Why pose estimation tracks body movement in Computer Vision - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the library used for pose estimation.

Computer Vision

import [1]

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Acv2

Btensorflow

Cnumpy

Dmatplotlib

Attempts:

3 left

💡 Hint

Common Mistakes

Importing tensorflow instead of cv2.

Importing numpy which is for arrays, not pose estimation.

Importing matplotlib which is for plotting.

The cv2 library (OpenCV) is commonly used for computer vision tasks including pose estimation.

2fill in blank

medium

Complete the code to read an image for pose estimation.

Computer Vision

image = cv2.[1]('person.jpg')

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Aimread

Bimwrite

Cimshow

Dresize

Attempts:

3 left

💡 Hint

Common Mistakes

Using imshow which displays images but does not read them.

Using imwrite which saves images to disk.

Using resize which changes image size.

The imread function reads an image from a file into memory.

3fill in blank

hard

Fix the error in the code to detect pose keypoints using a model.

Computer Vision

keypoints = model.[1](image)

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Aevaluate

Bfit

Cpredict

Dtrain

Attempts:

3 left

💡 Hint

Common Mistakes

Using train which is for training the model.

Using fit which is also for training.

Using evaluate which checks model performance.

The predict method runs the model on input data to get output keypoints.

4fill in blank

hard

Fill both blanks to create a dictionary of keypoints with their confidence scores.

Computer Vision

pose_data = {kp['name']: kp[1] for kp in keypoints if kp[2] > 0.5}

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A['score']

B['confidence']

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'confidence' which may not exist in the keypoint dictionary.

Using inconsistent keys for filtering and accessing scores.

Keypoints usually have a 'score' field indicating confidence. We filter keypoints with score above 0.5.

5fill in blank

hard

Fill all three blanks to calculate the average confidence score of detected keypoints.

Computer Vision

avg_score = sum(kp[1] for kp in keypoints if kp['score'][2] 0.5) / len([kp for kp in keypoints if kp['score'][3] 0.5])

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A['score']

B>

D['confidence']

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'confidence' instead of 'score'.

Using '<' instead of '>' for filtering.

We sum the 'score' values of keypoints with score greater than 0.5 and divide by their count to get average confidence.