root
130 lines
4.3 KiB
Python
130 lines
4.3 KiB
Python
#!/usr/bin/env python3
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"""Prepare a sharp, full-frame beef boneless product photo for the shop."""
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from __future__ import annotations
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import argparse
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import urllib.request
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from pathlib import Path
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from PIL import Image, ImageEnhance, ImageFilter, ImageOps, ImageStat
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ROOT = Path(__file__).resolve().parents[1]
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SITE = ROOT / "public" / "images" / "site"
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OUT_PATH = SITE / "beef-boneless.jpg"
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SOURCE_CACHE = SITE / "_beef_boneless_src.jpg"
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# High-res premium raw boneless beef (Unsplash)
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PRIMARY_URL = (
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"https://images.unsplash.com/photo-1603048297172-c92544798d5a"
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"?auto=format&fit=crop&w=1800&h=1800&crop=center&q=95"
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)
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# Original product reference (lower res fallback)
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FALLBACK_URL = (
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"https://www.hotcurrymarket.fi/wp-content/uploads/2025/01/"
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"FRESH-BEEF-BONELESS-WITHOUT-FAT-1KG.jpg"
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)
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OUTPUT_SIZE = (1400, 1400)
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def download(url: str, dest: Path) -> None:
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req = urllib.request.Request(
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url,
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headers={"User-Agent": "Mozilla/5.0 (compatible; Kottgard-site-builder/1.0)"},
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)
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with urllib.request.urlopen(req, timeout=60) as resp:
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dest.write_bytes(resp.read())
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def trim_near_white(img: Image.Image, tolerance: int = 18) -> Image.Image:
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"""Remove excess light borders from the source photo."""
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rgb = img.convert("RGB")
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pixels = rgb.load()
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w, h = rgb.size
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def row_is_border(y: int) -> bool:
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samples = [pixels[x, y] for x in range(0, w, max(1, w // 24))]
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return all(min(p) >= 255 - tolerance for p in samples)
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def col_is_border(x: int) -> bool:
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samples = [pixels[x, y] for y in range(0, h, max(1, h // 24))]
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return all(min(p) >= 255 - tolerance for p in samples)
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top = 0
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while top < h and row_is_border(top):
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top += 1
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bottom = h - 1
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while bottom > top and row_is_border(bottom):
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bottom -= 1
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left = 0
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while left < w and col_is_border(left):
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left += 1
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right = w - 1
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while right > left and col_is_border(right):
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right -= 1
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if right - left > 80 and bottom - top > 80:
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return rgb.crop((left, top, right + 1, bottom + 1))
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return rgb
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def enhance_photo(img: Image.Image) -> Image.Image:
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img = ImageOps.autocontrast(img, cutoff=0.5)
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img = ImageEnhance.Brightness(img).enhance(1.03)
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img = ImageEnhance.Contrast(img).enhance(1.08)
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img = ImageEnhance.Color(img).enhance(1.1)
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img = ImageEnhance.Sharpness(img).enhance(1.35)
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return img.filter(ImageFilter.UnsharpMask(radius=1.0, percent=110, threshold=2))
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def crop_center_cover(img: Image.Image, size: tuple[int, int]) -> Image.Image:
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tw, th = size
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w, h = img.size
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scale = max(tw / w, th / h)
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nw, nh = int(w * scale), int(h * scale)
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resized = img.resize((nw, nh), Image.Resampling.LANCZOS)
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left = (nw - tw) // 2
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top = (nh - th) // 2
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return resized.crop((left, top, left + tw, top + th))
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def prepare_product_image(source: Path, output: Path = OUT_PATH) -> None:
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beef = Image.open(source).convert("RGB")
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beef = trim_near_white(beef)
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beef = enhance_photo(beef)
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# Full-frame crop — meat fills the product image (no tiny subject in white void)
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beef = crop_center_cover(beef, OUTPUT_SIZE)
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beef.save(output, "JPEG", quality=96, optimize=True, subsampling=0)
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print(f"Saved product photo {OUTPUT_SIZE[0]}x{OUTPUT_SIZE[1]} → {output}")
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def main() -> None:
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parser = argparse.ArgumentParser(description="Prepare beef boneless product photo")
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parser.add_argument("--url", default=PRIMARY_URL)
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parser.add_argument("--input", type=Path)
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parser.add_argument("--output", type=Path, default=OUT_PATH)
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parser.add_argument("--use-cache", action="store_true")
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parser.add_argument("--fallback", action="store_true", help="Use hot curry source")
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args = parser.parse_args()
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if args.input:
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src = args.input
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elif args.use_cache and SOURCE_CACHE.exists():
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src = SOURCE_CACHE
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print(f"Using cached source → {src}")
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else:
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url = FALLBACK_URL if args.fallback else args.url
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print(f"Downloading source image…\n {url}")
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try:
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download(url, SOURCE_CACHE)
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except Exception as exc:
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print(f"Primary download failed ({exc}), trying fallback…")
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download(FALLBACK_URL, SOURCE_CACHE)
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src = SOURCE_CACHE
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prepare_product_image(src, args.output)
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if __name__ == "__main__":
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main() |