YOLOV5 + 雙目相機(jī)實(shí)現(xiàn)三維測(cè)距
1. zed + yolov7 實(shí)現(xiàn)雙目測(cè)距
2. zed+yolov4實(shí)現(xiàn)雙目測(cè)距（直接調(diào)用，免標(biāo)定）
3. zed+yolov5實(shí)現(xiàn)雙目測(cè)距（直接調(diào)用，免標(biāo)定）
4. 本文具體實(shí)現(xiàn)效果已在嗶哩嗶哩發(fā)布，點(diǎn)擊跳轉(zhuǎn)（歡迎投幣點(diǎn)贊）
5. 如果有用zed相機(jī)的，可以參考我上邊的兩邊文章??????直接調(diào)用內(nèi)部相機(jī)參數(shù)，精度比雙目測(cè)距好很多
下載鏈接1： https://download.csdn.net/download/qq_45077760/87233955 （CSDN）
下載鏈接2：https://github.com/up-up-up-up/yolov5_ceju （github）
注：我所做的是在10m以?xún)?nèi)的檢測(cè)，沒(méi)計(jì)算過(guò)具體誤差，當(dāng)然標(biāo)定誤差越小精度會(huì)好一點(diǎn)，其次注意光線(xiàn)、亮度等影響因素，當(dāng)然檢測(cè)范圍效果跟相機(jī)的好壞也有很大關(guān)系
在三維測(cè)距基礎(chǔ)上做了三維跟蹤，詳見(jiàn) YOLOv5+雙目實(shí)現(xiàn)三維跟蹤（python） 和 YOLOv7+雙目實(shí)現(xiàn)三維跟蹤（python）
1. 項(xiàng)目流程
大致流程: 雙目標(biāo)定→雙目校正→立體匹配→結(jié)合yolov5→深度測(cè)距
- 找到目標(biāo)識(shí)別源代碼中輸出物體坐標(biāo)框的代碼段。
- 找到雙目測(cè)距代碼中計(jì)算物體深度的代碼段。
- 將步驟2與步驟1結(jié)合，計(jì)算得到目標(biāo)框中物體的深度。
- 找到目標(biāo)識(shí)別網(wǎng)絡(luò)中顯示障礙物種類(lèi)的代碼段，將深度值添加到里面，進(jìn)行顯示
2. 測(cè)距原理
如果想了解雙目測(cè)距原理，請(qǐng)移步該文章 雙目三維測(cè)距（python）
3. 代碼部分解析
雙目相機(jī)參數(shù)stereoconfig.py
雙目相機(jī)標(biāo)定誤差越小越好，我這里誤差為0.1，盡量使誤差在0.2以下
import numpy as np
# 雙目相機(jī)參數(shù)
class stereoCamera(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
以下我stereo.py里的對(duì)圖像進(jìn)行處理的代碼
這些都是網(wǎng)上現(xiàn)成的，直接套用就可以
class stereo_dd:
def __init__(self,imgl,imgr):
self.left = imgl
self.right = imgr
# 預(yù)處理
def preprocess(self, img1, img2):
# 彩色圖->灰度圖
if(img1.ndim == 3):#判斷為三維數(shù)組
img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY) # 通過(guò)OpenCV加載的圖像通道順序是BGR
if(img2.ndim == 3):
img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
# 直方圖均衡
img1 = cv2.equalizeHist(img1)
img2 = cv2.equalizeHist(img2)
return img1, img2
'''
# 消除畸變
def undistortion(self, image, camera_matrix, dist_coeff):
undistortion_image = cv2.undistort(image, camera_matrix, dist_coeff)
return undistortion_image
'''
# 消除畸變
def undistortion(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
# 畸變校正和立體校正
def rectifyImage(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
# 立體校正檢驗(yàn)----畫(huà)線(xiàn)
def draw_line(self, image1, image2):
# 建立輸出圖像
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
# 繪制等間距平行線(xiàn)
line_interval = 50 # 直線(xiàn)間隔：50
for k in range(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
# 視差計(jì)算
def stereoMatchSGBM(self, left_image, right_image, down_scale=False):
# SGBM匹配參數(shù)設(shè)置
if left_image.ndim == 2:
img_channels = 1
else:
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
}
# 構(gòu)建SGBM對(duì)象
left_matcher = cv2.StereoSGBM_create(**paraml)
paramr = paraml
paramr['minDisparity'] = -paraml['numDisparities']
right_matcher = cv2.StereoSGBM_create(**paramr)
# 計(jì)算視差圖
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
測(cè)距代碼部分解析
這一部分我直接計(jì)算了目標(biāo)檢測(cè)框中心點(diǎn)的深度值，把中心點(diǎn)的深度值當(dāng)作了距離
你也可以寫(xiě)個(gè)循環(huán)，計(jì)算平均值或者中位數(shù)，把他們當(dāng)作深度值
if (accel_frame % fps_set == 0):
t3 = time.time()
thread.join()
points_3d = thread.get_result()
t4 = time.time()
a = points_3d[int(y_0), int(x_0), 0] / 1000
b = points_3d[int(y_0), int(x_0), 1] / 1000
c = points_3d[int(y_0), int(x_0), 2] / 1000
dis = ((a**2+b**2+c**2)**0.5)
這里我加入了檢測(cè)人與車(chē)之間的三維距離，分為了正常、中等、高風(fēng)險(xiǎn)三個(gè)距離等級(jí)
