From 1cc9009580802aa51d0d422e639a0dc2f4a21f42 Mon Sep 17 00:00:00 2001
From: Sakshum Gadyal <100090997+sxsmg@users.noreply.github.com>
Date: Tue, 11 Oct 2022 01:50:43 +0530
Subject: [PATCH] Update plot_face_compress.py

---
 examples/cluster/plot_face_compress.py | 62 ++++++++++++++++++++++++++
 1 file changed, 62 insertions(+)

diff --git a/examples/cluster/plot_face_compress.py b/examples/cluster/plot_face_compress.py
index b0c39f28f1d6b..ec172106edd0e 100644
--- a/examples/cluster/plot_face_compress.py
+++ b/examples/cluster/plot_face_compress.py
@@ -74,3 +74,65 @@
     plt.axvline(0.5 * (center_1 + center_2), color="b", linestyle="--")
 
 plt.show()
+
+''' Word Of Caution:
+Theoretically, the above example should demonstrate compression. But there's a catch when we try to check the size of images; 
+the compressed image actually ends up taking more memory space than the image itself. 
+Even though the number of unique values in the image is greater compared to the compressed image. 
+The catch comes from the fact that numpy arrays are contiguous and are required to have preallocated memory for each object. 
+This is where the numpy speed comes from. But for example, as above, it has caused problems. 
+The kmeans does reduce the dimensionality or number of unique values required to represent the image,
+ but they are still stored in the same length nparray. '''
+
+###Colored Image version With actual Compression:
+
+try:
+  face = face(gray=False)
+
+except ImportError:
+  face = sp.face(gray=False)
+
+n_clusters = 64 #clusters of colors
+np.random.seed(0)
+
+plt.imshow(face)
+
+w, h, d = original_shape = tuple(face.shape) # a colored image contains 3 stacked matrices  
+
+face = np.array(face, dtype=np.float64) / 255 #small number easier to multiply
+face_array = np.reshape(face, (w * h, d)) #reshape the image for Kmeans
+kmeans = cluster.KMeans(n_clusters=n_clusters, random_state=0).fit(face_array)
+labels = kmeans.predict(face_array)
+
+
+plt.figure(1)
+plt.clf()
+plt.axis("on")
+plt.title("Original image")
+plt.imshow(face)
+
+plt.figure(2)
+plt.clf()
+plt.axis("on")
+plt.title("Quantised image")
+img_re = kmeans.cluster_centers_[labels].reshape(w, h, -1)
+plt.imshow(img_re)
+
+plt.figure(3)
+plt.clf()
+plt.axis("on")
+plt.title("Quantised image Compressed")
+img_re_compressed = ((img_re*255).astype('uint8'))
+plt.imshow(img_re_compressed)
+
+#number of elements multiplied by byte size of each element
+img_re_size = img_re.itemsize * img_re.size     
+img_re_c_size = img_re_compressed.itemsize * img_re_compressed.size
+#number of unique elements reduce in compressed image due to clustering
+face_uniq = len(np.unique(face))
+img_re_c_uniq = len(np.unique(img_re_compressed))
+
+print(f"Size of reconstructed image: {img_re_size} bytes")
+print(f"Size of compressed reconstructed image: {img_re_c_size} bytes")
+print(f"number of unique values in original image: {face_uniq}")
+print(f"number of unique values in reconstructed image: {img_re_c_uniq}")