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YOLOV5 + PYQT5单目测距(三)_yolov5安卓单目测距
作者：菜鸟追梦旅行 | 2024-05-27 08:01:12
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yolov5安卓单目测距
1. 相关配置
系统：win 10
YOLO版本：yolov5 5.0
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. 测距源码
详见文章 YOLOV5 + 单目测距（python），下载源码后进入下一步
3. PYQT环境配置
首先安装一下pyqt5
pip install PyQt5
pip install PyQt5-tools
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接着再pycharm设置里配置一下
添加下面两个工具:
工具1：Qt Designer
Program D:\Anaconda3\Lib\site-packages\qt5_applications\Qt\bin\designer.exe#代码所用环境路径
Arauments : $FileName$
Working directory :$FileDir$
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工具2：PyUIC
Program D:\Anaconda3\Scripts\pyuic5.exe
Arguments : $FileName$ -o $FileNameWithoutExtension$.py
Working directory :$FileDir$
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4. 实验结果
4.1 界面1（简洁版）
在文件目录下创建一个main.py文件，将以下代码写入
import sys
import os
from PIL import Image
from PyQt5.QtCore import *
from PyQt5.QtGui import *
from PyQt5.QtWidgets import *
class filedialogdemo(QWidget):
def __init__(self, parent=None):
super(filedialogdemo, self).__init__(parent)
self.resize(500,500)
layout = QVBoxLayout()
self.btn = QPushButton("加载图片")
self.btn.clicked.connect(self.getfile)
layout.addWidget(self.btn)
self.le = QLabel(" csdn:积极向上的mr.d")
self.btn1 = QPushButton("加载本地摄像头")
self.btn1.clicked.connect(self.getfiles)
layout.addWidget(self.btn1)
layout.addWidget(self.le)
self.setLayout(layout)
self.setWindowTitle("双目测距系统")
def getfile(self):
'''
getOpenFileName():返回用户所选择文件的名称，并打开该文件
第一个参数用于指定父组件
第二个参数指定对话框标题
第三个参数指定目录
第四个参数是文件扩展名过滤器
'''
self.fname, _ = QFileDialog.getOpenFileName(self, 'Open file',r'C:\Users\hp\Desktop\sale\yolov5_ceju_pro\data\images',"Image files (*.jpg *.gif *.mp4)")
self.le.setPixmap(QPixmap(self.fname))
import shutil
shutil.rmtree('./runs/detect/exp')
str=(r'python C:\Users\hp\Desktop\sale\yolov5_ceju_pro\detect_01.py --source ' + self.fname+ ' --exist-ok ')
os.system(str) # 运行图片识别文件
path = os.listdir(r'C:\Users\hp\Desktop\sale\yolov5_ceju_pro\runs\detect\exp')
s = path[0]
pathend = r'C:\Users\hp\Desktop\sale\yolov5_ceju_pro\runs\detect\exp'+ '\\'+ s
I = Image.open(pathend)
I.show()
def getfiles(self): # 加载摄像头
str=(r'python C:\Users\hp\Desktop\sale\yolov5_ceju_pro\detect_01.py ') # python命令 + B.py + 参数：IC.txt'
os.environ['CUDA_LAUNCH_BLOCKING'] = '1' # 不加这个可能会报错
os.system(str)
if __name__ == '__main__':
app = QApplication(sys.argv)
ex = filedialogdemo()
ex.show()
sys.exit(app.exec_())
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运行main.py即可实现检测
4.2 界面2（改进版）
创建一个main.py文件，将以下代码写入
import sys
import cv2
import argparse
import random
import torch
import numpy as np
import torch.backends.cudnn as cudnn
from PyQt5 import QtCore, QtGui, QtWidgets
from utils.torch_utils import select_device
from models.experimental import attempt_load
from utils.general import check_img_size, non_max_suppression, scale_coords
from utils.datasets import letterbox
from utils.plots import plot_one_box
from distance import person_distance,car_distance
class Ui_MainWindow(QtWidgets.QMainWindow):
def __init__(self, parent=None):
super(Ui_MainWindow, self).__init__(parent)
self.timer_video = QtCore.QTimer()
