I’d like to get a 1000 x 1000 picture in Python from any input picture so that the input doesn’t lose it’s aspect ratio. In other words, I want to resize the input so that its longer dimension is 1000 pixels and "fill" the other dimension with the background color until it become 1000 x 1000 square. The original one must be in the center at the end.
You can use
resize() in OpenCV to resize the image up/down to the size you need. However,
resize() requires that you put in either the destination size (in both dimensions) or the scaling (in both dimensions), so you can’t just put one or the other in for 1000 and let it calculate the other for you. So the most robust way to do this is to find the aspect ratio and calculate what the smaller dimension would be when the bigger one is stretched to 1000. Then you can resize.
h, w = img.shape[:2] aspect = w/h
Note that if
aspect is greater than 1, then the image is oriented horizontally, while if it’s less than 1, the image is oriented vertically (and is square if
aspect = 1).
Different interpolation methods will look better depending on whether you’re stretching the image to a larger resolution, or scaling it down to a lower resolution. From the
To shrink an image, it will generally look best with CV_INTER_AREA interpolation, whereas to enlarge an image, it will generally look best with CV_INTER_CUBIC (slow) or CV_INTER_LINEAR (faster but still looks OK).
So, after resizing we’ll end up with a
Nx1000 image (where
N<=1000) and we’ll need to pad it with whatever background color you want on both sides to fill the image to
1000x1000. For this you can use
copyMakeBorder() for a pure OpenCV implementation, or since you’re using Python you can use
numpy.pad(). You’ll need to decide what to do in case an odd number of pixels needs to be added in order to make it
1000x1000, like whether the additional pixel goes to the left or right (or top or bottom, depending on the orientation of your image).
Here’s a script that defines a
resizeAndPad() function which automatically calculates the aspect ratio, scales accordingly, and pads as necessary, and then uses it on a horizontal, vertical, and square image:
import cv2 import numpy as np def resizeAndPad(img, size, padColor=0): h, w = img.shape[:2] sh, sw = size # interpolation method if h > sh or w > sw: # shrinking image interp = cv2.INTER_AREA else: # stretching image interp = cv2.INTER_CUBIC # aspect ratio of image aspect = w/h # if on Python 2, you might need to cast as a float: float(w)/h # compute scaling and pad sizing if aspect > 1: # horizontal image new_w = sw new_h = np.round(new_w/aspect).astype(int) pad_vert = (sh-new_h)/2 pad_top, pad_bot = np.floor(pad_vert).astype(int), np.ceil(pad_vert).astype(int) pad_left, pad_right = 0, 0 elif aspect < 1: # vertical image new_h = sh new_w = np.round(new_h*aspect).astype(int) pad_horz = (sw-new_w)/2 pad_left, pad_right = np.floor(pad_horz).astype(int), np.ceil(pad_horz).astype(int) pad_top, pad_bot = 0, 0 else: # square image new_h, new_w = sh, sw pad_left, pad_right, pad_top, pad_bot = 0, 0, 0, 0 # set pad color if len(img.shape) is 3 and not isinstance(padColor, (list, tuple, np.ndarray)): # color image but only one color provided padColor = [padColor]*3 # scale and pad scaled_img = cv2.resize(img, (new_w, new_h), interpolation=interp) scaled_img = cv2.copyMakeBorder(scaled_img, pad_top, pad_bot, pad_left, pad_right, borderType=cv2.BORDER_CONSTANT, value=padColor) return scaled_img v_img = cv2.imread('v.jpg') # vertical image scaled_v_img = resizeAndPad(v_img, (200,200), 127) h_img = cv2.imread('h.jpg') # horizontal image scaled_h_img = resizeAndPad(h_img, (200,200), 127) sq_img = cv2.imread('sq.jpg') # square image scaled_sq_img = resizeAndPad(sq_img, (200,200), 127)
And this gives the images:
ImageMagick is a simple, but well-built command-line interface to do basic image processing. It’s very easy to do what you want with a single command. See here for descriptions of the resizing commands.
$ convert v.jpg -resize 200x200 -background skyblue -gravity center -extent 200x200 scaled-v-im.jpg $ convert h.jpg -resize 200x200 -background skyblue -gravity center -extent 200x200 scaled-h-im.jpg $ convert sq.jpg -resize 200x200 -background skyblue -gravity center -extent 200x200 scaled-sq-im.jpg
Producing the images:
Answered By – alkasm
Answer Checked By – Terry (AngularFixing Volunteer)