遍历文件名

# -*- coding: utf-8 -*-
from __future__ import unicode_literals
import os
import numpy as np
import cv2 as cv
import sklearn.preprocessing as sp
fd = cv.CascadeClassifier('../../data/haar/face.xml')


def search_faces(directory):
    directory = os.path.normpath(directory)
    if not os.path.isdir(directory):
        raise IOError("The directory '" + directory +
                      "' doesn't exist!")
    faces = {}
    for curdir, subdirs, files in os.walk(directory):
        for jpeg in (file for file in files
                     if file.endswith('.jpg')):
            path = os.path.join(curdir, jpeg)
            label = path.split(os.path.sep)[-2]
            if label not in faces:
                faces[label] = []
            faces[label].append(path)
    return faces


train_faces = search_faces('../../data/faces/training')
codec = sp.LabelEncoder()
codec.fit(list(train_faces.keys()))
train_x, train_y = [], []
for label, filenames in train_faces.items():
    for filename in filenames:
        image = cv.imread(filename)
        gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
        faces = fd.detectMultiScale(gray, 1.1, 2,
                                    minSize=(100, 100))
        for l, t, w, h in faces:
            train_x.append(gray[t:t + h, l:l + w])
            train_y.append(codec.transform([label])[0])
train_y = np.array(train_y)
model = cv.face.LBPHFaceRecognizer_create()
model.train(train_x, train_y)
test_faces = search_faces('../../data/faces/testing')
test_x, test_y, test_z = [], [], []
for label, filenames in test_faces.items():
    for filename in filenames:
        image = cv.imread(filename)
        gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
        faces = fd.detectMultiScale(gray, 1.1, 2,
                                    minSize=(100, 100))
        for l, t, w, h in faces:
            test_x.append(gray[t:t + h, l:l + w])
            test_y.append(codec.transform([label])[0])
            a, b = int(w / 2), int(h / 2)
            cv.ellipse(image, (l + a, t + b), (a, b),
                       0, 0, 360, (255, 0, 255), 2)
            test_z.append(image)
test_y = np.array(test_y)
pred_test_y = []
for face in test_x:
    pred_code = model.predict(face)[0]
    pred_test_y.append(pred_code)
escape = False
while not escape:
    for code, pred_code, image in zip(
            test_y, pred_test_y, test_z):
        label, pred_label = \
            codec.inverse_transform(code), \
            codec.inverse_transform(pred_code)
        text = '{} {} {}'.format(
            label,
            '==' if label == pred_label else '!=',
            pred_label)
        cv.putText(image, text, (20, 50),
                   cv.FONT_HERSHEY_SIMPLEX, 2,
                   (255, 255, 255), 6)
        cv.imshow('Recognizing Face...', image)
        if cv.waitKey(1000) == 27:
            escape = True
            break