Geometric transforms (rotate, flip, crop) in Computer Vision

Introduction

Geometric transforms help change images by rotating, flipping, or cutting parts. This makes models see images in different ways and learn better.

When you want to make your image data bigger by creating new versions.

When you need to fix image orientation problems.

When you want to focus on a specific part of an image by cropping.

When you want your model to recognize objects no matter how they are turned or flipped.

When preparing images for training to improve model accuracy.

Syntax

Computer Vision

rotate(image, angle)
flip(image, mode)
crop(image, x_start, y_start, width, height)

rotate turns the image by the angle in degrees (positive for counterclockwise).

flip can be horizontal or vertical depending on mode.

crop cuts out a rectangle from the image starting at (x_start, y_start).

Examples

This rotates the image 90 degrees counterclockwise.

Computer Vision

rotated_img = rotate(image, 90)

This flips the image left to right.

Computer Vision

flipped_img = flip(image, 'horizontal')

This cuts a 100x100 pixel square starting at x=10, y=20.

Computer Vision

cropped_img = crop(image, 10, 20, 100, 100)

Sample Model

This code creates a simple image, then rotates it 45 degrees, flips it horizontally, and crops a 100x100 area from the top-left. It prints pixel values and cropped shape to show the changes.

Computer Vision

import cv2
import numpy as np

# Create a simple image: white square on black background
image = np.zeros((200, 200, 3), dtype=np.uint8)
cv2.rectangle(image, (50, 50), (150, 150), (255, 255, 255), -1)

# Rotate image 45 degrees
(h, w) = image.shape[:2]
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, 45, 1.0)
rotated = cv2.warpAffine(image, M, (w, h))

# Flip image horizontally
flipped = cv2.flip(image, 1)

# Crop image: 100x100 from top-left corner
cropped = image[0:100, 0:100]

# Check some pixel values to confirm changes
print('Original center pixel:', image[100, 100])
print('Rotated center pixel:', rotated[100, 100])
print('Flipped center pixel:', flipped[100, 100])
print('Cropped shape:', cropped.shape)

Important Notes

Rotation may cause parts of the image to go outside the frame or create black corners.

Flipping horizontally means left-right swap; vertically means top-bottom swap.

Cropping must stay inside image bounds to avoid errors.

Summary

Geometric transforms change images by rotating, flipping, or cropping.

They help models learn better by showing images in different views.

Use simple functions to apply these transforms safely within image size.

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