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fix(passport-photo): use bg-background for dropdown to match app theme

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This commit is contained in:
stirling-image
2026-04-14 16:01:35 +08:00
parent e7c1efaaf3
commit c2c104e887
5 changed files with 332 additions and 108 deletions
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packages/ai/python/detect_faces.py
+108 -45
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@@ -1,6 +1,7 @@
"""Face detection and blurring using MediaPipe."""
import sys
import json
import os
def emit_progress(percent, stage):
@@ -8,6 +9,92 @@ def emit_progress(percent, stage):
print(json.dumps({"progress": percent, "stage": stage}), file=sys.stderr, flush=True)
# ── Model path for new mp.tasks API ─────────────────────────────────
_FACE_DETECT_MODEL_URL = "https://storage.googleapis.com/mediapipe-models/face_detector/blaze_face_short_range/float16/latest/blaze_face_short_range.task"
_MODEL_DIR = os.path.join(os.path.dirname(__file__), "..", "..", "..", ".models")
_FACE_DETECT_MODEL_PATH = os.path.join(_MODEL_DIR, "blaze_face_short_range.task")
def _ensure_face_detect_model():
"""Download the face detector model if not present."""
if os.path.exists(_FACE_DETECT_MODEL_PATH):
return _FACE_DETECT_MODEL_PATH
os.makedirs(_MODEL_DIR, exist_ok=True)
import urllib.request
emit_progress(15, "Downloading face detection model")
urllib.request.urlretrieve(_FACE_DETECT_MODEL_URL, _FACE_DETECT_MODEL_PATH)
return _FACE_DETECT_MODEL_PATH
def _detect_with_solutions(img_array, min_confidence):
"""Detect faces using legacy mp.solutions API (mediapipe < 0.10.30)."""
import mediapipe as mp
mp_face = mp.solutions.face_detection
results = None
for model_sel in [0, 1]:
detector = mp_face.FaceDetection(
model_selection=model_sel,
min_detection_confidence=min_confidence,
)
results = detector.process(img_array)
detector.close()
if results.detections:
break
detections = results.detections or []
if not detections:
return []
ih, iw = img_array.shape[:2]
faces = []
for detection in detections:
bbox = detection.location_data.relative_bounding_box
faces.append({
"x": int(bbox.xmin * iw),
"y": int(bbox.ymin * ih),
"w": int(bbox.width * iw),
"h": int(bbox.height * ih),
})
return faces
def _detect_with_tasks(img_array, min_confidence):
"""Detect faces using new mp.tasks API (mediapipe >= 0.10.30)."""
import mediapipe as mp
model_path = _ensure_face_detect_model()
options = mp.tasks.vision.FaceDetectorOptions(
base_options=mp.tasks.BaseOptions(model_asset_path=model_path),
running_mode=mp.tasks.vision.RunningMode.IMAGE,
min_detection_confidence=min_confidence,
)
detector = mp.tasks.vision.FaceDetector.create_from_options(options)
mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=img_array)
result = detector.detect(mp_image)
detector.close()
faces = []
for detection in result.detections:
bbox = detection.bounding_box
faces.append({
"x": bbox.origin_x,
"y": bbox.origin_y,
"w": bbox.width,
"h": bbox.height,
})
return faces
def _detect_faces(img_array, min_confidence):
"""Detect faces, trying legacy API first then falling back to tasks API."""
try:
return _detect_with_solutions(img_array, min_confidence)
except AttributeError:
return _detect_with_tasks(img_array, min_confidence)
def main():
input_path = sys.argv[1]
output_path = sys.argv[2]
@@ -24,7 +111,6 @@ def main():
img = Image.open(input_path).convert("RGB")
try:
import mediapipe as mp
import numpy as np
emit_progress(20, "Ready")
@@ -34,56 +120,33 @@ def main():
min_confidence = max(0.1, 1.0 - sensitivity)
img_array = np.array(img)
mp_face = mp.solutions.face_detection
# Try short-range model first (model_selection=0, best for faces
# within ~2m which covers most photos), then fall back to
# full-range model (model_selection=1) for distant/group shots.
# Try legacy mp.solutions API first, fall back to mp.tasks
emit_progress(25, "Scanning for faces")
results = None
for model_sel in [0, 1]:
detector = mp_face.FaceDetection(
model_selection=model_sel,
min_detection_confidence=min_confidence,
)
results = detector.process(img_array)
detector.close()
if results.detections:
break
faces = []
detections = results.detections or []
num_faces = len(detections)
faces = _detect_faces(img_array, min_confidence)
num_faces = len(faces)
emit_progress(50, f"Found {num_faces} face{'s' if num_faces != 1 else ''}")
if num_faces > 0:
ih, iw = img_array.shape[:2]
for i, detection in enumerate(detections):
bbox = detection.location_data.relative_bounding_box
x = int(bbox.xmin * iw)
y = int(bbox.ymin * ih)
w = int(bbox.width * iw)
h = int(bbox.height * ih)
if num_faces > 0 and not detect_only:
for i, face in enumerate(faces):
x, y, w, h = face["x"], face["y"], face["w"], face["h"]
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)
# 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}",
)
faces.append({"x": x, "y": y, "w": w, "h": h})
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}",
)
if not detect_only:
emit_progress(95, "Saving result")