From 4c674d545211f2ac498387e6400dcefe1c544de8 Mon Sep 17 00:00:00 2001 From: qinxiaonan_branch <860289024@qq.com> Date: Wed, 15 May 2024 20:24:03 +0800 Subject: [PATCH] 1 --- .../yolov5-dnn-cpp-python-main/README.md | 24 + .../yolov5-dnn-cpp-python-main/bus.jpg | Bin 0 -> 487438 bytes .../yolov5-dnn-cpp-python-main/coco.names | 80 ++++ .../convert-onnx/common.py | 443 ++++++++++++++++++ .../convert-onnx/convert_onnx.py | 101 ++++ .../convert-onnx/yolov5l.py | 59 +++ .../convert-onnx/yolov5m.py | 59 +++ .../convert-onnx/yolov5s.py | 59 +++ .../convert-onnx/yolov5x.py | 59 +++ .../yolov5-dnn-cpp-python-main/dog.jpg | Bin 0 -> 163759 bytes .../yolov5-dnn-cpp-python-main/main_yolo.cpp | 136 ++++++ .../yolov5-dnn-cpp-python-main/main_yolov5.py | 121 +++++ .../yolov5-dnn-cpp-python-main/person.jpg | Bin 0 -> 113880 bytes .../yolov5-dnn-cpp-python-main/yolo.h | 52 ++ .../yolov5-dnn-cpp-python-main/zidane.jpg | Bin 0 -> 168949 bytes 15 files changed, 1193 insertions(+) create mode 100644 src/yolov5-dnn-cpp-python-main/yolov5-dnn-cpp-python-main/README.md create mode 100644 src/yolov5-dnn-cpp-python-main/yolov5-dnn-cpp-python-main/bus.jpg create mode 100644 src/yolov5-dnn-cpp-python-main/yolov5-dnn-cpp-python-main/coco.names create mode 100644 src/yolov5-dnn-cpp-python-main/yolov5-dnn-cpp-python-main/convert-onnx/common.py create mode 100644 src/yolov5-dnn-cpp-python-main/yolov5-dnn-cpp-python-main/convert-onnx/convert_onnx.py create mode 100644 src/yolov5-dnn-cpp-python-main/yolov5-dnn-cpp-python-main/convert-onnx/yolov5l.py create mode 100644 src/yolov5-dnn-cpp-python-main/yolov5-dnn-cpp-python-main/convert-onnx/yolov5m.py create mode 100644 src/yolov5-dnn-cpp-python-main/yolov5-dnn-cpp-python-main/convert-onnx/yolov5s.py create mode 100644 src/yolov5-dnn-cpp-python-main/yolov5-dnn-cpp-python-main/convert-onnx/yolov5x.py create mode 100644 src/yolov5-dnn-cpp-python-main/yolov5-dnn-cpp-python-main/dog.jpg create mode 100644 src/yolov5-dnn-cpp-python-main/yolov5-dnn-cpp-python-main/main_yolo.cpp create mode 100644 src/yolov5-dnn-cpp-python-main/yolov5-dnn-cpp-python-main/main_yolov5.py create mode 100644 src/yolov5-dnn-cpp-python-main/yolov5-dnn-cpp-python-main/person.jpg create mode 100644 src/yolov5-dnn-cpp-python-main/yolov5-dnn-cpp-python-main/yolo.h create mode 100644 src/yolov5-dnn-cpp-python-main/yolov5-dnn-cpp-python-main/zidane.jpg diff --git a/src/yolov5-dnn-cpp-python-main/yolov5-dnn-cpp-python-main/README.md b/src/yolov5-dnn-cpp-python-main/yolov5-dnn-cpp-python-main/README.md new file mode 100644 index 0000000..bdb70e7 --- /dev/null +++ b/src/yolov5-dnn-cpp-python-main/yolov5-dnn-cpp-python-main/README.md @@ -0,0 +1,24 @@ +# yolov5-dnn-cpp-py +yolov5s,yolov5l,yolov5m,yolov5x的onnx文件在百度云盘下载, +链接:https://pan.baidu.com/s/1d67LUlOoPFQy0MV39gpJiw +提取码:bayj + +python版本的主程序是main_yolov5.py,C++版本的主程序是main_yolo.cpp + +运行整套程序只需要安装opencv库(4.0以上版本的),彻底摆脱对深度学习框架的依赖 + +如果你想运行生成onnx文件的程序,那么就cd到convert-onnx文件夹,在百度云盘下载yolov5s,yolov5l,yolov5m,yolov5x的.pth文件放在该目录里, +百度云盘链接: https://pan.baidu.com/s/1oIdwpp6kuasANMInTpHnrw 密码: m3n1 + +这4个pth文件是从https://github.com/ultralytics/yolov5 的pth文件里抽取出参数,保存到顺序字典OrderedDict里,最后生成新的pth文件 +在convert-onnx文件夹里,我把4种yolov5的网络结构全都定义在.py文件里,这样便于读者直观的了解网络结构以及层与层的连接关系。 +下载完成pth文件后,运行convert_onnx.py就可以生成.onnx文件,这个程序需要依赖pytorch1.7.0框架,如果pytorch版本低了,程序运行会报错。 +因为在yolov5里有新的激活函数,旧版本pytorch可能不支持的 + +在编写这套程序时,遇到的bug和解决办法,可以阅读我的csdn博客 +https://blog.csdn.net/nihate/article/details/112731327 + + +2022年2月26日,看到https://github.com/ultralytics/yolov5 在最近更新的v6.1版本的, +我编写了分别使用OpenCV、ONNXRuntime部署yolov5-v6.1目标检测,包含C++和Python两个版本的程序。 +源码地址是: https://github.com/hpc203/yolov5-v6.1-opencv-onnxrun diff --git a/src/yolov5-dnn-cpp-python-main/yolov5-dnn-cpp-python-main/bus.jpg b/src/yolov5-dnn-cpp-python-main/yolov5-dnn-cpp-python-main/bus.jpg new file mode 100644 index 0000000000000000000000000000000000000000..b43e311165c785f000eb7493ff8fb662d06a3f83 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config.objThreshold; + strcpy_s(this->netname, config.netname.c_str()); + + ifstream ifs(this->classesFile.c_str()); + string line; + while (getline(ifs, line)) this->classes.push_back(line); + + string modelFile = this->netname; + modelFile += ".onnx"; + this->net = readNet(modelFile); +} + +void YOLO::drawPred(int classId, float conf, int left, int top, int right, int bottom, Mat& frame) // Draw the predicted bounding box +{ + //Draw a rectangle displaying the bounding box + rectangle(frame, Point(left, top), Point(right, bottom), Scalar(0, 0, 255), 3); + + //Get the label for the class name and its confidence + string label = format("%.2f", conf); + label = this->classes[classId] + ":" + label; + + //Display the label at the top of the bounding box + int baseLine; + Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine); + top = max(top, labelSize.height); + //rectangle(frame, Point(left, top - int(1.5 * labelSize.height)), Point(left + int(1.5 * labelSize.width), top + baseLine), Scalar(0, 255, 0), FILLED); + putText(frame, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 0.75, Scalar(0, 255, 0), 1); +} + +void YOLO::sigmoid(Mat* out, int length) +{ + float* pdata = (float*)(out->data); + int i = 0; + for (i = 0; i < length; i++) + { + pdata[i] = 1.0 / (1 + expf(-pdata[i])); + } +} + +void YOLO::detect(Mat& frame) +{ + Mat blob; + blobFromImage(frame, blob, 1 / 255.0, Size(this->inpWidth, this->inpHeight), Scalar(0, 0, 0), true, false); + this->net.setInput(blob); + vector outs; + this->net.forward(outs, this->net.getUnconnectedOutLayersNames()); + + /////generate proposals + vector classIds; + vector confidences; + vector boxes; + float ratioh = (float)frame.rows / this->inpHeight, ratiow = (float)frame.cols / this->inpWidth; + int n = 0, q = 0, i = 0, j = 0, nout = this->classes.size() + 5, c = 0; + for (n = 0; n < 3; n++) ///߶ + { + int num_grid_x = (int)(this->inpWidth / this->stride[n]); + int num_grid_y = (int)(this->inpHeight / this->stride[n]); + int area = num_grid_x * num_grid_y; + this->sigmoid(&outs[n], 3 * nout * area); + for (q = 0; q < 3; q++) ///anchor + { + const float anchor_w = this->anchors[n][q * 2]; + const float anchor_h = this->anchors[n][q * 2 + 1]; + float* pdata = (float*)outs[n].data + q * nout * area; + for (i = 0; i < num_grid_y; i++) + { + for (j = 0; j < num_grid_x; j++) + { + float box_score = pdata[4 * area + i * num_grid_x + j]; + if (box_score > this->objThreshold) + { + float max_class_socre = 0, class_socre = 0; + int max_class_id = 0; + for (c = 0; c < this->classes.size(); c++) //// get max socre + { + class_socre = pdata[(c + 5) * area + i * num_grid_x + j]; + if (class_socre > max_class_socre) + { + max_class_socre = class_socre; + max_class_id = c; + } + } + + if (max_class_socre > this->confThreshold) + { + float cx = (pdata[i * num_grid_x + j] * 2.f - 0.5f + j) * this->stride[n]; ///cx + float cy = (pdata[area + i * num_grid_x + j] * 2.f - 0.5f + i) * this->stride[n]; ///cy + float w = powf(pdata[2 * area + i * num_grid_x + j] * 2.f, 2.f) * anchor_w; ///w + float h = powf(pdata[3 * area + i * num_grid_x + j] * 2.f, 2.f) * anchor_h; ///h + + int left = (cx - 0.5*w)*ratiow; + int top = (cy - 0.5*h)*ratioh; ///껹ԭԭͼ + + classIds.push_back(max_class_id); + confidences.push_back(max_class_socre); + boxes.push_back(Rect(left, top, (int)(w*ratiow), (int)(h*ratioh))); + } + } + } + } + } + } + + // Perform non maximum