rgb in for loop

compressionRGB.py

@@ -1,8 +1,6 @@
 # -*- coding: utf-8 -*-
 import matplotlib.pyplot as plt
 import numpy as np 
-from PIL import Image  
-import PIL  
 
 
 #import image
@@ -10,114 +8,65 @@ imCol = plt.imread("mini.jpg", format=None)
 imRecons = np.zeros((len(imCol[:,0]), len(imCol[0,:]), 3))
 erreur = np.ones((len(imCol[:,0]), len(imCol[0,:]), 3))
 
-imRed = imCol[:,:,0]
+iterationsGlobales = 10
-imGreen = imCol[:,:,1]
+iterations = 10
-imBlue = imCol[:,:,2]
 
-iterationsGlobales = 30
+imRecons[:,:,:] = imCol[:,:,:]
-iterations = 20
 
-#RED
+# Compress all channels one by one
 
-# compress
+for channel in range(len(imCol[0,0,:])): # for each color channel of original image
     
+    FF= [[]]*len(imCol[:,:,channel]) # all channels should have the same dimsneions but eh
+    GG= [[]]*len(imCol[0,:,channel])
     
-
+    for x in range(iterationsGlobales):
-FF= [[]]*len(imRed[:,0])
+        F=np.ones((len(imRecons[:,0,channel]), 1))
-GG= [[]]*len(imRed[0,:])
+        G=np.ones((len(imRecons[0,:,channel]), 1))
-
-for x in range(iterationsGlobales):
-    F=np.ones((len(imRed[:,0]), 1))
-    G=np.ones((len(imRed[0,:]), 1))
         for y in range(iterations):
-        F = np.dot(imRed, G)               / np.dot(np.transpose(G), G)
+            F = np.dot(imRecons[:,:,channel], G)               / np.dot(np.transpose(G), G)
-        G = np.dot(np.transpose(imRed), F) / np.dot(np.transpose(F), F)
+            G = np.dot(np.transpose(imRecons[:,:,channel]), F) / np.dot(np.transpose(F), F)
         FF = np.append(FF, F, 1)
         GG = np.append(GG, G, 1)
         
+        imRecons[:,:,channel] = imRecons[:,:,channel] - np.dot(F,np.transpose(G))
     
-    
+    erreur[:,:,channel] = imRecons[:,:,channel] - np.dot(FF,np.transpose(GG))
-    imRed = imRed - np.dot(F,np.transpose(G))
+    imRecons[:,:,channel] = np.dot(FF,np.transpose(GG))
-
-erreur[:,:,0] = imRed - np.dot(FF,np.transpose(GG))
-imRecons[:,:,0] = np.dot(FF,np.transpose(GG))
-
-#GREEN
-
-# compress
-
-
-FF= [[]]*len(imGreen[:,0])
-GG= [[]]*len(imGreen[0,:])
-
-for x in range(iterationsGlobales):
-    F=np.ones((len(imGreen[:,0]), 1))
-    G=np.ones((len(imGreen[0,:]), 1))
-    for y in range(iterations):
-        F = np.dot(imGreen, G)               / np.dot(np.transpose(G), G)
-        G = np.dot(np.transpose(imGreen), F) / np.dot(np.transpose(F), F)
-    FF = np.append(FF, F, 1)
-    GG = np.append(GG, G, 1)
 
 
 
-    imGreen = imGreen - np.dot(F,np.transpose(G))
+#why does imRecons not fall into the 255 range ?
-
+# why does plt need it to be between 0 and 1 where imCol is fine being 0 to 255 ? because it's not an int ?
-erreur[:,:,1] = imGreen - np.dot(FF,np.transpose(GG))
+print(np.max(imRecons))
-imRecons[:,:,1] = np.dot(FF,np.transpose(GG))
+imRecons = imRecons/np.max(imRecons) 
-
-#BLUE
-
-# compress
-
-
-FF= [[]]*len(imBlue[:,0])
-GG= [[]]*len(imBlue[0,:])
-
-for x in range(iterationsGlobales):
-    F=np.ones((len(imBlue[:,0]), 1))
-    G=np.ones((len(imBlue[0,:]), 1))
-    for y in range(iterations):
-        F = np.dot(imBlue, G)               / np.dot(np.transpose(G), G)
-        G = np.dot(np.transpose(imBlue), F) / np.dot(np.transpose(F), F)
-    FF = np.append(FF, F, 1)
-    GG = np.append(GG, G, 1)
-    
-    
-    
-    imBlue = imBlue - np.dot(F,np.transpose(G))
-
-
-erreur[:,:,2] = imBlue - np.dot(FF,np.transpose(GG))
-imRecons[:,:,2] = np.dot(FF,np.transpose(GG))
-
-
-imRecons = imRecons/255
 #erreur = erreur/255
-#imRecons = np.round(imRecons, 4)
+
+#compression rate
+tauxComp = round(100-((len(imCol[:,0])+len(imCol[0,:]))*iterationsGlobales)/((len(imCol[:,0])*len(imCol[0,:])))*100,0)
+print(tauxComp, "% de compression")
 
 # plot
 plt.figure(dpi=300)
 
 
-plt.subplot(1, 2, 1)
+plt.subplot(1, 3, 1)
 plt.axis('off')
 
 plt.title("Original", loc='center', pad=None)
 plt.imshow(imCol, interpolation = "nearest")
 
 
-#plt.subplot(1, 3, 2)
+plt.subplot(1, 3, 2)
-#plt.axis('off')
-#plt.title("Erreur", fontdict=None, loc='center', pad=None)
-#plt.imshow(erreur, interpolation = "nearest")
-
-
-plt.subplot(1, 2, 2)
 plt.axis('off')
-plt.title("Compressed", fontdict=None, loc='center', pad=None)
+plt.title("Error", fontdict=None, loc='center', pad=None)
+plt.imshow(erreur, interpolation = "nearest")
+
+
+
+plt.subplot(1, 3, 3)
+plt.axis('off')
+plt.title("Compressed "+ str(tauxComp) +"%", fontdict=None, loc='center', pad=None)
 plt.imshow(imRecons, interpolation = "nearest")
 plt.show()
 
-tauxComp = round(100-((len(imCol[:,0])+len(imCol[0,:]))*iterationsGlobales)/((len(imCol[:,0])*len(imCol[0,:])))*100,0)
-print(tauxComp, "% de compression")

readme.md

@@ -1,6 +1,8 @@
 # Compression d'image
 
-Exercice de compression d'image pour apprendre le concept de base et constater le gain en taille de fichier
+Simple compression study in both RGB and monochrome images.
+
+Takes an image as input and generates a plot with a comparison of the original, error, and compressed image, as well as the space saved compared to the raw image data
 
 ## Example with 30 & 20 as parameters
 <img src="example.png" height="250" />