drone.py

# import the necessary packages
from imutils.video import VideoStream
import argparse
import imutils
import cv2
import datetime
from pyzbar import pyzbar
import datetime
import time

"""
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video", help="path to the video file")
args = vars(ap.parse_args())

# load the video
camera = cv2.VideoCapture(args["video"])

"""

# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-o", "--output", type=str, default="inventory.csv",
                help="path to output CSV file containing barcodes")
args = vars(ap.parse_args())

# initialize the video stream and allow the camera sensor to warm up
print("[INFO] starting video stream...")
# vs = VideoStream(src=0).start() --from your webcam
# vs = VideoStream('rtsp://192.168.0.40:1026/live.sdp').start()
vs = cv2.VideoCapture(0)
time.sleep(2.0)

# open the output CSV file for writing and initialize the set of
# barcodes found thus far
csv = open(args["output"], "w")
found = set()

# keep looping
while True:
    # grab the current frame and initialize the status text
    (grabbed, frame) = vs.read()
    frame = imutils.resize(frame, width=600)
    status = "No Targets"
# reading QR code
# find the barcodes in the frame and decode each of the barcodes
    barcodes = pyzbar.decode(frame)

    # loop over the detected barcodes
    for barcode in barcodes:
        # extract the bounding box location of the barcode and draw
        # the bounding box surrounding the barcode on the image
        (x, y, w, h) = barcode.rect
        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 2)

        # the barcode data is a bytes object so if we want to draw it
        # on our output image we need to convert it to a string first
        barcodeData = barcode.data.decode("utf-8")
        barcodeType = barcode.type

        # draw the barcode data and barcode type on the image
        text = "{} ({})".format(barcodeData, barcodeType)
        cv2.putText(frame, text, (x, y - 10),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)

        # if the barcode text is currently not in our CSV file, write
        # the timestamp + barcode to disk and update the set
        if barcodeData not in found:
            csv.write("{},{}\n".format(datetime.datetime.now(),
                                       barcodeData))
            csv.flush()
            found.add(barcodeData)

    # show the output frame
    cv2.imshow("Barcode Scanner", frame)
    key = cv2.waitKey(1) & 0xFF

    # check to see if we have reached the end of the video
    if not grabbed:
        break

    # convert the frame to grayscale, blur it, and detect edges
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    blurred = cv2.GaussianBlur(gray, (7, 7), 0)
    edged = cv2.Canny(blurred, 50, 150)

    # find contours in the edge map
    cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
                            cv2.CHAIN_APPROX_SIMPLE)
    cnts = imutils.grab_contours(cnts)
# loop over the contours
    for c in cnts:
        # approximate the contour
        peri = cv2.arcLength(c, True)
        approx = cv2.approxPolyDP(c, 0.01 * peri, True)

    # ensure that the approximated contour is "roughly" rectangular
        if len(approx) >= 4 and len(approx) <= 6:
            # compute the bounding box of the approximated contour and
            # use the bounding box to compute the aspect ratio
            (x, y, w, h) = cv2.boundingRect(approx)
            aspectRatio = w / float(h)

            # compute the solidity of the original contour
            area = cv2.contourArea(c)
            hullArea = cv2.contourArea(cv2.convexHull(c))
            solidity = area / float(hullArea)

            # compute whether or not the width and height, solidity, and
            # aspect ratio of the contour falls within appropriate bounds
            keepDims = w > 25 and h > 25
            keepSolidity = solidity > 0.9
            keepAspectRatio = aspectRatio >= 0.8 and aspectRatio <= 1.2

            # ensure that the contour passes all our tests
            if keepDims and keepSolidity and keepAspectRatio:
                # draw an outline around the target and update the status
                # text
                cv2.drawContours(frame, [approx], -1, (0, 0, 255), 4)
                status = "Target(s) Acquired"

                # compute the center of the contour region and draw the
                # crosshairs
                M = cv2.moments(approx)
                (cX, cY) = (int(M["m10"] // M["m00"]),
                            int(M["m01"] // M["m00"]))
                (startX, endX) = (int(cX - (w * 0.15)), int(cX + (w * 0.15)))
                (startY, endY) = (int(cY - (h * 0.15)), int(cY + (h * 0.15)))
                cv2.line(frame, (startX, cY), (endX, cY), (0, 0, 255), 3)
                cv2.line(frame, (cX, startY), (cX, endY), (0, 0, 255), 3)

            # draw the status text on the frame
    cv2.putText(frame, status, (20, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
                (0, 0, 255), 2)

    # show the frame and record if a key is pressed
    cv2.imshow("Frame", frame)
    key = cv2.waitKey(1) & 0xFF

    # if the 'q' key is pressed, stop the loop
    if key == ord("q"):
        break

# cleanup the camera and close any open windows
print("[INFO] cleaning up...")
vs.release()
cv2.destroyAllWindows()
csv.close()