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Add a new statement to import KMeans algorithm from sklearn package:
Define a new method, kmeans_seg(), to implement K-Means algorithm as follows:
Define a new method, seg_output(), to display segmented image and its histogram as follows:
Define rbstate() method to determine which radio button is clicked by user as follows:
Connect toggled() event of rbMultiple and rbKMeans to rbState() method and put them inside __init__() method as follows:
Run image_processing.py. Choose test image and click on rbMultiple widget. The result is shown in Figure below.
The click on rbKMeans widget and the result is shown in Figure below.
The following is image_processing.py script so far:
Edge Detection, Segmentation, and Denoising on Images with Python GUI (PyQt): Part 4
Part 1 Part 2
Tutorial Steps To Implement Image Segmentation Using Multiple Thresholding and K-Means Algorithm
Modify image_proc.ui, put two Radio Button widgets onto form and set their objectName properties to rbMultiple and rbKMeans. Set their text properties to Multiple Thresholding and K-Means Algorithm. Then, save form with the same name. Now, the form looks as shown in Figure below.
Define two new methods, multiple_thresh() and thresh_seg(), to implement multiple thresholding segmentation as follows:
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
Add a new statement to import KMeans algorithm from sklearn package:
from sklearn.cluster import KMeans
Define a new method, kmeans_seg(), to implement K-Means algorithm as follows:
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
Define a new method, seg_output(), to display segmented image and its histogram as follows:
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')
Define rbstate() method to determine which radio button is clicked by user as follows:
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)
Connect toggled() event of rbMultiple and rbKMeans to rbState() method and put them inside __init__() method as follows:
self.rbMultiple.toggled.connect(self.rbstate) self.rbKMeans.toggled.connect(self.rbstate)
Run image_processing.py. Choose test image and click on rbMultiple widget. The result is shown in Figure below.
The click on rbKMeans widget and the result is shown in Figure below.
The following is image_processing.py script so far:
#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.segmentation import active_contour from skimage.filters import gaussian import skimage.color as color import numpy as np 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) 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) 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) 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_())
Edge Detection, Segmentation, and Denoising on Images with Python GUI (PyQt): Part 4
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