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aa8e154 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | import cv2
import numpy as np
import base64
def b64_to_cv2(b64_str):
if "," in b64_str:
b64_str = b64_str.split(",")[1]
img_data = base64.b64decode(b64_str)
np_arr = np.frombuffer(img_data, np.uint8)
return cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
def extract_face_embedding(frame_bgr: np.ndarray):
"""
Dummy/basic face embedding extraction for proctoring.
We convert the face region to a color histogram.
"""
if frame_bgr is None:
return None
# For a simple face verification, just use cascade classifier
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
gray = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.1, 4)
if len(faces) == 0:
return None
# Use the largest face
faces = sorted(faces, key=lambda x: x[2]*x[3], reverse=True)
x, y, w, h = faces[0]
face_crop = frame_bgr[y:y+h, x:x+w]
face_crop = cv2.resize(face_crop, (128, 128))
hist = cv2.calcHist([face_crop], [0, 1, 2], None, [8, 8, 8], [0, 256, 0, 256, 0, 256])
cv2.normalize(hist, hist)
return hist.flatten()
def verify_face(reference_embedding, live_frame_bgr, threshold=0.6):
"""
Returns True if face matches, False otherwise.
"""
if reference_embedding is None:
return {"match": True, "alert": "No reference checking"}
live_embedding = extract_face_embedding(live_frame_bgr)
if live_embedding is None:
return {"match": False, "alert": "No face detected in live feed!"}
similarity = cv2.compareHist(
reference_embedding.astype(np.float32),
live_embedding.astype(np.float32),
cv2.HISTCMP_CORREL
)
match = similarity >= threshold
return {
"match": match,
"similarity": float(similarity),
"alert": "" if match else "Face mismatch detected! (Proctoring alert)"
}
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