exec_bin.py

import os.path
import time
from json import loads
from darkflow.net.build import TFNet
import cv2

options = {"model": "cfg/yolo.cfg", "load": "bin/yolo.weights", "threshold": 0.5, "gpu": 1.0}
tfnet = TFNet(options)

initial_file_name = 0
file_name = initial_file_name
seen_objects = set()
counter = 0
while True:
    counter = (counter + 1) % 2
    if True:
        files = os.listdir("./images")
        last_file = sorted(
            [x.split(".")[0] for x in files], key=lambda x: int(x), reverse=True
        )
        if len(last_file) >0:
            last_file = last_file[0]
            last_file = int(last_file)
            print("Inferencing file {}".format(last_file))
            if last_file > file_name:
                file_name = last_file -1

    if os.path.isfile("./images/{}.png".format(file_name)):
        imgcv = cv2.imread("./images/{}.png".format(file_name))
        result = tfnet.return_predict(imgcv)
        print(result)
        parsed_result = result
        if len(parsed_result) > 0:
            temp = sorted(parsed_result, key=lambda x: x["confidence"], reverse=True)
            if len(temp) > 2:
                if temp[0]["label"] in seen_objects:
                    detected_object = temp[1]
                else:
                    detected_object = temp[0]
            else:
                detected_object = temp[0]
            if not detected_object["label"] in seen_objects:
                with open("./label.txt", "w") as f:
                    f.write(str(detected_object["label"]))
                    seen_objects.add(detected_object["label"])
                    file_name += 5
    else:
        print("no file found")
        time.sleep(0.1)