DNN-based face detection in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load a pre-trained DNN face detector model.

Computer Vision

net = cv2.dnn.readNetFromCaffe('[1]', 'deploy.caffemodel')

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

Aconfig.json

Bmodel.pb

Cdeploy.prototxt

Dweights.h5

Attempts:

3 left

💡 Hint

Common Mistakes

Using the weights file instead of the prototxt file for architecture.

Confusing TensorFlow or Keras model files with Caffe files.

The prototxt file defines the model architecture for the Caffe DNN face detector.

2fill in blank

medium

Complete the code to prepare the input blob for the DNN face detector.

Computer Vision

blob = cv2.dnn.blobFromImage(image, 1.0, ([1], [1]), (104.0, 177.0, 123.0))

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A300

B224

C128

D512

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect input size causing detection failure.

Confusing input size with image resolution.

The face detector expects 300x300 input size for the blob.

3fill in blank

hard

Fix the error in the code to extract confidence scores from the detections.

Computer Vision

confidence = detections[0, 0, i, [1]]

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A1

B0

C3

D2

Attempts:

3 left

💡 Hint

Common Mistakes

Using index 1 or 3 which correspond to class or bounding box coordinates.

Confusing zero-based indexing.

Index 2 holds the confidence score in the detections array.

4fill in blank

hard

Fill both blanks to compute bounding box coordinates from detections.

Computer Vision

box = detections[0, 0, i, 3:7] * np.array([[1], [2], [1], [2]])

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Aw

Bh

Cimage.shape[1]

Dimage.shape[0]

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping width and height values.

Using fixed numbers instead of image dimensions.

Width is image.shape[1] and height is image.shape[0], used to scale box coordinates.

5fill in blank

hard

Fill all three blanks to filter detections by confidence and draw rectangles.

Computer Vision

if confidence > [1]:
    (startX, startY, endX, endY) = box.astype([2])
    cv2.rectangle(image, (startX, startY), (endX, endY), [3], 2)

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A0.5

Bint

C(0, 255, 0)

Dfloat

Attempts:

3 left

💡 Hint

Common Mistakes

Using too low or too high confidence threshold.

Not converting coordinates to int causing errors.

Using wrong color format for rectangle.

Confidence threshold is 0.5, box coordinates converted to int, green rectangle color.

Practice

1. What is the main purpose of using a DNN-based face detection model?

easy

A. To increase the size of an image

B. To find faces in images automatically

C. To compress images for storage

D. To change the color of an image

Solution

1. Step 1: Understand the goal of face detection

   Face detection means finding where faces are in pictures or videos.

2. Step 2: Recognize what DNN-based models do

   DNN models learn patterns to automatically locate faces without manual rules.

3. Final Answer:

   To find faces in images automatically -> Option B

4. Quick Check:

   Face detection = find faces [OK]

Hint: Face detection means locating faces in images [OK]

Common Mistakes:

• Confusing detection with image editing
• Thinking it changes image colors
• Mixing detection with compression

2. Which OpenCV function is used to load a pre-trained DNN face detection model?

easy

A. cv2.dnn.readNet()

B. cv2.imread()

C. cv2.resize()

D. cv2.cvtColor()

Solution

1. Step 1: Identify the function for loading DNN models

   OpenCV's DNN module uses cv2.dnn.readNet() to load models from files.

2. Step 2: Differentiate from other functions

   cv2.imread() loads images, cv2.resize() changes size, cv2.cvtColor() changes color space.

3. Final Answer:

   cv2.dnn.readNet() -> Option A

4. Quick Check:

   Load DNN model = cv2.dnn.readNet() [OK]

Hint: Use cv2.dnn.readNet() to load DNN models [OK]

Common Mistakes:

• Using cv2.imread() to load models
• Confusing image functions with model loading
• Trying to resize models with cv2.resize()

3. Given this code snippet, what will be the output type of detections?

net = cv2.dnn.readNet('model.caffemodel', 'deploy.prototxt')
blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300), (104, 177, 123))
net.setInput(blob)
detections = net.forward()

medium

A. A list of bounding box coordinates

B. A single integer count of faces

C. A string describing detected faces

D. A NumPy array with detection data

Solution

1. Step 1: Understand net.forward() output

   net.forward() returns a NumPy array containing detection results with scores and box info.

2. Step 2: Differentiate output types

   It is not a list, integer, or string but a multi-dimensional array with detection details.

3. Final Answer:

   A NumPy array with detection data -> Option D

4. Quick Check:

   net.forward() output = NumPy array [OK]

Hint: net.forward() returns a NumPy array of detections [OK]

Common Mistakes:

• Thinking output is a list or count
• Expecting a string description
• Confusing output with input image

4. You run this code but get no faces detected. What is the likely error?

blob = cv2.dnn.blobFromImage(image, 1.0, (224, 224), (104, 177, 123))
net.setInput(blob)
detections = net.forward()

What should you fix?

medium

A. Change input size to (300, 300) to match model requirements

B. Remove mean subtraction values from blobFromImage

C. Use cv2.imread() instead of blobFromImage

D. Call net.forward() before setInput()

Solution

1. Step 1: Check model input size requirements

   Many DNN face detectors expect input size (300, 300). Using (224, 224) causes mismatch and no detection.

2. Step 2: Correct the input size in blobFromImage

   Changing to (300, 300) matches model and enables detection.

3. Final Answer:

   Change input size to (300, 300) to match model requirements -> Option A

4. Quick Check:

   Input size must match model = (300, 300) [OK]

Hint: Match blob size to model input size [OK]

Common Mistakes:

• Using wrong input size for the model
• Calling net.forward() before setInput()
• Removing mean subtraction incorrectly

5. You want to improve face detection accuracy on images with multiple faces at different sizes. Which approach helps most?

hard

A. Reduce the input image size to speed up detection

B. Increase the learning rate during training

C. Use image pyramid to detect faces at multiple scales

D. Remove mean subtraction from preprocessing

Solution

1. Step 1: Understand multi-scale detection challenge

   Faces appear at different sizes; detecting all requires checking multiple scales.

2. Step 2: Use image pyramid technique

   Image pyramid resizes image multiple times to detect faces at various sizes, improving accuracy.

3. Final Answer:

   Use image pyramid to detect faces at multiple scales -> Option C

4. Quick Check:

   Multi-scale detection = image pyramid [OK]

Hint: Use image pyramid for faces at different sizes [OK]

Common Mistakes:

• Thinking higher learning rate fixes scale issues
• Reducing image size loses small faces
• Skipping mean subtraction harms accuracy