Template Matching using Python and OpenCV with the Help of Scaling

scaled_template_matching.py

"""
Usage: python scaled_template_matching.py --template tmp.jpg --image img.jpg

Installation: 
pip install opencv-contrib-python numpy imutils

blog post about this scale image & cv2.matchTemplate method:
https://www.pyimagesearch.com/2015/01/26/multi-scale-template-matching-using-python-opencv/
"""
import glob
import argparse

import numpy as np
import imutils
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(
    "-i", "--image", required=True,
    help="Path to the 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())

tmp_path = args['template']
img_path = args['image']
res_path = '{}_{}.jpg'.format(
    tmp_path.strip('.jpg').strip('.jpeg'),
    img_path.strip('.jpg').strip('.jpeg'),
)

# load the image image, convert it to grayscale, and detect edges
template = cv2.imread(args["template"])
# cv2.imshow("Template", template)
# cv2.waitKey(0)
# cv2.destroyAllWindows()

template = imutils.resize(template, width=200)
# cv2.imshow("Template", template)
# cv2.waitKey(0)
# cv2.destroyAllWindows()

template = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)
template = cv2.Canny(template, 50, 200)
(tH, tW) = template.shape[:2]
# cv2.imshow("Template", template)
# cv2.waitKey(0)
# cv2.destroyAllWindows()


# loop over the images to find the template in
# for imagePath in glob.glob(args["images"] + "/*.jpg"):
for imagePath in glob.glob(args["image"]):
    # load the image, convert it to grayscale, and initialize the
    # bookkeeping variable to keep track of the matched region
    image = cv2.imread(imagePath)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    found = None
    # loop over the scales of the image
    for scale in np.linspace(1, 2, 10)[::-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 update
        # the bookkeeping variable
        if found is None or maxVal > found[0]:
            found = (maxVal, maxLoc, r)

    # unpack the bookkeeping variable 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)
    cv2.destroyAllWindows()

    cv2.imwrite(res_path, image)