批量FaceAlignment using dlib

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最新推荐文章于 2025-04-09 14:15:04 发布

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本文提供了一段使用Python实现的人脸裁剪代码。该代码利用dlib库进行人脸检测及特征点定位，并从输入文件夹中批量处理图像，将检测到的人脸区域裁剪并保存至指定输出路径。

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# 批量人脸裁剪代码
import os

import cv2
import dlib


def FaceClip(fliePath, output_image_path):
    predictor_model = 'shape_predictor_68_face_landmarks.dat'
    detector = dlib.get_frontal_face_detector ()  # dlib人脸检测器
    predictor = dlib.shape_predictor (predictor_model)

    imagefile = os.listdir (filePath)

    print ('Images need to be processed: '+str(len (imagefile)))

    for file in os.listdir (filePath):
        print ('Processing file name: '+str(file))
        test_image_path = filePath
        print ('Processing file path: '+str(test_image_path))

        # cv2读取图像
        img = cv2.imread (str (test_image_path) + '\\' + str (file))
        img = cv2.cvtColor (img, cv2.COLOR_BGR2RGB)

        # 人脸数rects
        rects = detector (img, 0)

        # faces存储full_object_detection对象
        faces = dlib.full_object_detections ()

        for i in range (len (rects)):
            faces.append (predictor (img, rects[i]))

        for i in range (len (rects)):
            faces.append (predictor (img, rects[i]))

        face_images = dlib.get_face_chips (img, faces, size=320)
        for image in face_images:
            cv_bgr_img = cv2.cvtColor (image, cv2.COLOR_RGB2BGR)
            cv_bgr_img = cv2.resize (cv_bgr_img, (112, 112))
            output_path = os.path.join (output_image_path, file)
            print ('Output images path: '+str(output_path))
            cv2.imwrite (str (output_path), cv_bgr_img)


if __name__ == '__main__':
    filePath = 'E:\RealLifeMaskedFaceDataset\split\withoutmasktest'
    output_image_path = 'E:\RealLifeMaskedFaceDataset\split\outputtest'
    FaceClip (filePath, output_image_path)