Internship-day05

这个项目源码发布在github上：

face-match

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import os
import pandas as pd
import face_recognition


def find_person_info(image_path):
    # 看能不能找到给定路径的文件
    if not os.path.exists(image_path):
        return [], "No such img."
# 给定存放图片的文件夹
folder_path = "db"

# 加载给定路径的文件并生成特征编码，如果图片没有人脸则报错
unknown_image = face_recognition.load_image_file(image_path)
unknown_face_encodings = face_recognition.face_encodings(unknown_image)

if not unknown_face_encodings:
    return [], "No faces found in the unknown_image."

matched_persons = []  # 设计一个列表，用来存放匹配到人脸的信息

# 循环，找出提供照片中的每个人脸
for unknown_face_encoding in unknown_face_encodings:

    # 返回指定目录下的所有文件和目录名的列表
    for filename in os.listdir(folder_path):
        # 只处理.jpg和.png文件
        if filename.endswith(".jpg") or filename.endswith(".png"):
            # 加载给定路径的文件并生成特征编码
            known_image = face_recognition.load_image_file(os.path.join(folder_path, filename))
            known_face_encodings = face_recognition.face_encodings(known_image)
            
            # 假如在known_face_encodings中没找到人脸，那么跳出循环，下一张
            if not known_face_encodings:
                continue

            # 从 known_face_encodings 列表中取出第一个元素并赋值给 known_face_encoding
            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，取0.45差不多，还要调
            results = face_recognition.compare_faces([known_face_encoding], unknown_face_encoding, tolerance=0.5)

            # 打印结果
            if results[0]:
                # 取文件名为人物id
                person_id = filename.split('.')[0]

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

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

                # 假如找到了信息
                if not person_info.empty:
                    # 将这个人信息append加入到列表中
                    matched_persons.append(person_info.iloc[0].to_dict())

if matched_persons:
    return matched_persons, None
else:
    return [], "No match found."

打印 unknown_image：

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[[[ 32  71 112]
  [ 33  72 113]
  [ 33  72 113]
  ...
  [ 35  71 106]
  [ 35  71 106]
  [ 33  69 104]]

 [[ 33  72 113]
  [ 33  72 113]
  [ 34  73 114]
  ...
  [ 35  71 106]
  [ 35  71 106]
  [ 33  69 104]]

 [[ 34  73 114]
  [ 34  73 114]
  [ 35  74 115]
  ...
  [ 36  72 107]
  [ 36  72 107]
  [ 34  70 105]]

 ...

 [[  2  25  71]
  [  2  25  71]
  [  3  26  73]
  ...
  [ 14  21  74]
  [ 12  23  74]
  [ 12  23  74]]

 [[  0  23  64]
  [  0  24  64]
  [  2  25  68]
  ...
  [ 13  20  73]
  [ 10  22  73]
  [ 10  18  73]]

 [[  0  22  59]
  [  0  22  59]
  [  0  23  64]
  ...
  [ 13  20  73]
  [ 10  22  73]
  [  8  17  72]]]

打印 unknown_face_encodings：这个是用来存放unknown_face_encoding的一个array

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[array([-0.13223957,  0.16503266,  0.09020811,  0.00672515, -0.09350206,
       -0.00940245, -0.03665374, -0.09436678,  0.12680565,  0.01610284,
        0.23214346, -0.01058169, -0.17831463, -0.11720017, -0.02244026,
        0.12244396, -0.21053834, -0.13550632,  0.00286131,  0.04644092,
        0.10494904,  0.07014632,  0.0892531 ,  0.04381904, -0.07422439,
       -0.42399305, -0.04031111, -0.11812686,  0.02305087, -0.01154761,
        0.01216548,  0.0245845 , -0.25394315, -0.10557299,  0.03783333,
        0.07827907, -0.01626904, -0.00509987,  0.18849066, -0.0242721 ,
       -0.20650545,  0.11054654,  0.02895997,  0.31742305,  0.16261479,
        0.0759404 ,  0.05481248, -0.08809394,  0.09429981, -0.21823171,
        0.09824699,  0.16703583,  0.08863883, -0.01104275,  0.00278107,
       -0.14643633,  0.03028786,  0.02878108, -0.17623518,  0.05836982,
        0.1670046 , -0.01511078,  0.01745525, -0.01929321,  0.19001804,
        0.02725898, -0.07748701, -0.19153407,  0.02892923, -0.10864975,
       -0.05668222,  0.09141017, -0.1782237 , -0.1846647 , -0.32670057,
        0.0874635 ,  0.40891796,  0.07372297, -0.18304899,  0.03089732,
       -0.08080248,  0.08455542,  0.10734302,  0.0760516 , -0.0260337 ,
        0.018057  , -0.18947719,  0.06145783,  0.18782443, -0.03918557,
       -0.10583983,  0.22502539,  0.0415048 ,  0.03528464,  0.03694579,
        0.05772862, -0.02676605,  0.06341691, -0.20423572,  0.04492543,
        0.0347067 ,  0.04718253, -0.02277087,  0.10553455, -0.1783113 ,
        0.09799166, -0.03231913,  0.00251438, -0.03181266, -0.04052553,
       -0.17417844, -0.02148585,  0.10722338, -0.21874264,  0.25460005,
        0.16116001,  0.08119219,  0.13333398,  0.12037521, -0.00283147,
        0.00364753, -0.00507532, -0.1807882 , -0.06987992,  0.11120825,
        0.00986055,  0.10558957,  0.02575663])]

打印 unknown_face_encoding:

