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clarle/pedestrian.py

Created June 23, 2017 03:40
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  1. clarle created this gist Jun 23, 2017.
    39 changes: 39 additions & 0 deletions pedestrian.py
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    %matplotlib inline
    import numpy as np
    import cv2
    import imutils

    from matplotlib import pyplot as plt
    from imutils.object_detection import non_max_suppression

    hog = cv2.HOGDescriptor()
    hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())

    # Change to your own image with pedestrians
    image = cv2.imread('../data/diridon/P1070543.jpg')
    image = imutils.resize(image, width=min(1080, image.shape[1]))

    original = image.copy()

    # Detect people in the image
    (rects, weights) = hog.detectMultiScale(image, winStride=(4, 4), padding=(8, 8), scale=1.05)

    # Draw the original bounding boxes
    for (x, y, w, h) in rects:
    cv2.rectangle(original, (x, y), (x + w, y + h), (0, 0, 255), 2)

    # Apply non-maxima suppression to the bounding boxes using a
    # fairly large overlap threshold to try to maintain overlapping
    # boxes that are still people
    rects = np.array([[x, y, x + w, y + h] for (x, y, w, h) in rects])
    pick = non_max_suppression(rects, probs=None, overlapThresh=0.6)

    # Draw the final bounding boxes
    for (xA, yA, xB, yB) in pick:
    cv2.rectangle(image, (xA, yA), (xB, yB), (0, 255, 0), 2)

    print("Number of pedestrians: {}".format(len(pick)))

    # Show the output images
    plt.imshow(image)
    plt.show()