热门标签
热门文章
- 1conda安装gcc方法_conda 安装gcc
- 2Node.js基础---Express
- 3强化学习 PPO算法和代码_ppo算法参数
- 4Linux删除文件和目录
- 5UE5渲染视频教程推荐
- 6Secrets of Self-Made Millionaires, Adam Khoo 读书笔记_secrets of size
- 7两种方法教你在postman设置请求里带动态token_postman带token发请求
- 8给出两个整数a和b, 求他们的和, 但不能使用 + 等数学运算符。_int aplusb(int a, int b)
- 9python --Matplotlib详解_python matplotlib
- 10Python由浅入深学习路线笔记_python练习从浅入深
当前位置: article > 正文
使用ncnn在树莓派部署自己的yolov5lites模型
作者：2023面试高手 | 2024-02-25 10:35:13
赞
踩
使用ncnn在树莓派部署自己的yolov5lites模型
使用ncnn在树莓派部署自己的yolov5lites模型
文章目录
前言
记录一下入门小白的树莓派部署记录，前前后后走过不少坑。
一、windows10上训练自己的yolov5lites模型
1 下载yolov5lites源码
git clone https://github.com/ppogg/YOLOv5-Lite.git
- 1
2 配置yolov5lites运行环境
默认你在windows10上已经会配置环境，由于我本身已经配置好torch=1.7,torchvision=0.8，对应的cuda版本为11.0，还有对应的cudnn版本以及显卡驱动，我就在此环境下进行训练。并使用requirements.txt里的依赖(不需要重新配置相应的cuda以及cudnn)进行.onnx的导出
pip install -r requirements.txt
- 1
3 修改参数进行yolov5lites的训练
这一部分是记录转换xml格式为txt文件以及yolov5lites训练自己的模型等所需要的准备工作。
3.1 修改.yaml文件
为自己的训练种类以及个数
3.2 修改输入为[320,320]
将下载的v5lite-s.pt文件放在创建的weights/文件夹下。
3.3 进行训练
点击train.py，训练保存的权重在runs文件夹下
3.4 生成先验框
使用autoanchor.py，将生成的数据保存到v5lites.yaml
自己手动添加到对应的是C:\Users\jxbj2\Desktop\yolov5lite\YOLOv5-Lite-master\models\v5lite-s.yaml
3.5 可能遇到的bug，比如爆显存等等自己可以csdn一下。（坚持下去，多舔一舔）
3.6 这里贴一个将xml格式转换成yolov5lites训练所需的txt文件
文件夹准备如下，images放入图片，indata放入对应的xml格式文件，生成的txt文件会在labels文件夹下。
转换代码如下，如需调动测试以及验证数据集自己手动调yolov5lites的代码,换上自己对应修改的种类即可。
import xml.etree.ElementTree as ET import pickle import os from os import listdir , getcwd from os.path import join import glob classes = ["desk", "projector","cup","laptop","trash","box","mecanum"] def convert(size, box): dw = 1.0/size[0] dh = 1.0/size[1] x = (box[0]+box[1])/2.0 y = (box[2]+box[3])/2.0 w = box[1] - box[0] h = box[3] - box[2] x = x*dw w = w*dw y = y*dh h = h*dh return (x,y,w,h) def convert_annotation(image_name): in_file = open('./indata/'+image_name[:-3]+'xml') #xml文件路径 out_file = open('./labels/'+image_name[:-3]+'txt', 'w') #转换后的txt文件存放路径 f = open('./indata/'+image_name[:-3]+'xml') xml_text = f.read() root = ET.fromstring(xml_text) f.close() size = root.find('size') w = int(size.find('width').text) h = int(size.find('height').text) for obj in root.iter('object'): cls = obj.find('name').text if cls not in classes: print(cls) continue cls_id = classes.index(cls) xmlbox = obj.find('bndbox') b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text)) bb = convert((w,h), b) out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n') wd = getcwd() if __name__ == '__main__': #for image_path in glob.glob("./images/train/*.jpg"): #每一张图片都对应一个xml文件这里写xml对应的图片的路径 for image_path in glob.glob("./images/*.jpg"): #每一张图片都对应一个xml文件这里写xml对应的图片的路径 image_name = image_path.split('\\')[-1] convert_annotation(image_name)
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10
