import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle

def generate_square_image(size, square_size, noise_level=0.0):
    """
    Generates an image with a white square in the center.

    Args:
        size (int): The size of the image (size x size).
        square_size (int): The size of the square.
        noise_level (float): Standard deviation of Gaussian noise.

    Returns:
        numpy.ndarray: The image as a numpy array.
        numpy.ndarray: The mask.
        tuple: Bounding box (x_min, y_min, width, height).
    """

    # create mask
    mask = np.zeros((size, size))
    start = (size - square_size) // 2
    end = start + square_size
    mask[start:end, start:end] = 1

    # create bounding box
    bbox = (start, start, square_size, square_size)

    # create noisy image
    img = mask.copy()
    if noise_level > 0:
        noise = np.random.normal(0, noise_level, img.shape)
        img = np.clip(img + noise, 0, 1)

    return img, mask, bbox

# Example usage:
size = 100
square_size = 40
img, mask, bbox = generate_square_image(size, square_size, noise_level=0.1)

# Plot the image
fig, ax = plt.subplots(1, 3, figsize=(15, 5))
ax[0].imshow(img, cmap='gray')
ax[0].set_title('Generated Image')
ax[1].imshow(mask, cmap='gray')
ax[1].set_title('Mask')

# Display the bounding box overlayed on the image
ax[2].imshow(img, cmap='gray')
x, y, width, height = bbox
# The key fix: in matplotlib, the Rectangle coordinates start at the bottom-left corner
# But imshow displays arrays with the origin at the top-left corner
rect = Rectangle((x, y), width, height, linewidth=2, edgecolor='r', facecolor='none')
ax[2].add_patch(rect)
ax[2].set_title('Image with Bounding Box')

# Ensure origin is set to 'upper' to match imshow defaults
for a in ax:
    a.set_ylim([size, 0])  # Reverse y-axis to match array indexing

plt.tight_layout()
plt.show()

Question: What is the right code to align the box properly?

It seems to be the most straight forward approach to create one?

As you can see, I have already tried prompting this to work, but even that fix (which seems to be the one thing to explore here, which is the difference in coordinate systems) does not seem to work either.

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle

def generate_square_image(size, square_size, noise_level=0.0):
    """
    Generates an image with a white square in the center.

    Args:
        size (int): The size of the image (size x size).
        square_size (int): The size of the square.
        noise_level (float): Standard deviation of Gaussian noise.

    Returns:
        numpy.ndarray: The image as a numpy array.
        numpy.ndarray: The mask.
        tuple: Bounding box (x_min, y_min, width, height).
    """

    # create mask
    mask = np.zeros((size, size))
    start = (size - square_size) // 2
    end = start + square_size
    mask[start:end, start:end] = 1

    # create bounding box
    bbox = (start, start, square_size, square_size)

    # create noisy image
    img = mask.copy()
    if noise_level > 0:
        noise = np.random.normal(0, noise_level, img.shape)
        img = np.clip(img + noise, 0, 1)

    return img, mask, bbox

# Example usage:
size = 100
square_size = 40
img, mask, bbox = generate_square_image(size, square_size, noise_level=0.1)

# Plot the image
fig, ax = plt.subplots(1, 3, figsize=(15, 5))
ax[0].imshow(img, cmap='gray')
ax[0].set_title('Generated Image')
ax[1].imshow(mask, cmap='gray')
ax[1].set_title('Mask')

# Display the bounding box overlayed on the image
ax[2].imshow(img, cmap='gray')
x, y, width, height = bbox
# The key fix: in matplotlib, the Rectangle coordinates start at the bottom-left corner
# But imshow displays arrays with the origin at the top-left corner
rect = Rectangle((x, y), width, height, linewidth=2, edgecolor='r', facecolor='none')
ax[2].add_patch(rect)
ax[2].set_title('Image with Bounding Box')

# Ensure origin is set to 'upper' to match imshow defaults
for a in ax:
    a.set_ylim([size, 0])  # Reverse y-axis to match array indexing

plt.tight_layout()
plt.show()

Question: What is the right code to align the box properly?

It seems to be the most straight forward approach to create one?

As you can see, I have already tried prompting this to work, but even that fix (which seems to be the one thing to explore here, which is the difference in coordinate systems) does not seem to work either.

• python
• numpy
• matplotlib

Share Improve this question Follow edited Apr 1 at 8:38 Christoph Rackwitz 15.8k55 gold badges3939 silver badges5151 bronze badges asked Apr 1 at 6:18 AcademicAcademic 32533 silver badges1313 bronze badges

Add a comment  | 

1 Answer 1

Sorted by: Reset to default 3

Fixes:

1. Subtract 0.5 from x and y in Rectangle().Matplotlib positions pixels at the center of grid cells, but imshow() assumes pixel edges align exactly with grid lines. Adjusting by -0.5 shifts the bounding box to align properly.

2. origin='upper' ensures consistency with NumPy's top-left origin.

3. Hiding axis ticks makes visualization clearer.

The full corrected code is provided below:

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle

def generate_square_image(size, square_size, noise_level=0.0):
    """
    Generates an image with a white square in the center.

    Args:
        size (int): The size of the image (size x size).
        square_size (int): The size of the square.
        noise_level (float): Standard deviation of Gaussian noise.

    Returns:
        numpy.ndarray: The image as a numpy array.
        numpy.ndarray: The mask.
        tuple: Bounding box (x_min, y_min, width, height).
    """

    # Create mask
    mask = np.zeros((size, size))
    start = (size - square_size) // 2
    end = start + square_size
    mask[start:end, start:end] = 1

    # Create bounding box (x_min, y_min, width, height)
    bbox = (start, start, square_size, square_size)

    # Create noisy image
    img = mask.copy()
    if noise_level > 0:
        noise = np.random.normal(0, noise_level, img.shape)
        img = np.clip(img + noise, 0, 1)

    return img, mask, bbox

# Example usage:
size = 100
square_size = 40
img, mask, bbox = generate_square_image(size, square_size, noise_level=0.1)

# Plot the image
fig, ax = plt.subplots(1, 3, figsize=(15, 5))

# Display the generated image
ax[0].imshow(img, cmap='gray', origin='upper')
ax[0].set_title('Generated Image')

# Display the mask
ax[1].imshow(mask, cmap='gray', origin='upper')
ax[1].set_title('Mask')

# Display the image with bounding box
ax[2].imshow(img, cmap='gray', origin='upper')
x, y, width, height = bbox

# Adjust bounding box position to match imshow's top-left origin
rect = Rectangle((x - 0.5, y - 0.5), width, height, linewidth=2, edgecolor='r', facecolor='none')

ax[2].add_patch(rect)
ax[2].set_title('Image with Bounding Box')

# Ensure correct axis orientation
for a in ax:
    a.set_xticks([])
    a.set_yticks([])

plt.tight_layout()
plt.show()

OutPut: