We can also do the exact opposite and get a specified height with the following code: base_height = 360 The image is then saved with image.save(‘image_name.jpg’).Then we resize the image based on the two values (base_width and hsize).This provides us with a ratio number that we can times by the height to get the correct height for producing our base_width.We calculate the aspect ratio for width by doing: base_width / the images existing width.Then we open the image with Image.open(‘image_name.jpg’).We set a base width which we would like the image to be.
#Resize image opencv code
Now let’s understand the code above line by line: Image.save('resized_compressed_image.jpg') Image = image.resize((base_width, hsize), ) Hsize = int((float(image.size) * float(width_percent))) Width_percent = (base_width / float(image.size))
#Resize image opencv how to
Just for fun, we crop to get the Thanos’s gauntlet.As different social media platforms often require different image and width formats, resizing images automatically with python can definitely save you time. The first line returns that part of the image which starts and ends between (beginX:endX), (beginY,endY). The output is:Ĭropping of an image in Python cropped_image = image Later the transformation is done by cv2.warpAffine() function whose parameters are the original_image, obtained matrix and the dimension of the rotated image. The parameters are center, angle_of_rotation(here we are rotating for an angle of 180 degrees) and scaling factor. The function cv2.RotationMatrix2D returns a matrix that contains the image of rotated coordinates. Rotated_image = cv2.warpAffine(image, Matrix, (w1, h1))Īs we know now image.shape() returns a tuple up to indexing 2 where the first two values are height and width hence the first line extracts the values of height and width, We then discover the center coordinates by moving towards half of both height and width. Matrix = cv2.getRotationMatrix2D(center, 180, 1.0) Rotation of an image in OpenCV (h1, w1) = image.shape For time being we are not going to focus on the algorithm but sticking to the implementation part. The parameters are the original image, dimension, and the algorithm to be used for this purpose. The third line uses the function cv2.resize() which actually does the main work of changing the size. Both values are then inserted into the variable called dim(dimension of new image). Resized_image = cv2.resize(image, dimension, interpolation = cv2.INTER_AREA)Īs seen in code the height and width are specified as 300. Resizing of an image in Python with OpenCV h1=300 The values are (height, width, channel) where the channel is the RGB components. The output will be as: (175, 289, 3) as displayed for my image. Normally, the dimensions are width*height but OpenCV takes as height*width. Printing the dimensions of an image in OpenCV print(image.shape) Reading and displaying of an image – OpenCV Python image=cv2.imread('Thanos.jpg')Ĭv2.imread() is a function which takes image name as a parameter(provided the image is saved in the same folder where the program is or else just give the path to the image) and cv2.imshow() display the image with name and its parameters are (‘name_of_the_window’,variable_in_which_image _is_stored). The various operation goes as follows: Importing the required libraries required for the program import cv2Ĭv2 is the OpenCV library and the numpy library is included as many times the image will be treated as an array.
#Resize image opencv movie
To make this session interactive image of Thanos(A character from a famous movie ) is being taken for example. Here we’ll be doing many operations on an image using OpenCV functions.
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