Claude
df26e1d8e3
Phase-correlation on the treeline/horizon band (rows 70-90%) found: tx = -3324 px (north is 3324 px to the left of sunrise) ty = -85 px (north camera is 85 px higher — slight tilt difference) overlap = 516 px wide, feather-blended with a horizontal gradient Result in images/stitch-preview/north-sunrise-aligned-preview.jpg: 7164x2075 px panorama with continuous horizon and treeline. Cloud "V" at seam is a 36-second inter-shot artefact only; live video frames captured simultaneously will have no such discontinuity. stitch-cameras.py: accepts tx/ty overrides as argv[4]/argv[5] so the offsets can be tuned without code changes. https://claude.ai/code/session_01C4jbd3waXG3eKZYbGUjLUQ
80 lines
3.4 KiB
Python
80 lines
3.4 KiB
Python
#!/usr/bin/env python3
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"""
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Final stitch: north left, sunrise right.
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Translation from phase-correlation (tx=-3324, ty=-85).
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Feathered gradient blend across the overlap zone.
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"""
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import sys, cv2, numpy as np
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north_p, sunrise_p, out_p = sys.argv[1], sys.argv[2], sys.argv[3]
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tx_arg = int(sys.argv[4]) if len(sys.argv) > 4 else -3324
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ty_arg = int(sys.argv[5]) if len(sys.argv) > 5 else -85
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north = cv2.imread(north_p)
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sunrise = cv2.imread(sunrise_p)
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H, W = north.shape[:2]
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tx, ty = tx_arg, ty_arg # north → sunrise translation
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print(f"Using tx={tx} ty={ty}")
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# ── Canvas ────────────────────────────────────────────────────────────────────
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# Sunrise is at the canvas origin (0,0).
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# North is at (tx, ty) relative to sunrise — tx is negative so north is LEFT.
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canvas_x0 = min(0, tx) # leftmost pixel
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canvas_y0 = min(0, ty)
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canvas_x1 = max(W, W + tx)
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canvas_y1 = max(H, H + ty)
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cW = int(canvas_x1 - canvas_x0)
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cH = int(canvas_y1 - canvas_y0)
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# Canvas offsets for each image
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s_ox, s_oy = int(-canvas_x0), int(-canvas_y0) # sunrise top-left in canvas
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n_ox, n_oy = s_ox + tx, s_oy + ty # north top-left in canvas
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canvas = np.zeros((cH, cW, 3), dtype=np.uint8)
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# Paint north first (background)
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nx0, ny0 = int(n_ox), int(n_oy)
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canvas[ny0:ny0+H, nx0:nx0+W] = north
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# Paint sunrise on top (base)
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sx0, sy0 = int(s_ox), int(s_oy)
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canvas[sy0:sy0+H, sx0:sx0+W] = sunrise
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# ── Feathered blend in overlap zone ──────────────────────────────────────────
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# Overlap: columns where both images exist in the canvas
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ov_x0 = max(sx0, nx0)
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ov_x1 = min(sx0 + W, nx0 + W)
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ov_y0 = max(sy0, ny0)
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ov_y1 = min(sy0 + H, ny0 + H)
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if ov_x1 > ov_x0 and ov_y1 > ov_y0:
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ov_w = ov_x1 - ov_x0
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ov_h = ov_y1 - ov_y0
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print(f"Overlap zone: {ov_w}x{ov_h}px at canvas x=[{ov_x0},{ov_x1}]")
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# Horizontal gradient: sunrise fades OUT on the left (where north takes over)
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# north fades OUT on the right (where sunrise takes over)
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alpha = np.linspace(0.0, 1.0, ov_w, dtype=np.float32) # 0=north, 1=sunrise
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alpha = alpha[np.newaxis, :, np.newaxis] # shape (1, ov_w, 1)
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n_patch = north [ov_y0-ny0:ov_y1-ny0, ov_x0-nx0:ov_x1-nx0].astype(np.float32)
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s_patch = sunrise[ov_y0-sy0:ov_y1-sy0, ov_x0-sx0:ov_x1-sx0].astype(np.float32)
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blended = (s_patch * alpha + n_patch * (1.0 - alpha)).astype(np.uint8)
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canvas[ov_y0:ov_y1, ov_x0:ov_x1] = blended
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# ── Crop off the black margin at top/bottom from the ty offset ───────────────
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# After the shift there may be a thin black bar top or bottom — crop it.
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top_crop = max(sy0, ny0) # first row where BOTH images exist
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bottom_crop = min(sy0+H, ny0+H) # last row where either image exists
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canvas = canvas[top_crop:bottom_crop, :]
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cv2.imwrite(out_p, canvas, [cv2.IMWRITE_JPEG_QUALITY, 92])
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print(f"Output: {out_p} size={canvas.shape[1]}x{canvas.shape[0]}")
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# ── Save a 50% scaled preview for quick viewing ───────────────────────────────
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preview = cv2.resize(canvas, (canvas.shape[1]//2, canvas.shape[0]//2))
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cv2.imwrite(out_p.replace(".jpg", "-preview.jpg"), preview,
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[cv2.IMWRITE_JPEG_QUALITY, 85])
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print(f"Preview saved.")
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