画像処理：スキャンした画像を多くの同一機能を備えたテンプレート画像にマッピングする

Based on the clarifications provided by @it_guy, I have attempted to find all the crossed hearts using just the scanned image. I would have to try the algorithm on more images to check whether this approach will generalize or not.

1. Binarize the scanned image.

   gray_image = cv2.cvtColor(rgb_image, cv2.COLOR_BGR2GRAY)
   ret, thresh = cv2.threshold(gray_image, 180, 255, cv2.THRESH_BINARY_INV)
   

2. Perform dilation to close small gaps in the outline of the hearts, and the curves representing crosses. Note - The structuring element np.ones((1,2), np.uint8 can be changed by running the algorithm through multiple images and finding the most suitable structuring element.

closing_original = cv2.morphologyEx(original_binary, cv2.MORPH_DILATE, np.ones((1,2), np.uint8)). 

3. Find all the contours in the image. The contours include all hearts and the triangle at the bottom. We eliminate other contours like dots by placing constraints on the height and width of contours to filter them. Further, we also use contour hierachies to eliminate inner contours in cross hearts.

contours_original, hierarchy_original = cv2.findContours(closing_original, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)

4. We iterate through each of the filtered contours.

Contour with normal heart -

Contour with crossed heart -

Let us observe the difference between these two types of hearts. If we look at the transition from white-to-black pixel and black-to-white pixel ( from top to bottom ) inside the normal heart, we see that for majority of the image columns the number of such transitions are 4. ( Top border - 2 transitions, bottom border - 2 transitions )

white-to-black pixel - (255, 255, 0, 0, 0)

黒から白へのピクセル- （0、0、255、255、255）

ただし、交差した心臓の場合、交差した曲線/線が心臓内にさらに2つの遷移（最初に黒から白、次に白から黒）を追加するため、大部分の列の遷移の数は6でなければなりません。 ）。したがって、そのような遷移が4つ以上あるすべての画像列の中で、列の40％以上に6つの遷移がある場合、指定された輪郭は交差した輪郭を表します。結果-

コード-

import cv2
import numpy as np

def convert_to_binary(rgb_image):
    gray_image = cv2.cvtColor(rgb_image, cv2.COLOR_BGR2GRAY)
    ret, thresh = cv2.threshold(gray_image, 180, 255, cv2.THRESH_BINARY_INV)
    return gray_image, thresh

original = cv2.imread('original.jpg')
height, width = original.shape[:2]
original_gray, original_binary = convert_to_binary(original) # Get binary image
cv2.imwrite("binary.jpg", original_binary)
closing_original = cv2.morphologyEx(original_binary, cv2.MORPH_DILATE, np.ones((1,2), np.uint8)) # Close small gaps in the binary image
cv2.imwrite("closed.jpg", closing_original)
contours_original, hierarchy_original = cv2.findContours(closing_original, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE) # Get all the contours
bounding_rects_original = [cv2.boundingRect(c) for c in contours_original] # Get all contour bounding boxes
orig_boxes = list()

all_contour_image = original.copy()
for i, (x, y, w, h) in enumerate(bounding_rects_original):
    if h > height / 2 or w > width / 2:  # Eliminate extremely large contours
        continue
    if h < w / 2 or w < h / 2: # Eliminate vertical / horuzontal lines
        continue
    if w * h < 200: # Eliminate small area contours
        continue
    if hierarchy_original[0][i][3] != -1: # Eliminate contours created by heart crosses
        continue
    orig_boxes.append((x, y, w, h))
    cv2.rectangle(all_contour_image, (x,y), (x + w, y + h), (0, 255, 0), 3)
# cv2.imshow("warped", closing_original)
cv2.imwrite("all_contours.jpg", all_contour_image)

final_image = original.copy()
for x, y, w, h in orig_boxes:
    cropped_image = closing_original[y - 2 :y + h + 2, x: x + w] # Get the heart binary image

    col_pixel_diffs = np.abs(np.diff(cropped_image.T.astype(np.int16))/255) # Obtain all consecutive pixel differences in all the columns 

    column_sums = np.sum(col_pixel_diffs, axis=1) # Get the sum of each column's transitions. This results in an array of size equal 
    # to the number of columns, each element representing the number of black-white and white-black transitions. 

    percent_crosses = np.sum(column_sums >= 6)/ np.sum(column_sums >= 4) # Percentage of columns with 6 transitions among columns with 4 transitions
    if percent_crosses > 0.4: # Crossed heart criterion
        cv2.rectangle(final_image, (x,y), (x + w, y + h), (0, 255, 0), 3)
        cv2.imwrite("crossed_heart.jpg", cropped_image)
    else:
        cv2.imwrite("normal_heart.jpg", cropped_image)
cv2.imwrite("all_crossed_hearts.jpg", final_image)

このアプローチは、より多くの画像でテストして、その精度を見つけることができます。