Scale Image Until Either X or Y Is The Same as The Container and Then Crop The Rest

Mapping List Images into container to view them shows the same pic in all of the containers

You're using the same name for all the images, change your code to this:

finalCroppedImage.isNotEmpty
              ? finalCroppedImage.map((image) { //Instance of image from Image package
                  print(image.height); //here i have different values for the images
                  print(image.width);

                  File imageFinal = File(image!.path);
                  File(imageFinal!.path).writeAsBytesSync(encodePng(image)); // I'm trying to decode it to display in the container

                  return Container(
                    height: 300,
                    decoration: BoxDecoration(
                        image: DecorationImage(
                            fit: BoxFit.fitWidth,
                            image: FileImage(File(imageFinal!.path)))),
                  );
                }).toList()
              : [Text('Empty')],

Stretch and scale a CSS image in the background - with CSS only

CSS3 has a nice little attribute called background-size:cover.

This scales the image so that the background area is completely covered by the background image while maintaining the aspect ratio. The entire area will be covered. However, part of the image may not be visible if the width/height of the resized image is too large.

CSS Display an Image Resized and Cropped

You could use a combination of both methods eg.

    .crop {        width: 200px;        height: 150px;        overflow: hidden;    }
    .crop img {        width: 400px;        height: 300px;        margin: -75px 0 0 -100px;    }

    <div class="crop">        <img src="https://i.stack.imgur.com/wPh0S.jpg" alt="Donald Duck">    </div>

Resize image to maintain aspect ratio in Python, OpenCv

Using OpenCV

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 resize() docs:

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 1000xN or 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:

Using ImageMagick

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:

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