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osrs_yolov3

Yolov3 Object Detection In OSRS using Python code, Detecting Cows - Botting

name: GeForce GTX 1060 6GB major: 6 minor: 1 memoryClockRate(GHz): 1.7085 (Average: 12 FPS)

Quick Start

Download YOLOv3 weights from YOLO website. Run in Pycharm Terminal:

https://pjreddie.com/media/files/yolov3.weights ----- save this in 'model_data' directory

Alternative tiny weight file uses less computing but is less accurate useful for getting quicker detection rates

https://github.com/smarthomefans/darknet-test/blob/master/yolov3-tiny.weights ----- save this in 'model_data' directory

Convert the Darknet YOLO model to a Keras model.

type in terminal:

pip install -r requirements

then

python convert.py -w model_data/yolov3.cfg model_data/yolov3.weights model_data/yolov3.h5

Download Resources

Note: if there's issues with converting the weights to h5 use this yolo weights in the interim: https://drive.google.com/file/d/1_0UFHgqPFZf54InU9rI-JkWHucA4tKgH/view?usp=sharing

goto Google drive for large files and specifically the osrs cow and goblin weighted file: https://drive.google.com/folderview?id=1P6GlRSMuuaSPfD2IUA7grLTu4nEgwN8D

and see full video tutorial: https://youtu.be/X3snnwzJfEw

Check if your gpu will work: https://developer.nvidia.com/cuda-gpus and use the cuda for your model and the latest cudnn for the cuda version.

pycharm = https://www.jetbrains.com/pycharm/download/download-thanks.html?platform=windows&code=PCC

cuda 10.0 = https://developer.nvidia.com/compute/cuda/10.0/Prod/local_installers/cuda_10.0.130_411.31_win10

cudnn = https://developer.nvidia.com/rdp/cudnn-archive#a-collapse765-10

labelImg = https://github.com/heartexlabs/labelImg/releases

tesseract-ocr = https://sourceforge.net/projects/tesseract-ocr-alt/files/tesseract-ocr-setup-3.02.02.exe/download or for all versions: https://github.com/UB-Mannheim/tesseract/wiki

Credit to: https://github.com/pythonlessons/YOLOv3-object-detection-tutorial

Trouble Shooting:

Images and XML files for object detection

example unzip files in directory OID/Dataset/train/cow/: cows.z01 , cows.z02 , cows.z03

add image and xml files to folder OID//Dataset//train//name of class **** Add folder to train with the name of each class

***** IMAGES MUST BE IN JPG FORMAT (use png_jpg to convert png files to jpg files) *******

run voc_to_yolov3.py - this will create the images class path config file and the classes list config file

ezgif-1-5e2882085ca8.mp4

Changing default terminal and generating h5 file

Ensure you're using cmd for your terminal, by using the top tab 'file', then select 'settings'. using the left navigation pane select the section for 'Tools' then in the drop down select 'Terminal'. Within the section for Application Settings change/make sure the Shell Path: is your cmd.exe path. click apply then ok. close the project then reopen. In the terminal view type: cd venv cd scripts activate.bat

Untitled.Project.mp4

while using the pip -r requirements and still get the error: cannot import name 'batchnormalization' from 'keras.layers.normalization download this and save to model_data folder https://drive.google.com/file/d/1_0UFHgqPFZf54InU9rI-JkWHucA4tKgH/view?usp=sharing.

Resolving Batchnormalisation error

this is the error log for batchnormalisation: https://github.com/slyautomation/osrs_yolov3/blob/main/error_log%20batchnormalization.txt this is caused by having an incompatiable version of tensorflow. the version needed is 1.15.0

pip install --upgrade tensorflow==1.15.0

since keras has been updated but will still cause the batchnomralisation error, downgrade keras in the same way to 2.2.4:

pip install --upgrade keras==2.2.4

refer to successful log of python convert.py -w model_data/yolov3.cfg model_data/yolov3.weights model_data/yolov3.h5

https://github.com/slyautomation/osrs_yolov3/blob/main/successful_log%20no%20batchnormalisation%20issues.txt