YOLOv7+双目测距（python）
- YOLOv5+双目测距
- zed+yolov5实现双目测距（直接调用，免标定）
- zed+yolov4实现双目测距（直接调用，免标定）
- 本文具体实现效果已在Bilibili发布，点击跳转
- 如有需要，可以参考我上边的几篇文章进行对比👆👆👆
yolov7直接调用zed相机的代码也已经实现，可以运行10秒左右，会报cuda空间不足的错误，博主gpu为6G，可能是内存太小了。
1. 实验效果
经过一系列实验，结果表明yolov7结合双目实现测距效果不如yolov5，具体参数如下：
yolov5— 每帧速度：100-200ms
yolov7（不加多线程）— inference速度：400ms左右 NMS速度：1200-1500ms
yolov7（加多线程）— inference速度：400ms左右 NMS速度：200ms左右
inference：推理速度，指预处理之后的图像输入模型到模型输出结果的时间
NMS ：你可以理解为后处理时间，对模型输出结果经行转换等
2. 相关配置：
电脑系统：win10 (linux及Ubuntu同样适配)
Python版本：3.6
相机型号：zed2i （普通双目也可用）
所用分辨率：2560x720 （这个可以自己调节）
3. 测距原理
测距原理详见：双目三维测距（python）
4. 实验流程
yolov7实验步骤和yolov5一样，大致流程: 双目标定→双目校正→立体匹配→结合yolov7→深度测距
找到目标识别源代码中输出物体坐标框的代码段
找到双目测距代码中计算物体深度的代码段
将步骤2与步骤1结合，计算得到目标框中物体的深度
找到目标识别网络中显示障碍物种类的代码段，将深度值添加到里面，进行显示
5.相关代码
5.1 双目相机参数stereoconfig.py
双目相机标定误差越小越好，我这里误差为0.1，尽量使误差在0.2以下
import numpy as np
# 双目相机参数classstereoCamera(object):def__init__(self):
self.cam_matrix_left = np.array([[1101.89299,0,1119.89634],[0,1100.75252,636.75282],[0,0,1]])
self.cam_matrix_right = np.array([[1091.11026,0,1117.16592],[0,1090.53772,633.28256],[0,0,1]])
self.distortion_l = np.array([[-0.08369,0.05367,-0.00138,-0.0009,0]])
self.distortion_r = np.array([[-0.09585,0.07391,-0.00065,-0.00083,0]])
self.R = np.array([[1.0000,-0.000603116945856524,0.00377055351856816],[0.000608108737333211,1.0000,-0.00132288199083992],[-0.00376975166958581,0.00132516525298933,1.0000]])
self.T = np.array([[-119.99423],[-0.22807],[0.18540]])
self.baseline =119.99423
5.2 图像处理
以下是stereo.py里对图像进行处理的代码
这些都是网上现成的，直接套用就可以
classstereo_dd:def__init__(self,imgl,imgr):
self.left = imgl
self.right = imgr
# 预处理defpreprocess(self, img1, img2):# 彩色图->灰度图if(img1.ndim ==3):#判断为三维数组
img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)# 通过OpenCV加载的图像通道顺序是BGRif(img2.ndim ==3):
img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)# 直方图均衡
img1 = cv2.equalizeHist(img1)
img2 = cv2.equalizeHist(img2)return img1, img2
'''
# 消除畸变
def undistortion(self, image, camera_matrix, dist_coeff):
undistortion_image = cv2.undistort(image, camera_matrix, dist_coeff)
return undistortion_image
'''# 消除畸变defundistortion(self, imagleft,imagright, camera_matrix_left, camera_matrix_right, dist_coeff_left,dist_coeff_right):
undistortion_imagleft = cv2.undistort(imagleft, camera_matrix_left, dist_coeff_left )
undistortion_imagright = cv2.undistort(imagright, camera_matrix_right, dist_coeff_right)return undistortion_imagleft, undistortion_imagright
# 畸变校正和立体校正defrectifyImage(self, image1, image2, map1x, map1y, map2x, map2y):
rectifyed_img1 = cv2.remap(image1, map1x, map1y, cv2.INTER_AREA)
rectifyed_img2 = cv2.remap(image2, map2x, map2y, cv2.INTER_AREA)return rectifyed_img1, rectifyed_img2
# 立体校正检验----画线defdraw_line(self, image1, image2):# 建立输出图像
height =max(image1.shape[0], image2.shape[0])
width = image1.shape[1]+ image2.shape[1]
output = np.zeros((height, width,3), dtype=np.uint8)
output[0:image1.shape[0],0:image1.shape[1]]= image1
output[0:image2.shape[0], image1.shape[1]:]= image2
# 绘制等间距平行线
line_interval =50# 直线间隔：50for k inrange(height // line_interval):
cv2.line(output,(0, line_interval *(k +1)),(2* width, line_interval *(k +1)),(0,255,0), thickness=2, lineType=cv2.LINE_AA)return output
# 视差计算defstereoMatchSGBM(self, left_image, right_image, down_scale=False):# SGBM匹配参数设置if left_image.ndim ==2:
img_channels =1else:
img_channels =3
blockSize =3
paraml ={'minDisparity':0,'numDisparities':128,'blockSize': blockSize,'P1':8* img_channels * blockSize **2,'P2':32* img_channels * blockSize **2,'disp12MaxDiff':-1,'preFilterCap':63,'uniquenessRatio':10,'speckleWindowSize':100,'speckleRange':1,'mode': cv2.STEREO_SGBM_MODE_SGBM_3WAY
}# 构建SGBM对象
left_matcher = cv2.StereoSGBM_create(**paraml)
paramr = paraml
paramr['minDisparity']=-paraml['numDisparities']
right_matcher = cv2.StereoSGBM_create(**paramr)# 计算视差图
size =(left_image.shape[1], left_image.shape[0])if down_scale ==False:
disparity_left = left_matcher.compute(left_image, right_image)
disparity_right = right_matcher.compute(right_image, left_image)else:
left_image_down = cv2.pyrDown(left_image)
right_image_down = cv2.pyrDown(right_image)
factor = left_image.shape[1]/ left_image_down.shape[1]
disparity_left_half = left_matcher.compute(left_image_down, right_image_down)
disparity_right_half = right_matcher.compute(right_image_down, left_image_down)
disparity_left = cv2.resize(disparity_left_half, size, interpolation=cv2.INTER_AREA)
disparity_right = cv2.resize(disparity_right_half, size, interpolation=cv2.INTER_AREA)
disparity_left = factor * disparity_left
disparity_right = factor * disparity_right
trueDisp_left = disparity_left.astype(np.float32)/16.
trueDisp_right = disparity_right.astype(np.float32)/16.return trueDisp_left, trueDisp_right
5.3 测距代码
if save_img or view_img:# Add bbox to image
label =f'{names[int(cls)]}{conf:.2f}'
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=1)
x =(xyxy[0]+ xyxy[2])/2
y =(xyxy[1]+ xyxy[3])/2if(x <=1280):
t3 = time_synchronized()
p = num
height_0, width_0 = im0.shape[0:2]
iml = im0[0:int(height_0),0:int(width_0 /2)]
imr = im0[0:int(height_0),int(width_0 /2):int(width_0)]
height, width = iml.shape[0:2]
config = stereoconfig.stereoCamera()
map1x, map1y, map2x, map2y, Q = getRectifyTransform(height, width, config)
iml_rectified, imr_rectified = rectifyImage(iml, imr, map1x, map1y, map2x, map2y)
line = draw_line(iml_rectified, imr_rectified)
iml = undistortion(iml, config.cam_matrix_left, config.distortion_l)
imr = undistortion(imr, config.cam_matrix_right, config.distortion_r)
iml_, imr_ = preprocess(iml, imr)
iml_rectified_l, imr_rectified_r = rectifyImage(iml_, imr_, map1x, map1y, map2x, map2y)
disp, _ = stereoMatchSGBM(iml_rectified_l, imr_rectified_r,True)
points_3d = cv2.reprojectImageTo3D(disp, Q)
dis =((points_3d[int(y),int(x),0]**2+ points_3d[int(y),int(x),1]**2+ points_3d[int(y),int(x),2]**2)**0.5)/10
5.4 主代码
import argparse
import time
from pathlib import Path
import gol
import cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import random
from 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, TracedModel
from stereo.dianyuntu_yolo import getRectifyTransform
from stereo import stereoconfig
from stereo.stereo import stereo_threading, MyThread
import threading
defdetect(save_img=False):
source, weights, view_img, save_txt, imgsz, trace = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size,not opt.no_trace
save_img =not opt.nosave andnot source.endswith('.txt')# save inference images
webcam = source.isnumeric()or source.endswith('.txt')or source.lower().startswith(('rtsp://','rtmp://','http://','https://'))# Directories
save_dir = Path(increment_path(Path(opt.project)/ opt.name, exist_ok=opt.exist_ok))# increment run(save_dir /'labels'if save_txt else save_dir).mkdir(parents=True, exist_ok=True)# make dir# Initialize
set_logging()
device = select_device(opt.device)
half = device.type!='cpu'# half precision only supported on CUDA# Load model
model = attempt_load(weights, map_location=device)# load FP32 model
stride =int(model.stride.max())# model stride
imgsz = check_img_size(imgsz, s=stride)# check img_sizeif trace:
model = TracedModel(model, device, opt.img_size)if half:
model.half()# to FP16# Second-stage classifier
classify =Falseif classify:
modelc = load_classifier(name='resnet101', n=2)# initialize
modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()# Set Dataloader
