Inpainting and outpainting in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load an image and prepare a mask for inpainting.

Prompt Engineering / GenAI

from PIL import Image
image = Image.open('input.jpg')
mask = Image.new('L', image.size, [1])

Drag options to blanks, or click blank then click option'

A0

B255

C128

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using 255 instead of 0 for the initial mask value.

Confusing mask color values.

The mask for inpainting is usually black (0) where no change is needed and white (255) where inpainting is applied. Here, we start with a black mask.

2fill in blank

medium

Complete the code to apply inpainting using OpenCV.

Prompt Engineering / GenAI

import cv2
image = cv2.imread('input.jpg')
mask = cv2.imread('mask.png', 0)
inpainted = cv2.inpaint(image, mask, [1], cv2.INPAINT_TELEA)

Drag options to blanks, or click blank then click option'

A10

B0

C3

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using 0 radius which disables inpainting.

Using too large radius causing blurring.

The radius parameter controls the neighborhood size for inpainting. A small radius like 3 is typical.

3fill in blank

hard

Fix the error in the code to perform outpainting by extending the image canvas.

Prompt Engineering / GenAI

import numpy as np
from PIL import Image
image = Image.open('input.jpg')
width, height = image.size
new_width = width + 100
new_height = height + 50
new_image = Image.new('RGB', (new_width, new_height), [1])
new_image.paste(image, (50, 25))

Drag options to blanks, or click blank then click option'

A(255, 0, 0)

B(0, 0, 0)

C(128, 128, 128)

D(255, 255, 255)

Attempts:

3 left

💡 Hint

Common Mistakes

Using black background causing harsh edges.

Using a single integer instead of a tuple for color.

The new canvas background color is white (255,255,255) to blend well for outpainting.

4fill in blank

hard

Fill both blanks to create a mask dictionary for inpainting and outpainting regions.

Prompt Engineering / GenAI

masks = {
    'inpainting': [1],
    'outpainting': [2]
}

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A{'region': 'center', 'mask': mask_inpaint}

B{'region': 'border', 'mask': mask_outpaint}

C{'region': 'edges', 'mask': mask_outpaint}

D{'region': 'center', 'mask': mask_outpaint}

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping inpainting and outpainting masks.

Using the same mask for both regions.

Inpainting mask targets the center region, outpainting mask targets the border region.

5fill in blank

hard

Fill all three blanks to define a function that applies inpainting or outpainting based on mode.

Prompt Engineering / GenAI

def apply_painting(image, mask, mode):
    if mode == 'inpainting':
        method = [1]
    elif mode == 'outpainting':
        method = [2]
    else:
        raise ValueError('Invalid mode')
    return cv2.inpaint(image, mask, 3, method)  # radius fixed at 3

result = apply_painting(img, mask, [3])

Drag options to blanks, or click blank then click option'

Acv2.INPAINT_TELEA

Bcv2.INPAINT_NS

C'inpainting'

D'outpainting'

Attempts:

3 left

💡 Hint

Common Mistakes

Using the same method for both modes.

Passing mode as a variable instead of string.

Use TELEA method for inpainting, NS method for outpainting, and call function with mode 'outpainting'.

Practice

1. What is the main difference between inpainting and outpainting in image editing?

easy

A. Both inpainting and outpainting only remove unwanted parts from images.

B. Inpainting adds new areas around an image, outpainting removes parts inside it.

C. Inpainting fills missing parts inside an image, outpainting adds new areas around it.

D. Inpainting and outpainting are the same process with different names.

Solution

1. Step 1: Understand inpainting

   Inpainting is used to fill missing or unwanted parts inside an image, like fixing scratches or holes.

2. Step 2: Understand outpainting

   Outpainting extends the image by adding new content around the edges, making the image bigger.

