Geometric transforms (rotate, flip, crop) in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to rotate the image by 90 degrees clockwise using OpenCV.

Computer Vision

rotated_image = cv2.rotate(image, [1])

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

Acv2.ROTATE_90_COUNTERCLOCKWISE

Bcv2.ROTATE_180

Ccv2.ROTATE_90_CLOCKWISE

Dcv2.FLIP_HORIZONTAL

Attempts:

3 left

💡 Hint

Common Mistakes

Using the counterclockwise rotation constant instead.

Using flip constants instead of rotate.

To rotate an image 90 degrees clockwise in OpenCV, use cv2.ROTATE_90_CLOCKWISE as the flag.

2fill in blank

medium

Complete the code to flip the image vertically using OpenCV.

Computer Vision

flipped_image = cv2.flip(image, [1])

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

A2

B1

C-1

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using 1 which flips horizontally instead.

Using -1 which flips both axes.

In OpenCV, flipping with 0 flips the image vertically (upside down).

3fill in blank

hard

Fix the error in the code to crop the image correctly using NumPy slicing.

Computer Vision

cropped_image = image[[1]:[1]+100, 50:150]

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

A0

B50

C100

D150

Attempts:

3 left

💡 Hint

Common Mistakes

Starting crop at row 100 which may be outside image bounds.

Using column indices for row slicing.

To crop starting from the top, use 0 as the starting row index.

4fill in blank

hard

Fill both blanks to create a horizontal flip and then crop the center 100x100 pixels of the image.

Computer Vision

flipped = cv2.flip(image, [1])
cropped = flipped[[2]:[2]+100, 100:200]

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

A1

B0

C50

D100

Attempts:

3 left

💡 Hint

Common Mistakes

Using vertical flip code 0 instead of horizontal.

Starting crop at 100 which is too low for center crop.

Flip code 1 flips horizontally. Cropping starts at row 50 to get center 100 pixels vertically.

5fill in blank

hard

Fill all three blanks to rotate the image 180 degrees, flip vertically, and then crop the top-left 50x50 pixels.

Computer Vision

rotated = cv2.rotate(image, [1])
flipped = cv2.flip(rotated, [2])
cropped = flipped[[3]:[3]+50, 0:50]

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

Acv2.ROTATE_180

B0

Dcv2.ROTATE_90_CLOCKWISE

Attempts:

3 left

💡 Hint

Common Mistakes

Using 90 degree rotation instead of 180.

Using horizontal flip code 1 instead of vertical.

Starting crop at wrong row index.

Rotate 180 degrees uses cv2.ROTATE_180. Flip vertically uses code 0. Crop starts at row 0 for top-left corner.

Practice

1. Which geometric transform flips an image horizontally, like looking in a mirror?

easy

A. Horizontal flip

B. Rotate 90 degrees

C. Crop center

D. Vertical flip

Solution

1. Step 1: Understand horizontal flip

   Horizontal flip reverses the image left to right, like a mirror reflection.

2. Step 2: Compare with other transforms

   Rotate changes orientation, crop cuts part of image, vertical flip flips top to bottom.

3. Final Answer:

   Horizontal flip -> Option A

4. Quick Check:

   Mirror image = Horizontal flip [OK]

Hint: Mirror effect means horizontal flip [OK]

Common Mistakes:

• Confusing vertical flip with horizontal flip
• Thinking rotate is flipping
• Mixing crop with flip

2. Which Python code correctly rotates an image by 90 degrees clockwise using OpenCV?

easy

A. rotated = cv2.crop(image, (0,0,100,100))

B. rotated = cv2.flip(image, 1)

C. rotated = cv2.rotate(image, cv2.ROTATE_90_CLOCKWISE)

D. rotated = cv2.rotate(image, cv2.ROTATE_180)

Solution

1. Step 1: Identify OpenCV rotate syntax

   cv2.rotate(image, cv2.ROTATE_90_CLOCKWISE) rotates image 90 degrees clockwise.

