jetbot 06 之实时人脸表情检测

最新推荐文章于 2025-03-11 07:07:25 发布

原创

最新推荐文章于 2025-03-11 07:07:25 发布 · 893 阅读

13 ·

CC 4.0 BY-SA版权

本文介绍了一种基于实时人脸检测的表情识别方法，通过多种模型（包括Keras、Facial Emotion Recognition及ONNX模型）实现表情识别，并展示了不同模型在Jetson Nano平台上的应用实例。

在实时人脸检测的基础上，做表情的实时识别真的可以。但要有好的效果一要有好的模型，二要有很棒的图像预处理。

三检测的人要有丰富的表情，目光呆滞🤪的程序狗恐怕也让会算法素手无策。。当然可以找个表情丰富的妹子鉴定以下效果。

这里只讲模型的应用，开始。

项目github 地址：

https://github.com/walletiger/jetson_facial_emotion_examples

一把人脸检测封装成库

#darknet_fd.py

#!/usr/bin/python3
# -*- coding:utf-8 -*-
import sys

import cv2
import numpy as np
import time
from ctypes import *
from darknet import  detect_image
from darknet import  load_net_custom
from darknet import  load_meta
from darknet import  IMAGE
from darknet import network_width, network_height
from camera import JetCamera
import traceback 

class DarkNetFD(object):
    def __init__(self, config_path='yoloface-500k-v2.cfg', weight_path='yoloface-500k-v2.weights', meta_path='face.data'):
        self.net = load_net_custom(config_path.encode('utf-8'), weight_path.encode('utf-8'), 0, 1)
        self.meta = load_meta(meta_path.encode('utf-8'))
        self.thresh = .5
        self.hier_thresh = .5
        self.nms = .45

    def detect(self, img):
        image_list = [img]

        pred_height, pred_width, c = image_list[0].shape
        net_width, net_height = (network_width(self.net), network_height(self.net))
        img_list = []

        for custom_image_bgr in image_list:
            custom_image = cv2.cvtColor(custom_image_bgr, cv2.COLOR_BGR2RGB)
            custom_image = cv2.resize(
            custom_image, (net_width, net_height), interpolation=cv2.INTER_NEAREST)
            custom_image = custom_image.transpose(2, 0, 1)
            img_list.append(custom_image)

        arr = np.concatenate(img_list, axis=0)
        arr = np.ascontiguousarray(arr.flat, dtype=np.float32) / 255.0
        data = arr.ctypes.data_as(POINTER(c_float))
        im = IMAGE(net_width, net_height, c, data)

        ret_lst = detect_image(self.net, self.meta, im, self.thresh, self.hier_thresh, self.nms)
        ret_out_lst = []

        if ret_lst:
            for ret in ret_lst:
                x