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 as 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(base64_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('access-control-allow-origin', '*')
    response.headers.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

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