YOLO (Darknet): How to detect a whole directory of images? - machine-learning

Question

The Darknet guide to detect objects in images using pre-trained weights is here: https://pjreddie.com/darknet/yolo/
The command to run is:
./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg
The last argument is the path to a file, I've tried to change it to data/*.jpg but didn't work.
How to use Darknet to detect a whole directory of images?

Answer 1

As per the link mentioned below, one can use cv2.dnn.readNetFromDarknet module to read darknet, trained weights and configuration file to make a loaded model in python. Once the model is loaded, one can simply use for loop for prediction.
Please refer this link for further clarification

Answer 2

There is a simple way to detect objects on a list of images based on this repository AlexeyAB/darknet.
./darknet detector test cfg/obj.data cfg/yolov3.cfg yolov3.weights < images_files.txt
You can generate the file list either from the command line (Send folder files to txt ) or using a GUI tool like Nautilus on Ubuntu.
Two extra flags -dont_show -save_labels will disable the user interaction, and save the detection results to text files instead.

Answer 3

There's a trick to make Darknet executable load weights once and infer multiple image files. Use expect to do the trick.
Install expect:
sudo yum install expect -y
#sudo apt install expect -y
Do object detection on multiple images:
expect <<"HEREDOC"
puts "Spawning...";
spawn ./darknet detect cfg/yolov3-tiny.cfg yolov3-tiny.weights;
set I 0;
expect {
"Enter Image Path" {
set timeout -1;
if {$I == 0} {
send "data/dog.jpg\r";
incr I;
} elseif {$I == 1} {
send "data/kite.jpg\r";
incr I;
} else {
exit;
}
exp_continue;
}
}
HEREDOC

Answer 4

Another solution is loading Darknet from Python2 (not 3, Darknet is using Python2).
1a) Clone darknet as described in https://pjreddie.com/darknet/yolo/
1b) Go to the cloned dir, download yolov3-tiny.weights and yolov3.weights as said in https://pjreddie.com/darknet/yolo/
2) Copy darknet/examples/detector.py to darknet dir
3) Edit the new detector.py
Change .load_net line to use: cfg/yolov3-tiny.cfg and yolov3-tiny.weights
Change .load_meta line to use: cfg/coco.data
4a) Detect objects in images by adding some dn.dectect lines in detector.py
4b) Run detector.py