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feat(ai): parameterize outpaint pipeline with quality tier support

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Add TIER_PARAMS dict with fast/balanced/high presets controlling band
size, mask dilation, seam strip width, and Telea pre-inpainting. Parse
tier from sys.argv[7] with balanced fallback. Conditional Telea and
seam refinement steps skip cleanly for fast tier. Progressive outpaint
now accepts band_size and progress bounds for tier-appropriate scaling.
This commit is contained in:
SnapOtter
2026-05-13 15:45:19 +08:00
parent 3760885342
commit 55e544317c
1 changed files with 43 additions and 29 deletions
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packages/ai/python/outpaint.py
+43 -29
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@@ -5,9 +5,12 @@ import json
MODEL_SIZE = 512
BAND_SIZE = 128
MASK_DILATE_PX = 18
SEAM_STRIP_PX = 24
TIER_PARAMS = {
"fast": {"band_size": 192, "mask_dilate": 12, "seam_strip": 0, "use_telea": False},
"balanced": {"band_size": 128, "mask_dilate": 18, "seam_strip": 24, "use_telea": True},
"high": {"band_size": 72, "mask_dilate": 24, "seam_strip": 36, "use_telea": True},
}
def emit_progress(percent, stage):
@@ -85,7 +88,7 @@ def _run_lama(session, canvas, mask, feather_radius=5):
return np.clip(result, 0, 255).astype(np.uint8)
def _progressive_outpaint(session, canvas, mask):
def _progressive_outpaint(session, canvas, mask, band_size=128, progress_start=30, progress_end=75):
"""Process mask in concentric bands from original edge outward."""
import cv2
import numpy as np
@@ -103,7 +106,7 @@ def _progressive_outpaint(session, canvas, mask):
temp = remaining.copy()
while np.sum(temp > 127) > 0:
kernel = cv2.getStructuringElement(
cv2.MORPH_ELLIPSE, (BAND_SIZE * 2 + 1, BAND_SIZE * 2 + 1)
cv2.MORPH_ELLIPSE, (band_size * 2 + 1, band_size * 2 + 1)
)
eroded = cv2.erode(temp, kernel, iterations=1)
temp = eroded
@@ -115,7 +118,7 @@ def _progressive_outpaint(session, canvas, mask):
while np.sum(remaining > 127) > 0:
# Erode remaining mask to peel off outermost band
kernel = cv2.getStructuringElement(
cv2.MORPH_ELLIPSE, (BAND_SIZE * 2 + 1, BAND_SIZE * 2 + 1)
cv2.MORPH_ELLIPSE, (band_size * 2 + 1, band_size * 2 + 1)
)
eroded = cv2.erode(remaining, kernel, iterations=1)
@@ -128,9 +131,9 @@ def _progressive_outpaint(session, canvas, mask):
remaining = eroded
band_index += 1
# Scale progress between 30% and 75%
progress = 30 + int(45 * band_index / total_bands)
emit_progress(min(progress, 75), f"AI outpainting band {band_index}/{total_bands}")
progress_range = progress_end - progress_start
progress = progress_start + int(progress_range * band_index / total_bands)
emit_progress(min(progress, progress_end), f"AI outpainting band {band_index}/{total_bands}")
return current_canvas
@@ -143,6 +146,11 @@ def main():
extend_bottom = int(sys.argv[5])
extend_left = int(sys.argv[6])
tier = sys.argv[7] if len(sys.argv) > 7 else "balanced"
if tier not in TIER_PARAMS:
tier = "balanced"
params = TIER_PARAMS[tier]
try:
emit_progress(5, "Preparing")
from PIL import Image
@@ -187,38 +195,44 @@ def main():
# Dilate mask into original area for overlap
dilate_kernel = cv2.getStructuringElement(
cv2.MORPH_ELLIPSE, (MASK_DILATE_PX * 2 + 1, MASK_DILATE_PX * 2 + 1)
cv2.MORPH_ELLIPSE, (params["mask_dilate"] * 2 + 1, params["mask_dilate"] * 2 + 1)
)
mask = cv2.dilate(mask, dilate_kernel, iterations=1)
# Step 3: Telea pre-inpainting for gradient hints
emit_progress(25, "Pre-filling gradients")
canvas = cv2.inpaint(canvas, mask, 3, cv2.INPAINT_TELEA)
if params["use_telea"]:
emit_progress(25, "Pre-filling gradients")
canvas = cv2.inpaint(canvas, mask, 3, cv2.INPAINT_TELEA)
# Step 4: Progressive LaMa outpainting in concentric bands
canvas = _progressive_outpaint(session, canvas, mask)
if params["use_telea"]:
canvas = _progressive_outpaint(session, canvas, mask, params["band_size"], 30, 75)
else:
canvas = _progressive_outpaint(session, canvas, mask, params["band_size"], 20, 85)
# Step 5: Seam refinement -- second LaMa pass on thin boundary strip
emit_progress(80, "Refining seams")
seam_mask = np.zeros((new_h, new_w), dtype=np.uint8)
seam_strip = params["seam_strip"]
if seam_strip > 0:
emit_progress(80, "Refining seams")
seam_mask = np.zeros((new_h, new_w), dtype=np.uint8)
# Create thin strip along original image boundary
inner_kernel = cv2.getStructuringElement(
cv2.MORPH_ELLIPSE, (SEAM_STRIP_PX + 1, SEAM_STRIP_PX + 1)
)
outer_kernel = cv2.getStructuringElement(
cv2.MORPH_ELLIPSE, (SEAM_STRIP_PX * 2 + 1, SEAM_STRIP_PX * 2 + 1)
)
# Create thin strip along original image boundary
inner_kernel = cv2.getStructuringElement(
cv2.MORPH_ELLIPSE, (seam_strip + 1, seam_strip + 1)
)
outer_kernel = cv2.getStructuringElement(
cv2.MORPH_ELLIPSE, (seam_strip * 2 + 1, seam_strip * 2 + 1)
)
# Original region mask (before dilation)
orig_mask = np.zeros((new_h, new_w), dtype=np.uint8)
orig_mask[extend_top:extend_top + orig_h, extend_left:extend_left + orig_w] = 255
# Original region mask (before dilation)
orig_mask = np.zeros((new_h, new_w), dtype=np.uint8)
orig_mask[extend_top:extend_top + orig_h, extend_left:extend_left + orig_w] = 255
inner_edge = cv2.erode(orig_mask, inner_kernel, iterations=1)
outer_edge = cv2.dilate(orig_mask, outer_kernel, iterations=1)
seam_mask = cv2.subtract(outer_edge, inner_edge)
inner_edge = cv2.erode(orig_mask, inner_kernel, iterations=1)
outer_edge = cv2.dilate(orig_mask, outer_kernel, iterations=1)
seam_mask = cv2.subtract(outer_edge, inner_edge)
canvas = _run_lama(session, canvas, seam_mask)
canvas = _run_lama(session, canvas, seam_mask)
# Step 6: Poisson blending -- paste untouched original back
emit_progress(90, "Blending")