Python Flask搭建yolov3目标检测系统详解流程(检测系统目标)

【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')