calibrate.py

#!/usr/bin/env python3
"""Corner-calibration helper for digitization.

    python calibrate.py x1 y1 x2 y2 x3 y3 x4 y4   # TL TR BR BL (8x8 playing area)

Draws the chosen corners on the photo and the perspective-warped board with an
8x8 grid, so we can eyeball whether the corners are right before classifying.
"""
import sys
import cv2 as cv
import numpy as np

SIZE = 800  # warped board side (px)

img = cv.imread("samples/board.png")
H, W = img.shape[:2]

if len(sys.argv) == 9:
    pts = list(map(float, sys.argv[1:9]))
    corners = np.array([[pts[0], pts[1]], [pts[2], pts[3]],
                        [pts[4], pts[5]], [pts[6], pts[7]]], dtype=np.float32)
else:
    # first guess (TL, TR, BR, BL) on the 1920x1080 photo
    corners = np.array([[518, 130], [1210, 115], [1370, 900], [455, 915]], dtype=np.float32)

# --- overlay on the original ---
ov = img.copy()
labels = ["TL", "TR", "BR", "BL"]
for (x, y), lab in zip(corners, labels):
    cv.circle(ov, (int(x), int(y)), 12, (0, 0, 255), -1)
    cv.putText(ov, lab, (int(x) + 14, int(y)), cv.FONT_HERSHEY_SIMPLEX, 1.2, (0, 0, 255), 3)
cv.polylines(ov, [corners.astype(int)], True, (0, 255, 0), 3)
cv.imwrite("/tmp/overlay.png", ov)

# --- perspective warp to a square, with 8x8 grid ---
dst = np.array([[0, 0], [SIZE, 0], [SIZE, SIZE], [0, SIZE]], dtype=np.float32)
M = cv.getPerspectiveTransform(corners, dst)
warp = cv.warpPerspective(img, M, (SIZE, SIZE))
grid = warp.copy()
for i in range(9):
    p = i * SIZE // 8
    cv.line(grid, (p, 0), (p, SIZE), (0, 0, 255), 2)
    cv.line(grid, (0, p), (SIZE, p), (0, 0, 255), 2)
cv.imwrite("/tmp/warped_grid.png", grid)
cv.imwrite("/tmp/warped.png", warp)
print("wrote /tmp/overlay.png and /tmp/warped_grid.png")