北京网站建设公司现状,谷歌浏览器下载安装,太原seo网站优化,做网站大优惠程序示例精选 PythonYolov5舰船侦测识别 如需安装运行环境或远程调试#xff0c;见文章底部个人QQ名片#xff0c;由专业技术人员远程协助#xff01; 前言
这篇博客针对PythonYolov5舰船侦测识别编写代码#xff0c;代码整洁#xff0c;规则#xff0c… 程序示例精选 PythonYolov5舰船侦测识别 如需安装运行环境或远程调试见文章底部个人QQ名片由专业技术人员远程协助 前言
这篇博客针对PythonYolov5舰船侦测识别编写代码代码整洁规则易读。 学习与应用推荐首选。 文章目录
一、所需工具软件
二、使用步骤 1. 引入库 2. 代码实现 3. 运行结果
三、在线协助 一、所需工具软件
1. PythonPycharm
2. Yolov5 二、使用步骤
1.引入库
import argparse
import time
from pathlib import Pathimport cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import randomfrom models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages
from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path
from utils.plots import plot_one_box
from utils.torch_utils import select_device, load_classifier, time_synchronized
2. 代码实现
代码如下:
def detect(save_imgFalse):source, weights, view_img, save_txt, imgsz opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_sizewebcam source.isnumeric() or source.endswith(.txt) or source.lower().startswith((rtsp://, rtmp://, http://))# Directoriessave_dir Path(increment_path(Path(opt.project) / opt.name, exist_okopt.exist_ok)) # increment run(save_dir / labels if save_txt else save_dir).mkdir(parentsTrue, exist_okTrue) # make dir# Initializeset_logging()device select_device(opt.device)half device.type ! cpu # half precision only supported on CUDA# Load modelmodel attempt_load(weights, map_locationdevice) # load FP32 modelstride int(model.stride.max()) # model strideimgsz check_img_size(imgsz, sstride) # check img_sizeif half:model.half() # to FP16# Second-stage classifierclassify Falseif classify:modelc load_classifier(nameresnet101, n2) # initializemodelc.load_state_dict(torch.load(weights/resnet101.pt, map_locationdevice)[model]).to(device).eval()# Set Dataloadervid_path, vid_writer None, Noneif webcam:view_img check_imshow()cudnn.benchmark True # set True to speed up constant image size inferencedataset LoadStreams(source, img_sizeimgsz, stridestride)else:save_img Truedataset LoadImages(source, img_sizeimgsz, stridestride)# Get names and colorsnames model.module.names if hasattr(model, module) else model.namescolors [[random.randint(0, 255) for _ in range(3)] for _ in names]# Run inferenceif device.type ! cpu:model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run oncet0 time.time()# Apply NMSpred non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classesopt.classes, agnosticopt.agnostic_nms)t2 time_synchronized()# Apply Classifierif classify:pred apply_classifier(pred, modelc, img, im0s)# Process detectionsfor i, det in enumerate(pred): # detections per imageif webcam: # batch_size 1p, s, im0, frame path[i], %g: % i, im0s[i].copy(), dataset.countelse:p, s, im0, frame path, , im0s, getattr(dataset, frame, 0)p Path(p) # to Pathsave_path str(save_dir / p.name) # img.jpgtxt_path str(save_dir / labels / p.stem) ( if dataset.mode image else f_{frame}) # img.txts %gx%g % img.shape[2:] # print stringgn torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwhif len(det):# Rescale boxes from img_size to im0 sizedet[:, :4] scale_coords(img.shape[2:], det[:, :4], im0.shape).round()# Print resultsfor c in det[:, -1].unique():n (det[:, -1] c).sum() # detections per classs f{n} {names[int(c)]}{s * (n 1)}, # add to string# Write resultsfor *xyxy, conf, cls in reversed(det):if save_txt: # Write to filexywh (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywhline (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) # label formatwith open(txt_path .txt, a) as f:f.write((%g * len(line)).rstrip() % line \n)if save_img or view_img: # Add bbox to imagelabel f{names[int(cls)]} {conf:.2f}plot_one_box(xyxy, im0, labellabel, colorcolors[int(cls)], line_thickness3)# Print time (inference NMS)print(f{s}Done. ({t2 - t1:.3f}s))# Stream resultsif view_img:cv2.imshow(str(p), im0)cv2.waitKey(1) # 1 millisecond# Save results (image with detections)if save_img:if dataset.mode image:cv2.imwrite(save_path, im0)else: # videoif vid_path ! save_path: # new videovid_path save_pathif isinstance(vid_writer, cv2.VideoWriter):vid_writer.release() # release previous video writerfourcc mp4v # output video codecfps vid_cap.get(cv2.CAP_PROP_FPS)w int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))h int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))vid_writer cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h))vid_writer.write(im0)if save_txt or save_img:s f\n{len(list(save_dir.glob(labels/*.txt)))} labels saved to {save_dir / labels} if save_txt else print(fResults saved to {save_dir}{s})print(fDone. ({time.time() - t0:.3f}s))if __name__ __main__:parser argparse.ArgumentParser()parser.add_argument(--weights, nargs, typestr, defaultyolov5_crack_wall_epoach150_batchsize5.pt, helpmodel.pt path(s))parser.add_argument(--source, typestr, defaultdata/images, helpsource) # file/folder, 0 for webcamparser.add_argument(--img-size, typeint, default640, helpinference size (pixels))parser.add_argument(--conf-thres, typefloat, default0.4, helpobject confidence threshold)parser.add_argument(--iou-thres, typefloat, default0.45, helpIOU threshold for NMS)parser.add_argument(--device, default, helpcuda device, i.e. 0 or 0,1,2,3 or cpu)parser.add_argument(--view-img, actionstore_true, helpdisplay results)parser.add_argument(--save-txt, actionstore_true, helpsave results to *.txt)parser.add_argument(--save-conf, actionstore_true, helpsave confidences in --save-txt labels)parser.add_argument(--classes, nargs, typeint, helpfilter by class: --class 0, or --class 0 2 3)parser.add_argument(--agnostic-nms, actionstore_true, helpclass-agnostic NMS)parser.add_argument(--augment, actionstore_true, helpaugmented inference)parser.add_argument(--update, actionstore_true, helpupdate all models)parser.add_argument(--project, defaultruns/detect, helpsave results to project/name)parser.add_argument(--name, defaultexp, helpsave results to project/name)parser.add_argument(--exist-ok, actionstore_true, helpexisting project/name ok, do not increment)opt parser.parse_args()print(opt)check_requirements()with torch.no_grad():if opt.update: # update all models (to fix SourceChangeWarning)for opt.weights in [yolov5s.pt, yolov5m.pt, yolov5l.pt, yolov5x.pt]:detect()strip_optimizer(opt.weights)else:detect()3. 运行结果
三、在线协助 如需安装运行环境或远程调试见文章底部个人 QQ 名片由专业技术人员远程协助 1远程安装运行环境代码调试 2Qt, C, Python入门指导 3界面美化 4软件制作 博主推荐文章python人脸识别统计人数qt窗体-CSDN博客
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