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Divers/tutoriel29/README.md

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+# Tutoriel 29
+## Segmentation d'image avec l'algorithme des K-moyennes
+
+La vidéo de ce tutoriel est disponible l'adresse suivante:<br>
+https://www.youtube.com/watch?v=ytii3XvapRY
+
+

Divers/tutoriel29/ishihara.py

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+import numpy as np
+from matplotlib import pyplot as plt
+from sklearn.cluster import KMeans
+import cv2
+import glob
+
+k=2
+ESPACE="HSV"
+CH=[0, 2]
+size=200
+
+for image in glob.glob('.\images\*.png'):
+    print("Image:", image)
+
+    # Lecture et affichage de l'image
+    img=cv2.imread(image)
+    img=cv2.resize(img, (size, size))
+    cv2.imshow("image", img)
+
+    # Changement d'espace colorimétrique
+    img=cv2.cvtColor(img, eval("cv2.COLOR_BGR2"+ESPACE))
+    X=img[:, :, CH].reshape(img.shape[0]*img.shape[1], len(CH))
+
+    # Graph 2D des couches A et B
+    if len(CH)==2:
+        plt.scatter(X[:,0], X[:,1], s=3)#, marker='+')
+        plt.show()
+
+    # Algorithme K moyennes
+    kmeans=KMeans(n_clusters=k)
+    #kmeans.fit(X)
+    pred=kmeans.fit_predict(X)
+
+    # Graph 2D des couches A et B après utilisation de l'algorithme K moyennes
+    if len(CH)==2:
+        plt.scatter(X[:,0], X[:,1], c=pred, s=3) #10, marker='+')
+        plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=50, c='red')
+        plt.show()
+
+    # Affichage du résultat
+    pred=pred.reshape(img.shape[0], img.shape[1])
+    pred=pred/(k-1)
+    cv2.imshow("kmeans", pred)
+
+    if cv2.waitKey()&0xFF==ord('q'):
+        break

Divers/tutoriel29/ishihara2.py

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+import numpy as np
+from matplotlib import pyplot as plt
+from sklearn.cluster import KMeans
+import cv2
+import sys
+import glob
+
+min_clusters=2
+max_clusters=4
+ESPACES=["YCrCb", "HSV", "LAB"]
+COUCHES=[[1], [0, 2], [1, 2]]
+size=200
+
+for image in glob.glob('.\images\*.png'):
+    print("Image: {} ".format(image), end='')
+    tab=np.zeros([(len(ESPACES))*size, (max_clusters-min_clusters+1)*size], dtype=np.float32)
+    img=cv2.imread(image)
+    cv2.imshow("image", cv2.resize(img, (2*size, 2*size)))
+    img=cv2.resize(img, (size, size))
+
+    for index in range(len(ESPACES)):
+        img2=cv2.cvtColor(img, eval("cv2.COLOR_BGR2"+ESPACES[index]))
+        X=img2[:, :, COUCHES[index]].reshape(img2.shape[0]*img2.shape[1], len(COUCHES[index]))
+        for k in range(min_clusters, max_clusters+1):
+            sys.stdout.write('.')
+            sys.stdout.flush()
+            kmeans=KMeans(n_clusters=k)
+            pred=kmeans.fit_predict(X)
+            pred=pred.reshape(img2.shape[0], img2.shape[1])
+            pred=pred/(k-1)
+            tab[index*size:(index+1)*size, (k-min_clusters)*size:(k-min_clusters)*size+size]=pred
+    sys.stdout.write('\n')
+    cv2.imshow("kmeans", tab)
+    if cv2.waitKey()&0xFF==ord('q'):
+        break