self.setupUi(self)
self.init_logo()
self.init_slots()
self.cap = cv2.VideoCapture()
self.out = None
# self.out = cv2.VideoWriter('prediction.avi', cv2.VideoWriter_fourcc(*'XVID'), 20.0, (640, 480))
parser = argparse.ArgumentParser()
parser.add_argument('--weights', nargs='+', type=str,
default='yolov5s.pt', help='model.pt path(s)')
# file/folder, 0 for webcam
parser.add_argument('--source', type=str,
default='data/images', 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('--nosave', action='store_true',
help='do not save images/videos')
parser.add_argument('--classes', nargs='+', default=[2],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')
self.opt = parser.parse_args()
print(self.opt)
source, weights, view_img, save_txt, imgsz = self.opt.source, self.opt.weights, self.opt.view_img, self.opt.save_txt, self.opt.img_size
self.device = select_device(self.opt.device)
self.half = self.device.type != 'cpu' # half precision only supported on CUDA
cudnn.benchmark = True
# Load model
self.model = attempt_load(
weights, map_location=self.device) # load FP32 model
stride = int(self.model.stride.max()) # model stride
self.imgsz = check_img_size(imgsz, s=stride) # check img_size
if self.half:
self.model.half() # to FP16
# Get names and colors
self.names = self.model.module.names if hasattr(
self.model, 'module') else self.model.names
self.colors = [[random.randint(0, 255)
for _ in range(3)] for _ in self.names]
def setupUi(self, MainWindow):
MainWindow.setObjectName("MainWindow")
MainWindow.resize(800, 600)
self.centralwidget = QtWidgets.QWidget(MainWindow)
self.centralwidget.setObjectName("centralwidget")
self.pushButton = QtWidgets.QPushButton(self.centralwidget)
self.pushButton.setGeometry(QtCore.QRect(20, 130, 112, 34))
self.pushButton.setObjectName("pushButton")
self.pushButton_2 = QtWidgets.QPushButton(self.centralwidget)
self.pushButton_2.setGeometry(QtCore.QRect(20, 220, 112, 34))
self.pushButton_2.setObjectName("pushButton_2")
self.pushButton_3 = QtWidgets.QPushButton(self.centralwidget)
self.pushButton_3.setGeometry(QtCore.QRect(20, 300, 112, 34))
self.pushButton_3.setObjectName("pushButton_3")
self.groupBox = QtWidgets.QGroupBox(self.centralwidget)
self.groupBox.setGeometry(QtCore.QRect(160, 90, 611, 411))
self.groupBox.setObjectName("groupBox")
self.label = QtWidgets.QLabel(self.groupBox)
self.label.setGeometry(QtCore.QRect(10, 40, 561, 331))
self.label.setObjectName("label")
self.textEdit = QtWidgets.QTextEdit(self.centralwidget)
self.textEdit.setGeometry(QtCore.QRect(150, 10, 471, 51))
self.textEdit.setObjectName("textEdit")
MainWindow.setCentralWidget(self.centralwidget)
self.menubar = QtWidgets.QMenuBar(MainWindow)
self.menubar.setGeometry(QtCore.QRect(0, 0, 800, 30))
self.menubar.setObjectName("menubar")
MainWindow.setMenuBar(self.menubar)
self.statusbar = QtWidgets.QStatusBar(MainWindow)
self.statusbar.setObjectName("statusbar")
MainWindow.setStatusBar(self.statusbar)
self.retranslateUi(MainWindow)
QtCore.QMetaObject.connectSlotsByName(MainWindow)
def retranslateUi(self, MainWindow):
_translate = QtCore.QCoreApplication.translate
MainWindow.setWindowTitle(_translate("MainWindow", "双目测距系统"))
self.pushButton.setText(_translate("MainWindow", "图片检测"))
