Journal of Global Pharma Technology

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1 Survey about Shape Detection Jumana Waleed, Ahmed Brisam* Journal of Global Pharma Technology Available Online at RESEARCH ARTICLE 1 Department of Computer Science, College of Science, University of Diyala/Iraq. 2 College of Agriculture University of Al-Qadisiyah/Iraq. *Corresponding Author: abrisam@troy.edu Abstract ISSN: The simplifying original shoreline of vector data into direction-line is the source of picture of baselines of the shape detection. Applying standard algorithms of vector data confining, such as the Douglas-Peucker algorithm, to analyze the real shoreline within direction-line, disregards the specific elements of a guarantee of general marine interests.to solve this problem, this research suggested an improved Douglas-Peucker algorithm. First, determine the curved vertices of the original shoreline as a choice set of points; later filter division points from curved vertices based on corner and length; ultimately, separate the data into different parts in order to complete the Douglas-Peucker algorithm, and compare the threshold of each part to efficiently select specialty points and protect shape detection. This research uses Python language to carry out the algorithm design, and opencv2. The results show that, compared with the Douglas-Peucker algorithm, the improved Douglas-Peucker algorithm improves how detect the contours, and shape detection, and the accuracy of curve simplification also fixed. Introduction To start the project, define a shape detection module. The detect_shapes.py operator script was used to load a picture from disk, investigate it for shapes, and then make shape detector class (1). This proposed project presents a unique way to line generalization, which uses the idea of "effective area" for increasing simplification of a line by point elimination. Two methods are opted to compare the achievement of this, with one of the commonly used Douglas - Peucker, algorithm (1, 3). The area-based algorithm results compare equally with the standard generalization of the equivalent lines. It is can achieve both caricature generalizations and imperceptible minimal simplifications. Selecting the cut- off values carefully, it is likely to use the one algorithm for both independent generalizations and scaledependent (1). In the same way, independent of the camera settings this formula can take a shot at each camera marks. The same can be connected for video shading exchanges. With the Object and Shape Detection, we detect three-dimensional geometries and the position of things (4, 3). We develop the camera systems laser scanners but also custom-tailored light. These projects take measures at high velocity and with high accuracy, especially from running platforms. We concentrate specifically on activity, robustness and high service life of the operations and effective results evaluation. New applications contain mobile data record from the air, in liquid or by handheld operations (5, 2). Previous Works We could detect and follow an object applying color division. However, we could not recognize the contour of the contour. In this research, let's discuss how to identify a shape and position of an object using contours with Open CV. Using contours with Open CV, you can get a string of points of vertices of any white patch. Instance, you will get three vertices for a triangle, and four vertices for quadrilaterals.so, you can recognize any polygon by an amount of vertices of that polygon (2). You can also know features of polygons such as convexity, concavity, equilateral etc. by determining and comparing lengths between points.a binary form in which your objects should be white and the framework should be black.white observed that the widely used DP algorithm identified more of these critical points , JGPT. All Rights Reserved 231

