Hand and face landmark detection in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the MediaPipe Hands solution.

Computer Vision

import mediapipe as mp
mp_hands = mp.solutions.[1]

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

Ahands

Bface_mesh

Cdrawing_utils

Dpose

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'face_mesh' instead of 'hands' for hand detection.

Using 'pose' which is for body pose estimation.

Using 'drawing_utils' which is for drawing landmarks, not detection.

The MediaPipe Hands solution is accessed via mp.solutions.hands. This module provides hand landmark detection.

2fill in blank

medium

Complete the code to initialize the Hands model with static image mode enabled.

Computer Vision

with mp_hands.Hands(static_image_mode=[1], max_num_hands=2) as hands:

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AFalse

BNone

CTrue

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Setting static_image_mode to False when processing single images.

Using None or 0 which are invalid for this parameter.

Setting static_image_mode=True tells the model to treat input as static images, which is important for processing single images instead of video streams.

3fill in blank

hard

Fix the error in accessing the hand landmarks from the results object.

Computer Vision

if results.[1]:
    for hand_landmarks in results.multi_hand_landmarks:
        print(hand_landmarks)

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Amulti_hand_landmarks

Bhand_landmarks

Clandmarks

Dmulti_landmarks

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'hand_landmarks' which is not an attribute of results.

Using 'landmarks' or 'multi_landmarks' which do not exist.

The detected hand landmarks are stored in results.multi_hand_landmarks. This attribute contains a list of landmarks for each detected hand.

4fill in blank

hard

Fill both blanks to draw hand landmarks on the image using MediaPipe drawing utilities.

Computer Vision

mp_drawing.[1](image, hand_landmarks, mp_hands.[2])

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

Adraw_landmarks

Bdraw_detection

CHAND_CONNECTIONS

Dface_mesh

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'draw_detection' which is for object detection, not landmarks.

Using 'face_mesh' which is unrelated to hands.

The function draw_landmarks is used to draw landmarks on images. The connections for hands are accessed via mp_hands.HAND_CONNECTIONS.

5fill in blank

hard

Fill all three blanks to create a dictionary of landmark coordinates normalized to image size.

Computer Vision

landmark_dict = [1]: {'x': [2].x * image_width, 'y': [2].y * image_height} for [1], [2] in [3](hand_landmarks.landmark)

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Ai

Blandmark_dict

Cenumerate

Didx

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'landmark_dict' as a variable inside the comprehension.

Mixing variable names inconsistently.

Not using 'enumerate' to get index and value.

This dictionary comprehension uses enumerate to get the index idx and landmark i. The index is used as the key, and the landmark coordinates are scaled by image width and height.

Practice

1. What is the main purpose of hand and face landmark detection in computer vision?

easy

A. To compress video files

B. To increase image resolution

C. To change the color of images

D. To find key points on hands and faces in images or videos

Solution

1. Step 1: Understand the goal of landmark detection

   Landmark detection identifies important points on hands and faces to understand their shape and position.

2. Step 2: Compare options with the goal

   Only To find key points on hands and faces in images or videos matches this goal by describing key point detection on hands and faces.

3. Final Answer:

   To find key points on hands and faces in images or videos -> Option D

4. Quick Check:

   Landmark detection = key points detection [OK]

Hint: Landmark detection means finding important points [OK]

Common Mistakes:

• Confusing landmark detection with image enhancement
• Thinking it changes image colors
• Mixing it up with video compression

2. Which of the following is the correct way to import MediaPipe's hand landmark detection module in Python?

easy

A. import mediapipe.solutions.hands as mp_hands

B. import mediapipe.hands as mp_hands

C. import mediapipe as mp
mp.solutions.hands

D. from mediapipe import hands

Solution

1. Step 1: Recall MediaPipe import syntax

   MediaPipe modules are imported from mediapipe.solutions, e.g., mediapipe.solutions.hands.

2. Step 2: Check each option

   import mediapipe.solutions.hands as mp_hands correctly imports mediapipe.solutions.hands as mp_hands. Others are incorrect or incomplete.

3. Final Answer:

   import mediapipe.solutions.hands as mp_hands -> Option A

4. Quick Check:

   Correct import = mediapipe.solutions.hands [OK]

Hint: MediaPipe modules come from mediapipe.solutions [OK]

Common Mistakes:

• Using incorrect import paths
• Trying to import submodules directly without solutions
• Confusing alias names

3. Given the following Python code using MediaPipe for hand landmarks detection, what will be printed?

import mediapipe as mp
mp_hands = mp.solutions.hands
hands = mp_hands.Hands(static_image_mode=True)
results = hands.process(image_rgb)
print(len(results.multi_hand_landmarks))

Assuming image_rgb contains one clear hand.

medium

A. 1

B. Error

C. None

D. 0

Solution

1. Step 1: Understand the code flow

   The code processes an RGB image with one hand using MediaPipe Hands in static mode.

2. Step 2: Interpret the output

   Since one hand is present, results.multi_hand_landmarks will contain one set of landmarks, so its length is 1.

3. Final Answer:

   1 -> Option A

4. Quick Check:

   One hand detected = length 1 [OK]

Hint: Length of landmarks list equals number of detected hands [OK]

Common Mistakes:

• Assuming zero when hand is present
• Confusing None with empty list
• Expecting error without checking input

4. You wrote this code to detect face landmarks but get an error:

import mediapipe as mp
mp_face = mp.solutions.face_mesh
face_mesh = mp_face.FaceMesh()
results = face_mesh.process(image_bgr)
print(results.multi_face_landmarks)

What is the likely cause of the error?

medium

A. Missing import for cv2

B. FaceMesh class does not exist

C. Input image should be RGB, not BGR

D. process() method requires grayscale image

Solution

1. Step 1: Check input image format for MediaPipe FaceMesh

   MediaPipe expects RGB images, but the code uses image_bgr (BGR format).

2. Step 2: Understand error cause

   Using BGR instead of RGB causes wrong color channels and likely errors in detection.

3. Final Answer:

   Input image should be RGB, not BGR -> Option C

4. Quick Check:

   MediaPipe needs RGB input images [OK]

Hint: Always convert BGR to RGB before MediaPipe processing [OK]

Common Mistakes:

• Passing BGR images directly
• Assuming FaceMesh class is missing
• Thinking grayscale is required

5. You want to build a gesture recognition app using hand landmarks. Which approach best improves accuracy when hands are rotated or partially hidden?

hard

A. Only train on perfectly centered and clear hand images

B. Use data augmentation with rotated and occluded hand images during training

C. Ignore landmarks and use raw images directly

D. Use grayscale images instead of color

Solution

1. Step 1: Understand challenges in gesture recognition

   Hands can appear rotated or partly hidden, so model must handle variations.

2. Step 2: Choose best method to improve robustness

   Data augmentation with rotated and occluded images teaches model to recognize gestures despite changes.

3. Final Answer:

   Use data augmentation with rotated and occluded hand images during training -> Option B

4. Quick Check:

   Augmentation improves model robustness [OK]

Hint: Augment training data to handle rotations and occlusions [OK]

Common Mistakes:

• Training only on perfect images
• Ignoring landmarks reduces accuracy
• Using grayscale loses important info