基于openCV的鱼群密度速度检测程序.zip

import cv2 import numpy as np from PyQt5.QtCore import * from PyQt5.QtWidgets import * from PyQt5.QtGui import * from PyQt5 import QtCore, QtGui, QtWidgets import os import sys from pathlib import Path import time # coding=utf-8 # author Vicky-zhang # date 18/11/2022 # describe 鱼群密度检测。 def det_yolov5v6(info1): camera = cv2.VideoCapture(info1) # 参数0表示第一个摄像头 # 判断视频是否打开 if (camera.isOpened()): print('Open') else: print('摄像头未打开') # 测试用,查看视频size size = (int(camera.get(cv2.CAP_PROP_FRAME_WIDTH)), int(camera.get(cv2.CAP_PROP_FRAME_HEIGHT))) print('size:' + repr(size)) es = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (9, 4)) kernel = np.ones((5, 5), np.uint8) background = None fps = camera.get(cv2.CAP_PROP_FPS) # 视频平均帧率 while True: # 读取视频流 grabbed, frame_lwpCV = camera.read() if frame_lwpCV is None: break # 对帧进行预处理，先转灰度图，再进行高斯滤波。 # 用高斯滤波进行模糊处理，进行处理的原因：每个输入的视频都会因自然震动、光照变化或者摄像头本身等原因而产生噪声。对噪声进行平滑是为了避免在运动和跟踪时将其检测出来。 gray_lwpCV = cv2.cvtColor(frame_lwpCV, cv2.COLOR_BGR2GRAY) gray_lwpCV = cv2.GaussianBlur(gray_lwpCV, (21, 21), 0) # 将第一帧设置为整个输入的背景 if background is None: background = gray_lwpCV continue # 对于每个从背景之后读取的帧都会计算其与北京之间的差异，并得到一个差分图（different map）。 # 还需要应用阈值来得到一幅黑白图像，并通过下面代码来膨胀（dilate）图像，从而对孔（hole）和缺陷（imperfection）进行归一化处理 diff = cv2.absdiff(background, gray_lwpCV) diff = cv2.threshold(diff, 35, 255, cv2.THRESH_BINARY)[1] # 二值化阈值处理 diff = cv2.dilate(diff, es, iterations=2) # 形态学膨胀 # 显示矩形框 contours, hierarchy = cv2.findContours(diff.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # 该函数计算一幅图像中目标的轮廓 fishcount = 0 for c in contours: if cv2.contourArea(c) < 1000 or cv2.contourArea(c) > 3000: # 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 print(cv2.contourArea(c)) fishcount += 1 (x, y, w, h) = cv2.boundingRect(c) # 该函数计算矩形的边界框 # cv2.rectangle(frame_lwpCV, (x, y), (x+w, y+h), (0, 255, 0), 2) tl = round(0.002 * (frame_lwpCV.shape[0] + frame_lwpCV.shape[1]) / 2) + 1 # line/font thickness color = [0, 255, 0] c1, c2 = (int(x), int(y)), (int(x + w), int(y + h)) cv2.rectangle(frame_lwpCV, c1, c2, [0, 255, 0], thickness=tl, lineType=cv2.LINE_AA) # cv2.imshow('4', frame_lwpCV) # cv2.waitKey(0) label = str(cv2.contourArea(c) * 12 * 27 / (40000 * 500))[:4] + 'm/s' tf = max(tl - 1, 1) # font thickness t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 cv2.rectangle(frame_lwpCV, c1, c2, color, -1, cv2.LINE_AA) # filled cv2.putText(frame_lwpCV, label, (c1[0], c1[1] - 2), 0, 1, [0, 0, 255], thickness=tf, lineType=cv2.LINE_AA) #cv2.imshow('5', frame_lwpCV) #cv2.waitKey(0) label2 = 'fish numbers=' + str(fishcount) cv2.putText(frame_lwpCV, label2, (20, 20), 1, 2, [255, 215, 0], thickness=2, lineType=cv2.LINE_AA) ui.showimg(frame_lwpCV) QApplication.processEvents() time.sleep(1 / fps) # 按原帧率播放 # When everything done, release the capture camera.release() class Thread_1(QThread): # 线程1 def __init__(self,info1): super().