suppression to eliminate redundant overlapping boxes with + // lower confidences + vector indices; + NMSBoxes(boxes, confidences, this->confThreshold, this->nmsThreshold, indices); + for (size_t i = 0; i < indices.size(); ++i) + { + int idx = indices[i]; + Rect box = boxes[idx]; + this->drawPred(classIds[idx], confidences[idx], box.x, box.y, + box.x + box.width, box.y + box.height, frame); + } +} + +int main() +{ + YOLO yolo_model(yolo_nets[0]); + string imgpath = "bus.jpg"; + Mat srcimg = imread(imgpath); + yolo_model.detect(srcimg); + + static const string kWinName = "Deep learning object detection in OpenCV"; + namedWindow(kWinName, WINDOW_NORMAL); + imshow(kWinName, srcimg); + waitKey(0); + destroyAllWindows(); +} \ No newline at end of file diff --git a/src/yolov5-dnn-cpp-python-main/yolov5-dnn-cpp-python-main/main_yolov5.py b/src/yolov5-dnn-cpp-python-main/yolov5-dnn-cpp-python-main/main_yolov5.py new file mode 100644 index 0000000..f4db976 --- /dev/null +++ b/src/yolov5-dnn-cpp-python-main/yolov5-dnn-cpp-python-main/main_yolov5.py @@ -0,0 +1,121 @@ +import cv2 +import argparse +import numpy as np + +class yolov5(): + def __init__(self, yolo_type, confThreshold=0.5, nmsThreshold=0.5, objThreshold=0.5): + with open('coco.names', 'rt') as f: + self.classes = f.read().rstrip('\n').split('\n') ###这个是在coco数据集上训练的模型做opencv部署的,如果你在自己的数据集上训练出的模型做opencv部署,那么需要修改self.classes + self.colors = [np.random.randint(0, 255, size=3).tolist() for _ in range(len(self.classes))] + num_classes = len(self.classes) + anchors = [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]] + self.nl = len(anchors) + self.na = len(anchors[0]) // 2 + self.no = num_classes + 5 + self.grid = [np.zeros(1)] * self.nl + self.stride = np.array([8., 16., 32.]) + self.anchor_grid = np.asarray(anchors, dtype=np.float32).reshape(self.nl, 1, -1, 1, 1, 2) + + self.net = cv2.dnn.readNet(yolo_type + '.onnx') + self.confThreshold = confThreshold + self.nmsThreshold = nmsThreshold + self.objThreshold = objThreshold + + def _make_grid(self, nx=20, ny=20): + xv, yv = np.meshgrid(np.arange(ny), np.arange(nx)) + return np.stack((xv, yv), 2).reshape((1, 1, ny, nx, 2)).astype(np.float32) + + def postprocess(self, frame, outs): + frameHeight = frame.shape[0] + frameWidth = frame.shape[1] + ratioh, ratiow = frameHeight / 640, frameWidth / 640 + # Scan through all the bounding boxes output from the network and keep only the + # ones with high confidence scores. Assign the box's class label as the class with the highest score. + classIds = [] + confidences = [] + boxes = [] + for out in outs: + for detection in out: + scores = detection[5:] + classId = np.argmax(scores) + confidence = scores[classId] + if confidence > self.confThreshold and detection[4] > self.objThreshold: + center_x = int(detection[0] * ratiow) + center_y = int(detection[1] * ratioh) + width = int(detection[2] * ratiow) + height = int(detection[3] * ratioh) + left = int(center_x - width / 2) + top = int(center_y - height / 2) + classIds.append(classId) + confidences.append(float(confidence)) + boxes.append([left, top, width, height]) + + # Perform non maximum suppression to eliminate redundant overlapping boxes with + # lower confidences. + indices = cv2.dnn.NMSBoxes(boxes, confidences, self.confThreshold, self.nmsThreshold) + for i in indices: + i = i[0] + box = boxes[i] + left = box[0] + top = box[1] + width = box[2] + height = box[3] + frame = self.drawPred(frame, classIds[i], confidences[i], left, top, left + width, top + height) + return frame + def drawPred(self, frame, classId, conf, left, top, right, bottom): + # Draw a bounding box. + cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), thickness=4) + + label = '%.2f' % conf + label = '%s:%s' % (self.classes[classId], label) + + # Display the label at the top of the bounding box + labelSize, baseLine = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1) + top = max(top, labelSize[1]) + # cv.rectangle(frame, (left, top - round(1.5 * labelSize[1])), (left + round(1.5 * labelSize[0]), top + baseLine), (255,255,255), cv.FILLED) + cv2.putText(frame, label, (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), thickness=2) + return frame + def detect(self, srcimg): + blob = cv2.dnn.blobFromImage(srcimg, 1 / 255.0, (640, 640), [0, 0, 0], swapRB=True, crop=False) + # Sets the input to the network + self.net.setInput(blob) + + # Runs the forward pass to get output of the output layers + outs = self.net.forward(self.net.getUnconnectedOutLayersNames()) + + z = [] # inference output + for i in range(self.nl): + bs, _, ny, nx = outs[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) + # outs[i] = outs[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous() + outs[i] = outs[i].reshape(bs, self.na, self.no, ny, nx).transpose(0, 1, 3, 4, 2) + if self.grid[i].shape[2:4] != outs[i].shape[2:4]: + self.grid[i] = self._make_grid(nx, ny) + + y = 1 / (1 + np.exp(-outs[i])) ### sigmoid + ###其实只需要对x,y,w,h做sigmoid变换的, 不过全做sigmoid变换对结果影响不大,因为sigmoid是单调递增函数,那么就不影响类别置信度的排序关系,因此不影响后面的NMS + ###不过设断点查看类别置信度,都是负数,看来有必要做sigmoid变换把概率值强行拉回到0到1的区间内 + y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * int(self.stride[i]) + y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh + z.append(y.reshape(bs, -1, self.no)) + z = np.concatenate(z, axis=1) + return z + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--imgpath", type=str, default='bus.jpg', help="image path") + parser.add_argument('--net_type', default='yolov5s', choices=['yolov5s', 'yolov5l', 'yolov5m', 'yolov5x']) + parser.add_argument('--confThreshold', default=0.5, type=float, help='class confidence') + parser.add_argument('--nmsThreshold', default=0.5, type=float, help='nms iou thresh') + parser.add_argument('--objThreshold', default=0.5, type=float, help='object confidence') + args = parser.parse_args() + + yolonet = yolov5(args.net_type, confThreshold=args.confThreshold, nmsThreshold=args.nmsThreshold, objThreshold=args.objThreshold) + srcimg = cv2.imread(args.imgpath) + dets = yolonet.detect(srcimg) + srcimg = yolonet.postprocess(srcimg, dets) + + winName = 'Deep learning object detection in OpenCV' + cv2.namedWindow(winName, cv2.WINDOW_NORMAL) + cv2.imshow(winName, srcimg) + cv2.waitKey(0) + cv2.destroyAllWindows() diff --git a/src/yolov5-dnn-cpp-python-main/yolov5-dnn-cpp-python-main/person.jpg 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string netname; +}; + +class YOLO +{ + public: + YOLO(Net_config config); + void detect(Mat& frame); + private: + const float anchors[3][6] = {{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}}; + const float stride[3] = { 8.0, 16.0, 32.0 }; + const string classesFile = "coco.names"; + const int inpWidth = 640; + const int inpHeight = 640; + float confThreshold; + float nmsThreshold; + float objThreshold; + + char netname[20]; + vector classes; + Net net; + void drawPred(int classId, float conf, int left, int top, int right, int bottom, Mat& frame); + void sigmoid(Mat* out, int length); +}; + +static inline float sigmoid_x(float x) +{ + return static_cast(1.f / (1.f + exp(-x))); +} + +Net_config yolo_nets[4] = { + {0.5, 0.5, 0.5, "yolov5s"}, + {0.5, 0.5, 0.5, "yolov5m"}, + {0.5, 0.5, 0.5, "yolov5l"}, + {0.5, 0.5, 0.5, "yolov5x"} +}; diff --git a/src/yolov5-dnn-cpp-python-main/yolov5-dnn-cpp-python-main/zidane.jpg 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