Image interpolation in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the interpolation function from scipy.

SciPy

from scipy import [1]

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Aintegrate

Boptimize

Csignal

Dinterpolate

Attempts:

3 left

💡 Hint

Common Mistakes

Importing the wrong scipy submodule like optimize or integrate.

Forgetting to import the interpolate module.

The interpolate module in scipy contains functions for image interpolation.

2fill in blank

medium

Complete the code to create a 2D interpolation function from image data.

SciPy

interp_func = scipy.interpolate.RegularGridInterpolator((x, y), [1])

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Ax

Binterp_func

Cimage_data

Dy

Attempts:

3 left

💡 Hint

Common Mistakes

Passing coordinate arrays instead of image data.

Using the interpolation function variable itself as input.

The interpolation function needs the original image data array to create the interpolator.

3fill in blank

hard

Fix the error in the code to interpolate the image at new points.

SciPy

new_values = interp_func([1])

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A[[x_new, y_new]]

B[x_new, y_new]

C(x_new, y_new)

Dx_new, y_new

Attempts:

3 left

💡 Hint

Common Mistakes

Passing coordinates as a tuple or flat list instead of a 2D array.

Not wrapping the coordinates in an outer list.

The RegularGridInterpolator expects a 2D array of points, so the new points must be inside a list of lists.

4fill in blank

hard

Fill both blanks to create a dictionary of interpolated pixel values for points where x is greater than 5.

SciPy

interp_points = {point: interp_func([point])[1] point in points if point[0] [2] 5}

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A for

B >

C <

D if

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'if' instead of 'for' for the loop.

Using '<' instead of '>' for the condition.

The dictionary comprehension syntax requires 'for' before the loop variable, and the condition checks if the x-coordinate (point[0]) is greater than 5.

5fill in blank

hard

Fill all three blanks to create a new image by interpolating at scaled coordinates.

SciPy

scaled_image = [1]([2] * scale_factor, [3] * scale_factor, method='linear')

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Ascipy.interpolate.interpn

Bx_coords

Cy_coords

Dimage_data

Attempts:

3 left

💡 Hint

Common Mistakes

Using the image data as the first argument instead of the interpolation function.

Not scaling both x and y coordinates.

Using the wrong interpolation method.

The interpn function interpolates on a grid defined by coordinate arrays. We scale the x and y coordinates to get new points for interpolation.