the
Comparison of pedestrian recognition yolov5 and v7
yolo vehicle distance
source code: yolov5 binocular detection vehicle recognition (2023+monocular+binocular+python source code+graduation design) Shanghedao APP, open online blind box store
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In order to improve the traditional genetic algorithm (genetic algorithm, GA)IGA optimized BP network defects of too long iterative time and low precision, an improved genetic algorithm (improved genetic algorithm, GA)IGA optimized BP network was designed, and the double Visual positioning calculation. Improve the genetic algorithm to enhance the convergence ability of the BP network and obtain a stronger global optimization effect, significantly improve the processing efficiency and accuracy of the BP network, and finally promote the camera to obtain higher positioning accuracy and operation speed. The binocular vision positioning algorithm process of IGA optimized BP network is given, and the binocular vision positioning experiment is carried out. The research results show that the average error of the unoptimized coordinate prediction value is 0.66 mm, and the average error of the optimized coordinate is 0.08 mm. The binocular vision positioning accuracy achieved by the improved BP network reaches 0.12 mm, which is nearly 0.01 mm lower than the original predicted positioning error. The average accuracy of binocular vision positioning with BP network is 0.12 mm, and the actual accuracy of positioning with OpenCV is 0.10 mm. It is inferred that the binocular vision positioning accuracy condition is met when the neural network binocular vision is used for positioning.
1.1 Binocular Vision Positioning BP Network Structure
According to the method in Figure 1, the binocular vision positioning is performed through the BP network. A total of 3 layers of BP network are set, including input, output and hidden layers in sequence. The left and right cameras are used to capture the corner area of the checkerboard to obtain image data, and then the input neurons are formed by horizontal and vertical pixel coordinates, and then the actual coordinate parameters of the corner points are used to form the output neurons of the BP network. The hidden layer contains a total of 9 neurons, and the number of neurons in the hidden layer can be obtained by calculating twice the number of neurons in the input layer and adding 1. wij and wki represent the weights of the input and output layers relative to the hidden layer relative to the hidden layer. This study contains a total of 1,000 training samples, setting the target accuracy as 0.000 1, and controlling the upper limit of iterations to 10,000
. 
1.2 Binocular vision positioning IGA optimizes BP structure
Focused on the problems that need to be overcome when the genetic algorithm is used to achieve binocular vision positioning, on this basis, a better genetic algorithm is designed to improve the algorithm convergence ability and obtain a stronger global optimization effect, which significantly improves the BP network processing Efficiency and precision ultimately lead to higher positioning accuracy and computing speed of the camera.
1.2.1 Improved GA selection operator
To achieve the goal of improvement through the sorting method, the selection probability Pnew of the i-th individual after sorting is calculated by formula (1):
Using the same-name corner point detection and matching method, some pixel coordinates and coordinate parameters in Table 2 are extracted from it. The pixel coordinates are represented by (u,v), and u and v correspond to the horizontal and vertical directions in turn.
3.1 Accuracy test
This time, a total of 1,000 sets of pixel coordinates of the same-named corner points obtained by camera shooting and their corresponding actual coordinate data were selected to form the training set, and then the BP network and the BP network optimized through IGA were trained, and then 6 sets of data were imported to form the test set. Position the camera. Figure 4a is the predicted corner coordinates and actual values calculated by the unoptimized BP network. It can be found that the two curves form a similar trend pattern at this time, but there are also certain differences. Figure 4b shows the predicted results and actual values of the corner point coordinates of the BP network optimized by IGA. After comparison, it is found that the two curves have formed a good fit state at this time, and the precise two-dimensional and coordinate correspondence can be determined, ensuring double Visual vision can obtain lower positioning errors.
In order to evaluate the actual positioning performance of the improved neural network, the corresponding relationship between two-dimensional and three-dimensional space is constructed by improving the BP network, and the neural network is trained for 1000 times before the two-dimensional image corner data is input into the system for testing. Record the output results of the spatial coordinates of the two-dimensional image corner points in the reverse way, and then compare the difference with the coordinates of the positioning block to obtain the results shown in Table 2. The difference obtained according to the above method is used as the final positioning accuracy. Using the improved BP network calculated in Table 2, the binocular vision positioning accuracy reaches 0.12 mm, which is nearly 0.01 mm lower than the initial predicted positioning error. 
