YOLOv3 mAP

Hi, I was trying to use your code with YOLOv3, but I have some problems to make predictions txt files. With ./darknet detector test cfg/voc.data yolo-voc.cfg yolo-voc.weights -dont_show -ext_output < data/train.txt > result.txt I can generate a unique txt file with all results, but I really don’t have idea how to parse it. This is an example of result.txt file: <Total BFLOPS 65.864

seen 64 Enter Image Path: data/horses.jpg: Predicted in 42.076185 seconds. horse: 88% (left_x: 3 top_y: 185 width: 150 height: 167) horse: 99% (left_x: 5 top_y: 198 width: 307 height: 214) horse: 96% (left_x: 236 top_y: 180 width: 215 height: 169) horse: 99% (left_x: 440 top_y: 209 width: 156 height: 142) Enter Image Path: data/person.jpg: Predicted in 41.767213 seconds. dog: 99% (left_x: 58 top_y: 262 width: 147 height: 89) person: 100% (left_x: 190 top_y: 95 width: 86 height: 284) horse: 100% (left_x: 394 top_y: 137 width: 215 height: 206) Enter Image Path: >

Thank you so much for your help.

[vadimen]

here is a simple code, works for yolov3 of v4 or any other, adjust the paths, time or even the command for yourself

import os
import subprocess
import time

image_paths_file = '/home/darknet/vadim_configs/test.txt'
path_to_save_images = '/home/darknet/results/'

im_paths = open(image_paths_file, "r").readlines()

obj_dat = "/home/darknet/vadim_configs/obj.data"
yolo_cnf = "/home/darknet/vadim_configs/yolov4-custom.cfg"
yolo_wghts = "/home/darknet/backup/yolov4-custom_best.weights"

for im in im_paths:
    cmd = "./darknet detector test {} {} {} -dont_show \"{}\"".format(obj_dat, yolo_cnf, yolo_wghts, im)

    # The os.setsid() is passed in the argument preexec_fn so
    # it's run after the fork() and before  exec() to run the shell.

    p = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, preexec_fn=os.setsid)

    # it must be adjuste to your inference speed
    # on my rtx2080 takes 6 seconds max
    time.sleep(6)
    # Send the signal to all the process groups
    #os.killpg(os.getpgid(p.pid), signal.SIGTERM)

    image_name = (im.split('/')[-1]).split('.')[0]
    os.rename("predictions.jpg", os.path.join(path_to_save_images, image_name+'.jpg'))
    print("img was saved")

[Cartucho]

Done! Have a look here.

Basically, all you have to do is add the result.txt to the folder extra/ and run the command python convert_pred_yolo.py