《OpenCV视觉之眼》专栏图像处理总体项目

import math from matplotlib import pyplot as plt import cv2 import os import numpy as np from PyQt5.QtGui import QImage, QPixmap from PyQt5.QtWidgets import QMainWindow, QFileDialog, QMessageBox from main import Ui_MainWindow from detect import detect_thread class imagewindow(Ui_MainWindow,QMainWindow): def __init__(self): super(imagewindow,self).__init__() self.setupUi(self) self.setWindowTitle("Opencv图像处理") self.label.setScaledContents(True) #用户缩放 self.x=0.9 self.y=0.9 #旋转起始角度 self.angle=90 #鼠标事件 self.en = False self.progressBar.setValue(0) self.pushButton.clicked.connect(self.openImage) self.pushButton_2.clicked.connect(self.imageTogray) #灰度 self.pushButton_3.clicked.connect(self.Sketch) #素描 self.pushButton_4.clicked.connect(self.Nostalgia) #怀旧 self.pushButton_5.clicked.connect(self.paint) #油漆 self.pushButton_6.clicked.connect(self.Ground_glass) #毛玻璃 self.pushButton_7.clicked.connect(self.relief) #浮雕 self.pushButton_8.clicked.connect(self.threshold) #阈值化 self.pushButton_9.clicked.connect(self.zoom) #缩放 self.pushButton_10.clicked.connect(self.rotate) #旋转 self.pushButton_11.clicked.connect(self.GaussianFiltering) #高斯滤波 self.pushButton_12.clicked.connect(self.meanFiltering) #均值滤波 self.pushButton_13.clicked.connect(self.boxFiltering) #方框滤波 self.pushButton_14.clicked.connect(self.medianFiltering) #中值滤波 self.pushButton_15.clicked.connect(self.Lowpassfiltering) #低通滤波 self.pushButton_16.clicked.connect(self.Flooding) #泛洪 self.pushButton_17.clicked.connect(self.erode) #腐蚀 self.pushButton_18.clicked.connect(self.Canny) #Canny边缘算法 self.pushButton_19.clicked.connect(self.dilate) #膨胀 self.pushButton_20.clicked.connect(self.save_image) #保存处理后的图像 self.pushButton_21.clicked.connect(self.Filter)#滤镜 self.pushButton_22.clicked.connect(self.light)#光照 self.pushButton_23.clicked.connect(self.fleetingTime)#流连 self.pushButton_24.clicked.connect(self.gradient)#梯度运算 self.pushButton_25.clicked.connect(self.close)#闭运算 self.pushButton_26.clicked.connect(self.open)#开运算 self.pushButton_27.clicked.connect(self.Mosaic)#马赛克 self.pushButton_28.clicked.connect(self.quantification)#量化 self.pushButton_29.clicked.connect(self.topHat)#顶帽运算 self.pushButton_30.clicked.connect(self.hoffcircle)#霍夫圆检测 self.pushButton_31.clicked.connect(self.hightpassfiltering)#高通滤波 self.pushButton_32.clicked.connect(self.hoffline)#霍夫直线检测 self.pushButton_33.clicked.connect(self.blackHot)#黑帽运算 self.pushButton_34.clicked.connect(self.FourierTransform)#傅里叶变换 self.pushButton_35.clicked.connect(self.inFourierTransform)#傅里叶反变换 def faceRecognition(self): try: self.label_2.setScaledContents(True) # 读取图片 img = cv2.imread(self.path) img = cv2.resize(img, (240, 240), interpolation=cv2.INTER_CUBIC) gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) gradX = cv2.Sobel(gray, ddepth=cv2.CV_32F, dx=1, dy=0, ksize=-1) gradY = cv2.Sobel(gray, ddepth=cv2.CV_32F, dx=0, dy=1, ksize=-1) # subtract the y-gradient from the x-gradient gradient = cv2.subtract(gradX, gradY) gradient = cv2.convertScaleAbs(gradient) blurred = cv2.blur(gradient, (9, 9)) (_, thresh) = cv2.threshold(blurred, 90, 255, cv2.THRESH_BINARY) kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (25, 25)) closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel) # perform a series of erosions and dilations closed = cv2.erode(closed, None, iterations=4) closed = cv2.dilate(closed, None, iterations=4) (cnts, _) = cv2.findContours(closed.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) c = sorted(cnts, key=cv2.contourArea, reverse=True)[0] # compute the rotated bounding box of the largest contour rect = cv2.minAreaRect(c) box = np.int0(cv2.boxPoints(rect)) # draw a bounding box arounded the detected barcode and display the image cv2.drawContours(img, [box], -1, (0, 255, 0), 3) h, w = img.shape[:2] # 获取图像的长和宽 qimg = QImage(img, w, h, QImage.Format_BGR888) qpix = QPixmap.fromImage(qimg) self.label_2.setPixmap(qpix) except: QMessageBox.about(self, "提示", "请先选择人脸照片！") #自定义傅里叶变换函数 def dft(self,img): H, W, channel = img.shape # Prepare DFT coefficient G = np.zeros((H, W, channel), dtype=np.complex) # prepare processed index corresponding to original image positions x = np.tile(np.arange(W), (H, 1)) y = np.arange(H).repeat(W).reshape(H, -1) self.create_thread() # dft # pragma omp parallel for for c in range(channel): for v in range(H): for u in range(W): G[v, u, c] = np.sum(img[..., c] * np.exp(-2j * np.pi * (x * u / W + y * v / H))) / np.sqrt(H * W) if v % (int(H / 100)) == 0: self.detectThread.timerEvent() self.detectThread.quit() return G #傅里叶变换 def FourierTransform(self): try: self.label_2.setScaledContents(True) # 读取图片 img = cv2.imread(self.path) img = cv2.resize(img, (100, 100), interpolation=cv2.INTER_CUBIC) # 傅里叶库函数调用 #dft = cv2.dft(np.float32(img), flags=cv2.DFT_COMPLEX_OUTPUT) #dftshift = np.fft.fftshift(dft) #res1 = 20 * np.log(cv2.magnitude(dftshift[:, :, 0], dftshift[:, :, 1])) # 自定义傅里叶变换函数调用 self.result5 = self.dft(img) fshift = np.fft.fftshift(self.result5) fimg = np.log(np.abs(fshift)) plt.imshow(fimg, 'gray') plt.axis('off') plt.savefig("f.png", bbox_inches='tight', pad_inches=0) self.label_2.setPixmap(QPixmap("f.png")) os.remove("f.png") except: QMessageBox.about(self, "提示", "请先选择图片！") #自定义傅里叶反变换函数 def idft(self,G): # prepare out image H, W, channel = G.shape out = np.zeros((H, W, channel), dtype=np.float32) # 准备与原始图像位置相对应的处理索引 x = np.tile(np.arange(W), (H, 1)) y = np.arange(H).repeat(W).reshape(H, -1) self.create_thread() # idft for c in range(channel): for v in range(H): for u in range(W): out[v, u, c] = np.abs(np.sum(G[..., c] * np.exp(2j * np.pi * (x * u / W + y * v / H)))) / np.sqrt(W * H) if v % (int(H / 100)) == 0: self.detectThread.timerEvent() self.detectThread.quit() # clipping out = np.clip(out, 0, 255) out = out.astype(np.uint8) return out #傅里叶反变换 def inFourierTransform(self): try: self.label_2.setScaledContents(True) # 傅里叶库函数调用 #ishift = np.fft.ifftshift(self.result5) #iimg = cv2.idft(ishift)