OpenVINO-YoloV3 
I wrote an English article, here
#◆ はじめに
先に懺悔します。
今回はサンプルプログラムの丸パクリ記事です。 ( ´_ゝ`)
GPU も Neural Compute Stick も使わない、 CPU単体で男気実装シリーズ の第３弾。
今回は推論の難易度をセグメンテーションから１段階下げて、 OpenVINO + YoloV3 (Full Size) のオブジェクトディテクションをCPUで動作させました。
２４８MB のモデルをCPU単体で実行したときのスピードは下図。
ん〜〜〜・・・、遅いね・・・。
いや、CPU単体だとすると、めちゃくちゃ速いのか！？
＜YoloV3 (Full Size), Intel Core i7-8750H, CPU Only, 4 FPS - 5 FPS＞
tiny-YoloV3 にすれば爆速になる予感。
実効FPSに応じた動的フレームスキップのロジックを仕込めば、もっとスムーズに動作します。
みなさんの感想を伺いたいところです。
TwitterにGithubの内容を独り言ツイートしたら 10秒 でリツイートされました。
フォロワーなんてほとんど居ないはずなのに。
怖い。。。 Twitterってそういうもの？
是非試したい、という奇特な方は、ページトップの OpenVINO-YoloV3 という文字のリンクからどうぞ。
OpenVINOが導入済みであれば、コンパイル済みのバイナリ object_detection_demo_yolov3_async をキックするだけです。
環境キッティングの手順は LattePanda Alpha 864 (OS付属無し) にUbuntu16.04+OpenVINOを導入してNeural Compute Stick(NCS1) と Neural Compute Stick 2(NCS2) で爆速Semantic Segmentationを楽しむ をご覧ください。
IntelのCPUなら、LattePanda でなくても動作します。
#◆ 環境
- Ubuntu 16.04 x86_64
- OpenVINO toolkit 2018 R4 (2018.4.420)
- Python 3.5
- OpenCV 3.4.3
- YoloV3 (MS-COCO)
#◆ 実装
USBカメラを３台まで切り替え可能にしたことと、検出結果の枠や文字を見やすくしたぐらい。
TensorflowのモデルからOpenVINOのモデルへ変換する作業にとても時間を要しました。
下記に書き下す内容 および OpenVINO本体を除く全てのリソースはGithubにコミット済みです。
.pb と コンバート済みの lrモデル は私のGoogleドライブからダウンロードできるようにしてあります。
.pbファイル生成スクリプト
$ python3 convert_weights_pb.py \
--class_names coco.names \
--weights_file weights/yolov3.weights \
--data_format NHWC \
--output_graph pbmodels/frozen_yolo_v3.pb
.pbのサマリ
cd git;cd tensorflow
bazel-bin/tensorflow/tools/graph_transforms/summarize_graph --in_graph=/home/xxxx/git/OpenVINO-YoloV3/pbmodels/frozen_yolo_v3.pb
#========================================================================================================
Found 1 possible inputs: (name=inputs, type=float(1), shape=[?,416,416,3])
No variables spotted.
