Image inpainting concept in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load an image using OpenCV.

Computer Vision

import cv2
image = cv2.[1]('input.jpg')

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

Aimshow

Bresize

Cimwrite

Dimread

Attempts:

3 left

💡 Hint

Common Mistakes

Using cv2.imshow instead of cv2.imread

Trying to write the image instead of reading it

The cv2.imread function loads an image from a file into memory.

2fill in blank

medium

Complete the code to create a mask for the inpainting process.

Computer Vision

import numpy as np
mask = np.zeros(image.shape[:2], dtype=[1])

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

Afloat32

Buint8

Cbool

Dint32

Attempts:

3 left

💡 Hint

Common Mistakes

Using float32 which is not accepted as mask type

Using bool which may cause errors in OpenCV

The mask for inpainting is usually an 8-bit unsigned integer array where non-zero pixels mark areas to inpaint.

3fill in blank

hard

Fix the error in the inpainting function call to use the correct method.

Computer Vision

result = cv2.inpaint(image, mask, 3, [1])

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

Acv2.INPAINT_TELEA

Bcv2.INPAINT_NS

Ccv2.INPAINT_FAST

Dcv2.INPAINT_BASIC

Attempts:

3 left

💡 Hint

Common Mistakes

Using non-existent constants like INPAINT_FAST or INPAINT_BASIC

Using INPAINT_NS which is another method but less common

cv2.INPAINT_TELEA is a common and effective inpainting algorithm provided by OpenCV.

4fill in blank

hard

Fill both blanks to create a mask where pixels with value 0 are marked for inpainting.

Computer Vision

mask = np.where(image_gray [1] 0, [2], 0).astype(np.uint8)

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

A==

B255

C1

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '!=' instead of '=='

Setting mask pixels to 1 instead of 255

This code marks pixels equal to 0 in the grayscale image with 255 in the mask, indicating areas to inpaint.

5fill in blank

hard

Fill all three blanks to apply inpainting and display the result using OpenCV.

Computer Vision

result = cv2.inpaint([1], [2], 3, [3])
cv2.imshow('Inpainted Image', result)
cv2.waitKey(0)
cv2.destroyAllWindows()

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

Aimage

Bmask

Ccv2.INPAINT_TELEA

Dcv2.INPAINT_NS

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping image and mask arguments

Using wrong inpainting method constant

The cv2.inpaint function requires the original image, the mask, and the inpainting method (here INPAINT_TELEA).

Practice

1. What is the main purpose of image inpainting in computer vision?

easy

A. To fill missing or damaged parts of an image

B. To increase the resolution of an image

C. To classify objects in an image

D. To detect edges in an image

Solution

1. Step 1: Understand image inpainting definition

   Image inpainting is a technique used to restore or fill missing or damaged parts of an image.

2. Step 2: Compare options with definition

   Only To fill missing or damaged parts of an image matches the purpose of filling missing or damaged parts, while others describe different tasks.

3. Final Answer:

   To fill missing or damaged parts of an image -> Option A

4. Quick Check:

   Image inpainting = fill missing parts [OK]

Hint: Remember: inpainting means filling gaps in images [OK]

Common Mistakes:

• Confusing inpainting with image classification
• Thinking it increases image resolution
• Mixing it up with edge detection

2. Which of the following is the correct way to represent a mask for image inpainting?

easy

A. A binary image where 1 indicates missing pixels and 0 indicates known pixels

B. A color image with random colors

C. A text file describing image pixels

D. A grayscale image where white pixels mark missing areas

Solution

1. Step 1: Understand mask role in inpainting

   The mask guides the model where to fill missing parts, usually as a binary image.

2. Step 2: Identify correct mask format

   A binary image where 1 indicates missing pixels and 0 indicates known pixels correctly describes a binary mask with 1 for missing and 0 for known pixels.

3. Final Answer:

   A binary image where 1 indicates missing pixels and 0 indicates known pixels -> Option A

4. Quick Check:

   Mask = binary image marking missing areas [OK]

Hint: Mask uses 1 for missing, 0 for known pixels [OK]

Common Mistakes:

• Thinking mask is a color image
• Assuming mask is a text file
• Confusing grayscale with binary mask

3. Given this Python snippet for image inpainting using OpenCV:

import cv2
image = cv2.imread('photo.jpg')
mask = cv2.imread('mask.png', 0)
inpainted = cv2.inpaint(image, mask, 3, cv2.INPAINT_TELEA)
print(inpainted.shape)

What will be the output if photo.jpg is 640x480 pixels with 3 color channels?

medium

A. (640, 480, 3)

B. (480, 640, 3)

C. (480, 640)

D. (640, 480)

Solution

1. Step 1: Understand image shape format in OpenCV

   OpenCV images have shape (height, width, channels). For 640x480, height=480, width=640.

2. Step 2: Confirm inpainted image shape

   Inpainting returns an image of same size and channels as input, so shape is (480, 640, 3).

3. Final Answer:

   (480, 640, 3) -> Option B

4. Quick Check:

   OpenCV shape = (height, width, channels) [OK]

Hint: OpenCV shape order is height, width, channels [OK]

Common Mistakes:

• Swapping width and height in shape
• Ignoring color channels in output
• Assuming grayscale output

4. You run this code but get an error:

import cv2
image = cv2.imread('photo.jpg')
mask = cv2.imread('mask.png')
inpainted = cv2.inpaint(image, mask, 3, cv2.INPAINT_NS)

What is the likely cause of the error?

medium

A. Inpaint radius is too large

B. Image file path is incorrect

C. Using wrong inpainting method

D. Mask image is not loaded as a single channel (grayscale)

Solution

1. Step 1: Check mask loading method

   The mask must be loaded as grayscale (single channel) for inpainting, but code loads it as color by default.

2. Step 2: Identify cause of error

   Passing a 3-channel mask causes cv2.inpaint to fail, so loading mask with flag 0 is needed.

3. Final Answer:

   Mask image is not loaded as a single channel (grayscale) -> Option D

4. Quick Check:

   Mask must be grayscale for inpainting [OK]

Hint: Load mask with cv2.imread('mask.png', 0) [OK]

Common Mistakes:

• Loading mask as color image
• Ignoring file path errors
• Changing inpainting method unnecessarily

5. You want to remove a small object from a photo using image inpainting. Which steps should you follow?

hard

A. Crop the image to exclude the object

B. Blur the entire image to hide the object

C. Create a binary mask marking the object, then apply inpainting with that mask

D. Convert the image to grayscale and apply thresholding

Solution

1. Step 1: Mark the object with a mask

   Create a binary mask where the object pixels are 1 (missing) and others 0 (known).

2. Step 2: Apply inpainting using the mask

   Use an inpainting algorithm with the mask to fill the object area with surrounding pixels.

3. Final Answer:

   Create a binary mask marking the object, then apply inpainting with that mask -> Option C

4. Quick Check:

   Mask object + inpaint = object removal [OK]

Hint: Mask object area before inpainting to remove it [OK]

Common Mistakes:

• Blurring instead of inpainting
• Cropping loses image context
• Thresholding unrelated to object removal