2. Step 2: Check other options

   Flip flips image, crop is not a cv2 function, ROTATE_180 rotates 180 degrees, not 90.

3. Final Answer:

   rotated = cv2.rotate(image, cv2.ROTATE_90_CLOCKWISE) -> Option C

4. Quick Check:

   Rotate 90 clockwise = cv2.ROTATE_90_CLOCKWISE [OK]

Hint: Use cv2.rotate with ROTATE_90_CLOCKWISE for 90° turn [OK]

Common Mistakes:

• Using flip instead of rotate
• Wrong rotate constant
• Trying crop as a cv2 function

3. What will be the shape of the image after applying this code?

import cv2
image = cv2.imread('img.jpg')
cropped = image[50:150, 30:130]

medium

A. (100, 100, 3)

B. (150, 130, 3)

C. (80, 100, 3)

D. (100, 80, 3)

Solution

1. Step 1: Calculate crop height

   Height = 150 - 50 = 100 pixels.

2. Step 2: Calculate crop width

   Width = 130 - 30 = 100 pixels.

3. Step 3: Confirm channels

   Image has 3 color channels (RGB/BGR).

4. Final Answer:

   (100, 100, 3) -> Option A

5. Quick Check:

   Crop size = (height, width, channels) = (100, 100, 3) [OK]

Hint: Subtract start from end index for height and width [OK]

Common Mistakes:

• Mixing width and height order
• Forgetting zero-based indexing
• Ignoring color channels

4. This code tries to flip an image vertically but causes an error. What is wrong?

import cv2
image = cv2.imread('img.jpg')
flipped = cv2.flip(image, 2)

medium

A. image must be grayscale for flip

B. Flip code 2 is invalid; use 0 for vertical flip

C. cv2.flip requires three arguments

D. cv2.flip does not flip images

Solution

1. Step 1: Check cv2.flip flipCode values

   flipCode=0 flips vertically, 1 flips horizontally, -1 flips both axes.

2. Step 2: Identify invalid flipCode

   flipCode=2 is invalid and causes error.

3. Final Answer:

   Flip code 2 is invalid; use 0 for vertical flip -> Option B

4. Quick Check:

   Vertical flip = flipCode 0 [OK]

Hint: Use flipCode 0 for vertical flip in cv2.flip [OK]

Common Mistakes:

• Using wrong flipCode values
• Thinking flip needs grayscale
• Assuming cv2.flip needs extra args

5. You want to rotate an image 90 degrees clockwise and then crop the central 100x100 pixels. Which code correctly does this?

hard

A. crop = img[50:150, 50:150]
rot = cv2.rotate(crop, cv2.ROTATE_90_CLOCKWISE)

B. rot = cv2.flip(img, 1)
crop = rot[rot.shape[0]//2-50:rot.shape[0]//2+50, rot.shape[1]//2-50:rot.shape[1]//2+50]

C. rot = cv2.rotate(img, cv2.ROTATE_180)
crop = rot[0:100, 0:100]

D. rot = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE)
crop = rot[rot.shape[0]//2-50:rot.shape[0]//2+50, rot.shape[1]//2-50:rot.shape[1]//2+50]

Solution

1. Step 1: Rotate image 90 degrees clockwise

   Use cv2.rotate with ROTATE_90_CLOCKWISE on original image.

2. Step 2: Crop center 100x100 pixels

   Calculate center indices and slice 50 pixels before and after center in both dimensions.

3. Step 3: Verify order and correctness

   Rotation first, then crop center; other options rotate wrong angle or crop before rotate.

4. Final Answer:

   Rotate then crop center 100x100 -> Option D

5. Quick Check:

   Rotate 90° clockwise then center crop = rot = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE) crop = rot[rot.shape[0]//2-50:rot.shape[0]//2+50, rot.shape[1]//2-50:rot.shape[1]//2+50] [OK]

Hint: Rotate first, then crop center using shape indices [OK]

Common Mistakes:

• Cropping before rotating changes crop area
• Using wrong rotate angle
• Using flip instead of rotate