Object detection from aerial view in Drone Programming - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load an aerial image for object detection.

Drone Programming

image = cv2.imread([1])

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

Aimage.jpg

Bimg.png

Cload_image()

D'aerial_view.jpg'

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting quotes around the filename.

Using a function call instead of a filename string.

The correct way to load the aerial image is by specifying the filename as a string inside quotes.

2fill in blank

medium

Complete the code to convert the aerial image to grayscale for processing.

Drone Programming

gray_image = cv2.cvtColor(image, [1])

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Acv2.COLOR_BGR2GRAY

Bcv2.COLOR_BGR2RGB

Ccv2.COLOR_RGB2GRAY

Dcv2.COLOR_GRAY2BGR

Attempts:

3 left

💡 Hint

Common Mistakes

Using COLOR_BGR2RGB instead of grayscale conversion.

Using COLOR_GRAY2BGR which is the reverse conversion.

To convert a BGR image to grayscale, use cv2.COLOR_BGR2GRAY.

3fill in blank

hard

Fix the error in the code to detect objects using a pre-trained model.

Drone Programming

detections = model.[1](gray_image)

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Atransform

Bdetect

Cfit

Dpredict

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'predict' which is common in classification but not detection.

Using 'fit' which is for training, not detection.

The correct method to detect objects is 'detect', not 'predict' or others.

4fill in blank

hard

Fill both blanks to filter detected objects by confidence and draw bounding boxes.

Drone Programming

for obj in detections:
    if obj.confidence [1] 0.5:
        cv2.rectangle(image, obj.bbox[[2]], obj.bbox[2], (0,255,0), 2)

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A>

B<

C0

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using less than instead of greater than for confidence.

Using wrong bounding box index causing errors.

We filter objects with confidence greater than 0.5 and use index 0 for the bounding box start point.

5fill in blank

hard

Fill all three blanks to create a dictionary of detected object labels and their counts.

Drone Programming

counts = {}
for obj in detections:
    label = obj.[1]
    counts[label] = counts.get(label, [2]) + [3]

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

Alabel

B0

C1

Dconfidence

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'confidence' instead of 'label' for dictionary keys.

Starting counts at 1 instead of 0 causing wrong counts.

We use obj.label to get the label, default count 0, and add 1 for each detected object.