7/1/2023 0 Comments Cv2 image resizeThe difference in the sizes can be seen if we observe each image's x and y axes. Let us plot the images using matplotlib subplots for a better comparison of results. Res = cv2.resize(image, (120,120), interpolation=cv2.INTER_LINEAR) Smaller = cv2.resize(image, None, interpolation=cv2.INTER_AREA, fx=0.5, fy=0.5) cv2.INTER_LANCZOS4: Lanczos interpolation technique.īigger = cv2.resize(image, None, interpolation=cv2.INTER_CUBIC, fx=2, fy=2,).cv2.INTER_CUBIC: Bicubic interpolation technique.This is Extensively used to shrink the image cv2.INTER_AREA: Performs resampling using the pixel-area relationship.This is the default interpolation technique. cv2.INTER_LINEAR: Bilinear interpolation technique.cv2.INTER_NEAREST: Interpolation by using the nearest neighbor technique.There are various interpolation methods available in OpenCV. interpolation: (Optional) The interpolation method which is to be used.fy : (Optional) Scaling factor along the y axis of the image.fx : (Optional) Scaling factor along the x-axis of the image.dsize: The output dimension of the image.The cv2.resize() function takes the following parameters Another way is by mentioning a scaling factor. One way is by mentioning the output dimension directly. We can easily resize the image in two ways using the cv2.resize() function. The image that we are using here is the one shown below. Let us first import the necessary libraries and read the images. Image resizing with different Interpolation methods. Step 1: Import the libraries and read the image ![]() Step 1: Import the libraries and read the image.Recipe Objective: How to resize an image using OpenCV?.Img_new_padded = cv2.copyMakeBorder(img_new, 0, pad_bot, 0, pad_right, borderType=cv2.BORDER_CONSTANT, value=0)Ĭv2. Img_new = cv2.resize(img, (preferred_width, preferred_height)) Pad_right = get_nearest_larger(preferred_width) - preferred_width Preferred_width = round(preferred_height / height * width) Pad_bot = get_nearest_larger(preferred_height) - preferred_height Preferred_height = round(preferred_width / width * height) Remember the resolution parameter use column first then is row. image cv2.imread ('740411.jpg') cv2.imshow ('image', image) cv2.waitKey (0) cv2.destroyAllWindows () Here, it displays a 100 200 resolution image after resizing. Here's what it displays, a 740 411 resolution image. #gets the nearest larger 64, starting with 512 You can use cv2.resize (img, resolution) to solve the problem. New_filename = output_directory + "/" + f_name.replace("."+str(get_file_ext(filename)), "."+output_ext).replace(" ", "_") # this will return a tuple of root and extension Also I only pad to the right and to the bottom since in my experience, those are the areas for which inpainting is usually most useful. ![]() I have chosen a color of white for padding since the Stable Diffusion WebUI tool uses a black mask and this way I can easily see what has been masked and what hasn’t. OpenCV automatically determines the file format based on the extension, so you do not need to specify it explicitly. Output images are renamed to _scaled.png. Most other image file formats are supported though (such as JPEG, PNG, BMP, TIFF, WEBP and others). OpenCV does not support the AVIF file format (read here) that’s why I included the option to exclude extensions to allow processing of directories which have mixed content, for example obtained by scraping the web. It demonstrates how OpenCV can be used to easily perform basic tasks such as opening a file, resizing, padding, and writing the output back to the filesystem. ![]() ![]() The script is straightforward and can be viewed below or here. This can be done with: “pip install opencv-python” or “conda install -c conda-forge opencv” (whichever package manager you prefer). Batch resize and pad images using Python and OpenCV Batch resize and pad imagesįirst you need to install OpenCV. I chose to use OpenCV because it is a widely-used, easy-to-use library with powerful capabilities. To automate this process for a large number of images, I wrote a Python script using OpenCV. However, this results in a size that is not a multiple of 64, so the image needs to be padded to 1536×512 in order to be processed by the model. If you have an image that is not a multiple of 64, like 599×205 pixels, you can maintain the aspect ratio by resizing it to 1496×512. Stable Diffusion (at least 1.5) works best with images of 512 pixels in width or height. In case of Stable Diffusion, multiples of 64 are required. When using AI models like Stable Diffusion, sometimes input images need to be of a specific size.
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