Found 1 possible outputs: (name=output_boxes, op=ConcatV2)
Found 62002034 (62.00M) const parameters, 0 (0) variable parameters, and 0 control_edges
Op types used: 536 Const, 372 Identity, 87 Mul, 75 Conv2D, 72 FusedBatchNorm, 72 Maximum, 28 Add, \
24 Reshape, 14 ConcatV2, 9 Sigmoid, 6 Tile, 6 Range, 5 Pad, 4 SplitV, 3 Pack, 3 RealDiv, 3 Fill, \
3 Exp, 3 BiasAdd, 2 ResizeNearestNeighbor, 2 Sub, 1 Placeholder
To use with tensorflow/tools/benchmark:benchmark_model try these arguments:
bazel run tensorflow/tools/benchmark:benchmark_model -- \
--graph=/home/xxxx/git/OpenVINO-YoloV3/pbmodels/frozen_yolo_v3.pb \
--show_flops \
--input_layer=inputs \
--input_layer_type=float \
--input_layer_shape=-1,416,416,3 \
--output_layer=output_boxes
#========================================================================================================
Tensorflow--->OpenVINOへのコンバージョンスクリプト
$ sudo python3 /opt/intel/computer_vision_sdk/deployment_tools/model_optimizer/mo_tf.py \
--input_model pbmodels/frozen_yolo_v3.pb \
--output_dir lrmodels/YoloV3/FP32 \
--data_type FP32 \
--batch 1 \
--tensorflow_use_custom_operations_config yolo_v3_changed.json
慣れない C++ のロジックだったため、数行修正するのにも数時間掛けてしまいました。
次回は tiny-YoloV3 + Python へ焼き直ししたいと考えています。
小手先で少しだけカスタマイズを加えたYoloV3ロジック
#include <gflags/gflags.h>
#include <functional>
#include <iostream>
#include <fstream>
#include <random>
#include <memory>
#include <chrono>
#include <vector>
#include <string>
#include <algorithm>
#include <iterator>
#include <inference_engine.hpp>
#include <samples/common.hpp>
#include <samples/slog.hpp>
#include "object_detection_demo_yolov3_async.hpp"
#include <ext_list.hpp>
#include <opencv2/opencv.hpp>
using namespace InferenceEngine;
#define yolo_scale_13 13
#define yolo_scale_26 26
#define yolo_scale_52 52
bool ParseAndCheckCommandLine(int argc, char *argv[]) {
// ---------------------------Parsing and validating the input arguments--------------------------------------
gflags::ParseCommandLineNonHelpFlags(&argc, &argv, true);
if (FLAGS_h) {
showUsage();
return false;
}
slog::info << "Parsing input parameters" << slog::endl;
if (FLAGS_i.empty()) {
throw std::logic_error("Parameter -i is not set");
}
if (FLAGS_m.empty()) {
throw std::logic_error("Parameter -m is not set");
}
return true;
}
void FrameToBlob(const cv::Mat &frame, InferRequest::Ptr &inferRequest, const std::string &inputName) {
if (FLAGS_auto_resize) {
/* Just set input blob containing read image. Resize and layout conversion will be done automatically */
inferRequest->SetBlob(inputName, wrapMat2Blob(frame));
} else {
/* Resize and copy data from the image to the input blob */
Blob::Ptr frameBlob = inferRequest->GetBlob(inputName);
matU8ToBlob<uint8_t>(frame, frameBlob);
}
}
static int EntryIndex(int side, int lcoords, int lclasses, int location, int entry) {
int n = location / (side * side);
int loc = location % (side * side);
return n * side * side * (lcoords + lclasses + 1) + entry * side * side + loc;
}
struct DetectionObject {
int xmin, ymin, xmax, ymax, class_id;
float confidence;
DetectionObject(double x, double y, double h, double w, int class_id, float confidence, float h_scale, float w_scale) {
this->xmin = static_cast<int>((x - w / 2) * w_scale);
this->ymin = static_cast<int>((y - h / 2) * h_scale);
this->xmax = static_cast<int>(this->xmin + w * w_scale);
this->ymax = static_cast<int>(this->ymin + h * h_scale);
this->class_id = class_id;
this->confidence = confidence;
}
bool operator<(const DetectionObject &s2) const {
return this->confidence < s2.confidence;
}
};
double IntersectionOverUnion(const DetectionObject &box_1, const DetectionObject &box_2) {
double width_of_overlap_area = fmin(box_1.xmax, box_2.xmax) - fmax(box_1.xmin, box_2.xmin);
double height_of_overlap_area = fmin(box_1.ymax, box_2.ymax) - fmax(box_1.ymin, box_2.ymin);
double area_of_overlap;
if (width_of_overlap_area < 0 || height_of_overlap_area < 0)
area_of_overlap = 0;
else
area_of_overlap = width_of_overlap_area * height_of_overlap_area;
double box_1_area = (box_1.ymax - box_1.ymin) * (box_1.xmax - box_1.xmin);
double box_2_area = (box_2.ymax - box_2.ymin) * (box_2.xmax - box_2.xmin);
double area_of_union = box_1_area + box_2_area - area_of_overlap;
return area_of_overlap / area_of_union;
}
void ParseYOLOV3Output(const CNNLayerPtr &layer, const Blob::Ptr &blob, const unsigned long resized_im_h,
const unsigned long resized_im_w, const unsigned long original_im_h,
const unsigned long original_im_w,