3.2 Speed test
In order to verify the reliability of the neural network binocular vision positioning results, the binocular vision positioning test is completed through OpenCV and then compared with the positioning results. The traditional binocular vision positioning mode of OpenCV is used to determine the binocular vision parameters, and then the parameters after positioning are used for reverse calculation to obtain the three-dimensional space coordinate data of the corner points in the two-dimensional image plane. The coordinates of the corner points calculated in the above-mentioned three-dimensional space are compared with the coordinates of the corner points in the real three-dimensional space, so as to determine the positioning accuracy. According to Table 2, it can be seen that the average accuracy obtained when using BP network to locate binocular vision is 0.12 mm, and the actual accuracy of positioning with OpenCV is 0.10 mm. It can be inferred from this that the binocular vision positioning accuracy condition is met when the neural network binocular vision is used for positioning.
# -*- coding: utf-8 -*-
import argparse
import time
from pathlib import Path
import cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import random
import numpy as np
from PIL import Image, ImageDraw, ImageFont
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
from stereo.dianyuntu_yolo import preprocess, undistortion, getRectifyTransform, draw_line, rectifyImage,\
stereoMatchSGBM, hw3ToN3, DepthColor2Cloud, view_cloud
from stereo import stereoconfig_040_2
num = 210 #207 209 210 211
def detect(save_img=False):
num = 210
source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
('rtsp://', 'rtmp://', 'http://') )
# 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_size
if half:
model.half() # to FP16
# Second-stage classifier
classify = False
if 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, None
if 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:
save_img = True
dataset = LoadImages(source, img_size=imgsz, stride=stride)
print("img_size:")
print(imgsz)
# Get names and colors
names = model.module.names if hasattr(model, 'module') else model.names
colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
# Run inference
if device.type != 'cpu':
model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once
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 - 1.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
# Inference
t1 = time_synchronized()
pred = model(img, augment=opt.augment)[0]
# Apply NMS
pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
t2 = time_synchronized()
# Apply Classifier
if classify:
pred = apply_classifier(pred, modelc, img, im0s)
# Process detections
for i, det in enumerate(pred): # detections per image
if 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' else f'_{frame}') # img.txt
s += '%gx%g ' % img.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
if len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
# Print results
for 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 results
for *xyxy, conf, cls in reversed(det):
if save_txt: # Write to file
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
print("xywh x : %d, y : %d"%(xywh[0],xywh[1]) )
line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) # label format
with open(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=3)
##print label x,y zuobiao
x = (xyxy[0] + xyxy[2]) / 2
y = (xyxy[1] + xyxy[3]) / 2
#print(" %s is x: %d y: %d " %(label,x,y) )
height_0, width_0 = im0.shape[0:2]
if (x <= int(width_0/2) ):
t3 = time_synchronized()
################################
#stereo code
p = num
string = ''
#print("P is %d" %p )
# Read the image of the dataset
#iml = cv2.imread('./stereo/yolo/zuo/%szuo%d.bmp' %(string,p) ) # left picture
#imr = cv2.imread('./stereo/yolo/you/%syou%d.bmp' %(string,p) ) # right picture
#iml = cv2.imread('./stereo/yolo/zuo/%szuo%d.bmp' %(string,p) ) # left picture
#imr = cv2.imread('./stereo/yolo/you/%syou%d.bmp' %(string,p) ) # right picture
#height_0, width_0 = im0.shape[0:2]
#print("width_0 = %d " % width_0)
#print("height_0 = %d " % height_0)
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]
#cv2.imshow("iml",iml)
#cv2.imshow("imr",im0)
#cv2.waitKey(0)
#print("width = %d " % width)
#print("height = %d " % height)
# Read camera intrinsic and extrinsic parameters
config = stereoconfig_040_2.stereoCamera()
# stereo correction
map1x, map1y, map2x, map2y, Q = getRectifyTransform(height, width, config) # Gets the mapping matrix for distortion correction and stereo correction and the reprojection matrix for computing pixel space coordinates
#print("Print Q!")
