def detect_face(face_detector, img, align=True):
from retinaface import RetinaFace
from retinaface.commons import postprocess
#---------------------------------
resp = []
# The BGR2RGB conversion will be done in the preprocessing step of retinaface.
# img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) #retinaface expects RGB but OpenCV read BGR
"""
face = None
img_region = [0, 0, img.shape[0], img.shape[1]] #Really?
faces = RetinaFace.extract_faces(img_rgb, model = face_detector, align = align)
if len(faces) > 0:
face = faces[0][:, :, ::-1]
return face, img_region
"""
#--------------------------
obj = RetinaFace.detect_faces(img, model=face_detector, threshold=0.9)
if type(obj) == dict:
for key in obj:
identity = obj[key]
facial_area = identity["facial_area"]
y = facial_area[1]
h = facial_area[3] - y
x = facial_area[0]
w = facial_area[2] - x
img_region = [x, y, w, h]
#detected_face = img[int(y):int(y+h), int(x):int(x+w)] #opencv
detected_face = img[facial_area[1]:facial_area[3],
facial_area[0]:facial_area[2]]
if align:
landmarks = identity["landmarks"]
left_eye = landmarks["left_eye"]
right_eye = landmarks["right_eye"]
nose = landmarks["nose"]
#mouth_right = landmarks["mouth_right"]
#mouth_left = landmarks["mouth_left"]
detected_face = postprocess.alignment_procedure(
detected_face, right_eye, left_eye, nose)
resp.append((detected_face, img_region))
return resp
def extractForegroundFaces(model, image_path):
image = depthEstimation.generateDepthMap(model=model,
image_path=image_path)
faces = RetinaFace.detect_faces(image_path)
facial_depth = []
for key in faces:
facial_area = faces[key]['facial_area']
# create mask with zeros
mask = np.zeros((image.shape), dtype=np.uint8)
pts = np.array([[[facial_area[0], facial_area[1]],
[facial_area[0], facial_area[3]],
[facial_area[2], facial_area[3]],
[facial_area[2], facial_area[1]]]],
dtype=np.int32)
cv2.fillPoly(mask, pts, 255)
# get color values
values = image[np.where(mask == 255)]
facial_depth.append(values.mean())
return facial_depth
#! /usr/bin/python3
from retinaface import RetinaFace
resp = RetinaFace.detect_faces("img/gena1.jpg")
print(resp)
from retinaface import RetinaFace
import matplotlib.pyplot as plt
import cv2
img_path = "dataset/img3.jpg"
img = cv2.imread(img_path)
resp = RetinaFace.detect_faces(img_path, threshold=0.5)
print(resp)
for key in resp:
identity = resp[key]
#---------------------
landmarks = identity["landmarks"]
diameter = 1
cv2.circle(img, tuple(landmarks["left_eye"]), diameter, (0, 0, 255), -1)
cv2.circle(img, tuple(landmarks["right_eye"]), diameter, (0, 0, 255), -1)
cv2.circle(img, tuple(landmarks["nose"]), diameter, (0, 0, 255), -1)
cv2.circle(img, tuple(landmarks["mouth_left"]), diameter, (0, 0, 255), -1)
cv2.circle(img, tuple(landmarks["mouth_right"]), diameter, (0, 0, 255), -1)
facial_area = identity["facial_area"]
cv2.rectangle(img, (facial_area[2], facial_area[3]),
(facial_area[0], facial_area[1]), (255, 255, 255), 1)
#facial_img = img[facial_area[1]: facial_area[3], facial_area[0]: facial_area[2]]
#plt.imshow(facial_img[:, :, ::-1])
plt.imshow(img[:, :, ::-1])
plt.axis('off')