- 11
- 12
- 13
- 14
- 15
- 16
- 17
- 18
- 19
- 20
- 21
- 22
- 23
- 24
- 25
- 26
- 27
- 28
- 29
- 30
- 31
- 32
- 33
- 34
- 35
- 36
- 37
- 38
- 39
- 40
- 41
- 42
- 43
- 44
- 45
- 46
- 47
- 48
- 49
- 50
- 51
- 52
- 53
- 54
- 55
- 56
- 57
- 58
二、数据的导出以及转换
这部分的导出我是在windows上完成的，我尝试了一下在树莓派上去导出，但一直报导出错误。
1.last.onnx文件导出以及简化
python export.py --weights weights/last.pt
- 1
会在weights/文件夹下生成对应的last.onnx文件
使用onnx-simplifier对转换后的onnx进行简化,将last.onnx文件放到yolov5lite-master文件下输入在终端输入以下指令
python -m onnxsim last.onnx lastsim.onnx
- 1
将简化后的lastsim.onnx放入u盘，导入到树莓派中
三、树莓派环境依赖已经ncnn编译
1.树莓派环境依赖配置
sudo apt-get install git cmake
sudo apt-get install -y gfortran
sudo apt-get install -y libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
sudo apt-get install --no-install-recommends libboost-all-dev
sudo apt-get install -y libgflags-dev libgoogle-glog-dev liblmdb-dev libatlas-base-dev
- 1
- 2
- 3
- 4
- 5
2.ncnn配置以及编译
$ git clone https://gitee.com/Tencent/ncnn.git
cd ncnn
mkdir build
cd build
cmake ..
make -j4
make install
- 1
- 2
- 3
- 4
- 5
- 6
- 7
四、树莓派部署lite的ncnn细节
1.将lastsim.onnx转换为fp16的last.param和last.bin文件
对应路径自己修改（应当具备基础的命令行使用能力哈哈哈哈哈）
cd ncnn/build
./tools/onnx/onnx2ncnn lastsim.onnx lastsim.param lastsim.bin
./tools/ncnnoptimize lastsim.param lastsim.bin last.param last.bin 65536
- 1
- 2
- 3
2.添加yolov5lite.cpp到ncnn/examples文件夹下
具体代码如下所示
// Tencent is pleased to support the open source community by making ncnn available. // // Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved. // // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except // in compliance with the License. You may obtain a copy of the License at // // https://opensource.org/licenses/BSD-3-Clause // // Unless required by applicable law or agreed to in writing, software distributed // under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR // CONDITIONS OF ANY KIND, either express or implied. See the License for the // specific language governing permissions and limitations under the License. #include "layer.h" #include "net.h" #if defined(USE_NCNN_SIMPLEOCV) #include "simpleocv.h" #else #include <opencv2/core/core.hpp> #include <opencv2/highgui/highgui.hpp> #include <opencv2/imgproc/imgproc.hpp> #endif #include <float.h> #include <stdio.h> #include <vector> #include <sys/time.h> // 0 : FP16 // 1 : INT8 #define USE_INT8 0 // 0 : Image // 1 : Camera #define USE_CAMERA 1 struct Object { cv::Rect_<float> rect; int label; float prob; }; static inline float intersection_area(const Object& a, const Object& b) { cv::Rect_<float> inter = a.rect & b.rect; return inter.area(); } static void qsort_descent_inplace(std::vector<Object>& faceobjects, int left, int right) { int i = left; int j = right; float p = faceobjects[(left + right) / 2].prob; while (i <= j) { while (faceobjects[i].prob > p) i++; while (faceobjects[j].prob < p) j--; if (i <= j) { // swap std::swap(faceobjects[i], faceobjects[j]); i++; j--; } } #pragma omp parallel sections { #pragma omp section { if (left < j) qsort_descent_inplace(faceobjects, left, j); } #pragma omp section { if (i < right) qsort_descent_inplace(faceobjects, i, right); } } } static void qsort_descent_inplace(std::vector<Object>& faceobjects) { if (faceobjects.empty()) return; qsort_descent_inplace(faceobjects, 0, faceobjects.size() - 1); } static void nms_sorted_bboxes(const std::vector<Object>& faceobjects, std::vector<int>& picked, float nms_threshold) { picked.clear(); const int n = faceobjects.size(); std::vector<float> areas(n); for (int i = 0; i < n; i++) { areas[i] = faceobjects[i].rect.area(); } for (int i = 0; i < n; i++) { const Object& a = faceobjects[i]; int keep = 1; for (int j = 0; j < (int)picked.size(); j++) { const Object& b = faceobjects[picked[j]]; // intersection over union float inter_area = intersection_area(a, b); float union_area = areas[i] + areas[picked[j]] - inter_area; // float IoU = inter_area / union_area if (inter_area / union_area > nms_threshold) keep = 0; } if (keep) picked.push_back(i); } } static inline float sigmoid(float x) { return static_cast<float>(1.f / (1.f + exp(-x))); } // unsigmoid static inline float unsigmoid(float y) { return static_cast<float>(-1.0 * (log((1.0 / y) - 1.0))); } static void generate_proposals(const ncnn::Mat &anchors, int stride, const ncnn::Mat &in_pad, const ncnn::Mat &feat_blob, float prob_threshold, std::vector <Object> &objects) { const int num_grid = feat_blob.h; float unsig_pro = 0; if (prob_threshold > 0.6) unsig_pro = unsigmoid(prob_threshold); int num_grid_x; int num_grid_y; if (in_pad.w > in_pad.h) { num_grid_x = in_pad.w / stride; num_grid_y = num_grid / num_grid_x; } else { num_grid_y = in_pad.h / stride; num_grid_x = num_grid / num_grid_y; } const int num_class = feat_blob.w - 5; const int num_anchors = anchors.w / 2; for (int q = 0; q < num_anchors; q++) { const float anchor_w = anchors[q * 2]; const float anchor_h = anchors[q * 2 + 1]; const ncnn::Mat feat = feat_blob.channel(q); for (int i = 0; i < num_grid_y; i++) { for (int j = 0; j < num_grid_x; j++) { const float *featptr = feat.row(i * num_grid_x + j); // find class index with max class score int class_index = 0; float class_score = -FLT_MAX; float box_score = featptr[4]; if (prob_threshold > 0.6) { // while prob_threshold > 0.6, unsigmoid better than sigmoid if (box_score > unsig_pro) { for (int k = 0; k < num_class; k++) { float score = featptr[5 + k]; if (score > class_score) { class_index = k; class_score = score; } } float confidence = sigmoid(box_score) * sigmoid(class_score); if (confidence >= prob_threshold) { float dx = sigmoid(featptr[0]); float dy = sigmoid(featptr[1]); float dw = sigmoid(featptr[2]); float dh = sigmoid(featptr[3]); float pb_cx = (dx * 2.f - 0.5f + j) * stride; float pb_cy = (dy * 2.f - 0.5f + i) * stride; float pb_w = pow(dw * 2.f, 2) * anchor_w; float pb_h = pow(dh * 2.f, 2) * anchor_h; float x0 = pb_cx - pb_w * 0.5f; float y0 = pb_cy - pb_h * 0.5f; float x1 = pb_cx + pb_w * 0.5f; float y1 = pb_cy + pb_h * 0.5f; Object obj; obj.rect.x = x0; obj.rect.y = y0; obj.rect.width = x1 - x0; obj.rect.height = y1 - y0; obj.label = class_index; obj.prob = confidence; objects.push_back(obj); } } else { for (int k = 0; k < num_class; k++) { float score = featptr[5 + k]; if (score > class_score) { class_index = k; class_score = score; } } float confidence = sigmoid(box_score) * sigmoid(class_score); if (confidence >= prob_threshold) { float dx = sigmoid(featptr[0]); float dy = sigmoid(featptr[1]); float dw = sigmoid(featptr[2]); float dh = sigmoid(featptr[3]); float pb_cx = (dx * 2.f - 0.5f + j) * stride; float pb_cy = (dy * 2.f - 0.5f + i) * stride; float pb_w = pow(dw * 2.f, 2) * anchor_w; float pb_h = pow(dh * 2.f, 2) * anchor_h; float x0 = pb_cx - pb_w * 0.5f; float y0 = pb_cy - pb_h * 0.5f; float x1 = pb_cx + pb_w * 0.5f; float y1 = pb_cy + pb_h * 0.5f; Object obj; obj.rect.x = x0; obj.rect.y = y0; obj.rect.width = x1 - x0; obj.rect.height = y1 - y0; obj.label = class_index; obj.prob = confidence; objects.push_back(obj); } } } } } } } static int detect_yolov5(const cv::Mat& bgr, std::vector<Object>& objects) { ncnn::Net yolov5; #if USE_INT8 yolov5.opt.use_int8_inference=true; #else yolov5.opt.use_vulkan_compute = true; yolov5.opt.use_bf16_storage = true; #endif // original pretrained model from https://github.com/ultralytics/yolov5 // the ncnn model https://github.com/nihui/ncnn-assets/tree/master/models #if USE_INT8 yolov5.load_param("/home/corvin/Mask/weights/e.param"); yolov5.load_model("/home/corvin/Mask/weights/e.bin"); #else yolov5.load_param("/home/corvin/Mask/weights/eopt.param"); yolov5.load_model("/home/corvin/Mask/weights/eopt.bin"); #endif const int target_size = 320; const float prob_threshold = 0.60f; const float nms_threshold = 0.60f; int img_w = bgr.cols; int img_h = bgr.rows; // letterbox pad to multiple of 32 int w = img_w; int h = img_h; float scale = 1.f; if (w > h) { scale = (float)target_size / w; w = target_size; h = h * scale; } else { scale = (float)target_size / h; h = target_size; w = w * scale; } ncnn::Mat in = ncnn::Mat::from_pixels_resize(bgr.data, ncnn::Mat::PIXEL_BGR2RGB, img_w, img_h, w, h); // pad to target_size rectangle // yolov5/utils/datasets.py letterbox int wpad = (w + 31) / 32 * 32 - w; int hpad = (h + 31) / 32 * 32 - h; ncnn::Mat in_pad; ncnn::copy_make_border(in, in_pad, hpad / 2, hpad - hpad / 2, wpad / 2, wpad - wpad / 