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Face Extractor — OpenCV-based face detection and frame sampling
"""
import cv2
import numpy as np
from pathlib import Path
from typing import List, Tuple, Optional
class FaceExtractor:
"""
Extracts and crops face regions from video frames.
Uses OpenCV's Haar cascade (no extra dependencies).
"""
def __init__(self):
cascade_path = cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
self.detector = cv2.CascadeClassifier(cascade_path)
if self.detector.empty():
raise RuntimeError("Failed to load Haar cascade classifier.")
# ── Frame sampling ──────────────────────────────────────────────
def sample_frames(
self,
video_path: str,
max_frames: int = 32,
sample_fps: float = 2.0,
) -> List[Tuple[int, np.ndarray]]:
"""
Returns a list of (frame_index, BGR_frame) tuples.
"""
cap = cv2.VideoCapture(str(video_path))
if not cap.isOpened():
raise ValueError(f"Cannot open video: {video_path}")
video_fps = cap.get(cv2.CAP_PROP_FPS) or 25.0
total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
interval = max(1, int(video_fps / sample_fps))
frames: List[Tuple[int, np.ndarray]] = []
idx = 0
while cap.isOpened() and len(frames) < max_frames:
ret, frame = cap.read()
if not ret:
break
if idx % interval == 0:
frames.append((idx, frame))
idx += 1
cap.release()
return frames
# ── Face crop ───────────────────────────────────────────────────
def crop_face(
self,
frame: np.ndarray,
target_size: Tuple[int, int] = (224, 224),
padding_ratio: float = 0.25,
) -> Optional[np.ndarray]:
"""
Detects the largest face and returns a padded RGB crop.
Returns None if no face detected (caller should decide what to do).
"""
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = self.detector.detectMultiScale(
gray, scaleFactor=1.1, minNeighbors=4, minSize=(48, 48)
)
h, w = frame.shape[:2]
if len(faces) == 0:
# Fallback: centre-crop as square
size = min(h, w)
y0 = (h - size) // 2
x0 = (w - size) // 2
crop = frame[y0 : y0 + size, x0 : x0 + size]
else:
# Largest face
fx, fy, fw, fh = max(faces, key=lambda r: r[2] * r[3])
pad = int(max(fw, fh) * padding_ratio)
x1 = max(0, fx - pad)
y1 = max(0, fy - pad)
x2 = min(w, fx + fw + pad)
y2 = min(h, fy + fh + pad)
crop = frame[y1:y2, x1:x2]
if crop.size == 0:
return None
crop = cv2.resize(crop, target_size, interpolation=cv2.INTER_AREA)
crop = cv2.cvtColor(crop, cv2.COLOR_BGR2RGB)
return crop
# ── Full pipeline ────────────────────────────────────────────────
def extract(
self,
video_path: str,
max_frames: int = 32,
sample_fps: float = 2.0,
) -> List[dict]:
"""
Returns list of dicts: {frame_idx, face_rgb (H,W,3 uint8)}
"""
raw_frames = self.sample_frames(video_path, max_frames, sample_fps)
results = []
for fidx, frame in raw_frames:
face = self.crop_face(frame)
if face is not None:
results.append({"frame_idx": fidx, "face": face})
return results
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