const double threshold, std::vector<DetectionObject> &objects) {
// --------------------------- Validating output parameters -------------------------------------
if (layer->type != "RegionYolo")
throw std::runtime_error("Invalid output type: " + layer->type + ". RegionYolo expected");
const int out_blob_h = static_cast<int>(blob->getTensorDesc().getDims()[2]);
const int out_blob_w = static_cast<int>(blob->getTensorDesc().getDims()[3]);
if (out_blob_h != out_blob_w)
throw std::runtime_error("Invalid size of output " + layer->name +
" It should be in NCHW layout and H should be equal to W. Current H = " + std::to_string(out_blob_h) +
", current W = " + std::to_string(out_blob_h));
// --------------------------- Extracting layer parameters -------------------------------------
auto num = layer->GetParamAsInt("num");
try { num = layer->GetParamAsInts("mask").size(); } catch (...) {}
auto coords = layer->GetParamAsInt("coords");
auto classes = layer->GetParamAsInt("classes");
std::vector<float> anchors = {10.0, 13.0, 16.0, 30.0, 33.0, 23.0, 30.0, 61.0, 62.0, 45.0, 59.0, 119.0, 116.0, 90.0,
156.0, 198.0, 373.0, 326.0};
try { anchors = layer->GetParamAsFloats("anchors"); } catch (...) {}
auto side = out_blob_h;
int anchor_offset = 0;
switch (side) {
case yolo_scale_13:
anchor_offset = 2 * 6;
break;
case yolo_scale_26:
anchor_offset = 2 * 3;
break;
case yolo_scale_52:
anchor_offset = 2 * 0;
break;
default:
throw std::runtime_error("Invalid output size");
}
auto side_square = side * side;
const float *output_blob = blob->buffer().as<PrecisionTrait<Precision::FP32>::value_type *>();
// --------------------------- Parsing YOLO Region output -------------------------------------
for (int i = 0; i < side_square; ++i) {
int row = i / side;
int col = i % side;
for (int n = 0; n < num; ++n) {
int obj_index = EntryIndex(side, coords, classes, n * side * side + i, coords);
int box_index = EntryIndex(side, coords, classes, n * side * side + i, 0);
float scale = output_blob[obj_index];
if (scale < threshold)
continue;
double x = (col + output_blob[box_index + 0 * side_square]) / side * resized_im_w;
double y = (row + output_blob[box_index + 1 * side_square]) / side * resized_im_h;
double height = std::exp(output_blob[box_index + 3 * side_square]) * anchors[anchor_offset + 2 * n + 1];
double width = std::exp(output_blob[box_index + 2 * side_square]) * anchors[anchor_offset + 2 * n];
for (int j = 0; j < classes; ++j) {
int class_index = EntryIndex(side, coords, classes, n * side_square + i, coords + 1 + j);
float prob = scale * output_blob[class_index];
if (prob < threshold)
continue;
DetectionObject obj(x, y, height, width, j, prob,
static_cast<float>(original_im_h) / static_cast<float>(resized_im_h),
static_cast<float>(original_im_w) / static_cast<float>(resized_im_w));
objects.push_back(obj);
}
}
}
}
int main(int argc, char *argv[]) {
try {
/** This demo covers a certain topology and cannot be generalized for any object detection **/
std::cout << "InferenceEngine: " << GetInferenceEngineVersion() << std::endl;
// ------------------------------ Parsing and validating the input arguments ---------------------------------
if (!ParseAndCheckCommandLine(argc, argv)) {
return 0;
}
slog::info << "Reading input" << slog::endl;
cv::VideoCapture cap;
if (FLAGS_i == "cam0") {
cap.open(0);
} else if (FLAGS_i == "cam1") {
cap.open(1);
} else if (FLAGS_i == "cam2") {
cap.open(2);
} else if (!(cap.open(FLAGS_i.c_str()))) {
throw std::logic_error("Cannot open input file or camera: " + FLAGS_i);
}
// read input (video) frame
cv::Mat frame; cap >> frame;
cv::Mat next_frame;
const size_t width = (size_t) cap.get(cv::CAP_PROP_FRAME_WIDTH);
const size_t height = (size_t) cap.get(cv::CAP_PROP_FRAME_HEIGHT);
if (!cap.grab()) {
throw std::logic_error("This demo supports only video (or camera) inputs !!! "
"Failed to get next frame from the " + FLAGS_i);
}
// -----------------------------------------------------------------------------------------------------
// --------------------------- 1. Load Plugin for inference engine -------------------------------------
slog::info << "Loading plugin" << slog::endl;
InferencePlugin plugin = PluginDispatcher({"../../../lib/intel64", ""}).getPluginByDevice(FLAGS_d);
printPluginVersion(plugin, std::cout);
/**Loading extensions to the plugin **/
/** Loading default extensions **/
if (FLAGS_d.find("CPU") != std::string::npos) {
/**
* cpu_extensions library is compiled from the "extension" folder containing
* custom CPU layer implementations.
