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feat(smart-crop): overhaul with face detection, social presets, and 3 modes

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Replace the confusing 2-mode smart crop with a clear 3-mode system:
- Subject Focus: Sharp attention/entropy saliency crop with social media presets
- Face Focus: MediaPipe face detection with headshot framing presets
- Auto Trim: Border removal with optional pad-to-square

Adds detectFaces() to AI package, face preset constants, backward
compatibility for old mode names, and comprehensive integration tests.
This commit is contained in:
Siddharth Kumar Sah
2026-04-13 00:47:53 +08:00
parent 29fafd0722
commit 92d4d2d9c6
9 changed files with 798 additions and 277 deletions
Download Patch File Download Diff File Expand all files Collapse all files
packages/ai/python/detect_faces.py
+22 -17
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@@ -15,6 +15,7 @@ def main():
blur_radius = settings.get("blurRadius", 30)
sensitivity = settings.get("sensitivity", 0.5)
detect_only = settings.get("detectOnly", False)
try:
emit_progress(10, "Preparing")
@@ -64,26 +65,30 @@ def main():
w = int(bbox.width * iw)
h = int(bbox.height * ih)
# Add padding around the face
pad = int(max(w, h) * 0.1)
x1 = max(0, x - pad)
y1 = max(0, y - pad)
x2 = min(img.width, x + w + pad)
y2 = min(img.height, y + h + pad)
if not detect_only:
# Add padding around the face
pad = int(max(w, h) * 0.1)
x1 = max(0, x - pad)
y1 = max(0, y - pad)
x2 = min(img.width, x + w + pad)
y2 = min(img.height, y + h + pad)
face_region = img.crop((x1, y1, x2, y2))
blurred = face_region.filter(
ImageFilter.GaussianBlur(blur_radius)
)
img.paste(blurred, (x1, y1))
emit_progress(
50 + int((i + 1) / num_faces * 40),
f"Blurring face {i + 1} of {num_faces}",
)
face_region = img.crop((x1, y1, x2, y2))
blurred = face_region.filter(
ImageFilter.GaussianBlur(blur_radius)
)
img.paste(blurred, (x1, y1))
faces.append({"x": x, "y": y, "w": w, "h": h})
emit_progress(
50 + int((i + 1) / num_faces * 40),
f"Blurring face {i + 1} of {num_faces}",
)
emit_progress(95, "Saving result")
img.save(output_path)
if not detect_only:
emit_progress(95, "Saving result")
img.save(output_path)
print(
json.dumps(
{