vid_path, vid_writer =None,Noneif webcam:
view_img = check_imshow()
cudnn.benchmark =True# set True to speed up constant image size inference
dataset = LoadStreams(source, img_size=imgsz, stride=stride)else:
dataset = LoadImages(source, img_size=imgsz, stride=stride)# Get names and colors
names = model.module.names ifhasattr(model,'module')else model.names
colors =[[random.randint(0,255)for _ inrange(3)]for _ in names]# Run inferenceif device.type!='cpu':
model(torch.zeros(1,3, imgsz, imgsz).to(device).type_as(next(model.parameters())))# run once
old_img_w = old_img_h = imgsz
old_img_b =1
t0 = time.time()for path, img, im0s, vid_cap in dataset:
img = torch.from_numpy(img).to(device)
img = img.half()if half else img.float()# uint8 to fp16/32
img /=255.0# 0 - 255 to 0.0 - 1.0if img.ndimension()==3:
img = img.unsqueeze(0)# Warmupif device.type!='cpu'and(old_img_b != img.shape[0]or old_img_h != img.shape[2]or old_img_w != img.shape[3]):
old_img_b = img.shape[0]
old_img_h = img.shape[2]
old_img_w = img.shape[3]for i inrange(3):
model(img, augment=opt.augment)[0]# Inference
t1 = time_synchronized()with torch.no_grad():# Calculating gradients would cause a GPU memory leak
pred = model(img, augment=opt.augment)[0]
t2 = time_synchronized()# Apply NMS
pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
t3 = time_synchronized()# Apply Classifierif classify:
pred = apply_classifier(pred, modelc, img, im0s)# Process detectionsfor i, det inenumerate(pred):# detections per imageif webcam:# batch_size >= 1
p, s, im0, frame = path[i],'%g: '% i, im0s[i].copy(), dataset.count
else:
p, s, im0, frame = path,'', im0s,getattr(dataset,'frame',0)
p = Path(p)# to Path
save_path =str(save_dir / p.name)# img.jpg
txt_path =str(save_dir /'labels'/ p.stem)+(''if dataset.mode =='image'elsef'_{frame}')# img.txt
gn = torch.tensor(im0.shape)[[1,0,1,0]]# normalization gain whwhiflen(det):# Rescale boxes from img_size to im0 size
det[:,: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 class
s +=f"{n}{names[int(c)]}{'s'*(n >1)}, "# add to string# Write resultsfor*xyxy, conf, cls inreversed(det):if save_txt:# Write to file
xywh =(xyxy2xywh(torch.tensor(xyxy).view(1,4))/ gn).view(-1).tolist()# normalized xywh
line =(cls,*xywh, conf)if opt.save_conf else(cls,*xywh)# label formatwithopen(txt_path +'.txt','a')as f:
f.write(('%g '*len(line)).rstrip()% line +'\n')if save_img or view_img:# Add bbox to image
label =f'{names[int(cls)]}{conf:.2f}'
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=1)
x =(xyxy[0]+ xyxy[2])/2
y =(xyxy[1]+ xyxy[3])/2if(x <=1280):
t3 = time_synchronized()
p = num
height_0, width_0 = im0.shape[0:2]
iml = im0[0:int(height_0),0:int(width_0 /2)]
imr = im0[0:int(height_0),int(width_0 /2):int(width_0)]
height, width = iml.shape[0:2]
config = stereoconfig.stereoCamera()
map1x, map1y, map2x, map2y, Q = getRectifyTransform(height, width, config)
iml_rectified, imr_rectified = rectifyImage(iml, imr, map1x, map1y, map2x, map2y)
line = draw_line(iml_rectified, imr_rectified)
iml = undistortion(iml, config.cam_matrix_left, config.distortion_l)
imr = undistortion(imr, config.cam_matrix_right, config.distortion_r)
iml_, imr_ = preprocess(iml, imr)
iml_rectified_l, imr_rectified_r = rectifyImage(iml_, imr_, map1x, map1y, map2x, map2y)
disp, _ = stereoMatchSGBM(iml_rectified_l, imr_rectified_r,True)
points_3d = cv2.reprojectImageTo3D(disp, Q)
text_cxy ="*"
cv2.putText(im0, text_cxy,(int(x),int(y)), cv2.FONT_ITALIC,1.2,(0,0,255),3)print('点 (%d, %d) 的三维坐标 (x:%.1fcm, y:%.1fcm, z:%.1fcm)'%(int(x),int(y), points_3d[int(y),int(x),0]/10, points_3d[int(y),int(x),1]/10,
points_3d[int(y),int(x),2]/10))
