File size: 2,377 Bytes
7111e1a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | import os
from PIL import Image, ImageDraw, ImageFont
os.makedirs('test_images', exist_ok=True)
def load_font(size=22): # FIXED: was 20 — must match fix_data.py FONT_SIZE=22
"""Same font loader as fix_data.py — tries multiple paths."""
for fp in [
'arial.ttf', 'Arial.ttf',
'/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf',
'/System/Library/Fonts/Helvetica.ttc',
'C:/Windows/Fonts/arial.ttf',
]:
try:
return ImageFont.truetype(fp, size)
except Exception:
continue
print("WARNING: Could not load Arial/DejaVu font. Using default — predictions may be inaccurate.")
return ImageFont.load_default()
def create_image(text, filename):
"""Render text exactly the same way as fix_data.py training images."""
img = Image.new('RGB', (512, 64), color=(255, 255, 255))
draw = ImageDraw.Draw(img)
font = load_font(22)
bbox = draw.textbbox((0, 0), text, font=font)
x = max((512 - (bbox[2] - bbox[0])) // 2, 2)
y = max((64 - (bbox[3] - bbox[1])) // 2, 2)
draw.text((x, y), text, fill=(0, 0, 0), font=font)
img.save(filename)
print(f'Created: {filename}')
# ── Test samples ──────────────────────────────────────────────
create_image('Juan Dela Cruz', 'test_images/demo.jpg')
create_image('Juan Dela Cruz', 'test_images/name1.jpg')
create_image('01/15/1990', 'test_images/date1.jpg')
create_image('Tarlac City', 'test_images/place1.jpg')
create_image('Maria Santos', 'test_images/form1a_sample.jpg')
# ── Extra test cases (names, dates, addresses) ────────────────
create_image('Jose Dela Cruz Jr.', 'test_images/name2.jpg')
create_image('Ana Marie Reyes', 'test_images/name3.jpg')
create_image('03/22/1985', 'test_images/date2.jpg')
create_image('07/04/2000', 'test_images/date3.jpg')
create_image('Brgy. San Jose, Capas, Tarlac', 'test_images/place2.jpg')
create_image('78 MacArthur Hwy., Tarlac City', 'test_images/place3.jpg')
print('\nAll test images created!')
print('Font used matches training data — predictions should be accurate.') |