nms_slow.py

from .nms import non_max_suppression_slow
import numpy as np
import cv2

# construct a list containing the images that will be examined
# along with their respective bounding boxes
images = [
    ("images/audrey.jpg", np.array([
        (12, 84, 140, 212),
        (24, 84, 152, 212),
        (36, 84, 164, 212),
        (12, 96, 140, 224),
        (24, 96, 152, 224),
        (24, 108, 152, 236)])),
    ("images/bksomels.jpg", np.array([
        (114, 60, 178, 124),
        (120, 60, 184, 124),
        (114, 66, 178, 130)])),
    ("images/gpripe.jpg", np.array([
        (12, 30, 76, 94),
        (12, 36, 76, 100),
        (72, 36, 200, 164),
        (84, 48, 212, 176)]))]

# loop over the images
for (imagePath, boundingBoxes) in images:
    # load the image and clone it
    print(f'{len(boundingBoxes)} initial bounding boxes' )
    image = cv2.imread(imagePath)
    orig = image.copy()

    # loop over the bounding boxes for each image and draw them
    for (startX, startY, endX, endY) in boundingBoxes:
        cv2.rectangle(orig, (startX, startY), (endX, endY), (0, 0, 255), 2)

    # perform non-maximum suppression on the bounding boxes
    pick = non_max_suppression_slow(boundingBoxes, 0.3)
    print(f'[x] after applying non-maximum, {len(pick)} bounding boxes' )

    # loop over the picked bounding boxes and draw them
    for (startX, startY, endX, endY) in pick:
        cv2.rectangle(image, (startX, startY), (endX, endY), (0, 255, 0), 2)

    # display the images
    cv2.imshow("Original", orig)
    cv2.imshow("After NMS", image)
    cv2.waitKey(0)