Modules.py 文件源码

def face_extraction(path):
    path_str = path[:-1] if path.endswith('/') else path
    output_dir = path_str + '_faces'
    if not os.path.isdir(output_dir):
        os.makedirs(output_dir)

    detector = dlib.get_frontal_face_detector()
    predictor = dlib.shape_predictor(dat_face_landmark)
    face_cascade = cv2.CascadeClassifier(xml_face_classifier)
    undetectLst = list()

    numfile = get_dataInfo(path_str)
    not_detected = 0
    itr = 0

    for itr_file in os.listdir(path_str):
        if itr_file.endswith('.jpg'):
            file = "{:s}/{:s}".format(path_str, itr_file)
            image = cv2.imread(file)
            image = imutils.resize(image, width=500)
            bFace, faces = facial_landmark_detection(image, detector, predictor, file)

            if not bFace:
                bFace, faces = face_detect_classifier(image, face_cascade)
                if not bFace:
                    print file
                    undetectLst.append(file)
                    not_detected += 1
                    image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
                    cv2.imwrite("{:s}/{:s}".format(output_dir, itr_file), image)
                    continue
            x, y, w, h = faces
            crop_img = image[y:y + h, x:x + w]
            crop_img = cv2.cvtColor(crop_img, cv2.COLOR_BGR2GRAY)
            cv2.imwrite("{:s}/{:s}".format(output_dir, itr_file), crop_img)
            itr += 1 
        else:
            continue
    total = itr + not_detected
    print "{:s}: {:4d} of {:4d} file missed detected, detect rate {:2.2f}%"\
    .format(path_str, not_detected, total, 100.0 * itr / total)
    return undetectLst, total