自学教程：Python Flask搭建yolov3目标检测系统详解流程

from flask import Flask, request, jsonifyfrom PIL import Imageimport numpy as npimport base64import ioimport osfrom backend.tf_inference import load_model, inferenceos.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_requestdef add_headers(response):    response.headers.add('Access-Control-Allow-Origin', '*')    response.headers.add('Access-Control-Allow-Headers', 'Content-Type,Authorization')    return responseif __name__ == '__main__':    app.run(debug=True, host='0.0.0.0')

python app.py