Python Flask搭建yolov3目标检测系统详解流程

【人工智能项目】python Flask搭建yolov3目标检测系统

后端代码

from flask import Flask, request, jsonify
from PIL import Image
import numpy awww.cppcns.coms np
import base64
import io
import os

from backend.tf_inference import load_model, inference

os.environ['CUDA_VISIBLE_DEVICES'] = '0'

sess, detection_graph = load_model()

app = Flask(__name__)

@app.route('/api/', methods=["POST"])
def main_interface():
    response = request.get_json()
    data_str = response['image']
    point = data_str.find(',')
    base64_str = data_str[point:]  # remove unused part like this: "data:image/jpeg;base64,"

    image = base64.b64decode(base编程客栈64_str)       
    img = Image.open(io.BytesIO(image))

    if(img.mode!='RGB'):
        img = img.convert("RGB")
    
    # convert to numpy array.
    img_arr = np.array(img)

    # do object detection in inference function.
    results = inference(sess, detection_graph, img_arr, conf_thresh=0.7)
    print(results)

    return jsonify(results)

@app.after_request
def add_headers(response):
    response.headers.add('编程客栈Access-Control-Allo编程客栈w-Origin', '*')
    response.heade编程客栈rs.add('Access-Control-Allow-Headers', 'Content-Type,Authorization')
    return response


if __name__ == '__main__':
    app.run(debug=True, host='0.0.0.0')

展示部分

python -m http.server

python app.py

前端展示部分

到此这篇关于Python Flask搭建yolov3目标检测系统详解流程的文章就介绍到这了,更多相关Python 目标检测系统内容请搜索我们以前的文章或继续浏览下面的相关文章希望大家以后多多支持我们！