2, ncnn::BORDER_CONSTANT, 114.f); const float norm_vals[3] = {1 / 255.f, 1 / 255.f, 1 / 255.f}; in_pad.substract_mean_normalize(0, norm_vals); ncnn::Extractor ex = yolov5.create_extractor(); ex.input("images", in_pad); std::vector<Object> proposals; // stride 8 { ncnn::Mat out; ex.extract("451", out); ncnn::Mat anchors(6); anchors[0] = 10.f; anchors[1] = 13.f; anchors[2] = 16.f; anchors[3] = 30.f; anchors[4] = 33.f; anchors[5] = 23.f; std::vector<Object> objects8; generate_proposals(anchors, 8, in_pad, out, prob_threshold, objects8); proposals.insert(proposals.end(), objects8.begin(), objects8.end()); } // stride 16 { ncnn::Mat out; ex.extract("479", out); ncnn::Mat anchors(6); anchors[0] = 30.f; anchors[1] = 61.f; anchors[2] = 62.f; anchors[3] = 45.f; anchors[4] = 59.f; anchors[5] = 119.f; std::vector<Object> objects16; generate_proposals(anchors, 16, in_pad, out, prob_threshold, objects16); proposals.insert(proposals.end(), objects16.begin(), objects16.end()); } // stride 32 { ncnn::Mat out; ex.extract("507", out); ncnn::Mat anchors(6); anchors[0] = 116.f; anchors[1] = 90.f; anchors[2] = 156.f; anchors[3] = 198.f; anchors[4] = 373.f; anchors[5] = 326.f; std::vector<Object> objects32; generate_proposals(anchors, 32, in_pad, out, prob_threshold, objects32); proposals.insert(proposals.end(), objects32.begin(), objects32.end()); } // sort all proposals by score from highest to lowest qsort_descent_inplace(proposals); // apply nms with nms_threshold std::vector<int> picked; nms_sorted_bboxes(proposals, picked, nms_threshold); int count = picked.size(); objects.resize(count); for (int i = 0; i < count; i++) { objects[i] = proposals[picked[i]]; // adjust offset to original unpadded float x0 = (objects[i].rect.x - (wpad / 2)) / scale; float y0 = (objects[i].rect.y - (hpad / 2)) / scale; float x1 = (objects[i].rect.x + objects[i].rect.width - (wpad / 2)) / scale; float y1 = (objects[i].rect.y + objects[i].rect.height - (hpad / 2)) / scale; // clip x0 = std::max(std::min(x0, (float)(img_w - 1)), 0.f); y0 = std::max(std::min(y0, (float)(img_h - 1)), 0.f); x1 = std::max(std::min(x1, (float)(img_w - 1)), 0.f); y1 = std::max(std::min(y1, (float)(img_h - 1)), 0.f); objects[i].rect.x = x0; objects[i].rect.y = y0; objects[i].rect.width = x1 - x0; objects[i].rect.height = y1 - y0; } return 0; } static void draw_objects(const cv::Mat& bgr, const std::vector<Object>& objects) { static const char* class_names[] = { "face","face_mask" }; cv::Mat image = bgr.clone(); for (size_t i = 0; i < objects.size(); i++) { const Object& obj = objects[i]; fprintf(stderr, "%d = %.5f at %.2f %.2f %.2f x %.2f\n", obj.label, obj.prob, obj.rect.x, obj.rect.y, obj.rect.width, obj.rect.height); cv::rectangle(image, obj.rect, cv::Scalar(0, 255, 0)); char text[256]; sprintf(text, "%s %.1f%%", class_names[obj.label], obj.prob * 100); int baseLine = 0; cv::Size label_size = cv::getTextSize(text, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine); int x = obj.rect.x; int y = obj.rect.y - label_size.height - baseLine; if (y < 0) y = 0; if (x + label_size.width > image.cols) x = image.cols - label_size.width; cv::rectangle(image, cv::Rect(cv::Point(x, y), cv::Size(label_size.width, label_size.height + baseLine)), cv::Scalar(255, 255, 255), -1); cv::putText(image, text, cv::Point(x, y + label_size.height), cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 0)); } #if USE_CAMERA imshow("camera", image); cv::waitKey(1); #else cv::imwrite("result.jpg", image); #endif } #if USE_CAMERA int main(int argc, char** argv) { cv::VideoCapture capture; capture.open(0); //修改这个参数可以选择打开想要用的摄像头 cv::Mat frame; while (true) { capture >> frame; cv::Mat m = frame; std::vector<Object> objects; detect_yolov5(frame, objects); draw_objects(m, objects); if (cv::waitKey(30) >= 0) break; } } #else int main(int argc, char** argv) { if (argc != 2) { fprintf(stderr, "Usage: %s [imagepath]\n", argv[0]); return -1; } const char* imagepath = argv[1]; struct timespec begin, end; long time; clock_gettime(CLOCK_MONOTONIC, &begin); cv::Mat m = cv::imread(imagepath, 1); if (m.empty()) { fprintf(stderr, "cv::imread %s failed\n", imagepath); return -1; } std::vector<Object> objects; detect_yolov5(m, objects); clock_gettime(CLOCK_MONOTONIC, &end); time = (end.tv_sec - begin.tv_sec) + (end.tv_nsec - begin.tv_nsec); printf(">> Time : %lf ms\n", (double)time/1000000); draw_objects(m, objects); return 0; } #endif
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10
- 11
- 12
- 13
- 14
- 15
- 16
- 17
- 18
- 19
- 20
- 21
- 22
- 23
- 24
- 25
- 26
- 27
- 28
- 29
- 30
- 31
- 32
- 33
- 34
- 35
- 36
- 37
- 38
- 39
- 40
- 41
- 42
- 43
- 44
- 45
- 46
- 47
- 48
- 49
- 50
- 51
- 52
- 53
- 54
- 55
- 56
- 57
- 58
- 59
- 60
- 61
- 62
- 63
- 64
- 65
- 66
- 67
- 68
- 69
- 70
- 71
- 72
- 73
- 74
- 75
- 76
- 77
- 78
- 79
- 80
- 81
- 82
- 83
- 84
- 85
- 86
- 87
- 88
- 89
- 90
- 91
- 92
- 93
- 94
- 95
- 96
- 97
- 98
- 99
- 100
- 101
- 102
- 103
- 104
- 105
- 106
- 107
- 108
- 109
- 110
- 111
- 112
- 113
- 114
- 115
- 116
- 117
- 118
- 119
- 120
- 121
- 122
- 123
- 124
- 125
- 126
- 127
- 128
- 129
- 130
- 131
- 132
- 133
- 134
- 135
- 136
- 137
- 138
- 139
- 140
- 141
- 142
- 143
- 144
- 145
- 146
- 147
- 148
- 149
- 150
- 151
- 152
- 153
- 154
- 155
- 156
- 157
- 158
- 159
- 160
- 161
- 162
- 163
- 164
- 165
- 166
- 167
- 168
- 169
- 170
- 171
- 172
- 173
- 174
- 175
- 176
- 177
- 178
- 179
- 180
- 181
- 182
- 183
- 184
- 185
- 186
- 187
- 188
- 189
- 190
- 191
- 192
- 193
- 194
- 195
- 196
- 197
- 198
- 199
- 200
- 201
- 202
- 203
- 204
- 205
- 206
- 207
- 208
- 209
- 210
- 211
- 212
