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The function cv2.approxPolyDP() approximates a contour shape to another shape with less number of vertices. It accepts the following arguments − cnt − The array of the contour points. epsilon − Maximum distance from contour to approximated contour. A wise selection of epsilon is needed to get the correct output. SyntaxThe following syntax are used to approximate a contour shape epsilon = 0.01*cv2.arcLength(cnt, True) approx = cv2.approxPolyDP(cnt, epsilon, True) Steps You can use the following steps to approximate a contour shape in an image − Import the required library. In all the following Python examples, the required ... Read More
The contours of the objects in an image are very helpful to compute the area and perimeter of the image. A contour of an image is a curve joining all the continuous points along the boundary, having the same color or intensity. Contours are used for shape analysis and object detection and recognition etc. To compute the area and perimeter of an object, we first detect the contour of the object and then apply cv2.contourArea() and cv2.arcLength() functions respectively. Syntax The following syntax are used for the functions − area = cv2.contourArea(cnt) perimeter = cv2.arcLength(cnt, True) Where, "cnt" is ... Read More
The solidity of an object is computed as the ratio of contour area to its convex hull area. So to compute the solidity, we first have to find the contour area and convex hull area. The contour area of an object can be found using cv2.contourArea() function. Equivalent Diameter is the diameter of the circle whose area is the same as the contour area. The solidity and equivalent diameter can be computed as below − Syntax area = cv2.contourArea(cnt) hull = cv2.convexHull(cnt) hull_area = cv2.contourArea(hull) solidity = float(area)/hull_area equi_diameter = np.sqrt(4*area/np.pi) Where, cnt is a numpy array of the ... Read More
The extent of an object is computed as the ratio of contour area to its bounding rectangle area. So, to compute the extent, we first have to find the contour area and bounding rectangle area. The contour area of an object can be found using cv2.contourArea() function. Syntax The extent can be computed as follows − area = cv2.contourArea(cnt) x, y, w, h = cv2.boundingRect(cnt) rect_area = w*h extent = float(area)/rect_area Here, "cnt" is a numpy array of the contour points of an object in the image. Steps You can use the following steps to compute extent of an ... Read More
The aspect ratio of an object is computed as the ratio between the width and height of the bounding rectangle of the object. So, to compute the aspect ratio, we first have to find the bounding rectangle of the object. Bounding rectangle of an object can be found using cv2.boundingRect() function. It accepts the contour points of the object and returns top-left coordinate (x, y) and (width, height) of the bounding rectangle. We use the width and height to compute the aspect ratio. Syntax x, y, w, h = cv2.boundingRect(cnt) aspect_ratio = float(w)/h Here, "cnt" is a numpy array ... Read More
A bilateral filter operation is highly effective in smoothing the image and removing noises. The main advantage of the bilateral filtering is that it preserves the edges unlike in average and median filtering. Bilateral filtering operation is slower in comparison to other filters. We can perform bilateral filtering on an image using the cv2.bilateralFilter() method. Syntax Following is the syntax of this method. cv2.bilateralFilter(img, d, sigmaColor, sigmaSpace) This method accepts the following parameters − img − The input image on which the bilateral filter operation to be applied. d − A variable of the type integer representing the ... Read More
We can fit an ellipse to an object using the function cv2.fitEllipse(). The ellipse is inscribed in a rotated rectangle. The rotated rectangle is a bounding rectangle with minimum area enclosing the object. Syntax The syntax used for this function is − ellipse = cv2.fitEllipse(cnt) Where, "cnt" is the contour points. It is represented as an array of contour points. Output − It returns a tuple of tuples in ((x, y), (majorAxis, minorAxis), angle) format. (x, y) is the coordinates of center and (majorAxis, minorAxis) is the lengths of minor and major axes and angle is the rotation angle ... Read More
In OpenCV, a color (RGB) image is represented as a 3-dimensional numpy array. The pixel values of an image are stored using 8 bit unsigned integers (uint8) in range from 0 to 255. The bitwise OR operation on two images is performed on the binary representation of these pixel values of corresponding images. Syntax Here is the syntax to perform bitwise OR operation on two images − cv2.bitwise_or(img1, img2, mask=None) img1 and img2 are the two input images and mask is a mask operation. Steps To compute bitwise OR between two images, you can use the steps given below ... Read More
To add a watermark to an image, we will use the cv2.addWeighted() function from OpenCV. You can use the following steps to create a watermark on an input image − Import the required library. In all the following Python examples, the required Python library is OpenCV. Make sure you have already installed it. import cv2 Read the input image on which we are going to apply the watermark and read the watermark image. img = cv2.imread("panda.jpg") wm = cv2.imread("watermark.jpg") Access the height and width of the input image, and the height, width of the watermark image. h_img, w_img ... Read More
A minimum enclosing circle (circumcircle) of an object is a circle which completely covers the object with minimum area. We can find the minimum enclosing circle of an object using the function cv2.minEnclosingCircle(). Syntax The syntax of this function is − (x, y), radius = cv2.minEnclosingCircle(cnt) Where, "cnt" are the contour points. It is represented as an array of contour points. Output − It returns coordinate of center (x, y) and radius of minimum enclosing circle. (x, y) and radius are of float dtype. So, to draw a circle on the image, we convert them to integers. To draw ... Read More