im_h, im_w = data[0].shape[:]
padding_im = np.zeros(
(im_c, max_shape[1], max_shape[2]), dtype=np.float32)
padding_im[:, :im_h, :im_w] = data[0]
padding_batch.append((padding_im, ) + data[1:])
return padding_batch
def build_net(self, mode='train'):
train_pre_nms_top_n = 2000 if self.with_fpn else 12000
test_pre_nms_top_n = 1000 if self.with_fpn else 6000
......@@ -404,7 +386,7 @@ class FasterRCNN(BaseAPI):
batch_data = pool.map(transforms, images)
pool.close()
pool.join()
padding_batch = GenerateMiniBatch(batch_data)
padding_batch = generate_minibatch(batch_data)
im = np.array([data[0] for data in padding_batch])
im_resize_info = np.array([data[1] for data in padding_batch])
im_shape = np.array([data[2] for data in padding_batch])
......@@ -461,10 +443,10 @@ class FasterRCNN(BaseAPI):
res = {
k: (np.array(v), v.recursive_sequence_lengths())
for k, v in zip(list(test_outputs_keys), results)
for k, v in zip(list(self.test_outputs.keys()), result)
}
res['im_id'] = (np.array(
[[i] for i in range(batch_size)]).astype('int32'), [])
[[i] for i in range(len(images))]).astype('int32'), [])
preds = FasterRCNN._postprocess(res,
len(images), self.num_classes,
self.labels)
......@@ -510,10 +492,10 @@ class FasterRCNN(BaseAPI):
res = {
k: (np.array(v), v.recursive_sequence_lengths())
for k, v in zip(list(test_outputs_keys), results)
for k, v in zip(list(self.test_outputs.keys()), result)
}
res['im_id'] = (np.array(
[[i] for i in range(batch_size)]).astype('int32'), [])
[[i] for i in range(len(img_file_list))]).astype('int32'), [])
preds = FasterRCNN._postprocess(res,
len(img_file_list), self.num_classes,
self.labels)
......
paddlex/cv/models/mask_rcnn.py
浏览文件 @ 39347594
......@@ -22,6 +22,7 @@ import paddlex.utils.logging as logging
import paddlex
import copy
import os.path as osp
from paddlex.cv.transforms import arrange_transforms
from collections import OrderedDict
from .faster_rcnn import FasterRCNN
from .utils.detection_eval import eval_results, bbox2out, mask2out
......@@ -396,10 +397,10 @@ class MaskRCNN(FasterRCNN):
res = {
k: (np.array(v), v.recursive_sequence_lengths())
for k, v in zip(list(test_outputs_keys), results)
for k, v in zip(lists(self.test_outputs.keys()), result)
}
res['im_id'] = (np.array(
[[i] for i in range(batch_size)]).astype('int32'), [])
[[i] for i in range(len(images))]).astype('int32'), [])
res['im_shape'] = (np.array(im_shape), [])
preds = MaskRCNN._postprocess(res,
len(images), self.num_classes,
......@@ -446,12 +447,12 @@ class MaskRCNN(FasterRCNN):
res = {
k: (np.array(v), v.recursive_sequence_lengths())
for k, v in zip(list(test_outputs_keys), results)
for k, v in zip(list(self.test_outputs.keys()), result)
}
res['im_id'] = (np.array(
[[i] for i in range(batch_size)]).astype('int32'), [])
[[i] for i in range(len(img_file_list))]).astype('int32'), [])
res['im_shape'] = (np.array(im_shape), [])
preds = MaskRCNN._postprocess(res,
len(images), self.num_classes,
len(img_file_list), self.num_classes,
self.mask_head_resolution, self.labels)
return preds
paddlex/cv/models/yolo_v3.py
浏览文件 @ 39347594
......@@ -23,7 +23,7 @@ import paddlex.utils.logging as logging
import paddlex
import copy
from paddlex.cv.transforms import arrange_transforms
from paddlex.cv.datasets import GenerateMiniBatch
from paddlex.cv.datasets import generate_minibatch
from .base import BaseAPI
from collections import OrderedDict
from .utils.detection_eval import eval_results, bbox2out
......@@ -364,7 +364,7 @@ class YOLOv3(BaseAPI):
batch_data = pool.map(transforms, images)
pool.close()
pool.join()
padding_batch = GenerateMiniBatch(batch_data)
padding_batch = generate_minibatch(batch_data)
im = np.array(
[data[0] for data in padding_batch],
dtype=padding_batch[0][0].dtype)
......
