yolov5_dnn python/c++用opencv的dnn模块做yolov5目标检测

python/c++ opencv dnn模块调用yolov5-onnx权重文件做检测

非常简单的yolov5应用，不需要pytorch环境库，只需要opencv库即可，可以很简单很轻松的部署！！！

项目中自带yolov5n.onnx，如果需要yolov5其他模型onnx权重文件，可以从我的百度网盘提取。提取码： 1cj3

项目地址

github地址，yolov5_onnx_dnn(代码已注释)

如何在windows Visual Studio中配置 c++ opencv环境

配置 c++ opencv环境

本项目支持对图片和视频进行检测，也支持摄像头实时检测。 有问题请及时留言，如果帮助到了你，请在github留下star！

yolov5_non_max_suppression模块，需要将原始pytorch写的非极大值抑制代码，都转为numpy格式，方便摆脱pytorch框架。

 def non_max_suppression(self,prediction, conf_thres=0.25,agnostic=False):                                                 #25200 = 20*20*3 + 40*40*3 + 80*80*3
        xc = prediction[..., 4] > conf_thres  # candidates,获取置信度，prediction为所有的预测结果.shape(1, 25200, 21),batch为1，25200个预测结果，21 = x,y,w,h,c + class个数
        # Settings
        min_wh, max_wh = 2, 4096  # (pixels) minimum and maximum box width and height
        max_nms = 30000  # maximum number of boxes into torchvision.ops.nms()
        output = [np.zeros((0, 6))] * prediction.shape[0]
        # for p in prediction:
        #     for i in p:
        #         with open('./result.txt','a') as f:
        #             f.write(str(i) + '\n')
        for xi, x in enumerate(prediction):  # image index, image inference
            # Apply constraints
            x = x[xc[xi]]  # confidence，获取confidence大于conf_thres的结果
            if not x.shape[0]:
                continue
            # Compute conf
            x[:, 5:] *= x[:, 4:5]  # conf = obj_conf * cls_conf
            # Box (center x, center y, width, height) to (x1, y1, x2, y2)
            box = self.xywh2xyxy(x[:, :4])
            # Detections matrix nx6 (xyxy, conf, cls)
            conf = np.max(x[:, 5:], axis=1)    #获取类别最高的置信度
            j = np.argmax(x[:, 5:],axis=1)     #获取下标
            #转为array：  x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]
            re = np.array(conf.reshape(-1)> conf_thres)
            #转为维度
            conf =conf.reshape(-1,1)
            j = j.reshape(-1,1)
            #numpy的拼接
            x = np.concatenate((box,conf,j),axis=1)[re]
            # Check shape
            n = x.shape[0]  # number of boxes
            if not n:  # no boxes
                continue
            elif n > max_nms:  # excess boxes
                x = x[x[:, 4].argsort(descending=True)[:max_nms]]  # sort by confidence
            # Batched NMS
            c = x[:, 5:6] * (0 if agnostic else max_wh)  # classes
            boxes, scores = x[:, :4] + c, x[:, 4]  # boxes (offset by class), scores
            i = self.numpy_nms(boxes, scores, self.nmsThreshold)
            output[xi] = x[i]
        return output

yolov5_letterbox解释：以最长边放缩图片，然后最短边补灰边

def letterbox(self, im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):
     # Resize and pad image while meeting stride-multiple constraints
        shape = im.shape[:2]  # current shape [height, width]
        if isinstance(new_shape, int):
            new_shape = (new_shape, new_shape)

        # Scale ratio (new / old)
        r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
        if not scaleup:  # only scale down, do not scale up (for better val mAP)
            r = min(r, 1.0)

        # Compute padding
        ratio = r, r  # width, height ratios
        new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
        dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
        if auto:  # minimum rectangle
            dw, dh = np.mod(dw, stride), np.mod(dh, stride)  # wh padding
        elif scaleFill:  # stretch
            dw, dh = 0.0, 0.0
            new_unpad = (new_shape[1], new_shape[0])
            ratio = new_shape[1] / shape[1], new_shape[0] / shape[0]  # width, height ratios

        dw /= 2  # divide padding into 2 sides
        dh /= 2

        if shape[::-1] != new_unpad:  # resize
            im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
        top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
        left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
        im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
        return im, ratio, (dw, dh)