Detailed process of Python flask building yolov3 target detection system

Time:2022-5-13

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

Detailed process of Python flask building 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

Detailed process of Python flask building yolov3 target detection system

python app.py

Detailed process of Python flask building yolov3 target detection system

Front end display part

Detailed process of Python flask building yolov3 target detection system

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