一. 创建环境

1. anaconda下新建一个环境

   conda create -n yolo-sam python=3.8
   

   

2. 激活新建的环境

   conda activate yolo-sam
   

3. 更换conda镜像源

   conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
   conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
   conda config --set show_channel_urls yes
   conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/
   conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2/
   conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda/
   conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/menpo/
   conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/
   

   

4. 安装pytorch

   conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3
   

   

5. 下载官方代码，并解压

   git clone git@github.com:facebookresearch/segment-anything.git
   

   

   https://github.com/ultralytics/yolov5.git
   

   
   

6. 进入下载好的yolov5-6.1文件夹，打开cmd，激活环境，输入一下代码安装yolov5必须的库

   pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
   

   

7. 进入下载好的segment-anything文件夹，打开cmd，激活安装好的环境，运行以下代码

   pip install -e . -i https://mirrors.aliyun.com/pypi/simple/
   

   

8. 安装所需python库

   pip install opencv-python pycocotools matplotlib onnxruntime onnx flake8 isort black mypy -i https://mirrors.aliyun.com/pypi/simple/
   

   

二. 下载模型文件

1. 下载yolov5-6.1模型文件
   下载地址 https://github.com/ultralytics/yolov5/releases
   
2. 从官网下载sam模型
   
3. 在yolov5项目文件中创建一个文件夹，名为weights，把下载的权重文件放进去
   

三. 编辑代码

1. 将segment-anything项目下的文件夹segment_anything拷贝到yolov5-6.1项目文件夹下
   
2. 在yolov5-6.1项目下创建一个新的python文件，输入如下代码：

   import argparse
   import os
   import sys
   import numpy as np
   from pathlib import Path
   import cv2
   import json
   import torch
   from segment_anything import sam_model_registry, SamPredictor
   from models.common import DetectMultiBackend
   from utils.datasets import LoadImages
   from utils.general import (LOGGER, check_img_size, check_requirements, increment_path, non_max_suppression, print_args, scale_coords, colorstr)
   from utils.plots import Annotator, colors
   from utils.torch_utils import select_device, time_syncdef show_points(coords, labels, ax, marker_size=375):pos_points = coords[labels == 1]neg_points = coords[labels == 0]ax.scatter(pos_points[:, 0], pos_points[:, 1], color='green', marker='*', s=marker_size, edgecolor='white',linewidth=1.25)ax.scatter(neg_points[:, 0], neg_points[:, 1], color='red', marker='*', s=marker_size, edgecolor='white',linewidth=1.25)def show_box(box, img):x0, y0 = box[0], box[1]w, h = box[2] - box[0], box[3] - box[1]cv2.rectangle(img, (int(x0), int(y0)), (int(x0+w), int(y0+h)), (0, 255, 0), 1)def generate_json(masks, result, savePath):if len(masks) == 0:returnnum = 0shapes = []for mask in masks:mask = mask.cpu().numpy()[0]# 过滤面积比较小的物体if np.count_nonzero(mask == 1) >= 625:# 创建labelme格式tempData = {"label": "","points": [],"group_id": None,"shape_type": "polygon","flags": {}}tempData["label"] = str(result[num])num = num + 1# 找出物体轮廓objImg = np.zeros((mask.shape[0], mask.shape[1]), np.uint8)objImg[mask] = 255contours, hierarchy = cv2.findContours(objImg, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)# 找出轮廓最大的max_area = 0maxIndex = 0for i in range(0, len(contours)):area = cv2.contourArea(contours[i])if area >= max_area:max_area = areamaxIndex = i# 将每个物体轮廓点数限制在一定范围内if len(contours[maxIndex]) >= int(pow(np.count_nonzero(mask == 1), 0.5) / 2):contours = list(contours[maxIndex])contours = contours[::int(len(contours) / int(pow(np.count_nonzero(mask == 1), 0.5) / 2))]else:contours = list(contours[maxIndex])# 向labelme数据格式中添加轮廓点for point in contours:tempData["points"].append([int(point[0][0]), int(point[0][1])])# 添加物体标注信息shapes.append(tempData)jsonPath = savePath.replace(savePath.split(".")[-1], "json")  # 需要生成的文件路径print(jsonPath)# 创建json文件file_out = open(jsonPath, "w")# 载入json文件jsonData = {}# 8. 写入，修改json文件jsonData["version"] = "5.2.1"jsonData["flags"] = {}jsonData["shapes"] = shapesjsonData["imagePath"] = savePath.split("\\")[-1]jsonData["imageData"] = NonejsonData["imageHeight"] = mask.shape[0]jsonData["imageWidth"] = mask.shape[1]# 保存json文件file_out.write(json.dumps(jsonData, indent=4))  # 保存文件# 关闭json文件file_out.close()FILE = Path(__file__).resolve()
   ROOT = FILE.parents[0]  # YOLOv5 root directory
   if str(ROOT) not in sys.path:sys.path.append(str(ROOT))  # add ROOT to PATH
   ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative@torch.no_grad()
   def run(weights_sam=ROOT / 'sam_vit_b_01ec64.pth',  # model.pt path(s)weights_yolo=ROOT / 'yolov5s.pt',  # model.pt path(s)source=ROOT / 'data/images',  # file/dir/URL/glob, 0 for webcamdata=ROOT / 'data/coco128.yaml',  # dataset.yaml pathimgsz=(640, 640),  # inference size (height, width)conf_thres=0.25,  # confidence thresholdiou_thres=0.45,  # NMS IOU thresholdmax_det=1000,  # maximum detections per imagedevice='',  # cuda device, i.e. 0 or 0,1,2,3 or cpuview_img=False,  # show resultsnosave=False,  # do not save images/videosclasses=None,  # filter by class: --class 0, or --class 0 2 3agnostic_nms=False,  # class-agnostic NMSaugment=False,  # augmented inferenceproject=ROOT / 'runs/detect',  # save results to project/namename='exp',  # save results to project/nameexist_ok=False,  # existing project/name ok, do not increment):source = str(source)save_img = not nosave and not source.endswith('.txt')  # 是否保存检测结果图像标志位# 创建检测结果保存文件夹save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run(save_dir / 'labels' if False else save_dir).mkdir(parents=True, exist_ok=True)  # make dir# 载入模型device = select_device(device)sam = sam_model_registry["vit_" + str(weights_sam).split("_")[2]](checkpoint=weights_sam)sam.to(device="cuda")model = DetectMultiBackend(weights_yolo, device=device, dnn=False, data=data)stride, names, pt, jit, onnx, engine = model.stride, model.names, model.pt, model.jit, model.onnx, model.engineimgsz = check_img_size(imgsz, s=stride)  # 检查图像尺寸# 载入数据dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt)bs = 1  # batch_sizevid_path, vid_writer = [None] * bs, [None] * bs# 运行检测predictor = SamPredictor(sam)model.warmup(imgsz=(1 if pt else bs, 3, *imgsz), half=False)  # warmupdt, seen = [0.0, 0.0, 0.0], 0for path, im, im0s, vid_cap, s in dataset:t1 = time_sync()im = torch.from_numpy(im).to(device)im = im.float()im /= 255  # 0 - 255 to 0.0 - 1.0if len(im.shape) == 3:im = im[None]  # expand for batch dimt2 = time_sync()dt[0] += t2 - t1# 预测pred = model(im, augment=augment, visualize=False)t3 = time_sync()dt[1] += t3 - t2# 非极大值抑制NMSpred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)dt[2] += time_sync() - t3# Process predictionsfor i, det in enumerate(pred):  # per imageseen += 1p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)p = Path(p)  # to Pathsave_path = str(save_dir / p.name)  # im.jpgtxt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # im.txts += '%gx%g ' % im.shape[2:]  # print stringgn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwhimc = im0.copy() if False else im0  # for save_cropannotator = Annotator(im0, line_width=3, example=str(names))if len(det):# 将目标框从模型检测尺度变换到图像原始尺度det[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round()# 打印检测结果for c in det[:, -1].unique():n = (det[:, -1] == c).sum()  # detections per classs += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string# 写结果result = []for *xyxy, conf, cls in reversed(det):if save_img or view_img:  # Add bbox to imagec = int(cls)  # integer classresult.append(c)label = None if False else (names[c] if False else f'{names[c]} {conf:.2f}')annotator.box_label(xyxy, label, color=colors(c, True))image = im0s.copy()predictor.set_image(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))input_boxes = det[:, :4].clone().detach()  # 假设这是目标检测的预测结果transformed_boxes = predictor.transform.apply_boxes_torch(input_boxes, image.shape[:2])masks, _, _ = predictor.predict_torch(point_coords=None, point_labels=None, boxes=transformed_boxes, multimask_output=False)generate_json(masks, result, save_path)for mask in masks:mask = mask.cpu().numpy()color = np.concatenate([np.random.random(3) * 255], axis=0)h, w = mask.shape[-2:]mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)image = cv2.addWeighted(image, 1, np.array(mask_image, dtype=np.uint8), 0.4, 0)for box in input_boxes:show_box(box.cpu().numpy(), image)if view_img:cv2.imshow("mask", image)cv2.waitKey(0)# Save results (image with detections)if save_img:if dataset.mode == 'image':cv2.imwrite(save_path, image)# Print time (inference-only)LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')# Print resultst = tuple(x / seen * 1E3 for x in dt)  # speeds per imageLOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)if save_img:s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if False else ''LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")def parse_opt():parser = argparse.ArgumentParser()parser.add_argument('--weights-sam', nargs='+', type=str, default=ROOT / 'weights/sam_vit_h_4b8939.pth', help='model path(s)')parser.add_argument('--weights-yolo', nargs='+', type=str, default=ROOT / 'weights/airblow4s.pt', help='model path(s)')parser.add_argument('--source', type=str, default="D:\\20231126", help='file/dir/URL/glob, 0 for webcam')parser.add_argument('--data', type=str, default=ROOT / 'data/airblow_4.yaml', help='(optional) dataset.yaml path')parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')parser.add_argument('--conf-thres', type=float, default=0.5, help='confidence threshold')parser.add_argument('--iou-thres', type=float, default=0.1, help='NMS IoU threshold')parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')parser.add_argument('--view-img', action='store_true', help='show results')parser.add_argument('--nosave', action='store_true', help='do not save images/videos')parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3')parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')parser.add_argument('--augment', action='store_true', help='augmented inference')parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name')parser.add_argument('--name', default='exp', help='save results to project/name')parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')opt = parser.parse_args()opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expandprint_args(FILE.stem, opt)return optdef main(opt):check_requirements(exclude=('tensorboard', 'thop'))run(**vars(opt))if __name__ == "__main__":opt = parse_opt()main(opt)