dis =((points_3d[int(y),int(x),0]**2+ points_3d[int(y),int(x),1]**2+ points_3d[int(y),int(x),2]**2)**0.5)/10print('点 (%d, %d) 的 %s 距离左摄像头的相对距离为 %0.1f cm'%(x, y, label, dis))
text_x ="x:%.1fcm"%(points_3d[int(y),int(x),0]/10)
text_y ="y:%.1fcm"%(points_3d[int(y),int(x),1]/10)
text_z ="z:%.1fcm"%(points_3d[int(y),int(x),2]/10)
text_dis ="dis:%.1fcm"% dis
cv2.rectangle(im0,(int(xyxy[0]+(xyxy[2]- xyxy[0])),int(xyxy[1])),(int(xyxy[0]+(xyxy[2]- xyxy[0])+5+220),int(xyxy[1]+150)),
colors[int(cls)],-1)
cv2.putText(im0, text_x,(int(xyxy[0]+(xyxy[2]- xyxy[0])+5),int(xyxy[1]+30)),
cv2.FONT_ITALIC,1.2,(255,255,255),3)
cv2.putText(im0, text_y,(int(xyxy[0]+(xyxy[2]- xyxy[0])+5),int(xyxy[1]+65)),
cv2.FONT_ITALIC,1.2,(255,255,255),3)
cv2.putText(im0, text_z,(int(xyxy[0]+(xyxy[2]- xyxy[0])+5),int(xyxy[1]+100)),
cv2.FONT_ITALIC,1.2,(255,255,255),3)
cv2.putText(im0, text_dis,(int(xyxy[0]+(xyxy[2]- xyxy[0])+5),int(xyxy[1]+145)),
cv2.FONT_ITALIC,1.2,(255,255,255),3)
t4 = time_synchronized()print(f'Done. ({t4 - t3:.3f}s)')print(f'{s}Done. ({t2 - t1:.3f}s)')# Print time (inference + NMS)print(f'{s}Done. ({(1E3*(t2 - t1)):.1f}ms) Inference, ({(1E3*(t3 - t2)):.1f}ms) NMS')# Save results (image with detections)if save_img:if dataset.mode =='image':
cv2.imwrite(save_path, im0)print(f" The image with the result is saved in: {save_path}")else:# 'video' or 'stream'if vid_path != save_path:# new video
vid_path = save_path
ifisinstance(vid_writer, cv2.VideoWriter):
vid_writer.release()# release previous video writerif vid_cap:# video
fps = 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))else:# stream
fps, w, h =30, im0.shape[1], im0.shape[0]
save_path +='.mp4'
vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps,(w, h))
vid_writer.write(im0)
cv2.namedWindow("Video", cv2.WINDOW_NORMAL)
cv2.resizeWindow("Video",1280,480)
cv2.moveWindow("Video",0,0)
cv2.imshow("Video", im0)
cv2.waitKey(1)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(f"Results saved to {save_dir}{s}")print(f'Done. ({time.time()- t0:.3f}s)')if __name__ =='__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--weights', nargs='+',type=str, default='yolov7.pt',help='model.pt path(s)')
parser.add_argument('--source',type=str, default='inference/a5.mp4',help='source')# file/folder, 0 for webcam
parser.add_argument('--img-size',type=int, default=640,help='inference size (pixels)')
parser.add_argument('--conf-thres',type=float, default=0.25,help='object confidence threshold')
parser.add_argument('--iou-thres',type=float, default=0.45,help='IOU threshold for NMS')
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='display results')
parser.add_argument('--save-txt', action='store_true',help='save results to *.txt')
parser.add_argument('--save-conf', action='store_true',help='save confidences in --save-txt labels')
parser.add_argument('--nosave', action='store_true',help='do not save images/videos')
parser.add_argument('--classes', nargs='+',type=int,help='filter by class: --class 0, or --class 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('--update', action='store_true',help='update all models')
parser.add_argument('--project', default='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')
parser.add_argument('--no-trace', action='store_true',help='don`t trace model')
opt = parser.parse_args()print(opt)#check_requirements(exclude=('pycocotools', 'thop'))with torch.no_grad():if opt.update:# update all models (to fix SourceChangeWarning)for opt.weights in['yolov7.pt']:
detect()
strip_optimizer(opt.weights)else:
detect()
6.实验结果
效果图如下：
检测视频
源代码后续会开源，敬请期待…
如需要更快检测速度多线程代码，请私信我
本文转载自: https://blog.csdn.net/qq_45077760/article/details/129925445
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版权归原作者 积极向上的mr.d 所有， 如有侵权，请联系我们删除。