#print("Q[2,3]:%.3f"%Q[2,3])
iml_rectified, imr_rectified = rectifyImage(iml, imr, map1x, map1y, map2x, map2y)
# Draw equally spaced parallel lines to check the effect of stereo correction
line = draw_line(iml_rectified, imr_rectified)
#cv2.imwrite('./yolo/%s test%d.png' %(string,p), line)
# Eliminate distortion
iml = undistortion(iml, config.cam_matrix_left, config.distortion_l)
imr = undistortion(imr, config.cam_matrix_right, config.distortion_r)
# stereo matching
iml_, imr_ = preprocess(iml, imr) # Pretreatment can generally weaken the influence of uneven illumination, and it can be done without
iml_rectified_l, imr_rectified_r = rectifyImage(iml_, imr_, map1x, map1y, map2x, map2y)
disp, _ = stereoMatchSGBM(iml_rectified_l, imr_rectified_r, True)
#cv2.imwrite('./yolo/%s parallax%d.png' %(string,p), disp)
# Calculate the 3D coordinates of the pixels (under the left camera coordinate system)
points_3d = cv2.reprojectImageTo3D(disp, Q) # You can use the parameters given in stereo_config.py above
#points_3d = points_3d
'''
#print("x is :%.3f" %points_3d[int(y), int(x), 0] )
print('point (%d, %d) The three-dimensional coordinates of (x:%.3fcm, y:%.3fcm, z:%.3fcm)' % (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) )
'''
count = 0
#try:
while( (points_3d[int(y), int(x), 2] < 0) | (points_3d[int(y), int(x), 2] > 2500) ):
count += 1
x += count
if( 0 < points_3d[int(y), int(x), 2] < 2300 ):
break
y += count
if( 0 < points_3d[int(y), int(x), 2] < 2300 ):
break
count += 1
x -= count
if( 0 < points_3d[int(y), int(x), 2] < 2300 ):
break
y -= count
if( 0 < points_3d[int(y), int(x), 2] < 2300 ):
break
#if(count%2==1):
# x += 1
#else:
# y += 1
text_cxy = "*"
cv2.putText(im0, text_cxy, (x, y) , cv2.FONT_ITALIC, 1.2, (0,0,255), 3)
#print("count is %d" %count)
print('point (%d, %d) The three-dimensional coordinates of (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 ) / 10
print('point (%d, %d) of %s The relative distance from the left camera is %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,(xyxy[0]+(xyxy[2]-xyxy[0]),xyxy[1]),(xyxy[0]+(xyxy[2]-xyxy[0])+5+220,xyxy[1]+150),colors[int(cls)],-1);
cv2.putText(im0, text_x, (xyxy[0]+(xyxy[2]-xyxy[0])+5, xyxy[1]+30), cv2.FONT_ITALIC, 1.2, (255,255,255), 3)
cv2.putText(im0, text_y, (xyxy[0]+(xyxy[2]-xyxy[0])+5, xyxy[1]+65), cv2.FONT_ITALIC, 1.2, (255, 255, 255), 3)
cv2.putText(im0, text_z, (xyxy[0]+(xyxy[2]-xyxy[0])+5, xyxy[1]+100), cv2.FONT_ITALIC, 1.2, (255, 255, 255), 3)
cv2.putText(im0, text_dis, (xyxy[0]+(xyxy[2]-xyxy[0])+5, xyxy[1]+145), cv2.FONT_ITALIC, 1.2, (255, 255, 255), 3)
t4 = time_synchronized()
print(f'Done. ({t4 - t3:.3f}s)')
# Print time (inference + NMS)
print(f'{s}Done. ({t2 - t1:.3f}s)')
# Stream results
if 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: # 'video'
if vid_path != save_path: # new video
vid_path = save_path
if isinstance(vid_writer, cv2.VideoWriter):
vid_writer.release() # release previous video writer
fourcc = 'mp4v' # output video codec
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))
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(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='last_dead_fish_1000.pt', help='model.pt path(s)')
parser.add_argument('--source', type=str, default='./shuangmu_dead_fish_011.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('--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')
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()