Python and OpenCV for multi-scale template matching implementation

# USAGE
# python  --template cod_logo.png --images images
# USAGE2 Understanding Practical Inspection Principles and Details
# python  --template cod_logo.png --images images --visualize 1

# Import the necessary packages
import argparse  # argparse parses command line arguments
import glob  # Get the path to the input image

import cv2  # opencv bindings
import imutils  # Some methods of image processing
import numpy as np  # numpy for numerical processing

# Build command line and parse parameters
# --template Template path
# --images Original image path
# --visualize Flag whether to show the visualization results for each iteration
ap = ()
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 template image, convert grayscale map, detect edges
# Template matching using edges instead of the original image can greatly improve the accuracy of template matching.
template = (args["template"])
template = (template, cv2.COLOR_BGR2GRAY)
template = (template, 50, 200)
(tH, tW) = [:2]
("Template", template)

# Iterate over images to match templates
for imagePath in (args["images"] + "/*.jpg"):

    # Load image, convert to grayscale, initialize bookkeeping variables for tracking matching regions
    image = (imagePath)
    gray = (image, cv2.COLOR_BGR2GRAY)
    found = None

    # Iterate over image dimensions
    for scale in (0.2, 1.0, 20)[::-1]:
        # Scale the image according to scale and keep its aspect ratio
        resized = (gray, width=int([1] * scale))
        r = [1] / float([1])

        # Zoom to image smaller than template, terminate
        if [0] < tH or [1] < tW:
            break

        # Detect edges in scaled grayscale image for template matching
        # Compute a Canny edge representation of the image using exactly the same parameters as the template image;
        # Use application templates to match;
        # Get the correlation result and return a 4-tuple containing the min-correlation value, the max-correlation value, the (x, y)-coordinate of the min-value, and the (x, y)-coordinate of the max-value, respectively. We are only interested in the maximum and (x, y)-coordinates, so we keep only the maximum and discard the minimum.
        edged = (resized, 50, 200)
        result = (edged, template, cv2.TM_CCOEFF)
        (_, maxVal, _, maxLoc) = (result)

        # Check for visualization
        if ("visualize", False):
            # Bounding boxes drawn on detected areas
            clone = ([edged, edged, edged])
            (clone, (maxLoc[0], maxLoc[1]),
                          (maxLoc[0] + tW, maxLoc[1] + tH), (0, 0, 255), 2)
            ("Visualize", clone)
            (0)

        # If we find a new maximum correction value, update the value of the bookkeeping variable
        if found is None or maxVal > found[0]:
            found = (maxVal, maxLoc, r)

    # Unwrap the bookkeeping variables and calculate the bounding box (x, y) coordinates 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 bounding boxes on detection results and display images
    (image, (startX, startY), (endX, endY), (0, 0, 255), 2)
    ("Image", image)
    (0)