75 lines
2.4 KiB
Python
75 lines
2.4 KiB
Python
from pathlib import Path
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from PIL import Image
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from base_proxy import BASE_DIR
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from sheet_layout import SheetLayout, CardImageTransfer
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W_INCHES_EXPECTED = 2.72
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H_INCHES_EXPECTED = 3.7
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SAVE_INTERMEDIATE_FOR_DEBUG = False
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def make_intermediate_name(input_path: Path, infix: str):
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return BASE_DIR / 'cropped_cards' / f'{input_path.stem}.{infix}.jpg'
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class InputCardImage:
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def __init__(self, path: Path):
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self.path = path
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self.input_dpi = None
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self.original_im = None
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def load(self) -> None:
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with Image.open(self.path) as im:
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# print(im.info)
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rgb_im = im.convert('RGB')
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w, h = rgb_im.width, rgb_im.height
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dpi_by_w = w / W_INCHES_EXPECTED
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dpi_by_h = h / H_INCHES_EXPECTED
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print(f'Image is {w} x {h}, {dpi_by_w} dpi (by width)')
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if dpi_by_h != dpi_by_w:
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print(f'By height dpi is different: {dpi_by_h} dpi')
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self.input_dpi = dpi_by_w
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self.original_im = rgb_im
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def get_cropped_and_resized(self, layout: SheetLayout, transfer: CardImageTransfer) -> Image:
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new_dpi = layout.dpi
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self.load()
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im = self.original_im
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print(f'Input is {im.size[0]} x {im.size[1]}')
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# Resize to match sheet DPI
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new_w = layout.in_to_px(W_INCHES_EXPECTED)
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new_h = layout.in_to_px(H_INCHES_EXPECTED)
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print(f'Resized input to {new_w} x {new_h}')
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im = im.resize((new_w, new_h), resample=Image.Resampling.LANCZOS)
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if SAVE_INTERMEDIATE_FOR_DEBUG:
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im.save(make_intermediate_name(self.path, 'step1resize'), 'JPEG', dpi=(layout.dpi, layout.dpi), quality=100, subsampling=0)
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# Crop
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left = (new_w - layout.card_w) // 2
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right = left + layout.card_w
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top = (new_h - layout.card_h) // 2
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bottom = top + layout.card_h
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if transfer.bleeds[0]:
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top -= layout.leave_bleed
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if transfer.bleeds[1]:
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bottom += layout.leave_bleed
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if transfer.bleeds[2]:
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left -= layout.leave_bleed
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if transfer.bleeds[3]:
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right += layout.leave_bleed
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# Crop
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crop_box = (left, top, right, bottom)
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print(f'Cropped box {crop_box}')
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im = im.crop(crop_box)
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if SAVE_INTERMEDIATE_FOR_DEBUG:
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im.save(make_intermediate_name(self.path, 'step2crop'), 'JPEG', dpi=(layout.dpi, layout.dpi), quality=100, subsampling=0)
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return im
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