#!/usr/bin/env python3 """ Tatiana's PREMIUM embroidered patch composite. Focus on thread simulation, brightness, physical realism. """ from PIL import Image, ImageDraw, ImageFilter, ImageChops, ImageEnhance import numpy as np np.random.seed(17) base = Image.open("v1-03-mid-full-body.png").convert("RGBA") logo_raw = Image.open("ff-logo-mark.png").convert("RGBA") base_rgb = np.array(base.convert("RGB"), dtype=np.float32) W, H = base.size # ============================================================ # PLACEMENT # ============================================================ PATCH_W, PATCH_H = 80, 62 PASTE_X, PASTE_Y = 712, 740 arm = base_rgb[PASTE_Y:PASTE_Y+PATCH_H, PASTE_X:PASTE_X+PATCH_W] arm_lum = arm[:,:,0]*0.299 + arm[:,:,1]*0.587 + arm[:,:,2]*0.114 print(f"Arm lum: avg={arm_lum.mean():.1f} max={arm_lum.max():.1f}") # ============================================================ # PATCH FABRIC — with guaranteed min brightness # ============================================================ # Force patch to be visibly lighter than shadow arm (it's white fabric!) # Base off-white: ~210-220 range even in shadow (real white fabric behavior) # Modulate gently by local lighting (not color-match) lum_norm = np.clip(arm_lum.mean() / 160.0, 0.75, 1.15) fab_base = np.array([222, 216, 204], dtype=np.float32) * lum_norm fab_base = np.clip(fab_base, 165, 240) fab_r, fab_g, fab_b = int(fab_base[0]), int(fab_base[1]), int(fab_base[2]) print(f"Patch fabric: ({fab_r}, {fab_g}, {fab_b})") fabric = np.full((PATCH_H, PATCH_W, 4), [fab_r, fab_g, fab_b, 255], dtype=np.float32) # Fine weave texture noise = np.random.normal(0, 4.5, (PATCH_H, PATCH_W, 3)) # Coarser warp/weft pattern (fabric grain) grain_y = np.sin(np.arange(PATCH_H)[:, None] * 6.28 * 2 / 3) * 2.5 grain_x = np.sin(np.arange(PATCH_W)[None, :] * 6.28 * 2 / 4) * 2.5 grain = grain_y + grain_x fabric[:,:,:3] += noise + grain[:,:,np.newaxis] # Local lighting gradient applied to fabric arm_lum_norm = arm_lum / 255.0 lum_mod = 0.72 + 0.55 * arm_lum_norm lum_mod = np.clip(lum_mod, 0.60, 1.18)[:,:,np.newaxis] fabric[:,:,:3] = np.clip(fabric[:,:,:3] * lum_mod, 0, 255) # Patch edge shadow (stitching raises patch slightly off surface) edge_shadow = np.zeros((PATCH_H, PATCH_W), dtype=np.float32) for d in range(6): val = (6 - d) * 0.02 edge_shadow[d,:] += val edge_shadow[PATCH_H-1-d,:] += val edge_shadow[:,d] += val edge_shadow[:,PATCH_W-1-d] += val edge_shadow = np.clip(edge_shadow, 0, 0.12) fabric[:,:,:3] *= (1 - edge_shadow[:,:,np.newaxis]) fabric[:,:,3] = 255 patch = Image.fromarray(np.clip(fabric, 0, 255).astype(np.uint8), "RGBA") # ============================================================ # EMBROIDERY THREAD SIMULATION # ============================================================ logo_area_w = PATCH_W - 22 logo_area_h = PATCH_H - 18 logo_copy = logo_raw.copy() logo_copy.thumbnail((logo_area_w, logo_area_h), Image.LANCZOS) lw, lh = logo_copy.size logo_arr = np.array(logo_copy, dtype=np.float32) lum_logo = logo_arr[:,:,0]*0.299 + logo_arr[:,:,1]*0.587 + logo_arr[:,:,2]*0.114 logo_px = (lum_logo < 128) & (logo_arr[:,:,3] > 40) # --- Thread lighting simulation --- # Satin stitch: threads run in rows (horizontal), each thread is a small cylinder # Top of cylinder = highlight, edges = darker y_coords = np.arange(lh)[:, None] x_coords = np.arange(lw)[None, :] # Each thread row = 2px high, simulating individual thread bumps thread_period = 2.2 # Brightness modulation: peak at top of each thread, dark at bottom thread_phase = (y_coords % thread_period) / thread_period # 0..1 # Cylinder top (0.3) = bright, cylinder bottom (0.8) = dark thread_bright = np.cos((thread_phase - 0.3) * np.pi) * 0.5 + 0.5 # 0..1 # Also slight side sheen (highlight on one side of each thread) x_phase = (x_coords % 3.0) / 3.0 x_sheen = np.sin(x_phase * np.pi) * 0.3 # Base thread color range: 8 (dark) to 48 (highlight) thread_val = 8 + thread_bright * 32 + x_sheen * 12 thread_val = np.clip(thread_val, 4, 55) # Add micro-noise for fiber variation fiber_noise = np.random.normal(0, 2, (lh, lw)) thread_val += fiber_noise thread_val = np.clip(thread_val, 0, 65) # Build embroidery RGBA emb = np.zeros((lh, lw, 4), dtype=np.float32) emb[:,:,0] = logo_px * thread_val * 0.95 emb[:,:,1] = logo_px * thread_val * 0.85 emb[:,:,2] = logo_px * thread_val * 0.65 emb[:,:,3] = logo_px * 242 # high opacity # Slight shadow/depth: where logo pixel meets non-logo, add tiny shadow on non-logo side from scipy.ndimage import binary_dilation try: from scipy.ndimage import binary_dilation as dilate logo_dilated = dilate(logo_px, iterations=1) shadow_border = logo_dilated & ~logo_px emb[shadow_border, 0] = emb[shadow_border, 0] * 0 + fab_r * 0.82 emb[shadow_border, 1] = emb[shadow_border, 1] * 0 + fab_g * 0.82 emb[shadow_border, 2] = emb[shadow_border, 2] * 0 + fab_b * 0.82 emb[shadow_border, 3] = 140 # semi-transparent shadow print("Thread depth shadows: applied") except ImportError: print("scipy not available, skipping thread depth") emb_img = Image.fromarray(np.clip(emb, 0, 255).astype(np.uint8), "RGBA") emb_img = emb_img.filter(ImageFilter.GaussianBlur(0.35)) # Center on patch lx = (PATCH_W - lw) // 2 ly = (PATCH_H - lh) // 2 patch.paste(emb_img, (lx, ly), emb_img) # ============================================================ # STITCH BORDER — realistic double-stitch # ============================================================ draw = ImageDraw.Draw(patch) bm = 5 dark_thread = (30, 24, 16, 220) mid_thread = (55, 45, 30, 170) def stitches(d, x1, y1, x2, y2, col, orient, sl=3, sg=3): if orient == 'h': for x in range(x1, x2, sl + sg): d.line([(x, y1), (min(x+sl, x2), y1)], fill=col, width=1) else: for y in range(y1, y2, sl + sg): d.line([(x1, y), (x1, min(y+sl, y2))], fill=col, width=1) # Outer stitch row stitches(draw, bm, bm, PATCH_W-bm, bm, dark_thread, 'h') stitches(draw, bm, PATCH_H-bm-1, PATCH_W-bm, PATCH_H-bm-1,dark_thread, 'h') stitches(draw, bm, bm, bm, PATCH_H-bm, dark_thread, 'v') stitches(draw, PATCH_W-bm-1, bm, PATCH_W-bm-1,PATCH_H-bm, dark_thread, 'v') # Inner stitch row (double border) ib = bm + 4 stitches(draw, ib, ib, PATCH_W-ib, ib, mid_thread, 'h', sl=2, sg=2) stitches(draw, ib, PATCH_H-ib-1, PATCH_W-ib, PATCH_H-ib-1,mid_thread, 'h', sl=2, sg=2) stitches(draw, ib, ib, ib, PATCH_H-ib, mid_thread, 'v', sl=2, sg=2) stitches(draw, PATCH_W-ib-1, ib, PATCH_W-ib-1,PATCH_H-ib, mid_thread, 'v', sl=2, sg=2) # Outermost hairline (merrowed edge simulation) draw.rectangle([1, 1, PATCH_W-2, PATCH_H-2], outline=(45, 36, 25, 200), width=1) # Final integration blur patch = patch.filter(ImageFilter.GaussianBlur(0.3)) # ============================================================ # COMPOSITE # ============================================================ result = base.copy() # Directional drop shadow sh = Image.new("RGBA", (W, H), (0,0,0,0)) for i in range(8): a = max(0, 36 - i * 5) block = Image.new("RGBA", (PATCH_W+i*2, PATCH_H+i*2), (6, 5, 4, a)) block = block.filter(ImageFilter.GaussianBlur(1)) sh.paste(block, (PASTE_X - i + 2, PASTE_Y - i + 3), block) result = Image.alpha_composite(result, sh) # Patch pl = Image.new("RGBA", (W, H), (0,0,0,0)) pl.paste(patch, (PASTE_X, PASTE_Y), patch) result = Image.alpha_composite(result, pl) # ============================================================ # SAVE # ============================================================ out = result.convert("RGB") out.save("tatiana-patch-v2.jpg", quality=96) print("✓ Saved: tatiana-patch-v2.jpg") ctx = out.crop((595, 620, 895, 890)) ctx.save("tatiana-patch-v2-context.jpg", quality=95) zp = out.crop((PASTE_X-15, PASTE_Y-15, PASTE_X+PATCH_W+15, PASTE_Y+PATCH_H+15)) zp = zp.resize((zp.width*4, zp.height*4), Image.LANCZOS) zp.save("tatiana-patch-v2-zoom.jpg", quality=95) print("✓ Context + Zoom saved.")