__init__() self.info1=info1 self.run2(self.info1) def run2(self, info1): result = [] result = det_yolov5v6(info1) class Ui_MainWindow(object): def setupUi(self, MainWindow): MainWindow.setObjectName("MainWindow") MainWindow.resize(1280, 960) MainWindow.setStyleSheet("background-image: url(\"./template/bg1.jpg\")") self.centralwidget = QtWidgets.QWidget(MainWindow) self.centralwidget.setObjectName("centralwidget") self.label = QtWidgets.QLabel(self.centralwidget) self.label.setGeometry(QtCore.QRect(168, 60, 551, 71)) self.label.setAutoFillBackground(False) self.label.setStyleSheet("") self.label.setFrameShadow(QtWidgets.QFrame.Plain) self.label.setAlignment(QtCore.Qt.AlignCenter) self.label.setObjectName("label") self.label.setStyleSheet("font-size:42px;font-weight:bold;font-family:SimHei;background:rgba(255,255,255,0);") self.label_2 = QtWidgets.QLabel(self.centralwidget) self.label_2.setGeometry(QtCore.QRect(40, 188, 751, 501)) self.label_2.setStyleSheet("background:rgba(255,255,255,1);") self.label_2.setAlignment(QtCore.Qt.AlignCenter) self.label_2.setObjectName("label_2") self.textBrowser = QtWidgets.QTextBrowser(self.centralwidget) self.textBrowser.setGeometry(QtCore.QRect(40, 746, 951, 180)) self.textBrowser.setStyleSheet("background:rgba(0,0,0,0);") self.textBrowser.setObjectName("textBrowser") self.pushButton = QtWidgets.QPushButton(self.centralwidget) self.pushButton.setGeometry(QtCore.QRect(1020, 750, 150, 40)) self.pushButton.setStyleSheet("background:rgba(53,142,255,1);border-radius:10px;padding:2px 4px;") self.pushButton.setObjectName("pushButton") self.pushButton_2 = QtWidgets.QPushButton(self.centralwidget) self.pushButton_2.setGeometry(QtCore.QRect(1020, 810, 150, 40)) self.pushButton_2.setStyleSheet("background:rgba(53,142,255,1);border-radius:10px;padding:2px 4px;") self.pushButton_2.setObjectName("pushButton_2") self.pushButton_3 = QtWidgets.QPushButton(self.centralwidget) self.pushButton_3.setGeometry(QtCore.QRect(1020, 870, 150, 40)) self.pushButton_3.setStyleSheet("background:rgba(53,142,255,1);border-radius:10px;padding:2px 4px;") self.pushButton_3.setObjectName("pushButton_2") MainWindow.setCentralWidget(self.centralwidget) self.retranslateUi(MainWindow) QtCore.QMetaObject.connectSlotsByName(MainWindow) def retranslateUi(self, MainWindow): _translate = QtCore.QCoreApplication.translate MainWindow.setWindowTitle(_translate("MainWindow", "鱼群密度速度检测")) self.label.setText(_translate("MainWindow", "鱼群密度速度检测")) self.label_2.setText(_translate("MainWindow", "路径请勿包含中文")) self.pushButton.setText(_translate("MainWindow", "选择文件")) self.pushButton_2.setText(_translate("MainWindow", "开始识别")) self.pushButton_3.setText(_translate("MainWindow", "退出")) # 点击文本框绑定槽事件 self.pushButton.clicked.connect(self.openfile) self.pushButton_2.clicked.connect(self.click_1) self.pushButton_3.clicked.connect(self.handleCalc3) def openfile(self): global sname, filepath fname = QFileDialog() fname.setAcceptMode(QFileDialog.AcceptOpen) fname, _ = fname.getOpenFileName() if fname == '': return filepath = os.path.normpath(fname) sname = filepath.split(os.sep) ui.prin