Detailed process of Python flask building yolov3 target detection system

[artificial intelligence project] Python flask builds yolov3 target detection system:

 

Back end code

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')

 

Display part

python -m http.server

python app.py

Front end display part

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