2 , JGPT. All Rights Reserved 232 McMaster (5) repeatedly asserted Douglas- Peucker algorithm was "mathematically and perceptually superior" to other algorithms he evaluated because it picked out more of these critical points and produced least displacement from the original line proposed extensions to the algorithm; tag values were associated with each point as an indicator of its significance. The values, which are tagged, can be placed in an ascending order of the rank points towards the critical points. This concept of a fixed rank order of critical points is convenient since a tolerance parameter may be used to filter out the required points at run time.visvalingam and Whyatt (8, 10) utilize these tags to understand the predictions of their use, left over characteristics. They came to an understanding that the algorithm does not choose each points that are used for maps.even using relatively simple test data, White only found a 45% agreement between points selected by the algorithm and by respondents in their study(6). Alternative Algorithms for Line Simplification Reumann Witkam (1) Rather of using a point-to-point measure tolerance as a rejection, model (see Radial Distance in Fig. 1) and the O (n) Reumann Witkam algorithm uses a point to line to find distance tolerance. It represents a line through the first two vertices of the original poly line. For each successive vertex xi, its perpendicular distance to this line is determined (15,16). A new key is found at xi- 1, when this distance exceeds the specified tolerance. The pointsxi and xi+1 are then used to another new line. This algorithm is explained below, and it contains the following steps: Select the first point as the key point. If there is less than 3 points after the key point, end the algorithm. Create a line using the key vertex and the vertex following the key point. Select the point two vertices after they key point as the first test point. If the perpendicular distance from the test point to the line is less than a tolerance. Increment the test point. Repeat this step using the new test point. If the perpendicular distance from the test vertex to the line is greater than a tolerance. Remove all points between the key point and the point before the current test point. This keeps the key point and the point with the furthest perpendicular distance within tolerance. If there are no points between them (because the first test point was larger than the tolerance), no points are eliminated. Increment the key point and restart from step 2. Input of Data Requirements DIM represents the dimension, when DIM not equal 0. Change The Forward Iterator value to the value of the Output Iterator Fig. 1: Reumann Witkam The range between first point and last point contains vertex coordinates in multiples of DIM, e.g.: x, y, z, x, y, z, x, y, z when DIM = 4

3 The range between first point and last point contain at least two vertices To first step is not 0 Lang Simplification (17) This algorithm tries to fixed size searchregion. The start point and ends of that exploration area form a part. This section is used to determine the perpendicular distance to each intermediate point. If any computed distance is greater than the specified tolerance, the search region will be narrowed by excluding its last point. This process will continue until all calculated distances drop under the defined tolerance, or when there are no more common points (12, 13).All intermediate points are separated and a new search region is determined to begin at the last point from old search (see Fig. 2). The process is shown below and It contains the following steps: Select the first point as the key point. Select the last point as the end point. If there are no points between the key point and end point. And if the end point is not the last point. Make the end point the new key point. Make the last point the new end point. Restart step 3 using the new end point and key points. And if the end point is the last point. Exit the algorithm If there are points between the key point and the end point. Continue to step 5. Create a line between the key point and the end point. Find the point between the key point and the end point with the largest perpendicular distance from this line. If that distance is greater than some tolerance. Decrement the end point. Restart at step 3 using the new end point. If that distance is less than, or equal to, the tolerance. Eliminate all points between the key point and the end point. Make the end point the new key point. Make the last point the new end point. Restart at step 3. Fig. 2: Lang line simplification Input the Data Requirements DIM represents the dimension of the poly line, when DIM is not equal 0. Change Bidirectional Iterator value to the value of the Output Iterator. The range [first, last) contains vertex coordinates in multiples of DIM, e.g.: x, y, z, x, y, z, x, y, z when DIM = 3. The range between first point and last point contains at least 2 vertices. Look ahead is not equal 0. Douglas-Peucker Algorithm (3) This algorithm works a point-to-edge measure tolerance. The algorithm begins with a crude simplification that is the single edge joining the first point and last point of the initial poly line. It then calculates the , JGPT. All Rights Reserved 233

4 length of all between vertices to that edge. The point that is greatly away from that edge, and that has a calculated distance that is greater than a specified tolerance, will be marked as a key and added to the simplification (7, 9). This process will recurse for each edge in the current simplification until all points of the initial polyline are within the end of the simplification results. This method is drawn in Fig.3 and illustrated below: Choose the effective area for each point in the line. Remove the points with zero value and save them in a different list with this area. REPEAT Choose the point with the least effective area it called the current point. If its compute area first less than that of the last point to be eliminated, use the latter s, area instead. Remove the point from the original list and add this to the new list together with its associated area so that the line may be filtered at run time. Recalculate the effective area of the two adjoining points. Input the Data Requirements DIM represents the dimension of the polyline, when DIM is not 0. Change the Forward Iterator value to the value of the Output Iterator The range between first point and last point contains vertex coordinates in multiples of DIM, e.g.: x, y, z, x, y, z, x, y, z when DIM = 3 The range between first point and last point contains at least 2 vertices To first step is not 0. Constructing the Shape Detector We are creating some code to build the shape description logic. Class Shape Detector: Def init (point): Def detect (point, r): Shapes== "unidentified" Fig. 3 Douglas-Peucker Points peri = cv2(point).arc Length//(r, True) Approx = cv2. (Approx, r)poly DP(c, 0.07 * peri (point), True) Exposed= (Window, Worker), Promise<sequence<Detected Shape>>detect (Image Bitmap Source image); The detecting method detected first needs only a first argument, r, the contour of the shape that we are investigating to use contour approximation. The Contour approximation is an algorithm to reduce the amount of vertices in a line with a compressed set of points. This algorithm is commonly the merge and split algorithm (11). Contour approximation is already implemented in Open CV through. To make contour approximation, calculate the perimeter of the contour, actual contour approximation followed by creating perimeter of the contour , JGPT. All Rights Reserved 234

