hub serving start -m pyramidbox_lite_mobile

# coding: utf8
import requests
import json
import cv2
import base64


def cv2_to_base64(image):
    data = cv2.imencode('.jpg', image)[1]
    return base64.b64encode(data.tostring()).decode('utf8')


if __name__ == '__main__':
    # 獲取圖片的base64編碼格式 (記得修改你自己的圖片存放路徑)
    img1 = cv2_to_base64(cv2.imread("./static/Aaron_Peirsol_0001.jpg"))
    img2 = cv2_to_base64(cv2.imread("./static/Aaron_Peirsol_0002.jpg"))
    data = {'images': [img1, img2]}
    # 指定content-type
    headers = {"Content-type": "application/json"}
    # 發(fā)送HTTP請求
    url = "http://127.0.0.1:8866/predict/pyramidbox_lite_mobile"
    r = requests.post(url=url, headers=headers, data=json.dumps(data))

    # 打印預測結果
    print(r.json())

- templates
	- index.html
- app.py
- forms.py
- utils.py

from flask_wtf import FlaskForm
from flask_wtf.file import FileAllowed, FileRequired, FileSize, FileField

class ImageForm(FlaskForm):
    face_img = FileField("face_img", 
        validators=[
            FileRequired(message="不能為空"),
            FileAllowed(['jpg', 'png'], message="僅支持jpg/png格式"),
            FileSize(max_size=2048000, message="圖片不能大于2Mb")
        ],
        description="圖片不能大于2Mb，僅支持jpg/png格式"
    )

import base64
import numpy as np
import cv2


def base64_to_cv2(img: str):
    # base64 -> 二進制 -> ndarray -> cv2
    # 解碼為二進制數(shù)據(jù)
    img_codes = base64.b64decode(img)
    img_np = np.frombuffer(img_codes, np.uint8)
    img_cv2 = cv2.imdecode(img_np, cv2.IMREAD_COLOR)
    return img_cv2


def cv2_to_base64(image):
    data = cv2.imencode('.jpg', image)[1]
    return base64.b64encode(data.tostring()).decode('utf8')


# 顯示cv2格式的圖像 --> 開發(fā)過程中測試圖像是否正常時使用
def cv2_show(img_cv2):
    cv2.imshow('img', img_cv2)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

# 注：在推理前將圖像縮放到指定的尺寸，即能提升速度，有時也能提升精度（實測像素太高時識別效果也不好）
from flask import Flask, render_template, request
import requests
from forms import ImageForm
import cv2
import numpy as np
import json
import time
from utils import cv2_to_base64


app = Flask(__name__)
app.config['SECRET_KEY'] = 'your_secret_key_here'


@app.route('/', methods=['GET', 'POST'])
def predict():
    form = ImageForm()

    if form.validate_on_submit():

        # 1. 從前端表單獲取圖像文件
        file = form.face_img.data  # <class 'werkzeug.datastructures.FileStorage'>
        file_content = file.read()  # <class 'bytes'>

        # 2. 圖像文件轉cv2, 并縮放到指定尺寸 --> 尺寸太大或太小，識別精度都會變差
        img_cv2 = np.asarray(bytearray(file_content), dtype=np.uint8)  # (len,)
        img_cv2 = cv2.imdecode(img_cv2, cv2.IMREAD_COLOR)  # (w, h, c)
        img_cv2 = cv2.resize(img_cv2, (250, 250), interpolation=cv2.INTER_LINEAR)

        # 3. cv2轉str(base64)
        img_base64 = cv2_to_base64(img_cv2)

        # 4. str(base64)輸入模型 --> json --> 人臉框坐標
        data = {'images': [img_base64]}
        headers = {"Content-type": "application/json"}
        url = "http://127.0.0.1:8866/predict/pyramidbox_lite_mobile"

        start_time = time.time()
        r = requests.post(url=url, headers=headers, data=json.dumps(data))
        use_time = time.time() - start_time

        rectangle = r.json()['results'][0]['data'][0]  # 一張圖片 --> dict{confidence, left, top, right, bottom}

        # 5. cv2，json --> 畫矩形 --> cv2
        cv2.rectangle(
            img_cv2, 
            (rectangle['left'], rectangle['top']),
            (rectangle['right'], rectangle['bottom']),
            (255, 0, 0),  # 藍色
            thickness=2)

        # 6. cv2轉str(base64)
        img_base64 = cv2_to_base64(img_cv2)

        # 7. str(base64) 返回到前端
        return render_template(
            'index.html', form=form, img_base64=img_base64, 
            confidence=rectangle['confidence'], use_time=use_time)

    return render_template('index.html', form=form)

if __name__ == '__main__':
    app.run(debug=True, port=5000)

<h1>試試人臉檢測</h1>


<!-- 1. 上傳圖像的表單 -->
<form action="" method="post" class="mt-4" enctype="multipart/form-data">
    <!-- csrf這一句好像可以沒啥用 -->
    {{ form.csrf_token }}
    {{ form.face_img() }}
    <input type="submit" value="Submit">
</form>

<!-- 2. 顯示檢測結果 -->
{% if img_base64 %}
    <img src="data:image/jpeg;base64, {{ img_base64 }}" width="250" height="250">
    <p>置信度: {{ confidence }}</p>
    <p>推理耗時(秒): {{ use_time }}</p>
{% endif %}

<!-- 3. 顯示錯誤信息 -->
{% if form.face_img.errors %}
    <div class="alert alert-danger">
        {% for error in form.face_img.errors %}
            {{ error }}
        {% endfor %}
    </div>
{% endif %}

<form action="" method="post" class="mt-4">
    <!-- csrf這一句好像可以刪掉 -->
    {{ form.csrf_token }}
    {{ form.face_img() }}
    <input type="submit" value="Submit">
</form>