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Update utils.py
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utils.py
CHANGED
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@@ -9,40 +9,18 @@ from PIL import Image, ImageEnhance, ImageFilter, ImageDraw
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from config import COLOR_MATCH_CONFIG, FACE_MASK_CONFIG, AGE_BRACKETS
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# ============================================
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# NEW: Type Safety Helpers
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# ============================================
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def ensure_int(value):
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"""
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Convert numpy.int64 or similar to Python int.
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Prevents tensor construction errors with PIL dimensions.
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"""
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if isinstance(value, (int, float)):
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return int(value)
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if hasattr(value, 'item')
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return int(value.item())
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return int(value)
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def safe_image_size(image):
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"""
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Get image size as pure Python ints (not numpy.int64).
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Prevents errors when using PIL dimensions in tensor operations.
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Args:
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image: PIL Image
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Returns:
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Tuple of (width, height) as Python ints
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"""
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return (ensure_int(image.width), ensure_int(image.height))
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# ============================================
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# Original Utility Functions
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# ============================================
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def sanitize_text(text):
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"""
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Remove or replace problematic characters (emojis, special unicode)
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@@ -253,29 +231,15 @@ def create_face_mask(image, face_bbox, feather=None):
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return mask
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def draw_kps(image_pil, kps, color_list=[(255,0,0), (0,255,0), (0,0,255), (255,255,0), (255,0,255)]):
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"""
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Draw facial keypoints on image.
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Args:
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image_pil: PIL Image
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kps: Keypoints array from InsightFace
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color_list: List of colors for different keypoints
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Returns:
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PIL Image with keypoints drawn
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"""
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import cv2
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import numpy as np
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from PIL import Image
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stickwidth = 4
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limbSeq = np.array([[0, 2], [1, 2], [3, 2], [4, 2]])
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kps = np.array(kps)
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out_img = np.
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for i in range(len(limbSeq)):
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index = limbSeq[i]
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color = color_list[index[0]]
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@@ -284,9 +248,10 @@ def draw_kps(image_pil, kps, color_list=[(255,0,0), (0,255,0), (0,0,255), (255,2
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y = kps[index][:, 1]
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length = ((x[0] - x[1]) ** 2 + (y[0] - y[1]) ** 2) ** 0.5
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angle = math.degrees(math.atan2(y[0] - y[1], x[0] - x[1]))
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polygon = cv2.ellipse2Poly(
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out_img = cv2.fillConvexPoly(out_img.copy(), polygon, color)
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out_img = (out_img * 0.6).astype(np.uint8)
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for idx_kp, kp in enumerate(kps):
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@@ -294,56 +259,49 @@ def draw_kps(image_pil, kps, color_list=[(255,0,0), (0,255,0), (0,0,255), (255,2
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x, y = kp
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out_img = cv2.circle(out_img.copy(), (int(x), int(y)), 10, color, -1)
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return
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def get_facial_attributes(face):
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"""
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Extract facial attributes
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Args:
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face: InsightFace face detection object
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Returns:
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Dictionary of facial attributes
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"""
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attributes = {
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'description': [],
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'age': None,
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'gender': None,
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'expression': None,
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'
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'
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}
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# Age
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try:
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if hasattr(face, 'age'):
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age = int(face.age)
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attributes['age'] = age
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# Age bracket
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for min_age, max_age, label in AGE_BRACKETS:
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if min_age <= age < max_age:
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attributes['description'].append(label)
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break
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except (ValueError, TypeError, AttributeError):
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# Gender
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try:
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if hasattr(face, 'gender'):
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gender_code = int(face.gender)
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attributes['gender'] = gender_code
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if gender_code == 1:
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attributes['description'].append(
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elif gender_code == 0:
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attributes['description'].append(
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except (ValueError, TypeError, AttributeError):
