template_match_multiscale.py

#!/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]

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)

  # 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)