OpenPose overview in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load an OpenPose model using the OpenPose Python API.

Computer Vision

from openpose import pyopenpose as op
params = dict()
params["model_folder"] = "models/"
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.[1]()

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

Arun

Binit

Cload

Dstart

Attempts:

3 left

💡 Hint

Common Mistakes

Using run() instead of start(), which does not exist in the API.

Using load() which is not a method of the wrapper.

Using init() which is not the correct method name.

The start() method initializes and starts the OpenPose wrapper to process images.

2fill in blank

medium

Complete the code to process an image and get the pose keypoints.

Computer Vision

datum = op.Datum()
datum.cvInputData = image
opWrapper.emplaceAndPop([[1]])
keypoints = datum.poseKeypoints

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

AopWrapper

Bdatum

Cparams

Dimage

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the image directly instead of the datum object.

Passing the wrapper or params which are not valid inputs here.

The emplaceAndPop method takes a list of Datum objects to process. We pass datum which contains the input image.

3fill in blank

hard

Fix the error in the code to correctly extract the number of detected people from pose keypoints.

Computer Vision

num_people = datum.poseKeypoints.[1] if datum.poseKeypoints is not None else 0

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

Ashape[0]

Blen()

Csize

Dcount

Attempts:

3 left

💡 Hint

Common Mistakes

Using len() which causes an error because poseKeypoints is a NumPy array, not a list.

Using size which gives total elements, not number of people.

Using count which is not a valid attribute.

The poseKeypoints is a NumPy array where the first dimension is the number of people detected. Using shape[0] gives the count.

4fill in blank

hard

Fill both blanks to create a dictionary of body part names and their 2D coordinates for the first detected person.

Computer Vision

body_parts = {name: (datum.poseKeypoints[0, idx, [1]], datum.poseKeypoints[0, idx, [2]]) for idx, name in enumerate(body_part_names)}

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

A0

B1

C2

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping x and y indices.

Using index 2 which is confidence, not coordinate.

The 2D coordinates are stored in the second and first indices of the last dimension: 1 for x and 0 for y.

5fill in blank

hard

Fill all three blanks to filter detected people with confidence above 0.5 and create a list of their nose coordinates.

Computer Vision

nose_coords = [datum.poseKeypoints[i, nose_idx, [1]] for i in range(datum.poseKeypoints.shape[0]) if datum.poseKeypoints[i, nose_idx, [2]] > [3]]

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

A0

B2

C0.5

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong indices for coordinates or confidence.

Using a confidence threshold other than 0.5.

The x coordinate is at index 1, confidence at index 2, and the threshold is 0.5.

Practice

1. What is the main purpose of OpenPose in computer vision?

easy

A. To classify objects like cars and animals

B. To detect human body keypoints and poses in images or videos

C. To enhance image resolution

D. To generate 3D models from 2D images

Solution

1. Step 1: Understand OpenPose's function

   OpenPose is designed to find human body parts and poses in images or videos.

2. Step 2: Compare with other options

   Options B, C, and D describe different tasks unrelated to pose detection.

3. Final Answer:

   To detect human body keypoints and poses in images or videos -> Option B

4. Quick Check:

   OpenPose = Human pose detection [OK]

Hint: OpenPose = human pose keypoints detection [OK]

Common Mistakes:

• Confusing OpenPose with object classification
• Thinking OpenPose enhances image quality
• Assuming OpenPose creates 3D models

2. Which of the following is the correct step to use OpenPose in a program?

easy

A. Use OpenPose to classify image colors

B. Directly print the image without loading any model

C. Load the OpenPose model, process the image, then extract keypoints

D. Skip model loading and only display raw pixels

Solution

1. Step 1: Recall OpenPose usage steps

   OpenPose requires loading its model, processing images, and extracting keypoints.

2. Step 2: Eliminate incorrect options

   Options B, C, and D ignore model loading or misuse OpenPose for unrelated tasks.

3. Final Answer:

   Load the OpenPose model, process the image, then extract keypoints -> Option C

4. Quick Check:

   Model load + process + keypoints = correct usage [OK]

Hint: Always load model before processing images [OK]

Common Mistakes:

• Skipping model loading step
• Using OpenPose for color classification
• Trying to process images without model

3. Given this Python snippet using OpenPose:

keypoints = openpose.process(image)
print(len(keypoints))

What does len(keypoints) represent?

medium

A. The number of detected people in the image

B. The number of pixels in the image

C. The number of colors detected

D. The number of image channels

Solution

1. Step 1: Understand what keypoints hold

   OpenPose returns keypoints for each detected person; length equals number of people detected.

2. Step 2: Compare with other options

   Pixels, colors, and channels are unrelated to keypoints length.

3. Final Answer:

   The number of detected people in the image -> Option A

4. Quick Check:

   len(keypoints) = people count [OK]

Hint: Keypoints list length = people detected [OK]

Common Mistakes:

• Thinking length is pixel count
• Confusing keypoints with colors
• Assuming length is image channels

4. You run this code snippet but get an error:

keypoints = openpose.process(image)
print(keypoints.shape)

What is the likely cause?

medium

A. keypoints is a list, not a numpy array, so it has no shape attribute

B. The image variable is not defined

C. OpenPose model was not loaded

D. print() function is used incorrectly

Solution

1. Step 1: Identify error cause

   keypoints from OpenPose is usually a list, which does not have a .shape attribute.

2. Step 2: Check other options

   Image undefined or model not loaded would cause different errors; print() usage is correct.

3. Final Answer:

   keypoints is a list, not a numpy array, so it has no shape attribute -> Option A

4. Quick Check:

   List has no .shape attribute [OK]

Hint: Use len() for lists, not .shape [OK]

Common Mistakes:

• Assuming keypoints is a numpy array
• Ignoring variable definition errors
• Blaming print() function

5. You want to use OpenPose to analyze a video with multiple people moving. Which approach is best to get accurate pose tracking over time?

hard

A. Process only the first frame and reuse keypoints for all frames

B. Process frames randomly without linking keypoints

C. Skip OpenPose and use color detection instead

D. Process each video frame with OpenPose and link detected keypoints across frames

Solution

1. Step 1: Understand video pose tracking

   Accurate tracking requires processing each frame and linking poses over time.

2. Step 2: Evaluate other options

   Reusing first frame keypoints ignores movement; color detection is unrelated; random processing loses continuity.

3. Final Answer:

   Process each video frame with OpenPose and link detected keypoints across frames -> Option D

4. Quick Check:

   Frame-by-frame + linking = accurate tracking [OK]

Hint: Track poses frame-by-frame, link keypoints [OK]

Common Mistakes:

• Using only first frame keypoints
• Confusing color detection with pose tracking
• Ignoring temporal linking of poses