template_matching.py

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

def match_common_region(stage_image,wsi_image,visualize,name):
    # load the image image, convert it to grayscale, and detect edges
    template = cv2.imread(stage_image)
    template = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)
    template = cv2.Canny(template, 50, 200)
    (tH, tW) = template.shape[:2]
    #cv2.imshow("Template", template)

    # load the image, convert it to grayscale, and initialize the
    # bookkeeping variable to keep track of the matched region
    image = cv2.imread(wsi_image)
    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]:
        print('scale',scale)
        # 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)
        print(maxVal)
        # check to see if the iteration should be visualized
        
        if visualize==1:
            # 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))

    ndarray=np.asarray(image)
    cropped=ndarray[startY:endY,startX:endX]
    # draw a bounding box around the detected result and display the image
    timestamp=time.time()
    timestamp=datetime.datetime.fromtimestamp(timestamp).strftime('%Y-%m-%d-%H:%M:%S')
    filename=name+timestamp+".png"
    cv2.imwrite("./static/matching_region/"+filename,cropped)
    print(cropped.shape)
    cv2.rectangle(image, (startX, startY), (endX, endY), (0, 0, 255), 2)

    compute={"startX":startX,"endX":endX,"startY":startY,"endY":endY,"filename":filename}
    print("computed values")
    print(compute)
    return compute
    #cv2.imshow("Image", image)
    #cv2.waitKey(0)