你也可以替換成人與人或者車(chē)與車(chē)等等
########## plot_one_box 系列 ###########
if (distance != 0): ## Add bbox to image
label = f'{names[int(cls)]} {conf:.2f} '
'''下邊這幾行如果不需要，可以改成
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
我是做了分類(lèi)，是為了計(jì)算人與汽車(chē)之間的距離寫(xiě)的'''
if label is not None:
if (label.split())[0] == 'person':
people_coords.append(xyxy)
if (label.split())[0] == 'car' or (label.split())[0] =='truck':
car_coords.append(xyxy)
#plot_dots_on_car(xyxy, im0)
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)],line_thickness=3)
########## annotator.box_label 系列 ###########
if names[int(cls)] == "person":
people_coords.append(xyxy)
c = int(cls) # integer class 整數(shù)類(lèi) 1111111111
label = None if hide_labels else (
names[c] if hide_conf else f'{names[c]} {conf:.2f}') # 111
print("x:", point_cloud_value[0], "y:", point_cloud_value[1], "z:",
point_cloud_value[2], "dis:", distance, '', label)
# print("dis:", distance, "W:", wide)
txt = '{0} dis:{1} '.format(label, distance)
# annotator.box_label(xyxy, txt, color=(255, 0, 255))
annotator.box_label(xyxy, txt, color=colors(c, True))
if names[int(cls)] == "chair":
car_coords.append(xyxy)
c = int(cls) # integer class 整數(shù)類(lèi) 1111111111
label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}') # 111
print("x:", point_cloud_value[0], "y:", point_cloud_value[1], "z:",
point_cloud_value[2], "dis:", distance, '',label)
#print("dis:", distance, "W:", wide)
txt = '{0} dis:{1} '.format(label,distance)
#annotator.box_label(xyxy, txt, color=(255, 0, 255))
annotator.box_label(xyxy, txt, color = colors(c, True))
normal, intermediate, high = distancing(people_coords,car_coords, im0, intermediate, high,normal,dist_thres_lim=(2, 3))
主代碼
加入了多線(xiàn)程處理，加快處理速度
import argparse
import time
from pathlib import Path
import threading
from threading import Thread
import cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import random
import numpy as np
from utils.datasets import * ##1111111111
#from utils.utils import * # 111111111111
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, view_cloud ,DepthColor2Cloud
from stereo import stereoconfig
from stereo.stereo import stereo_dd
from stereo.stereo import get_median, stereo_threading, MyThread
intermediate = 0
high = 0
normal = 0
people_label = " "
normal_label = " "
inter_label = " "
high_label = " "
def detect(save_img=False):
accel_frame = 0
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://'))
# Initialize
set_logging()
device = select_device(opt.device)
half = device.type != 'cpu'
# Load model
model = attempt_load(weights, map_location=device)
stride = int(model.stride.max())
imgsz = check_img_size(imgsz,s=stride)
if half:
model.half()
# Second-stage classifier
classify = False
if classify:
modelc = load_classifier(name='resnet101', n=2)
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:
save_stream_dir = Path(
increment_path(Path("./runs/streams") / opt.name, exist_ok=opt.exist_ok))
(save_stream_dir / 'labels' if save_txt else save_stream_dir).mkdir(parents=True,
exist_ok=True)
flag = 0
view_img = check_imshow()
cudnn.benchmark = True
dataset = LoadStreams(source, img_size=imgsz, stride=stride)
# 獲取視頻信息，線(xiàn)程抓取圖片dataset類(lèi)中imgs[0]是0個(gè)攝像頭的圖片，LoadStreams是迭代類(lèi)---》dataset是一個(gè)迭代器
else:
