YOLOv7+单目测距（python）
- 1. 相关配置
- 2. 测距原理
- 3. 相机标定
- 3.1：标定方法1
- 3.2：标定方法2
- 4. 相机测距
- 4.1 测距添加
- 4.2 主代码
- 5. 实验效果
相关链接
1. YOLOV5 + 单目测距（python）
2. YOLOV5 + 双目测距（python）
3. YOLOV7 + 双目测距（python）
4. 具体实现效果已在Bilibili发布，点击跳转
本篇博文工程源码下载
链接1：https://download.csdn.net/download/qq_45077760/87708470
链接2：https://github.com/up-up-up-up/yolov7_Monocular_ranging
文章结构前三章节和 YOLOV5 + 单目测距 这篇博文一样，如看过该博文，直接跳转第四章节
1. 相关配置
系统：win 10
YOLO版本：yolov7
拍摄视频设备：安卓手机
电脑显卡：NVIDIA 2080Ti（CPU也可以跑，GPU只是起到加速推理效果）
2. 测距原理
单目测距原理相较于双目十分简单，无需进行立体匹配，仅需利用下边公式线性转换即可：
D = (F*W)/P
其中D是目标到摄像机的距离, Ｆ是摄像机焦距（焦距需要自己进行标定获取）, W是目标的宽度或者高度（行人检测一般以人的身高为基准）, P是指目标在图像中所占据的像素
了解基本原理后，下边就进行实操阶段
3. 相机标定
3.1：标定方法1
可以参考张学友标定法获取相机的焦距
3.2：标定方法2
直接使用代码获得焦距，需要提前拍摄一个矩形物体，拍摄时候相机固定，距离被拍摄物体自行设定，并一直保持此距离，背景为纯色，不要出现杂物；最后将拍摄的视频用以下代码检测：
import cv2
win_width = 1920
win_height = 1080
mid_width = int(win_width / 2)
mid_height = int(win_height / 2)
foc = 1990.0 # 根据教程调试相机焦距
real_wid = 9.05 # A4纸横着的时候的宽度，视频拍摄A4纸要横拍，镜头横，A4纸也横
font = cv2.FONT_HERSHEY_SIMPLEX
w_ok = 1
capture = cv2.VideoCapture('5.mp4')
capture.set(3, win_width)
capture.set(4, win_height)
while (True):
ret, frame = capture.read()
# frame = cv2.flip(frame, 1)
if ret == False:
break
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
ret, binary = cv2.threshold(gray, 140, 200, 60) # 扫描不到纸张轮廓时，要更改阈值，直到方框紧密框住纸张
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
binary = cv2.dilate(binary, kernel, iterations=2)
contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# cv2.drawContours(frame, contours, -1, (0, 255, 0), 2) # 查看所检测到的轮框
for c in contours:
if cv2.contourArea(c) < 1000: # 对于矩形区域，只显示大于给定阈值的轮廓，所以一些微小的变化不会显示。对于光照不变和噪声低的摄像头可不设定轮廓最小尺寸的阈值
continue
x, y, w, h = cv2.boundingRect(c) # 该函数计算矩形的边界框
if x > mid_width or y > mid_height:
continue
if (x + w) < mid_width or (y + h) < mid_height:
continue
if h > w:
continue
if x == 0 or y == 0:
continue
if x == win_width or y == win_height:
continue
w_ok = w
cv2.rectangle(frame, (x + 1, y + 1), (x + w_ok - 1, y + h - 1), (0, 255, 0), 2)
dis_inch = (real_wid * foc) / (w_ok - 2)
dis_cm = dis_inch * 2.54
# os.system("cls")
# print("Distance : ", dis_cm, "cm")
frame = cv2.putText(frame, "%.2fcm" % (dis_cm), (5, 25), font, 0.8, (0, 255, 0), 2)
frame = cv2.putText(frame, "+", (mid_width, mid_height), font, 1.0, (0, 255, 0), 2)
cv2.namedWindow('res', 0)
cv2.namedWindow('gray', 0)
cv2.resizeWindow('res', win_width, win_height)
cv2.resizeWindow('gray', win_width, win_height)
cv2.imshow('res', frame)
cv2.imshow('gray', binary)
c = cv2.waitKey(40)
if c == 27: # 按退出键esc关闭窗口
break
cv2.destroyAllWindows()
反复调节 ret, binary = cv2.threshold(gray, 140, 200, 60)这一行里边的三个参数，直到线条紧紧包裹住你所拍摄视频的物体，然后调整相机焦距直到左上角距离和你拍摄视频时相机到物体的距离接近为止
然后将相机焦距写进测距代码distance.py文件里，这里行人用高度表示，根据公式 D = (F*W)/P，知道相机焦距F、行人的高度66.9（单位英寸→170cm/2.54）、像素点距离 h，即可求出相机到物体距离D。 这里用到h-2是因为框的上下边界像素点不接触物体
foc = 1990.0 # 镜头焦距
real_hight_person = 66.9 # 行人高度
real_hight_car = 57.08 # 轿车高度
# 自定义函数，单目测距
def person_distance(h):
dis_inch = (real_hight_person * foc) / (h - 2)