5 Having the approximated contour, perform shape detection: If len(line, line ) == 3: Shape == triangle "triangle" If len (line, line ) == 4: (W, u, t, g) = cv2.bounding Rect (approx) ar = w / float(h) Shape = "square" if ar >= 0.85 and ar <= 1.15 else "rectangle" if len(approx) == 5: Shape = "pentagon" Else: Shape = "circle" Return x, In this list a contour, contain of a listing of points to check the number of notes, to limit the shape of an image. If it has four vertices or edges, then the shape must be both a rectangle and a square. To find that, measure the aspect ratio of the situation, that is only the width of the contour bounding will divide by the height. If the aspect ratio is ~1.0, then we are checking a square (since all sides have the approximately same length) (4).Unless the shape is a rectangle. If a shape has (5) several points, we can identify it as a pentagon. We can assume that the shape we are examining is a circle. Finally, to the calling method if we return the identified shape. Shape Detection with Open CV Having Shape Detector a defined class, create the detect shapes. Python driver script: From sd. shape detector Import shape Import imutils Import cv2 ap = = argparse. Argument Parser () ap. add_ argument ("-j", "++image", required==true, help="path to the input image") args == vars (ap.parse//args()) Handling parsing line discussions.a particular switch is needed, picture that is the way to where the image that we want to process on disk. Images == CV. I m read (args//image,["image"]) Ratio = image. Shape [0] / float ( resized resized. shape [0]) Gray == cv.cvt Color ( resized resized, cv2.color_rgb to GRAY) Blurred = cv. Gaussian Blur (gray gray, (5, 5), 0) Thresh == cv. threshold (blurred, 56, 255, CV. cnts = cv2.findcontours(thresh. copy(), cv2.retr_external, cv2.chain_approx_simple) cnts == cnts cnts if imutils. Is_ cv () else cnts sd= = Shape Detector image () We need resize it on after upload the image. Keeping the track of the ratio of the old height to the new resized height, the image has been binaries; the shapes will appear just two different colors as a white foreground against a black background. To find shapes in the binary image, the true tuple value from cv2.findcontours to eventually initialize the Shape Detector. D== cv. moments(r) rx = int((d["m10"] / D["m00"]) * ratio) cy = int((m["m01"] / M["m00"]) * ratio) shape = sd. detect detect (r) r = r. as type ("float") R == ratio r = r. as type ("int") Cv. draw Contours (image, [r], -1, (0, 255, 0), , JGPT. All Rights Reserved 235

