工程师&程序员的自我修养 Episode.6 深度学习实现人脸检测及人数统计项目

深度学习实现人脸检测及人数统计项目实现及代码示例

手动@王婆，自取不谢【doge】。
本项目利用Ultra-Light-Fast-Generic-Face-Detector-1MB模型完成人脸检测。该模型是针对边缘计算设备或低算力设备(如用ARM推理)设计的实时超轻量级通用人脸检测模型，可以在低算力设备中如用ARM进行实时的通用场景的人脸检测推理。
此处感谢参考部分来自@wangwei8638大佬的程序编写思路。

一、定义待预测数据

以本示例中文件夹下test_face_detection.jpg为待预测图片

!pip install paddlehub==1.6.0 -i https://pypi.tuna.tsinghua.edu.cn/simple

Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
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import paddle
import os

# 解压文件到指定路径
!unzip -o /home/aistudio/data/data.zip -d dataset/

Archive:  /home/aistudio/data/data.zip
  inflating: dataset/4.jpg           
  inflating: dataset/3.jpg           
  inflating: dataset/1.jpg           
  inflating: dataset/2.jpg           

# 待预测图片
test_img_path = ["dataset/1.jpg"]


import matplotlib.pyplot as plt 
import matplotlib.image as mpimg 

img = mpimg.imread(test_img_path[0]) 

# 展示待预测图片
plt.figure(figsize=(10,10))
plt.imshow(img) 
plt.axis('off') 
plt.show()

#定义全局变量
Filelist = []#存储文件路径的列表
path='dataset'

用户想要利用Ultra-Light-Fast-Generic-Face-Detector-1MB完成对该文件的人脸检测，只需读入该文件，将文件内容存成list，list中每个元素是待预测图片的存放路径。

import os 
def get_filelist(dir):
 
    data_dict={}.fromkeys(['path'])
    del_str='.ipynb_checkpoints'
    for home, dirs, files in os.walk(path):
        
        for filename in files:
            
            #忽略隐藏文件
            if del_str in filename:
                continue
            else:
                data_dict={}.fromkeys(['path'])
                # 文件名列表，包含完整路径
                #os.path.join实现文件路径的拼接
                img_path=os.path.join(home, filename)
                data_dict['path']=img_path
                #文件名列表，只包含文件名
                Filelist.append(data_dict)  
    return Filelist #返回文件路径列表和文件夹名称列表
get_filelist(path)
print(Filelist)

[{'path': 'dataset/1.jpg'}, {'path': 'dataset/2.jpg'}, {'path': 'dataset/4.jpg'}, {'path': 'dataset/3.jpg'}]

#path:图片文件路径
path ='dataset'

#调用get_filelist函数获取图片路径和图片类别
img_files=get_filelist(path)
print(type(img_files))
#生成训练数据集对应文本train_list
def dict_save(filename, data):#filename为写入txt文件的路径，data为要写入数据列表.
    with open(filename, 'w') as f:
        for i in data:
            train_data=i['path']+'\n'
            f.write(train_data)

    print("保存文件成功")  

train_file='data/train_list.txt'

dict_save(train_file,img_files)

保存文件成功

with open('data/train_list.txt', 'r') as f:
    test_img_path=[]
    for line in f:
        test_img_path.append(line.strip())
print(test_img_path)

['dataset/1.jpg', 'dataset/2.jpg', 'dataset/4.jpg', 'dataset/3.jpg']

二、加载预训练模型

Ultra-Light-Fast-Generic-Face-Detector-1MB提供了两种预训练模型，ultra_light_fast_generic_face_detector_1mb_320和ultra_light_fast_generic_face_detector_1mb_640。

• ultra_light_fast_generic_face_detector_1mb_320，在预测时会将图片输入缩放为320 * 240，预测速度更快。关于该模型更多介绍， 查看PaddleHub官网介绍
• ultra_light_fast_generic_face_detector_1mb_640，在预测时会将图片输入缩放为640 * 480，预测精度更高。关于该模型更多介绍， 查看PaddleHub官网介绍

用户根据需要，选择具体模型。利用PaddleHub使用该模型时，只需更改指定name，即可实现无缝切换。

import paddlehub as hub

module = hub.Module(name="ultra_light_fast_generic_face_detector_1mb_640")
# module = hub.Module(name="ultra_light_fast_generic_face_detector_1mb_320")

[32m[2021-06-01 17:02:41,276] [    INFO] - Installing ultra_light_fast_generic_face_detector_1mb_640 module[0m
[32m[2021-06-01 17:02:41,278] [    INFO] - Module ultra_light_fast_generic_face_detector_1mb_640 already installed in /home/aistudio/.paddlehub/modules/ultra_light_fast_generic_face_detector_1mb_640[0m

三、预测

PaddleHub对于支持一键预测的module，可以调用module的相应预测API，完成预测功能。

input_dict = {"image": test_img_path}
# execute predict and print the result
results = module.face_detection(data=input_dict, visualization=True)
cycle_counter = 0
for i in results:
    det =i['data']
    num = 0
    for condition in det:
        confidence = condition['confidence']
        if confidence >= 0.9:
            num = num +1
    cycle_counter = cycle_counter +1
    print("第",cycle_counter,"张图片的总人数为：",num)

# for result in results:
#     print(result)

# 预测结果展示
img = mpimg.imread("face_detector_640_predict_output/1.jpg")
plt.figure(figsize=(10,10))
plt.imshow(img) 
plt.axis('off') 
plt.show()

第 1 张图片的总人数为： 33
第 2 张图片的总人数为： 40
第 3 张图片的总人数为： 35
第 4 张图片的总人数为： 34