Claude
e34beeed00
North is projected onto a cylinder so its right edge curves naturally into sunrise's left edge, giving a continuous panoramic sweep from North to East. cyl-f1920-preview.jpg — f=1920 px (~90° HFOV): most natural perspective cyl-f1550-preview.jpg — f=1550 px (~105° HFOV): more pronounced curve stitch-cameras.py updated to use cylindrical projection. Focal length is a tunable argv[4]; refine once the user confirms which curve looks right. The 36-second inter-shot gap causes cloud drift at the seam — live video frames captured simultaneously will not have this artefact. https://claude.ai/code/session_01C4jbd3waXG3eKZYbGUjLUQ
109 lines
4.3 KiB
Python
109 lines
4.3 KiB
Python
#!/usr/bin/env python3
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"""
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Cylindrical panorama stitch.
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North faces ~0° azimuth, sunrise faces ~90°. Project both onto the same
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cylinder so the NE overlap region appears at the same y-coordinate in both
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and the scene is geometrically continuous.
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Usage:
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python3 stitch_cyl.py north.jpg sunrise.jpg out_prefix [focal_px]
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focal_px defaults to 1920 (= ~90° HFOV at 3840 px wide).
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Try 1300-1920 to span 90°-120° HFOV.
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"""
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import sys, cv2, numpy as np
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north_p = sys.argv[1]
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sunrise_p= sys.argv[2]
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prefix = sys.argv[3]
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f = float(sys.argv[4]) if len(sys.argv) > 4 else 1920.0
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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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cx, cy = W / 2.0, H / 2.0
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print(f"Images: {W}x{H} focal estimate: {f:.0f} px")
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# ── Cylindrical projection ────────────────────────────────────────────────────
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# Maps output (cylindrical) pixel (xc, yc) ← input (flat) pixel (xs, ys):
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# theta = xc / f (horizontal angle from optical axis)
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# xs = f * tan(theta) + cx
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# ys = yc / cos(theta) + cy
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def cylindrical_warp(img, f, cx, cy):
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h, w = img.shape[:2]
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xc = np.arange(w, dtype=np.float32) - cx
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yc = np.arange(h, dtype=np.float32) - cy
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XC, YC = np.meshgrid(xc, yc)
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theta = XC / f
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map_x = (f * np.tan(theta) + cx).astype(np.float32)
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map_y = (YC / np.cos(theta) + cy).astype(np.float32)
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return cv2.remap(img, map_x, map_y, cv2.INTER_LINEAR,
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borderMode=cv2.BORDER_CONSTANT, borderValue=0)
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n_cyl = cylindrical_warp(north, f, cx, cy)
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s_cyl = cylindrical_warp(sunrise, f, cx, cy)
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cv2.imwrite(f"{prefix}-north-cyl.jpg", n_cyl)
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cv2.imwrite(f"{prefix}-sunrise-cyl.jpg", s_cyl)
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print("Cylindrical projections saved.")
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# ── Phase-correlate on the treeline band of the cylindrical images ────────────
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y0, y1 = int(H * 0.70), int(H * 0.90)
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ow = int(W * 0.30) # look in outer 30% of each image
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n_strip = cv2.cvtColor(n_cyl[:, W-ow:], cv2.COLOR_BGR2GRAY)[y0:y1].astype(np.float64)
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s_strip = cv2.cvtColor(s_cyl[:, :ow], cv2.COLOR_BGR2GRAY)[y0:y1].astype(np.float64)
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# Normalised cross-power spectrum (phase correlation)
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N = np.fft.fft2(n_strip)
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S = np.fft.fft2(s_strip)
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R = N * np.conj(S)
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nrm = np.abs(R); nrm[nrm == 0] = 1
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corr = np.fft.ifft2(R / nrm).real
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peak = np.unravel_index(np.argmax(corr), corr.shape)
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dy_r, dx_r = peak
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rh, rw = corr.shape
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if dy_r > rh // 2: dy_r -= rh
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if dx_r > rw // 2: dx_r -= rw
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print(f"Phase-corr (cylindrical): dx={dx_r} dy={dy_r}")
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# Convert from band-strip offsets to full-image translation
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tx = -(W - ow + dx_r) # how far left north sits relative to sunrise
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ty = -dy_r
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print(f"Translation: tx={tx} ty={ty}")
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# ── Build panorama from cylindrical images ───────────────────────────────────
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xmin = min(0, tx); ymin = min(0, ty)
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xmax = max(W, W+tx); ymax = max(H, H+ty)
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cW = int(xmax-xmin); cH = int(ymax-ymin)
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sx0, sy0 = int(-xmin), int(-ymin) # sunrise origin
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nx0, ny0 = sx0+tx, sy0+ty # north origin
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nx0, ny0 = int(nx0), int(ny0)
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print(f"Canvas: {cW}x{cH}")
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canvas = np.zeros((cH, cW, 3), dtype=np.uint8)
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# North first (background)
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canvas[ny0:ny0+H, nx0:nx0+W] = n_cyl
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# Sunrise on top (base image)
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canvas[sy0:sy0+H, sx0:sx0+W] = s_cyl
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# Feathered blend in overlap
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ov_x0 = max(sx0, nx0); ov_x1 = min(sx0+W, nx0+W)
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ov_y0 = max(sy0, ny0); 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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alpha = np.linspace(0.0, 1.0, ov_w, dtype=np.float32)[np.newaxis, :, np.newaxis]
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n_patch = n_cyl[ov_y0-ny0:ov_y1-ny0, ov_x0-nx0:ov_x1-nx0].astype(np.float32)
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s_patch = s_cyl[ov_y0-sy0:ov_y1-sy0, ov_x0-sx0:ov_x1-sx0].astype(np.float32)
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canvas[ov_y0:ov_y1, ov_x0:ov_x1] = (s_patch*alpha + n_patch*(1-alpha)).astype(np.uint8)
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print(f"Overlap blend: {ov_w}x{ov_y1-ov_y0}px")
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# Crop black margin from ty offset
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top = max(sy0, ny0)
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bot = min(sy0+H, ny0+H)
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canvas = canvas[top:bot, :]
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cv2.imwrite(f"{prefix}-panorama.jpg", canvas, [cv2.IMWRITE_JPEG_QUALITY, 92])
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preview = cv2.resize(canvas, (canvas.shape[1]//2, canvas.shape[0]//2))
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cv2.imwrite(f"{prefix}-preview.jpg", preview, [cv2.IMWRITE_JPEG_QUALITY, 88])
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print(f"Panorama → {prefix}-panorama.jpg ({canvas.shape[1]}x{canvas.shape[0]})")
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