Edge Detection, Segmentation, and Denoising on Images with Python GUI (PyQt): Part 4

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Part 1 Part 2 Part 3

Tutorial Steps To Implement Image Denoising Using Non-local Means Denoising, Total Variation Filter, Bilateral Filter, and Wavelet Denoising Filter.

First, use cv2.fastNlMeansDenoisingColored() function which is the implementation of Non-local Means Denoising algorithm as defined below:

cv2.fastNlMeansDenoisingColored(src[, dst[, \
   h[, hColor[, templateWindowSize[, searchWindowSize]]]]])

The parameters are:

The three other algorithms you will use in this project are total variation filter, bilateral filter, and wavelet denoising filter. You need to import the three algorithms using this import statement as follows:

from skimage.restoration import (denoise_tv_chambolle,\
    denoise_bilateral, denoise_wavelet, estimate_sigma)

Add a Combo Box widget onto form in Qt Designer. Set its objectName property as cboDenoising. Double click on that widget and add four items as shown in Figure below.

Add five new Label widgets and set their text properties as h, patch, search, weight, and sigma.

Add a new Spin Box widget and set its objectName property as sbH. Set its value property to 15, its minimum property to 3, its maximum property to 99, and its singleStep to 1.

Add another Spin Box widget and set its objectName property as sbPatch. Set its value property to 13, its minimum property to 3, its maximum property to 99, and its singleStep to 2.

Add another Spin Box widget and set its objectName property as sbSearch. Set its value property to 29, its minimum property to 3, its maximum property to 99, and its singleStep to 2.

Add a new Horizonal Slider widget and set its objectName property as hsWeight. Set its value property to 0, its minimum property to 0, its maximum property to 10, and its singleStep to 1.

Add a new Horizonal Slider widget and set its objectName property as hsSigma. Set its value property to 0, its minimum property to 0, its maximum property to 100, and its singleStep to 1.

Add two new Line Edit widgets and set their objectName properties to leWeight and leSigma. Set their text properties to 0.1 and 0.25. The newly modified form is shown in Figure below

Define a new method, choose_denoising(), to read currentText property of cboDenoising and implement image denoising accordingly:

def choose_denoising(self,img): 
    strCB = self.cboDenoising.currentText()
    h = self.sbH.value()
    patch = self.sbPatch.value()
    size = self.sbSearch.value()
    weightVal = float(self.leWeight.text())
    sigmaVal = float(self.leSigma.text())
    noisy = self.choose_noise(img)
        
    if strCB == 'Non-Local Means Denoising': 
        self.hsWeight.setEnabled(False)
        self.leWeight.setEnabled(False)
        self.hsSigma.setEnabled(False)
        self.leSigma.setEnabled(False)
        self.sbH.setEnabled(True)
        self.sbPatch.setEnabled(True)
        self.sbSearch.setEnabled(True)
        denoised_img = cv2.fastNlMeansDenoisingColored(\
            noisy,None,h,h,patch,size)
        return denoised_img
        
    if strCB == 'Total Variation Filter': 
        self.hsWeight.setEnabled(True)
        self.leWeight.setEnabled(True)
        self.hsSigma.setEnabled(False)
        self.leSigma.setEnabled(False)
        self.sbH.setEnabled(False)
        self.sbPatch.setEnabled(False)
        self.sbSearch.setEnabled(False)
        denoised_img = denoise_tv_chambolle(noisy, \
            weight=weightVal, multichannel=True)
        cv2.normalize(denoised_img, \
           denoised_img, 0, 255, cv2.NORM_MINMAX, dtype=-1)
        denoised_img = denoised_img.astype(np.uint8)
        return denoised_img
    if strCB == 'Bilateral Filter': 
        self.hsWeight.setEnabled(False)
        self.leWeight.setEnabled(False)
        self.hsSigma.setEnabled(True)
        self.leSigma.setEnabled(True)
        self.sbH.setEnabled(False)
        self.sbPatch.setEnabled(False)
        self.sbSearch.setEnabled(False)
        denoised_img = denoise_bilateral(noisy, \
            sigma_color=sigmaVal, sigma_spatial=15, \
            multichannel=True)
        cv2.normalize(denoised_img, \
            denoised_img, 0, 255, cv2.NORM_MINMAX, dtype=-1)
        denoised_img = denoised_img.astype(np.uint8)
        return denoised_img
    if strCB == 'Wavelet Denoising Filter': 
        self.hsWeight.setEnabled(False)
        self.leWeight.setEnabled(False)
        self.hsSigma.setEnabled(False)
        self.leSigma.setEnabled(False)
        self.sbH.setEnabled(False)
        self.sbPatch.setEnabled(False)
        self.sbSearch.setEnabled(False)
        denoised_img = denoise_wavelet(noisy, \
            multichannel=True, convert2ycbcr=True, \
            rescale_sigma=True)
        cv2.normalize(denoised_img, denoised_img, 0, 255, \
            cv2.NORM_MINMAX, dtype=-1)
        denoised_img = denoised_img.astype(np.uint8)
        return denoised_img

