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#!/usr/bin/env python
# USAGE
# python template_match_multiscale.py --template template.png --image image.tif
# Adapted from: http://www.pyimagesearch.com/2015/01/26/multi-scale-template-matching-using-python-opencv/
# import the necessary packages
import numpy as np
import argparse
import glob
import cv2
def resize(image, width = None, height = None, inter = cv2.INTER_AREA):
# initialize the dimensions of the image to be resized and
# grab the image size
dim = None
(h, w) = image.shape[:2]
# if both the width and height are None, then return the
# original image
if width is None and height is None:
return image
# check to see if the width is None
if width is None:
# calculate the ratio of the height and construct the
# dimensions
r = height / float(h)
dim = (int(w * r), height)
# otherwise, the height is None
else:
# calculate the ratio of the width and construct the
# dimensions
r = width / float(w)
dim = (width, int(h * r))
# resize the image
resized = cv2.resize(image, dim, interpolation = inter)
# return the resized image
return resized
# 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", "--image", required=True,
help="Path to image 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["template"])
template = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)
template = cv2.Canny(template, 50, 200)
(tH, tW) = template.shape[:2]
cv2.imshow("Template", template)
imagePath = args["image"]
# load the image, convert it to grayscale, and initialize the
# bookkeeping variable to keep track of the matched region
gray = cv2.imread(imagePath, 0)
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 = 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))
print "%dx%d+%d+%d" % ((endX - startX), (endY - startY), startX, startY)
# draw a bounding box around the detected result and display the image
# cv2.rectangle(gray, (startX, startY), (endX, endY), (0, 0, 255), 2)
# cv2.imshow("Image", gray)
# cv2.waitKey(0)
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