Purple-Kernel-Eater-Monster-Ideation

This is task to detect the yellow seeds that will turn into black monster seeds. For more information visit this documentations

Prerequisites

• Python 3.6.8
• OpenCV 4.0.0
• Scikit-learn
• Download the dataset. Store it in project directory folder under images folder.

Goal

You can see the sample pair of image below. Goal is to achive the best accuracy for the prediction of which seeds will turn to black.

Day One	Day Two	Predicted Image

How to run the code

To train images and train model run following code

python train.py or

python train.py --datasetPath 'PATH_OF_DATASET' --outputPath 'PATH_OF_OUTPUT_DIR'

In above code both datasetPath and outputPath are optional.

To test and create model with predicted result file run following command

python test.py or

python test.py --datasetPath 'PATH_OF_DATASET' --outputPath 'PATH_OF_OUTPUT_DIR'

Insights

Training data

• First indexed_images.csv will be generated, it seperated the both 24 hours before and after images by it's name.
• Training file will crop the circles in cropped folder, both images folder before and after will be created inside cropped folder.
• Then the seeds are seperated from above folders. In root directory of project two folders will created named as before and after
• Store the seeds_converted.csv inside folder saved_csv where the seeds that are converted into purple monster will be saved.
• Now the size and position will be captured in final_data.csv. If the seeds conveted or not that detail will be stored in maindf.csv
• It's time to train model. Model will be created based on the images.

Testing data

• File will be cropped and will find the seeds details.
• Seeds details will act as an input for saved model
• If the seed detected as monster it will draw ellipse around it.