#!/usr/bin/env python3
"""
Final stitch: north left, sunrise right.
Translation from phase-correlation (tx=-3324, ty=-85).
Feathered gradient blend across the overlap zone.
"""
import sys, cv2, numpy as np

north_p, sunrise_p, out_p = sys.argv[1], sys.argv[2], sys.argv[3]
tx_arg = int(sys.argv[4]) if len(sys.argv) > 4 else -3324
ty_arg = int(sys.argv[5]) if len(sys.argv) > 5 else -85

north   = cv2.imread(north_p)
sunrise = cv2.imread(sunrise_p)
H, W = north.shape[:2]

tx, ty = tx_arg, ty_arg   # north → sunrise translation
print(f"Using tx={tx}  ty={ty}")

# ── Canvas ────────────────────────────────────────────────────────────────────
# Sunrise is at the canvas origin (0,0).
# North is at (tx, ty) relative to sunrise — tx is negative so north is LEFT.
canvas_x0 = min(0, tx)          # leftmost pixel
canvas_y0 = min(0, ty)
canvas_x1 = max(W, W + tx)
canvas_y1 = max(H, H + ty)
cW = int(canvas_x1 - canvas_x0)
cH = int(canvas_y1 - canvas_y0)

# Canvas offsets for each image
s_ox, s_oy = int(-canvas_x0), int(-canvas_y0)   # sunrise top-left in canvas
n_ox, n_oy = s_ox + tx, s_oy + ty                # north top-left in canvas

canvas = np.zeros((cH, cW, 3), dtype=np.uint8)

# Paint north first (background)
nx0, ny0 = int(n_ox), int(n_oy)
canvas[ny0:ny0+H, nx0:nx0+W] = north

# Paint sunrise on top (base)
sx0, sy0 = int(s_ox), int(s_oy)
canvas[sy0:sy0+H, sx0:sx0+W] = sunrise

# ── Feathered blend in overlap zone ──────────────────────────────────────────
# Overlap: columns where both images exist in the canvas
ov_x0 = max(sx0, nx0)
ov_x1 = min(sx0 + W, nx0 + W)
ov_y0 = max(sy0, ny0)
ov_y1 = min(sy0 + H, ny0 + H)

if ov_x1 > ov_x0 and ov_y1 > ov_y0:
    ov_w = ov_x1 - ov_x0
    ov_h = ov_y1 - ov_y0
    print(f"Overlap zone: {ov_w}x{ov_h}px  at canvas x=[{ov_x0},{ov_x1}]")

    # Horizontal gradient: sunrise fades OUT on the left (where north takes over)
    #                       north   fades OUT on the right (where sunrise takes over)
    alpha = np.linspace(0.0, 1.0, ov_w, dtype=np.float32)   # 0=north, 1=sunrise
    alpha = alpha[np.newaxis, :, np.newaxis]                 # shape (1, ov_w, 1)

    n_patch = north  [ov_y0-ny0:ov_y1-ny0, ov_x0-nx0:ov_x1-nx0].astype(np.float32)
    s_patch = sunrise[ov_y0-sy0:ov_y1-sy0, ov_x0-sx0:ov_x1-sx0].astype(np.float32)
    blended = (s_patch * alpha + n_patch * (1.0 - alpha)).astype(np.uint8)
    canvas[ov_y0:ov_y1, ov_x0:ov_x1] = blended

# ── Crop off the black margin at top/bottom from the ty offset ───────────────
# After the shift there may be a thin black bar top or bottom — crop it.
top_crop    = max(sy0, ny0)           # first row where BOTH images exist
bottom_crop = min(sy0+H, ny0+H)      # last row where either image exists
canvas = canvas[top_crop:bottom_crop, :]

cv2.imwrite(out_p, canvas, [cv2.IMWRITE_JPEG_QUALITY, 92])
print(f"Output: {out_p}  size={canvas.shape[1]}x{canvas.shape[0]}")

# ── Save a 50% scaled preview for quick viewing ───────────────────────────────
preview = cv2.resize(canvas, (canvas.shape[1]//2, canvas.shape[0]//2))
cv2.imwrite(out_p.replace(".jpg", "-preview.jpg"), preview,
            [cv2.IMWRITE_JPEG_QUALITY, 85])
print(f"Preview saved.")