self.pushButton_2.setText(_translate("MainWindow", "摄像头检测"))
self.pushButton_3.setText(_translate("MainWindow", "视频检测"))
self.groupBox.setTitle(_translate("MainWindow", "检测结果"))
self.label.setText(_translate("MainWindow", "TextLabel"))
self.textEdit.setHtml(_translate("MainWindow",
"<!DOCTYPE HTML PUBLIC \"-//W3C//DTD HTML 4.0//EN\" \"http://www.w3.org/TR/REC-html40/strict.dtd\">\n"
"<html><head><meta name=\"qrichtext\" content=\"1\" /><style type=\"text/css\">\n"
"p, li { white-space: pre-wrap; }\n"
"</style></head><body style=\" font-family:\'SimSun\'; font-size:9pt; font-weight:400; font-style:normal;\">\n"
"<p align=\"center\" style=\" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\"><span style=\" font-size:18pt; font-weight:600;\">双目测距系统</span></p></body></html>"))
def init_slots(self):
self.pushButton.clicked.connect(self.button_image_open)
self.pushButton_3.clicked.connect(self.button_video_open)
self.pushButton_2.clicked.connect(self.button_camera_open)
self.timer_video.timeout.connect(self.show_video_frame)
def init_logo(self):
pix = QtGui.QPixmap('wechat.jpg')
self.label.setScaledContents(True)
self.label.setPixmap(pix)
def button_image_open(self):
print('button_image_open')
name_list = []
img_name, _ = QtWidgets.QFileDialog.getOpenFileName(
self, "打开图片", "", "*.jpg;;*.png;;All Files(*)")
if not img_name:
return
img = cv2.imread(img_name)
print(img_name)
showimg = img
with torch.no_grad():
img = letterbox(img, new_shape=self.opt.img_size)[0]
# Convert
# BGR to RGB, to 3x416x416
img = img[:, :, ::-1].transpose(2, 0, 1)
img = np.ascontiguousarray(img)
img = torch.from_numpy(img).to(self.device)
img = img.half() if self.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
pred = self.model(img, augment=self.opt.augment)[0]
# Apply NMS
pred = non_max_suppression(pred, self.opt.conf_thres, self.opt.iou_thres, classes=self.opt.classes,
agnostic=self.opt.agnostic_nms)
print(pred)
# Process detections
for i, det in enumerate(pred):
if det is not None and len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(
img.shape[2:], det[:, :4], showimg.shape).round()
for *xyxy, conf, cls in reversed(det):
label = '%s %.2f' % (self.names[int(cls)], conf)
name_list.append(self.names[int(cls)])
plot_one_box(xyxy, showimg, label=label,
color=self.colors[int(cls)], line_thickness=2)
cv2.imwrite('prediction.jpg', showimg)
self.result = cv2.cvtColor(showimg, cv2.COLOR_BGR2BGRA)
self.result = cv2.resize(
self.result, (640, 480), interpolation=cv2.INTER_AREA)
self.QtImg = QtGui.QImage(
self.result.data, self.result.shape[1], self.result.shape[0], QtGui.QImage.Format_RGB32)
self.label.setPixmap(QtGui.QPixmap.fromImage(self.QtImg))
def button_video_open(self):
video_name, _ = QtWidgets.QFileDialog.getOpenFileName(
self, "打开视频", "", "*.mp4;;*.avi;;All Files(*)")
if not video_name:
return
flag = self.cap.open(video_name)
if flag == False:
QtWidgets.QMessageBox.warning(
self, u"Warning", u"打开视频失败", buttons=QtWidgets.QMessageBox.Ok, defaultButton=QtWidgets.QMessageBox.Ok)
else:
self.out = cv2.VideoWriter('prediction.avi', cv2.VideoWriter_fourcc(
*'MJPG'), 20, (int(self.cap.get(3)), int(self.cap.get(4))))
self.timer_video.start(30)
self.pushButton_3.setDisabled(True)