# Directories
save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)
save_img = True
dataset = LoadImages(source, img_size=imgsz,
stride=stride)
print("img_size:")
print(imgsz)
names = model.module.names if hasattr(model, 'module') else model.names
colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
################################ stereo #############################
if device.type != 'cpu':
model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once
t0 = time.time() # 整個(gè)推理過(guò)程的開(kāi)始計(jì)時(shí)
config = stereoconfig_040_2.stereoCamera()
# 立體校正
map1x, map1y, map2x, map2y, Q = getRectifyTransform(720, 1280, config)
# -----------------攝像頭從此處開(kāi)始反復(fù)循環(huán)-dataset為迭代器類(lèi)--------------------------------
for path, img, im0s, vid_cap in dataset:
img = torch.from_numpy(img).to(device)
img = img.half() if half else img.float()
img /= 255.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
t1 = time_synchronized()
pred = model(img, augment=opt.augment)[0]
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)
# List to store bounding coordinates of people 1111111
people_coords = [] # 1111111111
car_coords = []
# Process detections
for i, det in enumerate(pred):
if webcam:
p, s, im0, frame = path[i], '%g: ' % i, im0s[
i].copy(), dataset.count
else:
p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)
fps_set = 10 # setting the frame
if (accel_frame % fps_set == 0):
thread = MyThread(stereo_threading,args=(config, im0, map1x, map1y, map2x, map2y, Q))
thread.start()
print(threading.active_count())
print(threading.enumerate())
print("############## Frame is %d !##################" % accel_frame)
p = Path(p)
if webcam:
save_stream_path = str(save_stream_dir / "stream0.mp4")
else:
save_path = str(save_dir / p.name)
txt_path = str(save_dir / 'labels' / p.stem) + (
'' if dataset.mode == 'image' else f'_{frame}')
s += '%gx%g ' % img.shape[2:]
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]
if len(det):
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum()
s += f"{n} {names[int(c)]} {'s' * (n > 1)} , "
for *xyxy, conf, cls in reversed(det):
if (0 < xyxy[2] < 1280):
if save_txt:
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()
print("xywh x : %d, y : %d" % (xywh[0], xywh[1]))
line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh)
with open(txt_path + '.txt', 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')
if save_img or view_img:
x_center = (xyxy[0] + xyxy[2]) / 2
y_center = (xyxy[1] + xyxy[3]) / 2
x_0 = int(x_center)
y_0 = int(y_center)
if (0 < x_0 < 1280):
x1 = xyxy[0]
x2 = xyxy[2]
y1 = xyxy[1]
y2 = xyxy[3]
if (accel_frame % fps_set == 0):
t3 = time.time()
thread.join()
points_3d = thread.get_result()
t4 = time.time()
print(f'{s}Stereo Done. ({t4 - t3:.3f}s)')
a = points_3d[int(y_0), int(x_0), 0] / 1000
b = points_3d[int(y_0), int(x_0), 1] / 1000
c = points_3d[int(y_0), int(x_0), 2] / 1000
dis = ((a**2+b**2+c**2)**0.5)
distance = []
distance.append(dis)
if (distance != 0): ## Add bbox to image
label = f'{names[int(cls)]} {conf:.2f} '
'''下邊這幾行如果不需要，可以改成
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
我是做了分類(lèi)，是為了計(jì)算人與汽車(chē)之間的距離寫(xiě)的'''
if label is not None:
if (label.split())[0] == 'person':
