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ImportsSystem.Collections.GenericImportsSystem.ComponentModelImportsSystem.DataImportsSystem.DrawingImportsSystem.LinqImportsSystem.TextImportsSystem.Threading.TasksImportsSystem.Windows.Forms' make sure that using System.Diagnostics; is included ImportsSystem.Diagnostics' make sure that using System.Security.Principal; is included ImportsSystem.Security.Principal' make sure that using System.Net.NetworkInformation; is included ImportsSystem.Net.NetworkInformation' make sure that using System.Threading; is included for method Sleep ImportsSystem.ThreadingPublicClassForm1PublicSubNew()MyBase.New()InitializeComponent()EndSubPrivateSubbutton1_Click(senderAsObject,eAsEventArgs)Handlesbutton1.ClickForiAsInteger=0To3' Thread.Sleep(500); UsingpAsNewPing()' label1.Text = p.Send("www.google.com").RoundtripTime.ToString() + "ms"; listView1.Items.Add(p.Send("www.google.com").RoundtripTime.ToString()&"ms"&vbLf)EndUsingNextEndSubEndClass
""" Image classification using OpenCV CNN module using AlexNet and caffe pre-trained models (bvlc_alexnet.caffemodel not included because exceeds GitHub's file size limit of 100.00 MB) bvlc_alexnet.prototxt: https://github.com/opencv/opencv_extra/blob/master/testdata/dnn/bvlc_alexnet.prototxt bvlc_alexnet.caffemodel: http://dl.caffe.berkeleyvision.org/bvlc_alexnet.caffemodel """# Import required packages:importcv2importnumpyasnpfrommatplotlibimportpyplotaspltdefshow_img_with_matplotlib(color_img,title,pos):"""Shows an image using matplotlib capabilities"""img_RGB=color_img[:,:,::-1]ax=plt.subplot(1,1,pos)plt.imshow(img_RGB)plt.title(title)plt.axis('off')# Load the names of the classes:rows=open('synset_words.txt').read().strip().split('\n')classes=[r[r.find(' ')+1:].split(',')[0]forrinrows]# Load the serialized caffe model from disk:net=cv2.dnn.readNetFromCaffe("bvlc_alexnet.prototxt","bvlc_alexnet.caffemodel")# Load input image:image=cv2.imread("church.jpg")# Create the blob with a size of (227,227), mean subtraction values (104, 117, 123)blob=cv2.dnn.blobFromImage(image,1,(227,227),(104,117,123))print(blob.shape)# Feed the input blob to the network, perform inference and get the output:net.setInput(blob)preds=net.forward()# Get inference time:t,_=net.getPerfProfile()print('Inference time: %.2f ms'%(t*1000.0/cv2.getTickFrequency()))# Get the 10 indexes with the highest probability (in descending order)# This way, the index with the highest prob (top prediction) will be the first:indexes=np.argsort(preds[0])[::-1][:10]# We draw on the image the class and probability associated with the top prediction:text="label: {}\nprobability: {:.2f}%".format(classes[indexes[0]],preds[0][indexes[0]]*100)y0,dy=30,30fori,lineinenumerate(text.split('\n')):y=y0+i*dycv2.putText(image,line,(5,y),cv2.FONT_HERSHEY_SIMPLEX,0.8,(0,255,255),2)# Print top 10 prediction:for(index,idx)inenumerate(indexes):print("{}. label: {}, probability: {:.10}".format(index+1,classes[idx],preds[0][idx]))# Create the dimensions of the figure and set title:fig=plt.figure(figsize=(10,6))plt.suptitle("Image classification with OpenCV using AlexNet and caffe pre-trained models",fontsize=14,fontweight='bold')fig.patch.set_facecolor('silver')# Show the output image:show_img_with_matplotlib(image,"AlexNet and caffe pre-trained models",1)# Show the Figure:plt.show()
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