I am trying to multiscale in Python and it isnt working

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Python2.7

Line 37-39 do not work. Also, we do not know and cannot find out what the command lines and arguments for argparse are. We do need the visual line.

What I have tried:

# USAGE
# python match.py --template cod_logo.png --images images

# import the necessary packages
import numpy as np
import argparse
import imutils
import glob
import cv2

# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-t", "--template", required=True, help="Path to template image")
ap.add_argument("-i", "--images", required=True,
help="Path to images where template will be matched")
ap.add_argument("-v", "--visualize",
help="Flag indicating whether or not to visualize each iteration")
args = vars(ap.parse_args())

# load the image image, convert it to grayscale, and detect edges
template = cv2.imread(args["/home/pi/Downloads/opencv-template-for-matching.jpg"])
template = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)
template = cv2.Canny(template, 50, 200)
(tH, tW) = template.shape[:2]
cv2.imshow("Template", template)

# loop over the images to find the template in
for imagePath in glob.glob(args["images"] + "/*.jpg"):
# load the image, convert it to grayscale, and initialize the
# bookkeeping variable to keep track of the matched region
image = cv2.imread("/home/pi/Downloads/opencv-template-matching-python-tutorial.jpg")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
found = None

# loop over the scales of the image
for scale in np.linspace(0.2, 1.0, 20)[::-1]:
# resize the image according to the scale, and keep track
# of the ratio of the resizing
resized = imutils.resize(gray, width = int(gray.shape[1] * scale))
r = gray.shape[1] / float(resized.shape[1])

# if the resized image is smaller than the template, then break
# from the loop
if resized.shape[0] < tH or resized.shape[1] < tW:
break

# detect edges in the resized, grayscale image and apply template
# matching to find the template in the image
edged = cv2.Canny(resized, 50, 200)
result = cv2.matchTemplate(edged, template, cv2.TM_CCOEFF)
(_, maxVal, _, maxLoc) = cv2.minMaxLoc(result)

# check to see if the iteration should be visualized
if args.get("visualize", False):
# draw a bounding box around the detected region
clone = np.dstack([edged, edged, edged])
cv2.rectangle(clone, (maxLoc[0], maxLoc[1]),
(maxLoc[0] + tW, maxLoc[1] + tH), (0, 0, 255), 2)
cv2.imshow("Visualize", clone)
cv2.waitKey(0)

# if we have found a new maximum correlation value, then ipdate
# the bookkeeping variable
if found is None or maxVal > found[0]:
found = (maxVal, maxLoc, r)

# unpack the bookkeeping varaible and compute the (x, y) coordinates
# of the bounding box based on the resized ratio
(_, maxLoc, r) = found
(startX, startY) = (int(maxLoc[0] * r), int(maxLoc[1] * r))
(endX, endY) = (int((maxLoc[0] + tW) * r), int((maxLoc[1] + tH) * r))

# draw a bounding box around the detected result and display the image
cv2.rectangle(image, (startX, startY), (endX, endY), (0, 0, 255), 2)
cv2.imshow("Image", image)
cv2.waitKey(0)