answer.py

import cv2
import numpy as np

def func(input_path, dataset_path, output_path):
    # Load test image
    img = cv2.imread(input_path, cv2.IMREAD_GRAYSCALE)
    if img is None:
        raise FileNotFoundError(f"Input image not found: {input_path}")

    # Load templates
    templates = {}
    for i in range(10):
        path = dataset_path + "/" + str(i) + ".png"
        templates[str(i)] = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
    # Parameters
    threshold = 0.8
    detections = []

    # Run template matching for each digit
    for digit, tmpl in templates.items():
        result = cv2.matchTemplate(img, tmpl, cv2.TM_CCOEFF_NORMED)
        yloc, xloc = np.where(result >= threshold)

        for (x, y) in zip(xloc, yloc):
            h, w = tmpl.shape
            detections.append((x, y, x + w, y + h, digit, result[y, x]))

    # Sort detections by x coordinate
    detections.sort(key=lambda tup: tup[0])

    # Filter overlapping boxes (naively)
    filtered = []
    prev_x = -100
    for det in detections:
        x1, y1, x2, y2, digit, score = det
        if x1 - prev_x > 10:  # simple distance filter
            filtered.append((x1, y1, x2, y2, digit))
            prev_x = x1

    # Build recognized number
    recognized = ""
    for (_, _, _, _, digit) in filtered:
        recognized += digit

    # Save result to text file
    with open(output_path, 'w') as f:
        f.write(recognized)

func('simple_test.png', 'simple_templates', 'res.txt')