- 213
- 214
- 215
- 216
- 217
- 218
- 219
- 220
- 221
- 222
- 223
- 224
- 225
- 226
- 227
- 228
- 229
- 230
- 231
- 232
- 233
- 234
- 235
- 236
- 237
- 238
- 239
- 240
- 241
- 242
- 243
- 244
- 245
- 246
- 247
- 248
- 249
- 250
- 251
- 252
- 253
- 254
- 255
- 256
- 257
- 258
- 259
- 260
- 261
- 262
- 263
- 264
- 265
- 266
- 267
- 268
- 269
- 270
- 271
- 272
- 273
- 274
- 275
- 276
- 277
- 278
- 279
- 280
- 281
- 282
- 283
- 284
- 285
- 286
- 287
- 288
- 289
- 290
- 291
- 292
- 293
- 294
- 295
- 296
- 297
- 298
- 299
- 300
- 301
- 302
- 303
- 304
- 305
- 306
- 307
- 308
- 309
- 310
- 311
- 312
- 313
- 314
- 315
- 316
- 317
- 318
- 319
- 320
- 321
- 322
- 323
- 324
- 325
- 326
- 327
- 328
- 329
- 330
- 331
- 332
- 333
- 334
- 335
- 336
- 337
- 338
- 339
- 340
- 341
- 342
- 343
- 344
- 345
- 346
- 347
- 348
- 349
- 350
- 351
- 352
- 353
- 354
- 355
- 356
- 357
- 358
- 359
- 360
- 361
- 362
- 363
- 364
- 365
- 366
- 367
- 368
- 369
- 370
- 371
- 372
- 373
- 374
- 375
- 376
- 377
- 378
- 379
- 380
- 381
- 382
- 383
- 384
- 385
- 386
- 387
- 388
- 389
- 390
- 391
- 392
- 393
- 394
- 395
- 396
- 397
- 398
- 399
- 400
- 401
- 402
- 403
- 404
- 405
- 406
- 407
- 408
- 409
- 410
- 411
- 412
- 413
- 414
- 415
- 416
- 417
- 418
- 419
- 420
- 421
- 422
- 423
- 424
- 425
- 426
- 427
- 428
- 429
- 430
- 431
- 432
- 433
- 434
- 435
- 436
- 437
- 438
- 439
- 440
- 441
- 442
- 443
- 444
- 445
- 446
- 447
- 448
- 449
- 450
- 451
- 452
- 453
- 454
- 455
- 456
- 457
- 458
- 459
- 460
- 461
- 462
- 463
- 464
- 465
- 466
- 467
- 468
- 469
- 470
- 471
- 472
- 473
- 474
- 475
- 476
- 477
- 478
- 479
- 480
- 481
- 482
- 483
- 484
- 485
- 486
- 487
- 488
- 489
- 490
- 491
- 492
- 493
- 494
- 495
- 496
- 497
- 498
- 499
- 500
- 501
- 502
- 503
- 504
- 505
- 506
- 507
- 508
- 509
- 510
- 511
- 512
- 513
- 514
3.修改yolov5lite.cpp
3.1 修改classclass_names
修改成你所需要的检测种类,“desk”, “bicycle”, “cup”, “laptop”, “trash”, “box”, “mecanum”
3.2 修改anchor的数据
对应的是C:\Users\jxbj2\Desktop\yolov5lite\YOLOv5-Lite-master\models\v5lite-s.yaml
将上面这些anchor的数据（15，28，19，35，23，46）放入yolov5lite.cpp中的以下代码中，第一行的六个数据对应10，13，16，30，33，23）
对应的还有两个对应的地方也做出同样修改即可。
3.3 修改lastsim.param
将Reshape 0=x全部设置为0=-1，如画圈所示
3.4 修改yolov5lites的ex.extract
打开lastsim.param文件，对应上图三个方框里的 onnx：：Sigmoid_647改写到ex.extract里面。
3.5 在yolov5lites.cpp内修改路径
修改好yolov5.cpp中lastsim.param和lastsim.bin的路径，并放到测试的文件夹内（路径）。
3.6 修改CMakelists.txt
进入到ncnn/examples/CMakelist.txt，如下图所示
输入指令
cd ncnn/build
cmake ..
make
- 1
- 2
- 3
完成编译。
五、测试效果
打开测试的文件夹，将编译好的yolov5.cpp可执行文件放到测试文件夹下，（在yolov5lite.cpp文件内选择摄像头还是图片，如果有图片记得放在测试文件夹下）。
cd pi/ceshi
./yolov5_lite.cpp
- 1
- 2
说实话，我用的树莓派4B，yolov5lites感觉效果都不是很好，接下来我要继续试一下int8的量化。
声明：本文内容由网友自发贡献，不代表【wpsshop博客】立场，版权归原作者所有，本站不承担相应法律责任。如您发现有侵权的内容，请联系我们。转载请注明出处：https://www.wpsshop.cn/w/2023面试高手/article/detail/140383
推荐阅读
相关标签