Define a new method, do_denoising(), to call choose_denoising() and convert the result from BGR into RGB color space:

def do_denoising(self):
    denoised = self.choose_denoising(img)        
    height, width, channel = denoised.shape
    bytesPerLine = 3 * width  
    cv2.cvtColor(denoised, cv2.COLOR_BGR2RGB, denoised)
    qImg = QImage(denoised, width, height, \
        bytesPerLine, QImage.Format_RGB888)
    pixmap = QPixmap.fromImage(qImg)
    self.display_image(pixmap, denoised, self.labelImage, \
        self.widgetHistFilter, 'Histogram of Denoised Image')

Define two new methods, set_hsWeight() and set_hsSigma(), to set text properties of leWeight and leSigma and to invoke do_denoising() method.

def set_hsWeight(self, value):
    self.leWeight.setText(str(round((value/10),2)))
    self.do_denoising()
def set_hsSigma(self, value):
    self.leSigma.setText(str(round((value/100),2)))
    self.do_denoising()

Connect currentIndexChanged() event of cboDenoising to do_denoising(). Connect valueChanged() event of sbH, sbPatch, and sbSearch to do_denoising(). Put them inside __init__() method as follows:

self.cboDenoising.currentIndexChanged.connect(self.do_denoising)
self.sbH.valueChanged.connect(self.do_denoising)
self.sbPatch.valueChanged.connect(self.do_denoising)
self.sbSearch.valueChanged.connect(self.do_denoising)

Connect valueChanged() event of hsWeight to set_hsWeight() and valueChanged() event of hsSigma to set_hsSigma(). Put them inside __init__() method as follows:

self.hsWeight.valueChanged.connect(self.set_hsWeight)
self.hsSigma.valueChanged.connect(self.set_hsSigma)

Run image_processing.py. Choose test image and select Gaussian noise type to generate noisy image. Then select Non-Local Means Denoising and you can select h, patch, and search values that suit you better. The result is shown in Figure below.

Then select Total Variation Filter and you can select weight value that suits you better. The result is shown in Figure below.

Choose another test image and select Salt & Pepper noise type to generate noisy image. Then select Bilateral Filter and you can select sigma value that suits you better. The result is shown in Figure below.

Then select Wavelet Denoising Filter. The result is shown in Figure below.

The following is the last version of image_processing.py script:

