Unable to detect facial landmarks using OpenCV2

Jan 22 2021

I have developed a script using dlib and cv2 to draw facial landmarks on images having one face in that image. Here is the scripts;

import cv2
import dlib

img_path = 'landmarks.png'
detector = dlib.get_frontal_face_detector()

shape_predictor = 'shape_predictor_68_face_landmarks.dat'
predictor = dlib.shape_predictor(shape_predictor)

count = 1
ready = True
while ready:
    frame = cv2.imread("demo.jpg")
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    faces = detector(gray)
    for face in faces:
        x1 = face.left()
        y1 = face.top()
        x2 = face.right()
        y2 = face.bottom()
        cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 3)

        landmarks = predictor(gray, face)

        for n in range(0, 68):
            x = landmarks.part(n).x
            y = landmarks.part(n).y
            cv2.circle(frame, (x, y), 4, (255, 0, 0), -1)

    cv2.imshow("Frame", frame)
    ready = False

Now, here what makes me crazy. When I try to download any of the images(with or without mask) from google to test it, this script is working fine. Likewise, you can see these results such as,

But when I try over these following images, it does nothing.

I have made a couple of searches over the internet but I haven't found anything that is serving the current purpose.

Even, I have tried the combination of

  • cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
  • eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')
  • m_cascade = cv2.CascadeClassifier('haarcascade_mcs_mouth.xml')

I also have looked into the following useful links out there;

  • Face Bounding Box

  • Detect Face Landmarks in Android (Even not same domain)

  • Landmarks detection

  • OpenCV2 Detect Facial Landmarks

しかし、これらの画像でも機能していません。CV2 detector次のようなスクリプトを使用してデバッグすると、空のリストが表示されます。



import dlib

detector = dlib.get_frontal_face_detector()

img = dlib.load_rgb_image('demo.jpg')
dets, scores, idx = detector.run(img, 1, -1)
for i, d in enumerate(dets):
    print("Detection {}, score: {}, face_type:{}".format(
        d, scores[i], idx[i]))




j2abro Jan 26 2021 at 09:55

まず、dlibから信頼スコアを取得できるかどうかを確認しようと思うかもしれません。信頼度のしきい値が何であるかはわかりませんが、制限を下回る顔が検出された可能性があります。DLIB Gitのレポ、ここでの検出からの信頼を取得する方法の例です。

if (len(sys.argv[1:]) > 0):
    img = dlib.load_rgb_image(sys.argv[1])
    dets, scores, idx = detector.run(img, 1, -1)
    for i, d in enumerate(dets):
        print("Detection {}, score: {}, face_type:{}".format(
            d, scores[i], idx[i]))

または、別の顔検出器、たとえばこのMobileNetSSD顔検出器のようなCNNベースの検出器を検討してください。私はこの特定のモデルを使用していませんが、ここではGoogle TPUベースの顔検出器モデルのような同様のモデルを使用しており、非常に良い結果が得られています。

AliAhmad Jan 26 2021 at 13:15



import cv2
import dlib
import numpy as np

img= cv2.imread('Capture 8.PNG')
gray=cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

p = "shape_predictor_68_face_landmarks.dat"
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(p)
faces = detector(gray)

for face in faces:
  cv2.rectangle(img, (x1,y1), (x2,y2),(0,255,0),3)
  landmarks=predictor(gray, face)
  for n in range(0,68):
    cv2.circle(img, (x, y), 4, (0, 0, 255), -1)