**/
plugin.AddExtension(std::make_shared<Extensions::Cpu::CpuExtensions>());
}
if (!FLAGS_l.empty()) {
// CPU extensions are loaded as a shared library and passed as a pointer to the base extension
IExtensionPtr extension_ptr = make_so_pointer<IExtension>(FLAGS_l.c_str());
plugin.AddExtension(extension_ptr);
}
if (!FLAGS_c.empty()) {
// GPU extensions are loaded from an .xml description and OpenCL kernel files
plugin.SetConfig({{PluginConfigParams::KEY_CONFIG_FILE, FLAGS_c}});
}
/** Per-layer metrics **/
if (FLAGS_pc) {
plugin.SetConfig({ { PluginConfigParams::KEY_PERF_COUNT, PluginConfigParams::YES } });
}
// -----------------------------------------------------------------------------------------------------
// --------------- 2. Reading the IR generated by the Model Optimizer (.xml and .bin files) ------------
slog::info << "Loading network files" << slog::endl;
CNNNetReader netReader;
/** Reading network model **/
netReader.ReadNetwork(FLAGS_m);
/** Setting batch size to 1 **/
slog::info << "Batch size is forced to 1." << slog::endl;
netReader.getNetwork().setBatchSize(1);
/** Extracting the model name and loading its weights **/
std::string binFileName = fileNameNoExt(FLAGS_m) + ".bin";
netReader.ReadWeights(binFileName);
/** Reading labels (if specified) **/
std::string labelFileName = fileNameNoExt(FLAGS_m) + ".labels";
std::vector<std::string> labels;
std::ifstream inputFile(labelFileName);
std::copy(std::istream_iterator<std::string>(inputFile),
std::istream_iterator<std::string>(),
std::back_inserter(labels));
// -----------------------------------------------------------------------------------------------------
/** YOLOV3-based network should have one input and three output **/
// --------------------------- 3. Configuring input and output -----------------------------------------
// --------------------------------- Preparing input blobs ---------------------------------------------
slog::info << "Checking that the inputs are as the demo expects" << slog::endl;
InputsDataMap inputInfo(netReader.getNetwork().getInputsInfo());
if (inputInfo.size() != 1) {
throw std::logic_error("This demo accepts networks that have only one input");
}
InputInfo::Ptr& input = inputInfo.begin()->second;
auto inputName = inputInfo.begin()->first;
input->setPrecision(Precision::U8);
if (FLAGS_auto_resize) {
input->getPreProcess().setResizeAlgorithm(ResizeAlgorithm::RESIZE_BILINEAR);
input->getInputData()->setLayout(Layout::NHWC);
} else {
input->getInputData()->setLayout(Layout::NCHW);
}
// --------------------------------- Preparing output blobs -------------------------------------------
slog::info << "Checking that the outputs are as the demo expects" << slog::endl;
OutputsDataMap outputInfo(netReader.getNetwork().getOutputsInfo());
if (outputInfo.size() != 3) {
throw std::logic_error("This demo only accepts networks with three layers");
}
for (auto &output : outputInfo) {
output.second->setPrecision(Precision::FP32);
output.second->setLayout(Layout::NCHW);
}
// -----------------------------------------------------------------------------------------------------
// --------------------------- 4. Loading model to the plugin ------------------------------------------
slog::info << "Loading model to the plugin" << slog::endl;
ExecutableNetwork network = plugin.LoadNetwork(netReader.getNetwork(), {});
// -----------------------------------------------------------------------------------------------------
// --------------------------- 5. Creating infer request -----------------------------------------------
