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import cv2
import numpy as np
import dlib
dnn_net = cv2.dnn.readNetFromCaffe("deploy.prototxt", "res10_300x300_ssd_iter_140000.caffemodel")
# Initialize dlib's facial landmark predictor
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
dnn_net = cv2.dnn.readNetFromCaffe("deploy.prototxt", "res10_300x300_ssd_iter_140000.caffemodel")
# Initialize dlib's facial landmark predictor
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
def get_face(image, net=dnn_net, predictor=predictor):
# Prepare the image for DNN face detection
(h, w) = image.shape[:2]
blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0, (300, 300), (104.0, 177.0, 123.0))
net.setInput(blob)
detections = net.forward()
# Loop over the detections
for i in range(0, detections.shape[2]):
confidence = detections[0, 0, i, 2]
# Filter out weak detections
if confidence > 0.5:
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
# Convert bounding box to dlib rectangle format
dlib_rect = dlib.rectangle(int(startX), int(startY), int(endX), int(endY))
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
landmarks = predictor(gray, dlib_rect)
# Visualize landmarks
for p in landmarks.parts():
cv2.circle(image, (p.x, p.y), 2, (0, 255, 0), -1)
# Get the bounding box for the face based on landmarks
landmarks_np = np.array([[p.x, p.y] for p in landmarks.parts()])
x, y, w, h = cv2.boundingRect(landmarks_np)
x -= 25
y -= 25
w += 50
h += 50
x = max(0, x)
y = max(0, y)
w = min(w, image.shape[1] - x)
h = min(h, image.shape[0] - y)
# Crop and resize the face
face_crop = image[y-h//2:y+h, x:x+w]
face_crop = cv2.resize(face_crop, (224, 224))
return face_crop
return None
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