CV project workflow in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load images from a folder using OpenCV.

Computer Vision

import cv2
image = cv2.imread([1])
print(image.shape)

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

Afolder_path

Bimage.jpg

Ccv2.imread

D'image.jpg'

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting quotes around the filename

Passing a variable name without defining it

We need to provide the image filename as a string to cv2.imread. So, the correct answer is a string with quotes.

2fill in blank

medium

Complete the code to resize an image to 100x100 pixels.

Computer Vision

resized_image = cv2.resize(image, [1])
print(resized_image.shape)

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

A(100)

B(100, 100)

C[100, 100]

D100, 100

Attempts:

3 left

💡 Hint

Common Mistakes

Using a single integer instead of a tuple

Using a list instead of a tuple

The cv2.resize function expects the new size as a tuple (width, height). So, (100, 100) is correct.

3fill in blank

hard

Fix the error in the code to convert an image to grayscale.

Computer Vision

gray_image = cv2.cvtColor(image, [1])
print(gray_image.shape)

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

Acv2.COLOR_BGR2GRAY

Bcv2.COLOR_BGR2RGB

Ccv2.COLOR_RGB2GRAY

Dcv2.COLOR_GRAY2BGR

Attempts:

3 left

💡 Hint

Common Mistakes

Using RGB instead of BGR conversion code

Using grayscale to BGR conversion code

To convert a color image to grayscale, use cv2.COLOR_BGR2GRAY because OpenCV loads images in BGR format.

4fill in blank

hard

Fill both blanks to create a dictionary of image filenames and their sizes (width, height).

Computer Vision

image_sizes = {filename: (image.shape[[1]], image.shape[[2]]) for filename, image in images.items()}

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

A1

B0

C2

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping width and height indexes

Using channel index 2 instead of width or height

In OpenCV images, shape[0] is height and shape[1] is width. The dictionary stores (width, height), so width is shape[1] and height is shape[0].

5fill in blank

hard

Fill all three blanks to filter images with width greater than 200 and create a new dictionary with filename and width.

Computer Vision

filtered_images = {filename: image.shape[[1]] for filename, image in images.items() if image.shape[[2]] [3] 200}

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

A0

B1

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using height index 0 instead of width

Using less than operator instead of greater than

Width is shape[1]. We want images where width is greater than 200, so the condition is image.shape[1] > 200. The dictionary stores filename and width.

Practice

1. Which step comes first in a typical computer vision project workflow?

easy

A. Monitor model performance

B. Deploy the model to production

C. Tune hyperparameters

D. Define the problem and collect data

Solution

1. Step 1: Understand the project start

   The first step is to clearly define what problem you want to solve and gather the images or videos needed.

2. Step 2: Recognize the order of workflow steps

   Data collection must happen before training, tuning, or deployment.

3. Final Answer:

   Define the problem and collect data -> Option D

4. Quick Check:

   First step = Define problem and collect data [OK]

Hint: Start with problem definition and data collection [OK]

Common Mistakes:

• Thinking deployment is the first step
• Skipping problem definition
• Ignoring data collection importance

2. Which of the following is the correct syntax to split data into training and testing sets in Python using scikit-learn?

easy

A. train_test_split(data, test_size=0.2)

B. split_train_test(data, 0.2)

C. train_test(data, test=0.2)

D. train_test_split(data, test=0.2)

Solution

1. Step 1: Recall scikit-learn function name and parameters

   The correct function is train_test_split with parameter test_size to specify test data fraction.

2. Step 2: Check parameter correctness

   train_test_split(data, test_size=0.2) uses correct function and parameter names.

3. Final Answer:

   train_test_split(data, test_size=0.2) -> Option A

4. Quick Check:

   Correct function and parameter = train_test_split(data, test_size=0.2) [OK]

Hint: Remember exact function and parameter names from scikit-learn [OK]

Common Mistakes:

• Using wrong function name
• Using incorrect parameter names
• Confusing test_size with test

3. Given this code snippet for training a simple CNN model, what will be the printed output after training for 1 epoch?

import tensorflow as tf
model = tf.keras.Sequential([
  tf.keras.layers.Conv2D(16, 3, activation='relu', input_shape=(28,28,1)),
  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
history = model.fit(x_train, y_train, epochs=1, batch_size=32)
print(history.history['accuracy'][0])

medium

A. An error because 'accuracy' is not in history

B. The loss value after training

C. A float value between 0 and 1 representing training accuracy

D. The number of training samples

Solution

1. Step 1: Understand model.fit output

   The history object stores metrics per epoch. Accessing history.history['accuracy'][0] gives training accuracy after first epoch.

2. Step 2: Confirm metric requested

   Since metrics=['accuracy'] was set, accuracy is recorded and printed as a float between 0 and 1.

3. Final Answer:

   A float value between 0 and 1 representing training accuracy -> Option C

4. Quick Check:

   history.history['accuracy'][0] = training accuracy [OK]

Hint: history.history['accuracy'][0] holds first epoch accuracy [OK]

Common Mistakes:

• Confusing accuracy with loss
• Expecting an error accessing accuracy
• Thinking it prints sample count

4. You trained a model but it performs poorly on new images. Which step in the workflow might be causing this issue?

medium

A. Monitoring was set up correctly

B. Data preparation was insufficient or incorrect

C. Hyperparameters were tuned perfectly

D. Model deployment was done too early

Solution

1. Step 1: Analyze poor model performance cause

   Poor results on new data often mean the model did not learn well, usually due to bad or insufficient data preparation.

2. Step 2: Eliminate unrelated options

   Deployment timing, perfect hyperparameters, or monitoring setup do not directly cause poor initial performance.

3. Final Answer:

   Data preparation was insufficient or incorrect -> Option B

4. Quick Check:

   Poor performance = bad data prep [OK]

Hint: Check data prep first when model fails on new data [OK]

Common Mistakes:

• Blaming deployment timing
• Assuming hyperparameters are always perfect
• Ignoring data quality issues

5. In a computer vision project, after deploying your model, you notice accuracy drops over time. What is the best next step to maintain model performance?

hard

A. Collect new data and retrain the model regularly

B. Stop monitoring since model is deployed

C. Reduce the size of the training dataset

D. Ignore the drop as normal and do nothing

Solution

1. Step 1: Understand model drift after deployment

   Models can lose accuracy as data changes. Collecting new data and retraining helps adapt to changes.

2. Step 2: Evaluate other options

   Stopping monitoring or ignoring drops will worsen performance. Reducing training data size is counterproductive.

3. Final Answer:

   Collect new data and retrain the model regularly -> Option A

4. Quick Check:

   Maintain performance = retrain with new data [OK]

Hint: Retrain model regularly with fresh data after deployment [OK]

Common Mistakes:

• Ignoring monitoring after deployment
• Reducing training data size
• Assuming model never needs updates