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from PyQt5 import QtGui
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from PyQt5.QtWidgets import *
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import cv2
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import numpy as np
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import matplotlib
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matplotlib.use('Qt5Agg')
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from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
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from matplotlib.backends.backend_qt5 import NavigationToolbar2QT as NavigationToolbar
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import matplotlib.pyplot as plt
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from matplotlib.figure import Figure
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from math import sqrt,pow
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class pltFigure(FigureCanvas):
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def __init__(self, parent=None, width=5, height=3, dpi=100):
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plt.rcParams['font.sans-serif'] = ['SimHei']
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plt.rcParams['axes.unicode_minus'] = False
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self.fig = Figure(figsize=(width, height), dpi=dpi)
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FigureCanvas.__init__(self, self.fig)
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self.setParent(parent)
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self.axes = self.fig.add_subplot(111)
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FigureCanvas.setSizePolicy(self,
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QSizePolicy.Expanding,
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QSizePolicy.Expanding)
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FigureCanvas.updateGeometry(self)
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def init(self):
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self.unit5_img = np.ndarray(())
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self.unit5_imgOrg = np.ndarray(())
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self.unit5_img_channel = 1
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self.fig3 = pltFigure(width=5, height=3, dpi=80)
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self.fig_ntb3 = NavigationToolbar(self.fig3, self)
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self.gridlayout2 = QGridLayout(self.ui.label_48)
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self.gridlayout2.addWidget(self.fig3)
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self.gridlayout2.addWidget(self.fig_ntb3)
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self.unit5_img_channel=1
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def hist_refresh(self):
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hist = np.bincount(self.unit5_img.ravel(), minlength=256)
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self.fig3.axes.cla()
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self.fig3.axes.plot(hist)
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self.fig3.draw()
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def img_refresh(self):
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imgShow = self.unit5_img
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if self.unit5_img.size<=1:
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self.ui.label_46.setPixmap(QtGui.QPixmap(''))
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return
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h = 471
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w = 481
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M = np.float32([[1, 0, 0], [0, 1, 0]])
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h2, w2 = imgShow.shape
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print(h2)
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print(w2)
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if h2 / w2 == h / w:
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data = imgShow.tobytes()
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image = QtGui.QImage(data, w2, h2, w2 , QtGui.QImage.Format_Grayscale8)
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pix = QtGui.QPixmap.fromImage(image)
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scale_pix = pix.scaled(w, h)
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self.ui.label_46.setPixmap(scale_pix)
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print("branch1")
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return
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elif h2 / w2 > h / w:
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print("branch2")
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h_ = h2
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w_ = int(h2 * w / h + 0.5)
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M[0, 2] += (w_ - w2) / 2
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M[1, 2] += (h_ - h2) / 2
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print(M)
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else:
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print("branch3")
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h_ = int(w2 * h /w + 0.5)
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w_ = w2
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M[0, 2] += (w_ - w2) / 2
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M[1, 2] += (h_ - h2) / 2
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imgShow = cv2.warpAffine(imgShow, M, (w_, h_))
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data = imgShow.tobytes()
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image = QtGui.QImage(data, w_, h_, w_ , QtGui.QImage.Format_Grayscale8)
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pix = QtGui.QPixmap.fromImage(image)
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scale_pix = pix.scaled(w, h)
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self.ui.label_46.setPixmap(scale_pix)
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def img_save(self):
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if self.unit5_img.size > 1:
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fileName, tmp = QFileDialog.getSaveFileName(self, '保存图像', 'Image', '*.png *.jpg *.bmp *.jpeg')
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if fileName == '':
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return
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cv2.imwrite(fileName, self.unit5_img)
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msg_box = QMessageBox(QMessageBox.Information, '成功', '图像保存成功,保存路径为：' + fileName)
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msg_box.exec_()
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else:
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msg_box = QMessageBox(QMessageBox.Warning, '提示', '请选择图像')
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msg_box.exec_()
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def img_clear(self):
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if self.unit5_img.size > 1:
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init(self)
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img_refresh(self)
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else:
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msg_box = QMessageBox(QMessageBox.Warning, '无需清空', '没有图片')
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msg_box.exec_()
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def img_load(self):
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fileName, tmp = QFileDialog.getOpenFileName(self, '打开图像', 'Image', '*.png *.jpg *.bmp *.jpeg')
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if fileName == '':
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return
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init(self)
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self.unit5_img = cv2.imread(fileName, cv2.IMREAD_GRAYSCALE)
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self.unit5_imgOrg = self.unit5_img.copy()
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if self.unit5_img.size>= 1:
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print(self.unit5_img.shape)
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img_refresh(self)
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hist_refresh(self)
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def img_show(self):
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if self.unit5_img.size > 1:
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cv2.imshow('Original pic', self.unit5_img)
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cv2.waitKey(0)