3. Final Answer:

   Inpainting fills missing parts inside an image, outpainting adds new areas around it. -> Option C

4. Quick Check:

   Inpainting = fill inside, Outpainting = add outside [OK]

Hint: Inpainting fixes inside; outpainting grows outside [OK]

Common Mistakes:

• Confusing inpainting with outpainting
• Thinking both remove parts only
• Believing they are identical

2. Which of the following is the correct way to describe the input for an inpainting model?

easy

A. Only the new areas to add around the image.

B. An image with missing or masked areas to fill.

C. A complete image with no missing parts.

D. A text description of the image content.

Solution

1. Step 1: Identify input for inpainting

   Inpainting models require an image with missing or masked parts that need filling.

2. Step 2: Check other options

   Complete images or text descriptions are not direct inputs for inpainting; new areas relate to outpainting.

3. Final Answer:

   An image with missing or masked areas to fill. -> Option B

4. Quick Check:

   Inpainting input = image with holes [OK]

Hint: Inpainting needs holes in image input [OK]

Common Mistakes:

• Choosing complete images without masks
• Confusing input with outpainting requirements
• Selecting text descriptions as input

3. Given this Python pseudocode for outpainting, what will be the shape of the output image if the input image is 256x256 and the model adds 64 pixels on each side?

input_image = load_image('photo.png')  # shape (256, 256)
output_image = outpaint_model(input_image, border=64)
print(output_image.shape)

medium

A. (256, 256)

B. (192, 192)

C. (320, 320)

D. (384, 384)

Solution

1. Step 1: Calculate added pixels

   The model adds 64 pixels on each side, so total added width = 64 * 2 = 128 pixels.

2. Step 2: Calculate new image size

   Original size 256 + 128 = 384 pixels. This is 256 + 64 + 64 = 384, since 64 pixels on each side means adding 64 left and 64 right.

3. Step 3: Re-check options

   (384, 384) matches calculation. (320, 320) is 256 + 64, adding only one side.

4. Final Answer:

   (384, 384) -> Option D

5. Quick Check:

   256 + 64*2 = 384 [OK]

Hint: Add border pixels twice (both sides) to original size [OK]

Common Mistakes:

• Adding border only once
• Confusing inpainting with outpainting size change
• Ignoring both width and height increase

4. You run an inpainting model but the output image still has visible holes where the mask was applied. What is the most likely cause?

medium

A. The mask was not correctly applied to the input image.

B. The model was trained only for outpainting, not inpainting.

C. The input image was too large for the model to process.

D. The output image format does not support transparency.

Solution

1. Step 1: Check mask application

   If holes remain, the mask likely was not properly set, so the model didn't know where to fill.

2. Step 2: Evaluate other options

   Model type mismatch or image size issues usually cause errors or poor quality, not visible holes. Output format affects display but not hole filling.

3. Final Answer:

   The mask was not correctly applied to the input image. -> Option A

4. Quick Check:

   Mask error = holes remain [OK]

Hint: Check mask covers missing parts fully [OK]

Common Mistakes:

• Ignoring mask correctness
• Blaming model type without checking input
• Assuming output format causes holes

5. You want to create a larger scenic image by extending the edges of a 512x512 photo using outpainting. You also want to remove a small unwanted object inside the photo using inpainting. Which approach correctly combines both tasks?

hard

A. First apply inpainting on the original image to remove the object, then apply outpainting to extend the image edges.

B. Apply outpainting first to extend the image, then apply inpainting on the extended edges to remove the object.

C. Only use outpainting because it can both remove objects and extend images.

D. Only use inpainting because it can extend images and remove objects.

Solution

1. Step 1: Remove unwanted object first

   Inpainting fixes inside the image, so remove the object before changing image size.

2. Step 2: Extend image after cleanup

   Outpainting adds new areas around the cleaned image, so apply it after inpainting.

3. Step 3: Evaluate other options

   Outpainting cannot remove inside objects; inpainting cannot add new edges. Order matters for best results.

4. Final Answer:

   First apply inpainting on the original image to remove the object, then apply outpainting to extend the image edges. -> Option A

5. Quick Check:

   Fix inside first, then grow outside [OK]

Hint: Clean inside first (inpainting), then extend outside (outpainting) [OK]

Common Mistakes:

• Applying outpainting before inpainting
• Thinking one method does both tasks
• Ignoring task order importance