people_coords.append(xyxy)
if (label.split())[0] == 'car' or (label.split())[0] =='truck':
car_coords.append(xyxy)
#plot_dots_on_car(xyxy, im0)
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)],line_thickness=3)
text_xy_0 = "*"
cv2.putText(im0, text_xy_0, (int(x_0), int(y_0)), cv2.FONT_ITALIC, 1.2,(0, 0, 255), 3)
print()
print('點(diǎn) (%d, %d) 的 %s 距離左攝像頭的相對(duì)距離為 %0.2f m' % (x_center, y_center, label, distance))
text_dis_avg = "dis:%0.2fm" % distance
# only put dis on frame
cv2.rectangle(im0, (int(x1 + (x2 - x1)), int(y1)),(int(x1 + (x2 - x1) + 5 + 210), int(y1 + 40)), colors[int(cls)],-1) # 畫(huà)框存三維坐標(biāo)
cv2.putText(im0, text_dis_avg, (int(x1 + (x2 - x1) + 5), int(y1 + 30)),cv2.FONT_ITALIC, 1.2, (255, 255, 255), 3)
'''同理，下邊這一行如果不需要可以去除，我也是做行人與車(chē)輛之間距離用的'''
normal, intermediate, high = distancing(people_coords,car_coords, im0, intermediate, high,normal,dist_thres_lim=(2, 3))
t5 = time_synchronized() # stereo time end
print(f'{s}yolov5 Done. ({t2 - t1:.3f}s)')
if (accel_frame % fps_set == 0):
print(f'{s}yolov5+stereo Done. ({t5 - t1:.3f}s)')
if cv2.waitKey(1) & 0xFF == ord('q'):
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 ''
if view_img:
s = save_stream_path
print(f"Results saved to {s}")
print(f'All Done. ({time.time() - t0:.3f}s)')
vid_writer.release()
exit()
# Stream results
if view_img:
if (dataset.mode == 'stream') & (flag == 0):
if isinstance(vid_writer, cv2.VideoWriter):
vid_writer.release() # release previous video writer
fourcc = 'mp4v' # output video codec
fps = 24 # vid_cap.get(cv2.CAP_PROP_FPS)
w = 2560 # int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = 720 # int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
print("save_stream_dir is %s" % save_stream_dir)
print("save_stream_path is %s" % save_stream_path)
vid_writer = cv2.VideoWriter(save_stream_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h))
flag = 1
vid_writer.write(im0)
cv2.namedWindow("Webcam", cv2.WINDOW_NORMAL)
cv2.resizeWindow("Webcam", 1280, 480)
cv2.moveWindow("Webcam", 0, 100)
cv2.imshow("Webcam", im0)
cv2.waitKey(1)
# Save results
if save_img:
if dataset.mode == 'image':
cv2.imwrite(save_path, im0)
else:
if vid_path != save_path:
vid_path = save_path
if isinstance(vid_writer, cv2.VideoWriter):
vid_writer.release()
fourcc = 'mp4v'
fps = 24
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)
cv2.namedWindow("Video", cv2.WINDOW_NORMAL)
cv2.resizeWindow("Video", 1280, 480)
cv2.moveWindow("Video", 0, 0)
cv2.imshow("Video", im0)
cv2.waitKey(1)
print("frame %d is done!" % accel_frame)
accel_frame += 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'All Done. ({time.time() - t0:.3f}s)')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--weights', nargs='+', type=str, default='./yolov5s.pt', help='model.pt path(s)')
parser.add_argument('--source', type=str, default='./data/video/test.mp4',help='source')
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:
for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:
detect()
strip_optimizer(opt.weights)
else:
detect()
有點(diǎn)亂，后續(xù)會(huì)慢慢完善，下邊是測(cè)距后的圖，精度不是很高
4. 實(shí)驗(yàn)結(jié)果
單目標(biāo)測(cè)距
多目標(biāo)測(cè)距
目標(biāo)之間三維距離檢測(cè)計(jì)數(shù)
檢測(cè)效果（視頻展示）
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