self.pushButton.setDisabled(True)
self.pushButton_2.setDisabled(True)
def button_camera_open(self):
if not self.timer_video.isActive():
# 默认使用第一个本地camera
flag = self.cap.open(0)
if flag == False:
QtWidgets.QMessageBox.warning(
self, u"Warning", u"打开摄像头失败", buttons=QtWidgets.QMessageBox.Ok,
defaultButton=QtWidgets.QMessageBox.Ok)
else:
self.out = cv2.VideoWriter('prediction.avi', cv2.VideoWriter_fourcc(
*'MJPG'), 20, (int(self.cap.get(3)), int(self.cap.get(4))))
self.timer_video.start(30)
self.pushButton_3.setDisabled(True)
self.pushButton.setDisabled(True)
self.pushButton_2.setText(u"关闭摄像头")
else:
self.timer_video.stop()
self.cap.release()
self.out.release()
self.label.clear()
self.init_logo()
self.pushButton_3.setDisabled(False)
self.pushButton.setDisabled(False)
self.pushButton_2.setText(u"摄像头检测")
def show_video_frame(self):
name_list = []
flag, img = self.cap.read()
if img is not None:
showimg = img
with torch.no_grad():
img = letterbox(img, new_shape=self.opt.img_size)[0]
# Convert
# BGR to RGB, to 3x416x416
img = img[:, :, ::-1].transpose(2, 0, 1)
img = np.ascontiguousarray(img)
img = torch.from_numpy(img).to(self.device)
img = img.half() if self.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
pred = self.model(img, augment=self.opt.augment)[0]
# Apply NMS
pred = non_max_suppression(pred, self.opt.conf_thres, self.opt.iou_thres, classes=self.opt.classes,
agnostic=self.opt.agnostic_nms)
# Process detections
for i, det in enumerate(pred): # detections per image
if det is not None and len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(
img.shape[2:], det[:, :4], showimg.shape).round()
# Write results
for *xyxy, conf, cls in reversed(det):
x1 = int(xyxy[0])
y1 = int(xyxy[1])
x2 = int(xyxy[2])
y2 = int(xyxy[3])
h = y2 - y1
c = int(cls) # integer class 整数类 1111111111
label = '%s %.2f' % (self.names[int(cls)], conf)
dis_m = car_distance(h)
label += f' {dis_m}m'
plot_one_box(
xyxy, showimg, label=label, color=self.colors[int(cls)], line_thickness=2)
self.out.write(showimg)
show = cv2.resize(showimg, (640, 480))
self.result = cv2.cvtColor(show, cv2.COLOR_BGR2RGB)
showImage = QtGui.QImage(self.result.data, self.result.shape[1], self.result.shape[0],
QtGui.QImage.Format_RGB888)
self.label.setPixmap(QtGui.QPixmap.fromImage(showImage))
else:
self.timer_video.stop()
self.cap.release()
self.out.release()
self.label.clear()
self.pushButton_3.setDisabled(False)
self.pushButton.setDisabled(False)
self.pushButton_2.setDisabled(False)
self.init_logo()
if __name__ == '__main__':
stylesheet = """
Ui_MainWindow {
background-image: url("01.jpg");
background-repeat: no-repeat;
background-position: center;
}
"""
app = QtWidgets.QApplication(sys.argv)
app.setStyleSheet(stylesheet)
ui = Ui_MainWindow()
ui.show()
sys.exit(app.exec_())
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本文采用coco数据集，包含80种类别，如果想检测想要类别，将parser.add_argument(‘–classes’, nargs=‘+’, default=[2],type=int,help=‘filter by class: --class 0, or --class 0 2 3’)中default=[2]改成所需类别索引号即可
4.3 界面卡住解决方案
如果点击图片检测或者视频检测按钮卡住，大概律师代码中间出现了错误，这时候是看不到报错的，具体可以把以下这个功能打开，这样就可以看到报错
5. 实现效果
工程源码下载：https://github.com/up-up-up-up/pyqt5-yolov5-5.0-Monocular/tree/main
文章内容后续会慢慢完善…
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