InferRequest::Ptr async_infer_request_next = network.CreateInferRequestPtr();
InferRequest::Ptr async_infer_request_curr = network.CreateInferRequestPtr();
// -----------------------------------------------------------------------------------------------------
// --------------------------- 6. Doing inference ------------------------------------------------------
slog::info << "Start inference " << slog::endl;
bool isLastFrame = false;
bool isAsyncMode = false; // execution is always started using SYNC mode
bool isModeChanged = false; // set to TRUE when execution mode is changed (SYNC<->ASYNC)
typedef std::chrono::duration<double, std::ratio<1, 1000>> ms;
auto total_t0 = std::chrono::high_resolution_clock::now();
auto wallclock = std::chrono::high_resolution_clock::now();
double ocv_decode_time = 0, ocv_render_time = 0;
while (true) {
auto t0 = std::chrono::high_resolution_clock::now();
// Here is the first asynchronous point:
// in the Async mode, we capture frame to populate the NEXT infer request
// in the regular mode, we capture frame to the CURRENT infer request
if (!cap.read(next_frame)) {
if (next_frame.empty()) {
isLastFrame = true; // end of video file
} else {
throw std::logic_error("Failed to get frame from cv::VideoCapture");
}
}
if (isAsyncMode) {
if (isModeChanged) {
FrameToBlob(frame, async_infer_request_curr, inputName);
}
if (!isLastFrame) {
FrameToBlob(next_frame, async_infer_request_next, inputName);
}
} else if (!isModeChanged) {
FrameToBlob(frame, async_infer_request_curr, inputName);
}
auto t1 = std::chrono::high_resolution_clock::now();
ocv_decode_time = std::chrono::duration_cast<ms>(t1 - t0).count();
t0 = std::chrono::high_resolution_clock::now();
// Main sync point:
// in the true Async mode, we start the NEXT infer request while waiting for the CURRENT to complete
// in the regular mode, we start the CURRENT request and wait for its completion
if (isAsyncMode) {
if (isModeChanged) {
async_infer_request_curr->StartAsync();
}
if (!isLastFrame) {
async_infer_request_next->StartAsync();
}
} else if (!isModeChanged) {
async_infer_request_curr->StartAsync();
}
if (OK == async_infer_request_curr->Wait(IInferRequest::WaitMode::RESULT_READY)) {
t1 = std::chrono::high_resolution_clock::now();
ms detection = std::chrono::duration_cast<ms>(t1 - t0);
t0 = std::chrono::high_resolution_clock::now();
ms wall = std::chrono::duration_cast<ms>(t0 - wallclock);
wallclock = t0;
t0 = std::chrono::high_resolution_clock::now();
std::ostringstream out;
out << "OpenCV cap/render time: " << std::fixed << std::setprecision(2)
<< (ocv_decode_time + ocv_render_time) << " ms";
cv::putText(frame, out.str(), cv::Point2f(0, 25), cv::FONT_HERSHEY_TRIPLEX, 0.6, cv::Scalar(0, 255, 0), 1, cv::LINE_AA);
out.str("");
out << "Wallclock time " << (isAsyncMode ? "(TRUE ASYNC): " : "(SYNC, press Tab): ");
out << std::fixed << std::setprecision(2) << wall.count() << " ms (" << 1000.f / wall.count() << " fps)";
cv::putText(frame, out.str(), cv::Point2f(0, 50), cv::FONT_HERSHEY_TRIPLEX, 0.6, cv::Scalar(0, 0, 255), 1, cv::LINE_AA);
if (!isAsyncMode) { // In the true async mode, there is no way to measure detection time directly
out.str("");
out << "Detection time : " << std::fixed << std::setprecision(2) << detection.count()
<< " ms ("
<< 1000.f / detection.count() << " fps)";
cv::putText(frame, out.str(), cv::Point2f(0, 75), cv::FONT_HERSHEY_TRIPLEX, 0.6, cv::Scalar(255, 0, 0), 1, cv::LINE_AA);
}
// ---------------------------Processing output blobs--------------------------------------------------