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else:
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msg_box = QMessageBox(QMessageBox.Warning, '提示', '请选择图像')
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msg_box.exec_()
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def Laplacian(self):
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if self.unit5_img.size>1:
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imarr = self.unit5_img
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height, width = imarr.shape
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fft = np.fft.fft2(imarr)
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fft = np.fft.fftshift(fft)
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for i in range(height):
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for j in range(width):
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fft[i, j] *= -((i - (height - 1) / 2) ** 2 + (j - (width - 1) / 2) ** 2)
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fft = np.fft.ifftshift(fft)
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ifft = np.fft.ifft2(fft)
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ifft = np.real(ifft)
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max = np.max(ifft)
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min = np.min(ifft)
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res = np.zeros((height, width), dtype="uint8")
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for i in range(height):
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for j in range(width):
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res[i, j] = 255 * (ifft[i, j] - min) / (max - min)
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self.unit5_img = res
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img_refresh(self)
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hist_refresh(self)
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else:
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msg_box = QMessageBox(QMessageBox.Warning, '提示', '请选择图像 ')
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msg_box.exec_()
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def Idea(self):
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if self.unit5_img.size>1:
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new_img = self.unit5_img
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# pencv中的傅立叶变化
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dft = cv2.dft(np.float32(new_img), flags=cv2.DFT_COMPLEX_OUTPUT)
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dtf_shift = np.fft.fftshift(dft)
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# np.fft.fftshift()函数来实现平移,让直流分量在输出图像的重心
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rows, cols = new_img.shape
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crow, ccol = int(rows / 2), int(cols / 2) # 计算频谱中心
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mask = np.zeros((rows, cols, 2), np.uint8) # 生成rows行cols列的2纬矩阵，数据格式为uint8
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mask[crow - 30:crow + 30, ccol - 30:ccol + 30] = 1 # 将靠近频谱中心的部分低通信息 设置为1，属于低通滤波
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fshift = dtf_shift * mask
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# 傅立叶逆变换
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f_ishift = np.fft.ifftshift(fshift)
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img_back = cv2.idft(f_ishift)
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img_back = cv2.magnitude(img_back[:, :, 0], img_back[:, :, 1]) # 计算像素梯度的绝对值
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img_back = np.abs(img_back)
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img_back = (img_back - np.amin(img_back)) / (np.amax(img_back) - np.amin(img_back))
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self.unit5_img = (img_back * 255).astype(np.uint8)
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img_refresh(self)
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hist_refresh(self)
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else:
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msg_box = QMessageBox(QMessageBox.Warning, '提示', '请选择图像 ')
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msg_box.exec_()
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def Gaussion(self):
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if self.unit5_img.size > 1:
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sigma =1
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imarr = self.unit5_img
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height, width = imarr.shape
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fft = np.fft.fft2(imarr)
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fft = np.fft.fftshift(fft)
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for i in range(height):
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for j in range(width):
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fft[i, j] *= (1 - np.exp(-((i - (height - 1) / 2) ** 2 + (j - (width - 1) / 2) ** 2) / 2 / sigma ** 2))
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fft = np.fft.ifftshift(fft)
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ifft = np.fft.ifft2(fft)
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ifft = np.real(ifft)
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max = np.max(ifft)
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min = np.min(ifft)
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res = np.zeros((height, width), dtype="uint8")
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for i in range(height):
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for j in range(width):
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res[i, j] = 255 * (ifft[i, j] - min) / (max - min)
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self.unit5_img = res
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img_refresh(self)
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hist_refresh(self)
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else:
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msg_box = QMessageBox(QMessageBox.Warning, '提示', '请选择图像 ')
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msg_box.exec_()
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#def Butterworth(self):
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#if self.unit5_img.size>1:
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#image = self.unit5_img
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# d = 40
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# f = np.fft.fft2(image)
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#fshift = np.fft.fftshift(f)
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# transfor_matrix = np.zeros(image.shape)
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# M = transfor_matrix.shape[0]
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# N = transfor_matrix.shape[1]
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# for u in range(M):
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# for v in range(N):
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# D = sqrt((u - M / 2) ** 2 + (v - N / 2) ** 2)
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# transfor_matrix[u, v] = 1 / (1 + pow(D / d, 16))
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# print(transfor_matrix.dtype)
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# new_img = np.abs(np.fft.ifft2(np.fft.ifftshift(fshift * transfor_matrix)))
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# new_img = new_img .astype(np.float32)
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# self.unit5_img = (new_img * 255).astype(np.uint8)
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# self.unit5_img = new_img
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# print(self.unit5_img.shape)
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# img_refresh(self)
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# #hist_refresh(self)
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# else:
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# msg_box = QMessageBox(QMessageBox.Warning, '没有图像', '请先选择一副图像 ')
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# msg_box.exec_()
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def img_reset(self):
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if self.unit5_img.size>1:
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self.unit5_img = self.unit5_imgOrg
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img_refresh(self)
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hist_refresh(self)
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else:
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msgbox = QMessageBox(QMessageBox.Warning, "提示", "请选择图像")
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msgbox.exec_()
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