【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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