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#image_processing.py import cv2 import numpy as np from PyQt5.QtWidgets import* from PyQt5 import QtGui, QtCore from PyQt5.uic import loadUi from matplotlib.backends.backend_qt5agg import (NavigationToolbar2QT as NavigationToolbar) from PyQt5.QtWidgets import QDialog, QFileDialog from PyQt5.QtGui import QIcon, QPixmap, QImage from PIL import Image from skimage.util import random_noise from sklearn.cluster import KMeans from skimage.filters import gaussian import skimage.color as color import numpy as np from skimage.restoration import (denoise_tv_chambolle, denoise_bilateral, denoise_wavelet, estimate_sigma) fname = "" class FormImageProcessing(QMainWindow): def __init__(self): QMainWindow.__init__(self) loadUi("image_proc.ui",self) self.setWindowTitle("Image Processing") self.pbImage.clicked.connect(self.open_file) self.setState('START') self.hsMean.valueChanged.connect(self.set_hsMean) self.hsVar.valueChanged.connect(self.set_hsVar) self.hsAmount.valueChanged.connect(self.set_hsAmount) self.leMean.textEdited.connect(self.do_noise) self.leVar.textEdited.connect(self.do_noise) self.leAmount.textEdited.connect(self.do_noise) self.cboNoise.currentIndexChanged.connect(self.do_noise) self.sbMinVal.valueChanged.connect(self.set_minval) self.sbMaxVal.valueChanged.connect(self.set_maxval) self.sbKernel.valueChanged.connect(self.set_kernel) self.leMinVal.textEdited.connect(self.do_edge) self.leMaxVal.textEdited.connect(self.do_edge) self.leKernel.textEdited.connect(self.do_edge) self.cboEdge.currentIndexChanged.connect(self.do_edge) self.rbMultiple.toggled.connect(self.rbstate) self.rbKMeans.toggled.connect(self.rbstate) self.cboDenoising.currentIndexChanged.connect(self.do_denoising) self.sbH.valueChanged.connect(self.do_denoising) self.sbPatch.valueChanged.connect(self.do_denoising) self.sbSearch.valueChanged.connect(self.do_denoising) self.hsWeight.valueChanged.connect(self.set_hsWeight) self.hsSigma.valueChanged.connect(self.set_hsSigma) def set_hsWeight(self, value): self.leWeight.setText(str(round((value/10),2))) self.do_denoising() def set_hsSigma(self, value): self.leSigma.setText(str(round((value/100),2))) self.do_denoising() def choose_denoising(self,img): strCB = self.cboDenoising.currentText() h = self.sbH.value() patch = self.sbPatch.value() size = self.sbSearch.value() weightVal = float(self.leWeight.text()) sigmaVal = float(self.leSigma.text()) noisy = self.choose_noise(img) if strCB == 'Non-Local Means Denoising': self.hsWeight.setEnabled(False) self.leWeight.setEnabled(False) self.hsSigma.setEnabled(False) self.leSigma.setEnabled(False) self.sbH.setEnabled(True) self.sbPatch.setEnabled(True) self.sbSearch.setEnabled(True) denoised_img = cv2.fastNlMeansDenoisingColored(noisy,None,\ h,h,patch,size) return denoised_img if strCB == 'Total Variation Filter': self.hsWeight.setEnabled(True) self.leWeight.setEnabled(True) self.hsSigma.setEnabled(False) self.leSigma.setEnabled(False) self.sbH.setEnabled(False) self.sbPatch.setEnabled(False) self.sbSearch.setEnabled(False) denoised_img = denoise_tv_chambolle(noisy, \ weight=weightVal, multichannel=True) cv2.normalize(denoised_img, denoised_img, 0, 255, \ cv2.NORM_MINMAX, dtype=-1) denoised_img = denoised_img.astype(np.uint8) return denoised_img if strCB == 'Bilateral Filter': self.hsWeight.setEnabled(False) self.leWeight.setEnabled(False) self.hsSigma.setEnabled(True) self.leSigma.setEnabled(True) self.sbH.setEnabled(False) self.sbPatch.setEnabled(False) self.sbSearch.setEnabled(False) denoised_img = denoise_bilateral(noisy, sigma_color=sigmaVal, \ sigma_spatial=25, multichannel=True) cv2.normalize(denoised_img, denoised_img, 0, 255, \ cv2.NORM_MINMAX, dtype=-1) denoised_img = denoised_img.astype(np.uint8) return denoised_img if strCB == 'Wavelet Denoising Filter': self.hsWeight.setEnabled(False) self.leWeight.setEnabled(False) self.hsSigma.setEnabled(False) self.leSigma.setEnabled(False) self.sbH.setEnabled(False) self.sbPatch.setEnabled(False) self.sbSearch.setEnabled(False) denoised_img = denoise_wavelet(noisy, multichannel=True, \ convert2ycbcr=True, rescale_sigma=True) cv2.normalize(denoised_img, denoised_img, 0, 255, \ cv2.NORM_MINMAX, dtype=-1) denoised_img = denoised_img.astype(np.uint8) return denoised_img def do_denoising(self): denoised = self.choose_denoising(img) height, width, channel = denoised.shape bytesPerLine = 3 * width cv2.cvtColor(denoised, cv2.COLOR_BGR2RGB, denoised) qImg = QImage(denoised, width, height, \ bytesPerLine, QImage.Format_RGB888) pixmap = QPixmap.fromImage(qImg) self.display_image(pixmap, denoised, self.labelImage, \ self.widgetHistFilter, 'Histogram of Denoised Image') def thresh_seg(self,img): img_seg = np.zeros(img.shape, np.float32) if len(img.shape) == 2: img_seg = self.multiple_thresh(img) else: img_seg[:, :, 0] = self.multiple_thresh(img[:, :, 0]) img_seg[:, :, 1] = self.multiple_thresh(img[:, :, 1]) img_seg[:, :, 2] = self.multiple_thresh(img[:, :, 2]) cv2.normalize(img_seg, img_seg, 0, 255, cv2.NORM_MINMAX, dtype=-1) img_seg = img_seg.astype(np.uint8) return img_seg def multiple_thresh(self, img): img_thresh= 255*img.reshape(img.shape[0]*img.shape[1]) thresh_mean = img_thresh.mean() for i in range(img_thresh.shape[0]): if img_thresh[i] > thresh_mean: img_thresh[i] = 3 elif img_thresh[i] > 0.5: img_thresh[i] = 2 elif img_thresh[i] > 0.25: img_thresh[i] = 1 else: img_thresh[i] = 0 img_mul = img_thresh.reshape(img.shape[0],img.shape[1]) return img_mul def kmeans_seg(self,img): img_seg = np.zeros(img.shape, np.float32) img_n = img.reshape(img.shape[0]*img.shape[1], img.shape[2]) kmeans = KMeans(n_clusters=5, random_state=0).fit(img_n) pic2show = kmeans.cluster_centers_[kmeans.labels_] img_seg = pic2show.reshape(img.shape[0], img.shape[1], img.shape[2]) cv2.normalize(img_seg, img_seg, 0, 255, cv2.NORM_MINMAX, dtype=-1) img_seg = img_seg.astype(np.uint8) return img_seg def rbstate(self): noisy = self.choose_noise(img) if self.rbMultiple.isChecked() == True: output = self.thresh_seg(noisy) self.seg_output(output) if self.rbKMeans.isChecked() == True: output = self.kmeans_seg(noisy) self.seg_output(output) def seg_output(self,output): height, width, channel = output.shape bytesPerLine = 3 * width cv2.cvtColor(output, cv2.COLOR_BGR2RGB, output) qImg = QImage(output, width, height, \ bytesPerLine, QImage.Format_RGB888) pixmap = QPixmap.fromImage(qImg) self.display_image(pixmap, output, self.labelFilter, \ self.widgetHistFilter, 'Histogram of Segmented Image') def open_file(self): global img self.fname = QFileDialog.getOpenFileName(self, 'Open file', 'd:\\',"Image Files (*.jpg *.gif *.bmp *.png)") pixmap = QPixmap(self.fname[0]) img = cv2.imread(self.fname[0], cv2.IMREAD_COLOR) self.display_image(pixmap, img, self.labelImage, \ self.widgetHistIm, 'Histogram of Original Image') self.setState('RUN') self.hsAmount.setEnabled(False) self.leAmount.setEnabled(False) def display_image(self, pixmap, img, label, qwidget1, title): label.setPixmap(pixmap) label.setScaledContents(True); self.display_histogram(img, qwidget1, title) def