// Processing results of the CURRENT request
unsigned long resized_im_h = inputInfo.begin()->second.get()->getDims()[0];
unsigned long resized_im_w = inputInfo.begin()->second.get()->getDims()[1];
std::vector<DetectionObject> objects;
// Parsing outputs
for (auto &output : outputInfo) {
auto output_name = output.first;
CNNLayerPtr layer = netReader.getNetwork().getLayerByName(output_name.c_str());
Blob::Ptr blob = async_infer_request_curr->GetBlob(output_name);
ParseYOLOV3Output(layer, blob, resized_im_h, resized_im_w, height, width, FLAGS_t, objects);
}
// Filtering overlapping boxes
std::sort(objects.begin(), objects.end());
for (int i = 0; i < objects.size(); ++i) {
if (objects[i].confidence == 0)
continue;
for (int j = i + 1; j < objects.size(); ++j)
if (IntersectionOverUnion(objects[i], objects[j]) >= FLAGS_iou_t)
objects[j].confidence = 0;
}
// Drawing boxes
for (auto &object : objects) {
if (object.confidence < FLAGS_t)
continue;
auto label = object.class_id;
float confidence = object.confidence;
if (FLAGS_r) {
std::cout << "[" << label << "] element, prob = " << confidence <<
" (" << object.xmin << "," << object.ymin << ")-(" << object.xmax << "," << object.ymax << ")"
<< ((confidence > FLAGS_t) ? " WILL BE RENDERED!" : "") << std::endl;
}
if (confidence > FLAGS_t) {
/** Drawing only objects when >confidence_threshold probability **/
std::ostringstream conf;
conf << ":" << std::fixed << std::setprecision(3) << confidence;
cv::putText(frame,
(label < labels.size() ? labels[label] : std::string("label #") + std::to_string(label))
+ conf.str(),
cv::Point2f(object.xmin, object.ymin - 5), cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 255), 1, cv::LINE_AA);
cv::rectangle(frame, cv::Point2f(object.xmin, object.ymin), cv::Point2f(object.xmax, object.ymax), cv::Scalar(0, 0, 255), 1, cv::LINE_AA);
}
}
}
cv::imshow("Detection results", frame);
t1 = std::chrono::high_resolution_clock::now();
ocv_render_time = std::chrono::duration_cast<ms>(t1 - t0).count();
if (isLastFrame) {
break;
}
if (isModeChanged) {
isModeChanged = false;
}
// Final point:
// in the truly Async mode, we swap the NEXT and CURRENT requests for the next iteration
frame = next_frame;
next_frame = cv::Mat();
if (isAsyncMode) {
async_infer_request_curr.swap(async_infer_request_next);
}
const int key = cv::waitKey(1);
if (27 == key) // Esc
break;
if (9 == key) { // Tab
isAsyncMode ^= true;
isModeChanged = true;
}
}
// -----------------------------------------------------------------------------------------------------
auto total_t1 = std::chrono::high_resolution_clock::now();
ms total = std::chrono::duration_cast<ms>(total_t1 - total_t0);
std::cout << "Total Inference time: " << total.count() << std::endl;
/** Showing performace results **/
if (FLAGS_pc) {
printPerformanceCounts(*async_infer_request_curr, std::cout);
}
}
catch (const std::exception& error) {
std::cerr << "[ ERROR ] " << error.what() << std::endl;
return 1;
}
catch (...) {
std::cerr << "[ ERROR ] Unknown/internal exception happened." << std::endl;
return 1;
}
slog::info << "Execution successful" << slog::endl;
return 0;
}
#◆ 最後に
モデルの変換、鬼畜めいた大変さです。
また、OpenVINOからリターンされる推論結果の形式が想定と大きく異なっていたり、OpenVINOがYoloV3の全てのレイヤーをフォローしているわけでは無いため、ある程度自力でモデル構造を変更する必要があります。
正直言って、沼です。
tiny-YoloV3 は、もっと沼です。
前回記事 のように、かなりトリッキーな実装をしないとならない感覚。
これまでまじめに返信し過ぎたためか、国外の方々からの問い合わせが多くなってきて正直面倒くさいです。
Youtubeに Step-by-Step の手順動画を投稿してくれ、とか。
英語しゃべれないっつーに。
ちなみに日本人からの問い合わせは、ほぼ無いw
こういった手抜き記事のどのあたりが心にヒットしているのかが全く謎ですね。
#◆ 次回予告
とか言いながら、 せっかくなので tiny-YoloV3 の実装にもチャレンジしてみたい。
いやぁ、 泥沼な予感。。。
もう既に２日間格闘しているし、めちゃくちゃ時間が掛かりそう。。。