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3D RGB Color Histogram using python,index dataset
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3D histogram is to uses the conjunctive AND, it searches for green pixel,red pixels then feature vectors are stored and compared
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Performing a image Search using python
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The first thing, the first time. Import the package we will need. The oursearcherclass stored in the pyimagesearchmodule. Then, we define our arguments in the same way as we did in the indexing steps. Finally, we use pickleto to load. We close the disk index and initialize oursearcher. Line 3: index each image as a query to see what we get. In general, you query outside rather than part of the dataset, but before we get down to business, let's just do some sample searches. Line 5: This is where the actual search takes place. We view the current image as our query and perform the search. Line 8 11: load and display our query image. Line 17 35: show the results of the top 10, I have decided to use two montage images. The first montage shows 1-5 and the second montage shows 6-10. The name of image and distance is provided online 27. Line 38-40: finally, we show our search results to users.
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K-mean clustering
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K-means is a clustering algorithm. The goal is to partition n data points into different K groups. Each n data points will be assigned to the nearest mean cluster. Each cluster is referred to as its & quot; prime & quot; or & quot; Center & quot;. In general, the original n data points of the cluster using k-means return rate k alone. A group of data points in a cluster are considered as & quot; more similar & quot; data points that belong to other clusters than each other. In our example, we will cluster the pixel strengths of RGB images. Given the mxn size of the image, we have mxn pixels, each of which consists of three parts: red, green and blue, respectively. We will treat these mxn pixels as our data points and use k-means clustering. Pixels belonging to a given cluster will be more similar in color than pixels belonging to a separate cluster.
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An image into a document scanner using python
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You see, scanning a file using a smartphone can be broken down into three simple steps: Step 1: edge detection. Step 2: use the edge in the image to find the contour (contour) to indicate that a piece of paper is scanned. Step 3: apply a perspective transformation to obtain a top view of the file. Line 2-7 handle import we need the necessary Python package. We're going to talk about our four by importing what I discussed last week_ point_ The transform function starts. We will also use the imutils module, which contains convenient features for resizing, rotating, cropping and image editing. You can read more about imutils after my basic image manipulation. Next, let's import the threshold of scikit image_ Adaptive function. The feature will help us get a "black and white" feel of our scanned images. Finally, we will use numpy's numerical processing, argparse to parse the command line parameters, and CV2 to bind our OpenCV. Lines 10-13 handle parsing our command line parameters. We only need the image of a switch, - image, which is the image of the path containing the document we want to scan. Now that we have the path to our image, we can move on to step 1: edge detection. Line 61 performs warping modification. As a matter of fact, all the heavy work is done by four_ point_ Transform function. Also, you can read more about last week's post in this feature. We will pass two parameters to four_ point_ Transform: the first one is ours. We loaded the original image of the disk (not one of the sizes). The second parameter is to represent the file, multiplied by the contour of the adjusted size scale. So, you may wonder, why do we multiply by the adjustment ratio? We multiplied the adjusted ratio because we performed edge detection and found that the contour height = 500 pixels on the adjusted image. However, we want to perform the scan on the original image instead of the resized image, so we multiply the contour points by the resizing ratio. To get a black-and-white image, we take a distorted image, convert it to grayscale and apply adaptive threshold to line 65-67.
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This simulation is an example of combination of wi
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I have attached Network simulator code for cellular communication
for both wired and wireless topologies
initially a linux platform has to be installed ..then proceed on with Ns2 installation
create code in Ns2 desktop
call the code via Terminal
Code explanation:
i nitially u need to describe the
channel of propagation
Anntena type ,direction
number nodes
wired nodes, wireless nodes ,type of routing mode eg: adhoc Routing etc
then form a group..via programming describe the location of nodes and then further communication is established through Ns2 simulation
Address parameter of the nodes are also included then the channel establishment and link is also established
Comments are written beside code i have attached.
and the code is made to run..
All the Best
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SIMULATION OF DISTANCE VECTOR ROUTING USING NS2 SI
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SIMULATION OF DISTANCE VECTOR ROUTING USING NS2 SIMULATOR In this project it describes the simulation of distance vector routing using Ns2 simulator I have attched the snapshop of all output also Follow the steps by installing linux followed by ns2 simulator To study the performance of Distance vector routing. We design anetwork as follows: siva502034 2016-08-23
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Cell zooming in cellular network and its performa
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Software required for this project
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