FaceRecog/model_test.py at main · Lixtt/FaceRecog · GitHub

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# -*-coding: utf-8 -*-


from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from utils import file_processing, image_processing, debug
import face_recognition
import predict

resize_width = 160
resize_height = 160


def face_face_recognition_batch(
    model_path, dataset_path, filename, filePath_list, label_list, threshold
):
    # 加载数据库的数据
    dataset_emb, names_list = predict.load_dataset(dataset_path, filename)
    print("loadind dataset...\n names_list:{}".format(names_list))
    # 初始化mtcnn人脸检测
    face_detect = face_recognition.Facedetection()
    # 初始化facenet
    face_net = face_recognition.facenetEmbedding(model_path)

    right_num = 0
    wrong_num = 0
    detection_num = 0
    test_num = len(filePath_list)
    for image_path, label_name in zip(filePath_list, label_list):
        print("image_path:{}".format(image_path))
        # 读取图片
        image = image_processing.read_image_gbk(image_path)
        # 人脸检测
        T0 = debug.TIME()
        bboxes, landmarks = face_detect.detect_face(image)
        bboxes, landmarks = face_detect.get_square_bboxes(
            bboxes, landmarks, fixed="height"
        )
        T1 = debug.TIME()

        if bboxes == [] or landmarks == []:
            print("-----no face")
            continue
        if len(bboxes) >= 2 or len(landmarks) >= 2:
            print("-----image have {} faces".format(len(bboxes)))
            continue
        T2 = debug.TIME()
        # 获得人脸框区域
        face_images = image_processing.get_bboxes_image(
            image, bboxes, resize_height, resize_width
        )
        face_images = image_processing.get_prewhiten_images(
            face_images, normalization=True
        )
        # face_images = image_processing.get_prewhiten_images(face_images,normalization=True)
        pred_emb = face_net.get_embedding(face_images)
        T3 = debug.TIME()
        pred_name, pred_score = predict.compare_embadding(
            pred_emb, dataset_emb, names_list, threshold=threshold
        )
        # 在图像上绘制人脸边框和识别的结果
        # show_info = [n + ':' + str(s)[:5] for n, s in zip(pred_name, pred_score)]
        # image_processing.show_image_text("face_recognition", image, bboxes, show_info)

        index = 0
        pred_name = pred_name[index]
        pred_score = pred_score[index]
        if pred_name == label_name:
            right_num += 1
        else:
            wrong_num += 1
        detection_num += 1
        print(
            "--detect face time:{}ms,recognition:{}ms,label_name:{},pred_name:{},score:{:3.4f},status:{}".format(
                debug.RUN_TIME(T1 - T0),
                debug.RUN_TIME(T3 - T2),
                label_name,
                pred_name,
                pred_score,
                (label_name == pred_name),
            )
        )
    # 准确率
    accuracy = right_num / detection_num
    # 漏检率
    misdetection = (test_num - detection_num) / test_num
    print(
        "-------------right_num/detection_num:{}/{},accuracy rate:{}".format(
            right_num, detection_num, accuracy
        )
    )
    print(
        "-------------misdetection/all_num:{}/{},misdetection rate:{}".format(
            (test_num - detection_num), test_num, misdetection
        )
    )


def face_recognition_(model_path, dataset_path, test_dataset, filename, threshold):
    # 获得测试图片的路径和label
    filePath_list, label_list = file_processing.gen_files_labels(
        test_dataset, postfix=None
    )
    label_list = [name.split("_")[0] for name in label_list]
    print("filePath_list:{},label_list{}".format(len(filePath_list), len(label_list)))
    face_face_recognition_batch(
        model_path, dataset_path, filename, filePath_list, label_list, threshold
    )


if __name__ == "__main__":
    model_path = "models/20200808-102900"
    dataset_path = "dataset/emb/faceEmbedding.npy"
    filename = "dataset/emb/name.txt"
    # image_path='dataset/test_images/1.jpg'
    # test_dataset='E:/Face/dataset/bzl/test2/test_dataset'
    # test_dataset='E:/Face/dataset/bzl/test3/test_dataset'
    test_dataset = "../database/lfw-align-128/Grady_Irvin_Jr"
    threshold = 1.14
    face_recognition_(model_path, dataset_path, test_dataset, filename, threshold)