Omair Saleh
ac19afce4e
HERO IMAGE: - Generated 3 concepts with gemini-3-pro-image-preview, picked #1 - Phone showing 'Payment Received' notification at a charity gala dinner - Warm tungsten bokeh chandeliers against dark bg-gray-950 - Directly visualizes the headline: 'money in the bank' - Candid documentary angle, not looking at camera, brand compliant IMAGE OPTIMIZATION (85% total reduction): - All 21 images resized: landscape max 1200px, portrait max 1000px - Compressed JPEG quality 80, progressive encoding, EXIF stripped - Total: 13.6MB -> 2.1MB (saved 11.5MB) - Individual savings: 81-90% per image NEXT.JS IMAGE PIPELINE: - Added sharp (10x faster than squoosh for image processing) - next.config.mjs: WebP format, proper device/image sizes, 1yr cache TTL - Dockerfile: libc6-compat + NEXT_SHARP_PATH for Alpine sharp support - First request: ~3s (processing), cached: <1s WebP served sizes: hero 52KB, cards 32-40KB (vs original 500-800KB JPEGs)
83 lines
2.7 KiB
Python
83 lines
2.7 KiB
Python
"""Optimize all landing images: resize + compress + strip EXIF.
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Also set hero-concept-1.jpg as the new hero image."""
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import sys, os, shutil
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sys.stdout.reconfigure(encoding='utf-8')
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from PIL import Image
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IMG_DIR = "pledge-now-pay-later/public/images/landing"
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BRAND_DIR = "pledge-now-pay-later/brand/photography"
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# Max dimensions per image type
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# Portrait (4:5 or 1:1) → max 1000px long side
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# Landscape (16:9) → max 1200px wide
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MAX_LANDSCAPE = 1200
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MAX_PORTRAIT = 1000
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QUALITY = 80
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def optimize(path, max_long_side):
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"""Resize + compress + strip EXIF. Returns (old_size, new_size)."""
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old_size = os.path.getsize(path)
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img = Image.open(path)
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# Strip EXIF by creating new image
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if img.mode != "RGB":
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img = img.convert("RGB")
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# Resize if larger than max
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w, h = img.size
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long_side = max(w, h)
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if long_side > max_long_side:
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ratio = max_long_side / long_side
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new_w = int(w * ratio)
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new_h = int(h * ratio)
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img = img.resize((new_w, new_h), Image.LANCZOS)
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# Save with progressive JPEG, quality 80, optimized
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img.save(path, "JPEG", quality=QUALITY, optimize=True, progressive=True)
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new_size = os.path.getsize(path)
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return old_size, new_size, img.size
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# Step 1: Set hero-concept-1 as the main hero
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hero_src = os.path.join(IMG_DIR, "hero-concept-1.jpg")
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hero_dst = os.path.join(IMG_DIR, "00-hero.jpg")
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if os.path.exists(hero_src):
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shutil.copy2(hero_src, hero_dst)
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# Also copy to brand dir
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shutil.copy2(hero_src, os.path.join(BRAND_DIR, "00-hero.jpg"))
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print(f"Set hero: hero-concept-1.jpg -> 00-hero.jpg")
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# Step 2: Clean up concept images
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for f in ["hero-concept-1.jpg", "hero-concept-2.jpg", "hero-concept-3.jpg"]:
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p = os.path.join(IMG_DIR, f)
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if os.path.exists(p):
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os.remove(p)
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print(f"Cleaned: {f}")
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# Step 3: Optimize all images
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total_old = 0
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total_new = 0
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for fname in sorted(os.listdir(IMG_DIR)):
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if not fname.endswith(".jpg"):
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continue
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path = os.path.join(IMG_DIR, fname)
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img = Image.open(path)
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w, h = img.size
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img.close()
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# Determine max size based on orientation
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if w > h:
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max_side = MAX_LANDSCAPE # landscape
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else:
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max_side = MAX_PORTRAIT # portrait or square
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old_size, new_size, final_dims = optimize(path, max_side)
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total_old += old_size
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total_new += new_size
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saved_pct = (1 - new_size / old_size) * 100 if old_size > 0 else 0
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print(f" {fname:45s} {final_dims[0]:>5d}x{final_dims[1]:<5d} {old_size//1024:>4d}KB -> {new_size//1024:>4d}KB ({saved_pct:+.0f}%)")
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print(f"\n{'='*60}")
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print(f"Total: {total_old//1024}KB -> {total_new//1024}KB ({(1 - total_new/total_old)*100:.0f}% reduction)")
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print(f"{'='*60}")
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