paddlex/cv/transforms/cls_transforms.py
浏览文件 @ 39347594
......@@ -68,13 +68,14 @@ class Compose(ClsTransform):
if isinstance(im, np.ndarray):
if len(im.shape) != 3:
raise Exception(
"im should be 3-dimension, but now is {}-dimensions".format(
len(im.shape)))
"im should be 3-dimension, but now is {}-dimensions".
format(len(im.shape)))
else:
try:
im = cv2.imread(im).astype('float32')
im = cv2.imread(im)
except:
raise TypeError('Can\'t read The image file {}!'.format(im))
im = im.astype('float32')
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
for op in self.transforms:
if isinstance(op, ClsTransform):
......@@ -139,8 +140,8 @@ class RandomCrop(ClsTransform):
tuple: 当label为空时，返回的tuple为(im, )，对应图像np.ndarray数据；
当label不为空时，返回的tuple为(im, label)，分别对应图像np.ndarray数据、图像类别id。
"""
im = random_crop(im, self.crop_size, self.lower_scale, self.lower_ratio,
self.upper_ratio)
im = random_crop(im, self.crop_size, self.lower_scale,
self.lower_ratio, self.upper_ratio)
if label is None:
return (im, )
else:
......@@ -270,12 +271,14 @@ class ResizeByShort(ClsTransform):
im_short_size = min(im.shape[0], im.shape[1])
im_long_size = max(im.shape[0], im.shape[1])
scale = float(self.short_size) / im_short_size
if self.max_size > 0 and np.round(scale * im_long_size) > self.max_size:
if self.max_size > 0 and np.round(scale *
im_long_size) > self.max_size:
scale = float(self.max_size) / float(im_long_size)
resized_width = int(round(im.shape[1] * scale))
resized_height = int(round(im.shape[0] * scale))
im = cv2.resize(
im, (resized_width, resized_height), interpolation=cv2.INTER_LINEAR)
im, (resized_width, resized_height),
interpolation=cv2.INTER_LINEAR)
if label is None:
return (im, )
......
paddlex/cv/transforms/det_transforms.py
浏览文件 @ 39347594
......@@ -108,10 +108,11 @@ class Compose(DetTransform):
im = im_file
else:
try:
im = cv2.imread(im_file).astype('float32')
im = cv2.imread(im_file)
except:
raise TypeError('Can\'t read The image file {}!'.format(
im_file))
im = im.astype('float32')
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
# make default im_info with [h, w, 1]
im_info['im_resize_info'] = np.array(
......
paddlex/cv/transforms/seg_transforms.py
浏览文件 @ 39347594
......@@ -81,9 +81,10 @@ class Compose(SegTransform):
format(len(im.shape)))
else:
try:
im = cv2.imread(im).astype('float32')
im = cv2.imread(im)
except:
raise ValueError('Can\'t read The image file {}!'.format(im))
im = im.astype('float32')
if self.to_rgb:
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
if label is not None:
......@@ -399,7 +400,8 @@ class ResizeByShort(SegTransform):
im_short_size = min(im.shape[0], im.shape[1])
im_long_size = max(im.shape[0], im.shape[1])
scale = float(self.short_size) / im_short_size
if self.max_size > 0 and np.round(scale * im_long_size) > self.max_size:
if self.max_size > 0 and np.round(scale *
im_long_size) > self.max_size:
scale = float(self.max_size) / float(im_long_size)
resized_width = int(round(im.shape[1] * scale))
resized_height = int(round(im.shape[0] * scale))
......
paddlex/deploy.py
浏览文件 @ 39347594
......@@ -144,7 +144,7 @@ class Predictor:
res['im_info'] = im_resize_info
res['im_shape'] = im_shape
elif self.model_type == "segmenter":
im, im_imfo = DeepLabv3p._preprocess(
im, im_info = DeepLabv3p._preprocess(
image,
self.transforms,
self.model_type,
......
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