dis_cm = dis_inch * 2.54
dis_cm = int(dis_cm)
dis_m = dis_cm/100
return dis_m
def car_distance(h):
dis_inch = (real_hight_car * foc) / (h - 2)
dis_cm = dis_inch * 2.54
dis_cm = int(dis_cm)
dis_m = dis_cm/100
return dis_m
4. 相机测距
4.1 测距添加
主要是把测距部分加在了画框附近，首先提取边框的像素点坐标，然后计算边框像素点高度，在根据 公式 D = (F*W)/P 计算目标距离
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
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
x1 = int(xyxy[0]) # 获取四个边框坐标
y1 = int(xyxy[1])
x2 = int(xyxy[2])
y2 = int(xyxy[3])
h = y2 - y1
label = f'{names[int(cls)]} {conf:.2f}'
if label is not None:
if (label.split())[0] == 'person':
dis_m = person_distance(h) # 调用函数，计算行人实际高度
label += f' {dis_m}m' # 将行人距离显示写在标签后
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=1)
if (label.split())[0] == 'car' or (label.split())[0] == 'truck':
dis_m = car_distance(h) # 调用函数，计算行人实际高度
label += f' {dis_m}m' # 将行人距离显示写在标签后
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=1)
4.2 主代码
import argparse
import time
from pathlib import Path
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 distance import person_distance,car_distance
def detect(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 and not 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_size
if trace:
model = TracedModel(model, device, opt.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:
dataset = LoadImages(source, img_size=imgsz, stride=stride)
# 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
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 - 1.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
# Warmup
if 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 in range(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 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
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
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
x1 = int(xyxy[0]) # 获取四个边框坐标
y1 = int(xyxy[1])
x2 = int(xyxy[2])
y2 = int(xyxy[3])
h = y2 - y1
label = f'{names[int(cls)]} {conf:.2f}'
if label is not None:
if (label.split())[0] == 'person':
dis_m = person_distance(h) # 调用函数，计算行人实际高度
label += f' {dis_m}m' # 将行人距离显示写在标签后
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=1)
if (label.split())[0] == 'car' or (label.split())[0] == 'truck':
dis_m = car_distance(h) # 调用函数，计算行人实际高度
label += f' {dis_m}m' # 将行人距离显示写在标签后
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=1)
# Print time (inference + NMS)
print(f'{s}Done. ({(1E3 * (t2 - t1)):.1f}ms) Inference, ({(1E3 * (t3 - t2)):.1f}ms) NMS')
# 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)
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
if isinstance(vid_writer, cv2.VideoWriter):
vid_writer.release() # release previous video writer
if 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)
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/images/2.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()
5. 实验效果
由于yolov7和yolov5机制问题，yolov7推理时间相较于yolov5较长，实验效果如下
