Start splitting up app functionality

py/.gitignore

@@ -1,2 +1,2 @@
 env
-
+__pycache__

py/install.sh

@@ -0,0 +1,8 @@
+#!/bin/bash
+
+if [ ! -d env ]; then
+	python -m venv env
+fi
+source env/bin/activate
+python -m pip install -r requirements.txt
+echo source env/bin/activate

py/posterize.py

@@ -0,0 +1,21 @@
+from sklearn.cluster import MiniBatchKMeans
+import numpy as np
+import argparse
+import cv2
+
+class Posterize:
+	"""Posterize an image and then find nearest colors to use"""
+	colors = []
+	#def __init__ (self) :
+		
+
+	def closest(self, colors, color):
+		colors = np.array(colors)
+		color = np.array(color)
+		distances = np.sqrt(np.sum((colors - color) ** 2, axis=1))
+		index_of_smallest = np.where(distances == np.amin(distances))
+		smallest_distance = colors[index_of_smallest]
+		return smallest_distance 
+
+if __name__ == "__main__" :
+	posterize = Posterize()

py/requirements.txt

@@ -1 +1,3 @@
-opencv-python
+opencv-python
+numpy
+scikit-learn

py/separator.py

@@ -1,12 +1,21 @@
 import argparse
+from posterize import Posterize
+from os.path import abspath
 
 from os.path import isfile
 
 parser = argparse.ArgumentParser(description='Separate an image into most similar colors specified')
 
 parser.add_argument('input', type=str, help='Input image to separate')
+parser.add_argument('output', type=str, help='Output dir to write to')
 
-args = parser.parse_args()
+class Separator :
+	posterize = Posterize()
+	def __init__ (self, args) :
+		print(args)
 
 
+if __name__ == "__main__" :
+	args = parser.parse_args()
+	Separator(args)