NeuroFind

A solution to the task given by the Oberseminar of Messtechnik Institute of TU Dresden in 2021

Installation

please install on a linux operating system

Install both Faster R-CNN and Yolov5

git clone https://github.com/Anxum/NeuroFind.git
cd NeuroFind
git clone https://github.com/ultralytics/yolov5.git temp
mv temp/.git yolov5/.git
mv temp/* yolov5
rm -rf temp
cd yolov5
pip3 install -r requirements.txt
cd ../Faster_R-CNN
git clone https://github.com/tensorflow/models.git
pip3 install lxml
mkdir models/protoc
mv protoc-3.19.3-linux-x86_64.zip models/protoc
cd models/protoc
unzip protoc-3.19.3-linux-x86_64.zip
rm -r protoc-3.19.3-linux-x86_64.zip
cd ../research
../protoc/bin/protoc object_detection/protos/*.proto --python_out=.
export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim
cp object_detection/packages/tf2/setup.py .
python3 -m pip install --use-feature=2020-resolver .
cd ../../../..

Only Install Yolov5

git clone https://github.com/Anxum/NeuroFind.git
cd NeuroFind
git clone https://github.com/ultralytics/yolov5.git temp
mv temp/.git yolov5/.git
mv temp/* yolov5
rm -rf temp
cd yolov5
pip3 install -r requirements.txt 
cd ../..

Only Install Faster R-CNN

git clone https://github.com/Anxum/NeuroFind.git
cd NeuroFind/Faster_R-CNN
git clone https://github.com/tensorflow/models.git
pip3 install lxml
mkdir models/protoc
mv protoc-3.19.3-linux-x86_64.zip models/protoc
cd models/protoc
unzip protoc-3.19.3-linux-x86_64.zip
rm -r protoc-3.19.3-linux-x86_64.zip
cd ../research
../protoc/bin/protoc object_detection/protos/*.proto --python_out=.
export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim
cp object_detection/packages/tf2/setup.py .
python3 -m pip install --use-feature=2020-resolver .
cd ../../../..

Weights

In addition to the installation guide above please download the weights for the neuronal networks from: --insert url--

Standardized paths for YOLOv5 and Faster R-CNN:

Neuronal Network	Put Weights to:
YOLOv5	NeuroFind/yolov5/runs/train/weights
Faster R-CNN	NeuroFind/Faster_R-CNN/export

If you choose to put the weights into another folder you must refer to these paths if you wish to use the inference algorithms later.

Inference

Please download weigts first as described above or train the network yourself before Inference

YOLOv5

For Inference please run (from NeuroFind/yolov5)

python3 yolo_detect.py

Possible arguments include:

Argument	Description	Input format	Default Value	Comment
--weights	Put path to weights	OS_PATH	./runs/train/weights/best.pt
--source	path to file or dir	OS_PATH	../Images	URL, glob and webcam(0) could be possible, not tested
--conf-thres	minimal value of confidence for bounding boxes	float, [0..1]	0.25
--device	Specify the device to run Inference	0,1,2,3,..., cpu for CPU
--nosave	Don't save the images after inference		False
--project	Save to project/name	OS_PATH	./runs/detect
--name	Save to project/name	str	exp
--line-thickness	Thickness of lines of bounding boxes on the image	int	2

Arguments that are not tested or will not work or should not be specified

Not testet	Will not work	Please don't use
--exist-ok	--visualize	--hide labels
--classes	--view-img	--hide-conf
--half		--save-conf
--dnn		--imgsz
--update		--save-txt
--augment
--agnostic-nms
--iou-thres
--max-det

Faster R-CNN

For Inference please run (from NeuroFind/Faster_R-CNN)

python3 frcnn_detect.py

Possible arguments include:

Argument	Description	Input format	Default Value
--config	Put path to config	OS_PATH	./export/pipeline.config
--source	path to file or dir	OS_PATH	../Images
--checkpt	Path to checkpoint	OS_PATH	./export/checkpoint/ckpt-0
--labelmap	Path to labelmap	OS_PATH	./export/label_map.pbtxt
--conf-thres	minimal value of confidence for bounding boxes	float, [0..1]	0.25
--output	Save files to this path	OS_PATH	./runs/detect/exp
--nosave	Don't save the images after inference		False
--line-thickness	Thickness of lines of bounding boxes on the image	int	2

Training

YOLOv5

to create a training dataset please use( in NeuroFind/training_data_yolo):

python3 crop_image.py

For training, please refer to: https://github.com/ultralytics/yolov5.git

Faster R-CNN

For training, please refer to: https://github.com/tensorflow/models.git