display_histogram(self, img, qwidget1, title): qwidget1.canvas.axes1.clear() channel = len(img.shape) if channel == 2: #grayscale image histr = cv2.calcHist([img],[0],None,[256],[0,256]) qwidget1.canvas.axes1.plot(histr,\ color = 'yellow',linewidth=3.0) qwidget1.canvas.axes1.set_ylabel('Frequency',\ color='white') qwidget1.canvas.axes1.set_xlabel('Intensity', \ color='white') qwidget1.canvas.axes1.tick_params(axis='x', colors='white') qwidget1.canvas.axes1.tick_params(axis='y', colors='white') qwidget1.canvas.axes1.set_title(title,color='white') qwidget1.canvas.axes1.set_facecolor('xkcd:black') qwidget1.canvas.axes1.grid() qwidget1.canvas.draw() else : #color image color = ('b','g','r') for i,col in enumerate(color): histr = cv2.calcHist([img],[i],None,[256],[0,256]) qwidget1.canvas.axes1.plot(histr,\ color = col,linewidth=3.0) qwidget1.canvas.axes1.set_ylabel('Frequency',\ color='white') qwidget1.canvas.axes1.set_xlabel('Intensity', \ color='white') qwidget1.canvas.axes1.tick_params(axis='x', colors='white') qwidget1.canvas.axes1.tick_params(axis='y', colors='white') qwidget1.canvas.axes1.set_title(title,color='white') qwidget1.canvas.axes1.set_facecolor('xkcd:black') qwidget1.canvas.axes1.grid() qwidget1.canvas.draw() def setState(self, state): if state == 'START': self.cboNoise.setEnabled(False) self.hsMean.setEnabled(False) self.hsVar.setEnabled(False) self.hsAmount.setEnabled(False) self.leMean.setEnabled(False) self.leVar.setEnabled(False) self.leAmount.setEnabled(False) self.leMinVal.setEnabled(False) self.leMaxVal.setEnabled(False) self.leKernel.setEnabled(False) self.sbMinVal.setEnabled(False) self.sbMaxVal.setEnabled(False) self.sbKernel.setEnabled(False) self.cboEdge.setEnabled(False) self.rbMultiple.setEnabled(False) self.rbKMeans.setEnabled(False) self.cboDenoising.setEnabled(False) self.sbH.setEnabled(False) self.sbPatch.setEnabled(False) self.sbSearch.setEnabled(False) self.hsWeight.setEnabled(False) self.leWeight.setEnabled(False) self.hsWeight.setEnabled(False) self.hsSigma.setEnabled(False) self.leSigma.setEnabled(False) else: self.cboNoise.setEnabled(True) self.hsMean.setEnabled(True) self.hsVar.setEnabled(True) self.hsAmount.setEnabled(True) self.leMean.setEnabled(True) self.leVar.setEnabled(True) self.leAmount.setEnabled(True) self.leMinVal.setEnabled(True) self.leMaxVal.setEnabled(True) self.leKernel.setEnabled(True) self.sbMinVal.setEnabled(True) self.sbMaxVal.setEnabled(True) self.sbKernel.setEnabled(True) self.cboEdge.setEnabled(True) self.rbMultiple.setEnabled(True) self.rbKMeans.setEnabled(True) self.cboDenoising.setEnabled(True) self.sbH.setEnabled(True) self.sbPatch.setEnabled(True) self.sbSearch.setEnabled(True) self.hsWeight.setEnabled(True) self.leWeight.setEnabled(True) self.hsWeight.setEnabled(True) self.hsSigma.setEnabled(True) self.leSigma.setEnabled(True) def set_hsMean(self, value): self.leMean.setText(str(round((value/64),2))) self.do_noise() def set_hsVar(self, value): self.leVar.setText(str(round((value/64),2))) self.do_noise() def set_hsAmount(self, value): self.leAmount.setText(str(round((value/10),2))) self.do_noise() def choose_noise(self,img): strCB = self.cboNoise.currentText() mean = float(self.leMean.text()) var = float(self.leVar.text()) amount = float(self.leAmount.text()) sigma = var**0.5 row,col,ch= img.shape if strCB == 'Gaussian': self.hsAmount.setEnabled(False) self.leAmount.setEnabled(False) self.hsMean.setEnabled(True) self.leMean.setEnabled(True) self.hsVar.setEnabled(True) self.leVar.setEnabled(True) noisy_image = self.gaussian_noise(img, mean, sigma, row, col) return noisy_image if strCB == 'Speckle': self.hsAmount.setEnabled(False) self.leAmount.setEnabled(False) self.hsMean.setEnabled(True) self.leMean.setEnabled(True) self.hsVar.setEnabled(True) self.leVar.setEnabled(True) noisy_image = self.speckle_noise(img, mean, sigma, row, col) return noisy_image if strCB == 'Poisson': self.hsMean.setEnabled(False) self.leMean.setEnabled(False) self.hsVar.setEnabled(False) self.leVar.setEnabled(False) self.hsAmount.setEnabled(True) self.leAmount.setEnabled(True) noisy_image = self.poisson_noise(img, amount) return noisy_image if strCB == 'Salt & Pepper': self.hsMean.setEnabled(False) self.leMean.setEnabled(False) self.hsVar.setEnabled(False) self.leVar.setEnabled(False) self.hsAmount.setEnabled(True) self.leAmount.setEnabled(True) noisy_image = self.salt_pepper_noise(img, amount) return noisy_image def gaussian_noise2(self,img, mean, sigma, row, col): gaussian = np.random.normal(mean, sigma, \ (row,col)) # np.zeros((224, 224), np.float32) noisy_image = np.zeros(img.shape, np.float32) if len(img.shape) == 2: noisy_image = img + gaussian else: noisy_image[:, :, 0] = img[:, :, 0] + gaussian noisy_image[:, :, 1] = img[:, :, 1] + gaussian noisy_image[:, :, 2] = img[:, :, 2] + gaussian cv2.normalize(noisy_image, noisy_image, 0, 255, \ cv2.NORM_MINMAX, dtype=-1) noisy_image = noisy_image.astype(np.uint8) return noisy_image def speckle_noise2(self,img, mean, sigma, row, col): gaussian = np.random.normal(mean, sigma, \ (row,col)) # np.zeros((224, 224), np.float32) noisy_image = np.zeros(img.shape, np.float32) if len(img.shape) == 2: noisy_image = img + img*gaussian else: noisy_image[:, :, 0] = img[:, :, 0] + gaussian * img[:, :, 0] noisy_image[:, :, 1] = img[:, :, 1] + gaussian * img[:, :, 1] noisy_image[:, :, 2] = img[:, :, 2] + gaussian * img[:, :, 2] cv2.normalize(noisy_image, noisy_image, 0, 255, \ cv2.NORM_MINMAX, dtype=-1) noisy_image = noisy_image.astype(np.uint8) return noisy_image def gaussian_noise(self,img, mean, sigma, row, col): # Generate Gaussian noise gauss = np.random.normal(mean,sigma,img.size) gauss = gauss.reshape(img.shape[0],img.shape[1],\ img.shape[2]).astype('uint8') # Add the Gaussian noise to the image img_gauss = cv2.add(img,gauss) return img_gauss def speckle_noise(self,img, mean, sigma, row, col): # Generate Gaussian noise gauss = np.random.normal(mean,sigma,img.size) gauss = gauss.reshape(img.shape[0],img.shape[1],\ img.shape[2]).astype('uint8') # Add the Gaussian noise to the image img_sp = cv2.add(img,img*gauss) return img_sp def salt_pepper_noise(self, img, val): # Add salt-and-pepper noise to the image. noise_img = random_noise(img, mode='s&p',amount=val) # The above function returns a floating-point image # on the range [0, 1], thus we changed it to 'uint8' # and from [0,255] imgsnp = np.array(255*noise_img, dtype = 'uint8') return imgsnp def poisson_noise(self, img, peak): pois = np.random.poisson(img / 255.0 * peak) / peak * 255 # The above function returns a floating-point image # on the range [0, 1], thus we changed it to 'uint8' # and from [0,255] imgpois = np.array(255*pois, dtype = 'uint8') return imgpois def do_noise(self): noisy = self.choose_noise(img) height, width, channel = noisy.shape bytesPerLine = 3 * width cv2.cvtColor(noisy, cv2.COLOR_BGR2RGB, noisy) qImg = QImage(noisy, width, height, \ bytesPerLine, QImage.Format_RGB888) pixmap = QPixmap.fromImage(qImg) self.display_image(pixmap, noisy, self.labelFilter, \ self.widgetHistFilter, 'Histogram of Noisy Image') def choose_edge(self,img): strCB = self.cboEdge.currentText() minVal = float(self.leMinVal.text()) maxVal = float(self.leMaxVal.text()) kernel = int(self.leKernel.text()) noisy = self.choose_noise(img) if strCB == 'Canny': self.leKernel.setEnabled(False) self.sbKernel.setEnabled(False) self.leMinVal.setEnabled(True) self.sbMinVal.setEnabled(True) self.leMaxVal.setEnabled(True) self.sbMaxVal.setEnabled(True) edge_im = self.canny_edge(noisy, minVal, maxVal) return edge_im if strCB == 'Sobel X': self.leKernel.setEnabled(True) self.sbKernel.setEnabled(True) self.leMinVal.setEnabled(False) self.sbMinVal.setEnabled(False) self.leMaxVal.setEnabled(False) self.sbMaxVal.setEnabled(False) edge_im = self.sobelx_edge(noisy, 1, 0, kernel) return edge_im if strCB == 'Sobel Y': self.leKernel.setEnabled(True) self.sbKernel.setEnabled(True) self.leMinVal.setEnabled(False) self.sbMinVal.setEnabled(False) self.leMaxVal.setEnabled(False) self.sbMaxVal.setEnabled(False) edge_im = self.sobelx_edge(noisy, 0, 1, kernel) return edge_im if strCB == 'Laplacian': self.leKernel.setEnabled(True) self.sbKernel.setEnabled(True) self.leMinVal.setEnabled(False) self.sbMinVal.setEnabled(False) self.leMaxVal.setEnabled(False) self.sbMaxVal.setEnabled(False) edge_im = self.laplacian_edge(noisy, kernel) return edge_im def canny_edge(self, img, minVal, maxVal): edge_im = np.zeros(img.shape, np.float32) if len(img.shape) == 2: edge_im = cv2.Canny(img,minVal, maxVal) else: edge_im[:, :, 0] = cv2.Canny(img[:, :, 0],minVal, maxVal) edge_im[:, :, 1] = cv2.Canny(img[:, :, 1],minVal, maxVal) edge_im[:, :, 2] = cv2.Canny(img[:, :, 2],minVal, maxVal) cv2.normalize(edge_im, edge_im, 0, 255, cv2.NORM_MINMAX, dtype=-1) edge_im = edge_im.astype(np.uint8) return edge_im def sobelx_edge(self, img, d1, d2, kernel): edge_im = np.zeros(img.shape, np.float32) if len(img.shape) == 2: edge_im = cv2.Sobel(img,cv2.CV_64F, d1, d2, kernel) else: edge_im[:, :, 0] = cv2.Sobel(img[:, :, 0],cv2.CV_64F, d1, d2, kernel) edge_im[:, :, 1] = cv2.Sobel(img[:, :, 1],cv2.CV_64F, d1, d2, kernel) edge_im[:, :, 2] = cv2.Sobel(img[:, :, 2],cv2.CV_64F, d1, d2, kernel) cv2.normalize(edge_im, edge_im, 0, 255, cv2.NORM_MINMAX, dtype=-1) edge_im = edge_im.astype(np.uint8) return edge_im def laplacian_edge(self, img, kernel): edge_im = np.zeros(img.shape, np.float32) if len(img.shape) == 2: edge_im = cv2.Laplacian(img, cv2.CV_64F, kernel) else: edge_im[:, :, 0] = cv2.Laplacian(img[:, :, 0], cv2.CV_64F, kernel) edge_im[:, :, 1] = cv2.Laplacian(img[:, :, 1], cv2.CV_64F, kernel) edge_im[:, :, 2] = cv2.Laplacian(img[:, :, 2], cv2.CV_64F, kernel) cv2.normalize(edge_im, edge_im, 0, 255, cv2.NORM_MINMAX, dtype=-1) edge_im = edge_im.astype(np.uint8) return edge_im def do_edge(self): edges = self.choose_edge(img) height, width, channel = edges.shape bytesPerLine = 3 * width cv2.cvtColor(edges, cv2.COLOR_BGR2RGB, edges) qImg = QImage(edges, width, height, \ bytesPerLine, QImage.Format_RGB888) pixmap = QPixmap.fromImage(qImg) self.display_image(pixmap, edges, self.labelFilter, \ self.widgetHistFilter, 'Histogram of Edge Detection') def set_minval(self): self.leMinVal.setText(str(self.sbMinVal.value())) self.do_edge() def set_maxval(self): self.leMaxVal.setText(str(self.sbMaxVal.value())) self.do_edge() def set_kernel(self): self.leKernel.setText(str(self.sbKernel.value())) self.do_edge() if __name__=="__main__": import sys app = QApplication(sys.argv) w = FormImageProcessing() w.show() sys.exit(app.exec_())