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"""Face crop preprocessing for model input."""
import cv2
import numpy as np
from typing import List
def preprocess(img: np.ndarray, model_img_size: int) -> np.ndarray:
"""Resize with letterboxing, normalize to [0,1], convert to CHW."""
new_size = model_img_size
old_size = img.shape[:2]
ratio = float(new_size) / max(old_size)
scaled_shape = tuple([int(x * ratio) for x in old_size])
interpolation = cv2.INTER_LANCZOS4 if ratio > 1.0 else cv2.INTER_AREA
img = cv2.resize(
img, (scaled_shape[1], scaled_shape[0]), interpolation=interpolation
)
delta_w = new_size - scaled_shape[1]
delta_h = new_size - scaled_shape[0]
top, bottom = delta_h // 2, delta_h - (delta_h // 2)
left, right = delta_w // 2, delta_w - (delta_w // 2)
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_REFLECT_101)
img = img.transpose(2, 0, 1).astype(np.float32) / 255.0
return img
def preprocess_batch(face_crops: List[np.ndarray], model_img_size: int) -> np.ndarray:
"""Preprocess multiple face crops into a batched array."""
if not face_crops:
raise ValueError("face_crops list cannot be empty")
batch = np.zeros(
(len(face_crops), 3, model_img_size, model_img_size), dtype=np.float32
)
for i, face_crop in enumerate(face_crops):
batch[i] = preprocess(face_crop, model_img_size)
return batch
def crop(img: np.ndarray, bbox: tuple, bbox_expansion_factor: float) -> np.ndarray:
"""Extract square face crop from bbox with expansion. Pad edges with reflection."""
original_height, original_width = img.shape[:2]
x, y, w, h = bbox
w = w - x
h = h - y
if w <= 0 or h <= 0:
raise ValueError("Invalid bbox dimensions")
max_dim = max(w, h)
center_x = x + w / 2
center_y = y + h / 2
x = int(center_x - max_dim * bbox_expansion_factor / 2)
y = int(center_y - max_dim * bbox_expansion_factor / 2)
crop_size = int(max_dim * bbox_expansion_factor)
crop_x1 = max(0, x)
crop_y1 = max(0, y)
crop_x2 = min(original_width, x + crop_size)
crop_y2 = min(original_height, y + crop_size)
top_pad = int(max(0, -y))
left_pad = int(max(0, -x))
bottom_pad = int(max(0, (y + crop_size) - original_height))
right_pad = int(max(0, (x + crop_size) - original_width))
if crop_x2 > crop_x1 and crop_y2 > crop_y1:
img = img[crop_y1:crop_y2, crop_x1:crop_x2, :]
else:
img = np.zeros((0, 0, 3), dtype=img.dtype)
result = cv2.copyMakeBorder(
img,
top_pad,
bottom_pad,
left_pad,
right_pad,
cv2.BORDER_REFLECT_101,
)
if result.shape[0] != crop_size or result.shape[1] != crop_size:
raise ValueError(
f"Crop size mismatch: expected {crop_size}x{crop_size}, got {result.shape[0]}x{result.shape[1]}"
)
return result
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