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This article will explain in detail how to build a yolov3 target detection system in Flask in Python. The editor thinks it is very practical, so I share it with you as a reference. I hope you can get something after reading this article.

The backend code from flask import Flask, request, jsonifyfrom PIL import Imageimport numpy as npimport base64import ioimport osfrom backend.tf_inference import load_model, inferenceos.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_ strpoint:] # remove unused part like this: _ "data:image/jpeg Base64, "image = base64.b64decode (base64_str) img = Image.open (io.BytesIO (image)) if (IMG. Modewords): 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_requestdef add_headers (response): response.headers.add ('Access-Control-Allow-Origin',' *') response.headers.add ('Access-Control-Allow-Headers',' Content-Type,Authorization') return responseif _ _ name__ = ='_ main__': app.run (debug=True Host='0.0.0.0') display section

Python-m http.server

Python app.py

Front-end display part

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