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# Expression
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try:
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if hasattr(face, 'emotion'):
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# Some InsightFace models provide emotion
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@@ -464,7 +422,7 @@ def calculate_optimal_size(original_width, original_height, recommended_sizes=No
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Returns:
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Tuple of (optimal_width, optimal_height) as multiples of 64
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"""
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# Ensure pure Python ints
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original_width = ensure_int(original_width)
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original_height = ensure_int(original_height)
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@@ -565,4 +523,4 @@ def enhance_face_crop(face_crop):
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return face_crop_final
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print("[OK] Utilities loaded
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from config import COLOR_MATCH_CONFIG, FACE_MASK_CONFIG, AGE_BRACKETS
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def ensure_int(value):
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"""Convert numpy.int64 or similar to Python int"""
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if isinstance(value, (int, float)):
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return int(value)
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return int(value.item()) if hasattr(value, 'item') else int(value)
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def safe_image_size(image):
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"""Get image size as pure Python ints to avoid numpy.int64 issues"""
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return (ensure_int(image.width), ensure_int(image.height))
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def sanitize_text(text):
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"""
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Remove or replace problematic characters (emojis, special unicode)
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return mask
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def draw_kps(image_pil, kps, color_list=[(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0), (255, 0, 255)]):
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"""Draw facial keypoints on image for InstantID ControlNet"""
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stickwidth = 4
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limbSeq = np.array([[0, 2], [1, 2], [3, 2], [4, 2]])
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kps = np.array(kps)
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w, h = image_pil.size
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out_img = np.zeros([h, w, 3])
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for i in range(len(limbSeq)):
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index = limbSeq[i]
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color = color_list[index[0]]
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y = kps[index][:, 1]
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length = ((x[0] - x[1]) ** 2 + (y[0] - y[1]) ** 2) ** 0.5
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angle = math.degrees(math.atan2(y[0] - y[1], x[0] - x[1]))
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polygon = cv2.ellipse2Poly(
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(int(np.mean(x)), int(np.mean(y))), (int(length / 2), stickwidth), int(angle), 0, 360, 1
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)
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out_img = cv2.fillConvexPoly(out_img.copy(), polygon, color)
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out_img = (out_img * 0.6).astype(np.uint8)
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for idx_kp, kp in enumerate(kps):
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x, y = kp
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out_img = cv2.circle(out_img.copy(), (int(x), int(y)), 10, color, -1)
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out_img_pil = Image.fromarray(out_img.astype(np.uint8))
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return out_img_pil
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def get_facial_attributes(face):
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"""
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Extract comprehensive facial attributes.
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Returns dict with age, gender, expression, quality metrics.
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"""
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attributes = {
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'age': None,
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'gender': None,
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'expression': None,
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'quality': 1.0,
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'pose_angle': 0,
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'description': []
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}
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# Age extraction
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try:
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if hasattr(face, 'age'):
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age = int(face.age)
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attributes['age'] = age
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for min_age, max_age, label in AGE_BRACKETS:
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if min_age <= age < max_age:
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attributes['description'].append(label)
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break
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except (ValueError, TypeError, AttributeError) as e:
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print(f"[WARNING] Age extraction failed: {e}")
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# Gender extraction
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try:
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if hasattr(face, 'gender'):
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gender_code = int(face.gender)
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attributes['gender'] = gender_code
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if gender_code == 1:
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attributes['description'].append("male")
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elif gender_code == 0:
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attributes['description'].append("female")
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except (ValueError, TypeError, AttributeError) as e:
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print(f"[WARNING] Gender extraction failed: {e}")
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# Expression/emotion detection (if available)
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try:
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if hasattr(face, 'emotion'):
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# Some InsightFace models provide emotion
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Returns:
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Tuple of (optimal_width, optimal_height) as multiples of 64
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"""
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# Ensure pure Python ints to avoid numpy.int64 issues
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original_width = ensure_int(original_width)
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original_height = ensure_int(original_height)
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return face_crop_final
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print("[OK] Utilities loaded")
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