Internship-day04

In the first beginning, I write a function with DeepFace

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from deepface import DeepFace
import pandas as pd
import datetime
# 这个文件用之前要改成main.py

# Representations stored in db/representations_vgg_face.pkl file.Please delete this file when you add new identities in your database.
# 每次动db文件夹，也就是数据库，都要把里面的pkl文件删掉

# 读取 Excel 文件
df = pd.read_excel('info.xlsx')

a=datetime.datetime.now()

# 比对图片和数据库
# 这个是模版
# found = DeepFace.find(img_path = "img.png", db_path = "db")
found = DeepFace.find(img_path="img.png", db_path="db", model_name='DeepFace')

b=datetime.datetime.now()

# 如果找到了匹配的图片
if found:
    # 取第一张匹配的图片
    match = found[0]

    # 提取出不含后缀的文件名，假设找到的图片的文件名（不包括扩展名）是人的 ID
    person_id = str(match['identity']).split('/')[-1].split('.')[0]

    # 在 Excel 文件中查找这个人的信息
    person_info = df[df['id'].astype(str) == person_id]

    # 如果找到了这个人的信息
    if not person_info.empty:
        # 打印这个人的信息
        print(person_info.iloc[0])

# print("used time:", b-a)
# # 这里用来测试打印
# print(df)
# print('found:', found)
# print('person_id:', person_id)
# print('person_info:', person_info)

But the effectiveness of the model and algorithms are poor. When “model_name=’DeepFace’”, the recognition speed is fast, with the sacrifice of accuracy. When “model_name=’VGG’”, the recognition speed is slow, about 15s per request.

Consequently, I used ‘face_recognition’ from GitHub, which turned out to be of great effectiveness.

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import face_recognition
import os
import pandas as pd


def find_person_info(image_path):
    # 文件夹路径
    folder_path = "db"

    # 加载你想要识别的图片
    unknown_image = face_recognition.load_image_file(image_path)
    unknown_face_encodings = face_recognition.face_encodings(unknown_image)

    if not unknown_face_encodings:
        print("No faces found in the unknown_image.")
        return None

    # 获取未知图片中的面部编码
    unknown_face_encoding = unknown_face_encodings[0]

    # 遍历文件夹中的每一张图片
    for filename in os.listdir(folder_path):
        # 只处理图片文件
        if filename.endswith(".jpg") or filename.endswith(".png"):
            # 加载已知的图片
            known_image = face_recognition.load_image_file(os.path.join(folder_path, filename))
            known_face_encodings = face_recognition.face_encodings(known_image)

            if not known_face_encodings:
                print(f"No faces found in the image {filename}.")
                continue

            # 获取已知图片中的面部编码
            known_face_encoding = known_face_encodings[0]

            # 比较两个面部编码，看是否匹配，调整tolerance参数
            results = face_recognition.compare_faces([known_face_encoding], unknown_face_encoding, tolerance=0.45)

            # 打印结果
            if results[0]:
                # 提取出不含后缀的文件名，假设文件名（不包括扩展名）是人的名字
                person_id = filename.split('.')[0]
                print(f"The unknown_image has a face that matches {person_id}!")

                df = pd.read_excel('info.xlsx')

                person_info = df[df['id'].astype(str) == person_id]

                # 如果找到了这个人的信息
                if not person_info.empty:
                    # 返回这个人的信息

                    print(person_info.iloc[0])

                    # 返回to_dict()格式
                    return person_info.iloc[0].to_dict()

    return 0

the key steps include:

  1. load the unknown picture and get the encoding

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    # 文件夹路径
    folder_path = "db"
       
    # 加载你想要识别的图片
    unknown_image = face_recognition.load_image_file(image_path)
    unknown_face_encodings = face_recognition.face_encodings(unknown_image)

  2. traverse pictures in ‘db’ files and get all encodings

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    # 遍历文件夹中的每一张图片
       for filename in os.listdir(folder_path):
           # 只处理图片文件
           if filename.endswith(".jpg") or filename.endswith(".png"):
               # 加载已知的图片
               known_image = face_recognition.load_image_file(os.path.join(folder_path, filename))
               known_face_encodings = face_recognition.face_encodings(known_image)
       
               if not known_face_encodings:
                   print(f"No faces found in the image {filename}.")
                   continue
       
               # 获取已知图片中的面部编码
               known_face_encoding = known_face_encodings[0]

  3. compare unknown_encodings with known_encodings, adjusting parameters: tolerance. Then transfer the output type of name of the picture from int to string

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    # 比较两个面部编码，看是否匹配，调整tolerance参数
                results = face_recognition.compare_faces([known_face_encoding], unknown_face_encoding, tolerance=0.45)

                # 打印结果
                if results[0]:
                    # 提取出不含后缀的文件名，假设文件名（不包括扩展名）是人的名字
                    person_id = filename.split('.')[0]
                    print(f"The unknown_image has a face that matches {person_id}!")

                    df = pd.read_excel('info.xlsx')

                    person_info = df[df['id'].astype(str) == person_id]

                    # 如果找到了这个人的信息
                    if not person_info.empty:
                        # 返回这个人的信息

                        print(person_info.iloc[0])

                        # 返回to_dict()格式
                        return person_info.iloc[0].to_dict()

Make sure that, the id value in excel file, equals to names of the known pictures(without postfix)

明天做异常处理、报错，多人图片，性能优化