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[-0.13223957  0.16503266  0.09020811  0.00672515 -0.09350206 -0.00940245
 -0.03665374 -0.09436678  0.12680565  0.01610284  0.23214346 -0.01058169
 -0.17831463 -0.11720017 -0.02244026  0.12244396 -0.21053834 -0.13550632
  0.00286131  0.04644092  0.10494904  0.07014632  0.0892531   0.04381904
 -0.07422439 -0.42399305 -0.04031111 -0.11812686  0.02305087 -0.01154761
  0.01216548  0.0245845  -0.25394315 -0.10557299  0.03783333  0.07827907
 -0.01626904 -0.00509987  0.18849066 -0.0242721  -0.20650545  0.11054654
  0.02895997  0.31742305  0.16261479  0.0759404   0.05481248 -0.08809394
  0.09429981 -0.21823171  0.09824699  0.16703583  0.08863883 -0.01104275
  0.00278107 -0.14643633  0.03028786  0.02878108 -0.17623518  0.05836982
  0.1670046  -0.01511078  0.01745525 -0.01929321  0.19001804  0.02725898
 -0.07748701 -0.19153407  0.02892923 -0.10864975 -0.05668222  0.09141017
 -0.1782237  -0.1846647  -0.32670057  0.0874635   0.40891796  0.07372297
 -0.18304899  0.03089732 -0.08080248  0.08455542  0.10734302  0.0760516
 -0.0260337   0.018057   -0.18947719  0.06145783  0.18782443 -0.03918557
 -0.10583983  0.22502539  0.0415048   0.03528464  0.03694579  0.05772862
 -0.02676605  0.06341691 -0.20423572  0.04492543  0.0347067   0.04718253
 -0.02277087  0.10553455 -0.1783113   0.09799166 -0.03231913  0.00251438
 -0.03181266 -0.04052553 -0.17417844 -0.02148585  0.10722338 -0.21874264
  0.25460005  0.16116001  0.08119219  0.13333398  0.12037521 -0.00283147
  0.00364753 -0.00507532 -0.1807882  -0.06987992  0.11120825  0.00986055
  0.10558957  0.02575663]

将这个方法的各个小功能拖出来，写成更小的函数，便于维护：

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import os
import pandas as pd
import face_recognition


def check_image_exists(image_path):
    if not os.path.exists(image_path):
        raise FileNotFoundError("No such img.")
    return True


def load_image_and_generate_encodings(image_path):
    image = face_recognition.load_image_file(image_path)
    face_encodings = face_recognition.face_encodings(image)
    if not face_encodings:
        raise ValueError("No faces found in the image.")
    return face_encodings


def get_known_face_encodings(folder_path):
    known_face_encodings = {}
    for filename in os.listdir(folder_path):
        if filename.endswith(".jpg") or filename.endswith(".png"):
            image = face_recognition.load_image_file(os.path.join(folder_path, filename))
            face_encodings = face_recognition.face_encodings(image)
            if face_encodings:
                known_face_encodings[filename] = face_encodings[0]
    return known_face_encodings


def compare_faces(unknown_face_encodings, known_face_encodings):
    matched_faces = []
    for unknown_face_encoding in unknown_face_encodings:
        for filename, known_face_encoding in known_face_encodings.items():
            results = face_recognition.compare_faces([known_face_encoding], unknown_face_encoding, tolerance=0.5)
            if results[0]:
                matched_faces.append(filename.split('.')[0])
    return matched_faces


def get_person_info(matched_faces):
    df = pd.read_excel('info.xlsx')
    matched_persons = []
    for person_id in matched_faces:
        person_info = df[df['id'].astype(str) == person_id]
        if not person_info.empty:
            matched_persons.append(person_info.iloc[0].to_dict())
    return matched_persons


def find_person_info(image_path):
    check_image_exists(image_path)
    unknown_face_encodings = load_image_and_generate_encodings(image_path)
    known_face_encodings = get_known_face_encodings("db")
    matched_faces = compare_faces(unknown_face_encodings, known_face_encodings)
    matched_persons = get_person_info(matched_faces)
    return matched_persons

相应地，更改 app.py 接口

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from flask import Flask, request, jsonify
from function import find_person_info  # 确保你的function.py文件在同一目录下，或者在Python的搜索路径中

app = Flask(__name__)


@app.route('/find_person_info', methods=['POST'])
def find_person_info_api():
    image_path = request.json['image_path']
    try:
        persons_info = find_person_info(image_path)
        return jsonify(persons_info), 200
    except FileNotFoundError:
        return jsonify({"message": "The image does not exist."}), 200
    except ValueError as e:
        if str(e) == "No faces found in the image.":
            return jsonify({"message": "No faces found in the uploaded image."}), 200
        elif "No faces found in the image" in str(e):
            return jsonify({"message": "No faces found in one of the known images."}), 200
    except Exception:
        return jsonify({"message": "No match found."}), 200


if __name__ == '__main__':
    app.run(debug=True)

from function import * 这行代码的意思是从 function 模块（也就是 function.py 文件）中导入所有的公有函数和变量。

这种导入方式被称为 “星号” 或 “通配符” 导入。它会导入 function 模块中所有没有以下划线 _ 开头的函数和变量。这种导入方式可以方便地导入模块中的所有内容，但是也有一些缺点：

  1. 如果两个模块都定义了一个同名的函数或变量，那么后导入的模块会覆盖先导入的模块中的函数或变量。
  2. 当你查看代码时，可能不清楚一个函数或变量是从哪个模块导入的，这会使代码更难理解和维护。

因此，尽管 from function import * 这种导入方式很方便，但是在编写大型项目时，通常推荐明确地导入需要的函数和变量，例如 from function import find_person_info。