【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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