harris.py

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# Example : harris feature points from a video file
# specified on the command line (e.g. python FILE.py video_file) or from an
# attached web camera

# Author : Toby Breckon, toby.breckon@durham.ac.uk

# Copyright (c) 2015 School of Engineering & Computing Science,
#                    Durham University, UK
# License : LGPL - http://www.gnu.org/licenses/lgpl.html

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import cv2
import argparse
import sys
import numpy as np

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keep_processing = True

# parse command line arguments for camera ID or video file

parser = argparse.ArgumentParser(
    description='Perform ' +
    sys.argv[0] +
    ' example operation on incoming camera/video image')
parser.add_argument(
    "-c",
    "--camera_to_use",
    type=int,
    help="specify camera to use",
    default=0)
parser.add_argument(
    "-r",
    "--rescale",
    type=float,
    help="rescale image by this factor",
    default=1.0)
parser.add_argument(
    'video_file',
    metavar='video_file',
    type=str,
    nargs='?',
    help='specify optional video file')
args = parser.parse_args()

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# this function is called as a call-back everytime the trackbar is moved
# (here we just do nothing)


def nothing(x):
    pass

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# define video capture object


try:
    # to use a non-buffered camera stream (via a separate thread)

    if not (args.video_file):
        import camera_stream
        cap = camera_stream.CameraVideoStream()  # T-API breaks code
    else:
        cap = cv2.VideoCapture()  # not needed for video files

except BaseException:
    # if not then just use OpenCV default

    print("INFO: camera_stream class not found - camera input may be buffered")
    cap = cv2.VideoCapture()

# define display window name

window_name = "Live Camera Input"  # window name

# if command line arguments are provided try to read video_name
# otherwise default to capture from attached H/W camera

if (((args.video_file) and (cap.open(str(args.video_file))))
        or (cap.open(args.camera_to_use))):

    # create window by name (as resizable)

    cv2.namedWindow(window_name, cv2.WINDOW_NORMAL)

    # add some track bar controllers for settings

    neighbourhood = 3
    cv2.createTrackbar(
        "neighbourhood, N",
        window_name,
        neighbourhood,
        15,
        nothing)

    while (keep_processing):

        # if video file successfully open then read frame from video

        if (cap.isOpened):
            ret, frame = cap.read()

            # when we reach the end of the video (file) exit cleanly

            if (ret == 0):
                keep_processing = False
                continue

            # rescale if specified

            if (args.rescale != 1.0):
                frame = cv2.resize(
                    frame, (0, 0), fx=args.rescale, fy=args.rescale)

        # convert to single channel grayscale image
        # with 32-bit float representation per pixel

        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        gray = np.float32(gray)

        # get parameters from track bars

        neighbourhood = cv2.getTrackbarPos("neighbourhood, N", window_name)

        # check neighbourhood is greater than 3 and odd

        neighbourhood = max(3, neighbourhood)
        if not (neighbourhood % 2):
            neighbourhood = neighbourhood + 1

        # find harris corners (via the good features to track function)

        corners = cv2.goodFeaturesToTrack(
            gray,
            maxCorners=500,
            qualityLevel=0.01,
            minDistance=10,
            blockSize=neighbourhood,
            useHarrisDetector=True,
            k=0.01)
        corners = np.intp(corners)

        for i in corners:
            x, y = i.ravel()
            cv2.circle(frame, (x, y), 3, (0, 255, 0), -1)

        # alternatively get the raw harris eigenvalue response

        # dst = cv2.cornerHarris(gray,neighbourhood,neighbourhood, 0.01)

        # Threshold for an optimal value, it may vary depending on the image.

        # frame[dst>0.005*dst.max()]=[0,255,0]

        # display image

        cv2.imshow(window_name, frame)

        # start the event loop - essential

        # cv2.waitKey() is a keyboard binding function (argument is the time in
        # milliseconds). It waits for specified milliseconds for any keyboard
        # event. If you press any key in that time, the program continues.
        # If 0 is passed, it waits indefinitely for a key stroke.
        # (bitwise and with 0xFF to extract least significant byte of
        # multi-byte response)

        # wait 40ms (i.e. 1000ms / 25 fps = 40 ms)

        key = cv2.waitKey(40) & 0xFF

        # It can also be set to detect specific key strokes by recording which
        # key is pressed

        # e.g. if user presses "x" then exit  / press "f" for fullscreen
        # display

        if (key == ord('x')):
            keep_processing = False
        elif (key == ord('f')):
            cv2.setWindowProperty(
                window_name,
                cv2.WND_PROP_FULLSCREEN,
                cv2.WINDOW_FULLSCREEN)

    # close all windows

    cv2.destroyAllWindows()

else:
    print("No video file specified or camera connected.")

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