Motion detection basics in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read a video frame using OpenCV.

Computer Vision

ret, frame = cap.[1]()

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

Aread

Bgrab

Ccapture

Dfetch

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'capture()' which is not a valid OpenCV method for reading frames.

Using 'grab()' which only grabs the frame but does not decode it.

Using 'fetch()' which does not exist in OpenCV.

The read() method reads the next frame from the video capture object.

2fill in blank

medium

Complete the code to convert a frame to grayscale for motion detection.

Computer Vision

gray = cv2.[1](frame, cv2.COLOR_BGR2GRAY)

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AtransformColor

BconvertColor

CchangeColor

DcvtColor

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'convertColor' which is not the exact OpenCV function name.

Using 'changeColor' or 'transformColor' which do not exist.

The cvtColor() function converts images between color spaces, here from BGR to grayscale.

3fill in blank

hard

Fix the error in the code to compute the absolute difference between two frames.

Computer Vision

diff = cv2.[1](frame1, frame2)

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Aabsdiff

BabsDiff

CabsoluteDifference

Dabs_diff

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect capitalization like 'absDiff'.

Using non-existent functions like 'absoluteDifference' or 'abs_diff'.

The correct OpenCV function to compute absolute difference is absdiff() all lowercase.

4fill in blank

hard

Fill both blanks to threshold the difference image and find contours.

Computer Vision

ret, thresh = cv2.[1](diff, 25, 255, cv2.[2])
contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

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Athreshold

BTHRESH_BINARY

CadaptiveThreshold

DTHRESH_OTSU

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'adaptiveThreshold' which requires different parameters.

Using 'THRESH_OTSU' which is a flag for Otsu's method, not binary threshold.

threshold() applies a fixed threshold, and THRESH_BINARY is the thresholding type to create a binary image.

5fill in blank

hard

Fill all three blanks to draw rectangles around detected motion areas.

Computer Vision

for contour in contours:
    if cv2.contourArea(contour) > [1]:
        (x, y, w, h) = cv2.[2](contour)
        cv2.[3](frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

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A500

BboundingRect

Crectangle

DcontourArea

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'contourArea' as a function call in the wrong place.

Using wrong function names like 'contourRect' or 'drawRect'.

We filter small contours with area > 500, get bounding rectangles with boundingRect(), and draw rectangles with rectangle().