TheAlgorithms/Python · error · ValueError

Destination width/height should be > 0

Error message

Destination width/height should be > 0

What it means

Raised by NearestNeighbour.__init__ (digital_image_processing/resize/resize.py:15) when dst_width or dst_height is negative. Caveat grounded in the source: the guard tests `< 0`, but the message says '> 0' — so zero passes validation and then crashes later with ZeroDivisionError at `self.src_w / self.dst_w`. Treat any value <= 0 as invalid even though the library only rejects negatives.

Source

Thrown at digital_image_processing/resize/resize.py:15

"""Multiple image resizing techniques"""

import numpy as np
from cv2 import destroyAllWindows, imread, imshow, waitKey


class NearestNeighbour:
    """
    Simplest and fastest version of image resizing.
    Source: https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation
    """

    def __init__(self, img, dst_width: int, dst_height: int):
        if dst_width < 0 or dst_height < 0:
            raise ValueError("Destination width/height should be > 0")

        self.img = img
        self.src_w = img.shape[1]
        self.src_h = img.shape[0]
        self.dst_w = dst_width
        self.dst_h = dst_height

        self.ratio_x = self.src_w / self.dst_w
        self.ratio_y = self.src_h / self.dst_h

        self.output = self.output_img = (
            np.ones((self.dst_h, self.dst_w, 3), np.uint8) * 255
        )

    def process(self):
        for i in range(self.dst_h):
            for j in range(self.dst_w):
                self.output[i][j] = self.img[self.get_y(i)][self.get_x(j)]

View on GitHub (pinned to f5988cc097)

Solutions

1. Validate dst_width > 0 and dst_height > 0 at the caller before constructing (cover the zero hole the library misses)
2. Clamp computed dimensions: `max(1, int(round(scale * src_w)))`
3. If you control the library, fix the guard to `<= 0` so the message matches behavior

Example fix

// before
resizer = NearestNeighbour(img, int(w * scale), int(h * scale))  # 0 or negative slips through

# after
dst_w = max(1, int(round(img.shape[1] * scale)))
dst_h = max(1, int(round(img.shape[0] * scale)))
resizer = NearestNeighbour(img, dst_w, dst_h)

if dst_width <= 0 or dst_height <= 0:  # strict: library only rejects negatives, zero slips to ZeroDivisionError
    raise ValueError('width/height must be positive')
resizer = NearestNeighbour(img, dst_width, dst_height)

def are_valid_dimensions(w, h) -> bool:
    return isinstance(w, int) and isinstance(h, int) and w > 0 and h > 0

try:
    resizer = NearestNeighbour(img, w, h)
except ValueError:
    resizer = NearestNeighbour(img, 1, 1)

When it happens

Trigger: NearestNeighbour(img, -100, 50) raises immediately; NearestNeighbour(img, 0, 50) passes the check and raises ZeroDivisionError when computing ratio_x/ratio_y.

Common situations: Computing target dimensions from aspect-ratio math that can go negative or collapse to zero (rounding tiny images), or exposing width/height as user config without validation.

Related errors

• level must be between -255.0 (black) and 255.0 (white)
• Factor value should be from 0 to {self.max_threshold}
• ksize must be in {tuple(kernels)}
• number must be positive
• The value of input must be non-negative

AI-assisted analysis of TheAlgorithms/Python@f5988cc097 (2026-08-14). Data as JSON: /api/errors/1164e5c6782dc7fc. Report an issue: GitHub.