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Lab 1 : Summer Orienteering In this lab, you will be generating optimal paths for orienteering. In the sport (or "activity", depending onyour level of fitness/competitiveness) of orienteering, you are given a map with terrain information,elevation contours, and a set or sequence of locations to visit ("controls"). There is a combination ofathletic skills and planning skills required to succeed - a smarter competitor who can figure out the bestway to get from point to point may beat out a more athletic competitor who makes poor choices! In thislab, you are given computer-friendly inputs so that you can use an algorithm to determine the best path foryou to take depending on your ability. Credit: Eric Dudley The map(s) In an ordinary orienteering event, the map you get will be quite detailed. Different background colorsshow the type of terrain (see the table below), while buildings, boulders, man-made objects and othernotable features are shown with different symbols. For this assignment, you are given two separate inputs,both representing Mendon Ponds Park: a text representation of the elevations within an area (500 lines of400 double values, each representing an elevation in meters) and a 395x500 simplified color-only terrainmap (color legend below). To address the difference in width between the elevation and terrain files youshould just ignore the last five values on each line of the elevation file. Also, the real-world pixel size isdetermined by that of the National Elevation Dataset, which in our area is one third of an arc-second,equivalent to 10.29 m in longitude (X) and 7.55 m in latitude (Y). You must take these dimensions intoyour account of distance. The basic event As for the points you will need to go visit, those will come in a simple text file, two integers per line,representing the (x,y) pixel (origin at upper left) in the terrain map containing the location. In the classicevent type that we are considering, the sequence of points must be visited in the order given. One suchclassic event was the World Deaf Orienteering Championships, held in Mendon Ponds Park. The localclub also had an event using the same courses, the results and maps of which can be seen here. I havedone my best to convert the white, brown and red courses into the relevant text files. The planning So, you have to get to some controls. However, going in a straight line, even if possible, is often notadvisable. First of all, you will be able to proceed at different speeds through different terrains. Ratherthan telling you how fast, you need to decide based on some representative photos how fast you can travelthrough these terrains: Terrain type Color on map Photo (legend) Open land #F89412 (248,148,18) ARough meadow #FFC000 (255,192,0) BEasy movement forest #FFFFFF (255,255,255) C · DSlow run forest #02D03C (2,208,60) E Walk forest #028828 (2,136,40) F Impassible vegetation #054918 (5,73,24) GLake/Swamp/Marsh #0000FF (0,0,255) H · I · J Paved road #473303 (71,51,3) K · LFootpath #000000 (0,0,0) M · NOut of bounds #CD0065 (205,0,101) Then we get to the planning. This is a large environment, so while breadth-first search might beacceptable for individual paths, it is much better (and not much harder!) to implement an A* search tohandle complete events. However, consider your heuristic function carefully. Showing that it is admissible(or it is not quite admissible, but you can bound its error) is another important part of your writeup. Notethat if any of these alterations would make your heuristic inadmissible, you should change it! Input Name your program 'lab1'. It should take 4 arguments, in order: terrain-image, elevation-file, path-file,output-image-filename. Python would look like, $python3 lab1.py terrain.png mpp.txt red.txt redOut.png Output You should output an image of the input map with the optimal path drawn on top of it. You should alsooutput the total path length in meters either to the terminal or drawn on the map itself. Here is an examplepath for the brown trail. Note that your solution may produce a different result and still be correct. This isespecially true on the park map where our terrain penalties will differ. The test cases linked below haveless variance and your output should match those more closely. Writeup As discussed above, your writeup should include all relevant decisions made in the implementation of your code. That is, how you implemented your cost function, how you implemented your heuristic, andjustification for their correctness. Some hints/tips You are welcome to write your solution in Python, or Java. If you would like to use a differentlanguage, please consult with me first. Regardless, your program should run on a Linux-based CSmachine, and your code should operate there without modification. In Python, you can use the Python Image Library to both read an image in to an array of pixels aswell as to modify those pixels to output your results. (I used Image.open(), .load() and .save() onthe CS machines.) In Java, you can use the ImageIO class to read in an image into a BufferedImage object and get/setpixels from there. You are welcome to hard-code things which make sense to hard code, such as the color values. Becareful if you hard code file names that it will still work when downloaded and run on a differentmachine. Some overall suggestions to approach the work: First just get a single simple path planned. Here isa simple one, but try some others and some longer ones. Get the graphical output working first, itwill help you debug everything. A pixel's neighbors can be defined as either the 4 pixels that share an edge (the cardinal directions)or the 8 pixels that share either an edge or a corner (the cardinal directions and north-east, northwest, etc). Either is acceptable and test cases have been generated for both. You do not have to do anything fancy like moving faster or slower depending on elevation - it issufficient for the elevation to simply add to the 3d distance between pixels. For those who insist ondoing something beyond that, get everything working with a simple handling of elevation firstbefore trying anything special. Grading Proper interpretation of input files: 5% Proper A* search algorithm and heuristic: 25% Testcases: 20% Efficiency of solution*: 15% Human-readable output (drawing path and reporting distance): 20%Writeup: 15% * As a point of comparison, most solutions in Python produces a plan for the red course above in about 3seconds with a conservative admissible heuristic. If your program takes longer than 20 seconds there islikely something wrong with your heuristic. Path finding on the testcases that serpentine often will takelonger. Anything over a minute indicates you are using inefficient data structures. Testcases Check these tests cases out! If your solution runs in a reasonable amount of time and matches the suppliedoutput then you are probably fine.