6 2) Cv. put Text (image, shape, shape (cx, cy), cv. FONT_HERSHEY_SIMPLEX, CV. I am show ("Image", image) Cv. waits Key (0) Looping will start over all of the individual shapes, to calculate the center of the contour for every one of them, followed by performing shape detection and labeling. The shapes selected from the resized image, it is needed to multiply the shapes and center (x, y)- coordinates by the resized ratio. These results will give the right (x, y) coordinates for both the shapes and centroid of the initial picture. Why Choosing Douglas-Peucker Algorithm The Ramer-Douglas Peucker algorithm is an algorithm for reducing the amount of vertices in a curve that is approaching by a set of points. It does so by "thinking" of a line between the front and end point in a set of details that form the curve. It examines which point in connecting is distant away from this line. The Douglas-Peucker algorithm uses a pointto-edge distance tolerance. The algorithm has a simple start, which follows joining the edges of vertices from first and the last. Then it calculates all the distances which have immediate vertices to the that particular edge. Specified tolerance is known when the vertex that is far away from the edge and that which has a computed distance, simplifies the References 1. Reumann K, Witkam APM (1974) Optimizing curve segmentation in computer graphics, in Proceedings of International Computing Symposium, , North Holland Publishing Company, Amsterdam. 2. Chrisman, Nicholas R (1983) Epsilon Filtering: A Technique for Automated Scale Changing, Technical Paper of43rd Annual ACSM Meetings, Washington D.C., Douglas David, H Thomas, K Peucker (1973) Algorithms for Reduction of the Number of Points Required Representing a Digitized Line or its Caricature. The Canadian Cartographer, 10(2): end result key to any simplification. This process helps for all the present simplification edges, till the vertices of all the original polyline are within the simplification lines. Conclusion There are several type of shape detection algorithms; each type of algorithm has its own advantages and limitation. Salient character or caricatures of a line are identified as central to the process of the generalization of the line. The caricatures produced by the Douglas-Peucker algorithm are believed to be the most successful. Shape preserving points on a line can be taken to choose the crucial with the help of the stated algorithm. However, majority of the researchers do not agree with the same thinking about the other methods. These techniques are considered to be suitable only for smallest simplification, and not for generalization, especially of complicated lines. Future Work Now, we know how to achieve shape detection by use python and Open CV. We leveraged contour approach, the method of reducing the amount of points on a curve to a simpler approximated version. Later, based on this shape similarity, we checked an amount of vertices each shape has. Released the vertex number, we were able to correctly label each of the shapes. Our future goal is to get deeper geometric understanding of robot localization. It is well known that shape detection, performs a primary role in human usual understanding. Our research shows that mapping tasks are also based on shape representation and shape matching. 4. Imai, Hiroshi, Masao Iri (1986) Computational Geometric Methods for Polygonal Approximations of a Curve, Computer Vision, Graphics, and Image Processing, 36: McMaster, Robert B, K Stuart Shea, (1992) Generalization in Digital Cartography, Washington, DC: Association of American Geographers. 6. Williams CM (1998) An Efficient Algorithm for the Piecewise Linear Approximation of a Polygonal Curves in the Plane, Computer Vision, Graphics, and Image Processing, Cromley Robert G (1991) Hierarchical Methods of Line Simplification, Cartography and , JGPT. All Rights Reserved 236

7 Geographic Information System, 18(2): Visvalingam M, Whyatt, J D (1990) " The Douglas-Peucker Algorithm for Line Simplification: Re- evaluation through Visualization", Computer Graphics Forum, 9 (3): Visvalingam M, Whyatt, J D (1991a) "Cartographic Algorithms: Problems of Implementation and Evaluation and the Impact of Digitizing Errors", Computer Graphics Forum, 10 (3) Visvalingam M, Whyatt, J D (1991b) " The Importance of Detailed Specification, Consistent Implementation and Rigorous Testing of Cartographic Software", in K. Rybaczuk and M. Blakemore (eds) Mapping The Nations vol 2, 15th Conference of Int. Cartographic Assoc. (September, Bournemouth), White, E R (1983) "Perceptual Evaluation of Line Generalization Algorithms, Unpublished Master s Thesis, University of Oklahoma. 12. Topfer R, Pillewizer W (1999) "The Principles of Selection, The Cartographic Journal, 3 (1): Robinson AH, Sale RD, Morrision JL, Muehrcke PC (1994) Elements of Cartography (Wiley & Sons, New York) 5th Edition. 14. Ramer U (1992) "An Iterative Procedure f) r the Polygonal Approximation of Plane Curves, Computer Graphics and Image Processing, 1: McMaster RB (1997) "Automated Line Generalization", Cartographical, 24 (2): Jenks, G F (1989) "Geographic Logic in Line Generalization, Cartographical, 26 (1): Lang T (1969) Rules for robot draughts men, The Geographical Magazine, 42: , JGPT. All Rights Reserved 237