fitting check

import cv2
import numpy as np

def nothing(x):
    pass

win_w, win_h = 800, 100
img = np.zeros((win_h,win_w,3), np.uint8)
cv2.imshow('image', img)
cv2.createTrackbar('1cm', 'image', 1, 50, nothing)
cv2.createTrackbar('0.01cm', 'image', 0, 100, nothing)
cv2.createTrackbar('offset(0.1cm)', 'image', 0, 500, nothing)
cv2.waitKey(1)

# centimeters
vals = (0, 7.5, 9.5, 8.5, 9.5, 9, 65, 9, 9.5, 8.5, 9.5, 7.5, 7.5, 9.5, 8.5, 9.5, 9, 65, 9, 9.5, 8.5, 9.5, 7.5)
dist = 0.0
total_dist = 0
for val in vals:
    total_dist += val

# pixels
xs = 50
xe = 750
ys = 20
ye = 50
yt = 80

# conversion (cm -> px)
conv = lambda a: int(a / total_dist * (xe - xs) + xs)

while 1:
    img = cv2.rectangle(img, (0, 0), (win_w, win_h), (255, 255, 255), -1)

    dist = 0
    for val in vals:
        dist += val
        xpos = conv(dist)
        img = cv2.line(img, (xpos, ys), (xpos, ye), (0, 0, 0), 2)

    coarse = cv2.getTrackbarPos('1cm','image')
    coarse = max(coarse, 1) # prevent infinite loop
    fine = cv2.getTrackbarPos('0.01cm', 'image') / 100
    offset = cv2.getTrackbarPos('offset(0.1cm)', 'image') / 10
    dist = offset
    ticks = 0
    while dist < total_dist:
        xpos = conv(dist)
        img = cv2.line(img, (xpos, ye), (xpos, yt), (255, 0, 0), 2)
        dist += coarse + fine
        ticks += 1

    print(f'{ticks} ticks, {coarse+fine}cm gap, {offset}cm offset              ', end='\r')
    cv2.imshow('image', img)

    if cv2.waitKey(1) & 0xFF == ord('e'):
        break

cv2.destroyAllWindows()