树莓派4B使用NCNN部署Yolov5

2023-04-11 18:07| 来源: 网络整理| 查看: 265

文章目录

前言

一、树莓派配置NCNN

1.安装依赖

2.下载NCNN并编译

二、Yolov5-lite模型训练

1.源码地址

2.安装所需要的包

3.训练自己的数据集(YOLO格式)

4.模型训练

5.模型转换

6.onnx模型简化

三、树莓派部署lite模型

1.将onnx模型转换为ncnn

2.添加Yolov5-lite.cpp

3.修改eopt.param

4.修改yolov5_lite.cpp

5.修改CMakeLists.txt

四、最终运行效果

总结

前言

记录一下流程，方便下次再用

一、树莓派配置NCNN 1.安装依赖 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 2.下载NCNN并编译 $ git clone https://gitee.com/Tencent/ncnn.git cd ncnn mkdir build cd build cmake .. make -j4 make install

完成后ncnn文件夹如下

二、Yolov5-lite模型训练 1.源码地址

https://gitee.com/seaflyren/YOLOv5-Lite

下载后的文件如图所示

2.安装所需要的包 pip install -r requirements.txt 3.训练自己的数据集(YOLO格式)

data文件夹下新建mydata.yaml，复制coco.yaml内容并粘贴

根据自己的数据集修改类别数nc和类名classname以及训练集和验证集路径

修改模型yaml文件中的nc数，和mydata.yaml保持一致

4.模型训练

以lite-e为例，打开终端输入命令

python train.py --weights '预训练权重路径/v5lite-e.pt' --data 'data/mydata.yaml' --cfg 'models/v5lite-e.yaml' --epoch 300 --batch-size 16 --adam 5.模型转换 python export.py --weights 'weights/last.pt' --batch-size 1 --img_size 320 6.onnx模型简化

使用onnx-simplifier对转换后的onnx进行简化

pip install onnxsimplifier python -m onnxsim last.onnx e.onnx 三、树莓派部署lite模型 1.将onnx模型转换为ncnn cd ncnn/build ./tools/onnx/onnx2ncnn e.onnx e.param e.bin # 模型优化为fp16 ./tools/onnxoptimize e.param e.bin eopt.param eopt.bin 65536 2.添加Yolov5-lite.cpp cd ncnn/examples touch yolov5_lite.cpp

将下面的代码复制到cpp文件中

// 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 #include #include #endif #include #include #include #include // 0 : FP16 // 1 : INT8 #define USE_INT8 0 // 0 : Image // 1 : Camera #define USE_CAMERA 1 struct Object { cv::Rect_ rect; int label; float prob; }; static inline float intersection_area(const Object& a, const Object& b) { cv::Rect_ inter = a.rect & b.rect; return inter.area(); } static void qsort_descent_inplace(std::vector& faceobjects, int left, int right) { int i = left; int j = right; float p = faceobjects[(left + right) / 2].prob; while (i p) i++; while (faceobjects[j].prob < p) j--; if (i nms_threshold) keep = 0; } if (keep) picked.push_back(i); } } static inline float sigmoid(float x) { return static_cast(1.f / (1.f + exp(-x))); } // unsigmoid static inline float unsigmoid(float y) { return static_cast(-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 &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& 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 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 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 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 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 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& 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 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 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 3.修改eopt.param

bug1:Squeeze not supported yet!

生成param文件时如果遇到Squeeze not supported yet!等提示，解决方法为使用onnxsimplifier优化onnx模型在转换为param

打开eopt.param，将所有Reshape修改为0=-1，此步是为了能够动态输入