Initial commit

Divers/tutoriel37/compare_textures.py

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+from skimage import feature
+import matplotlib.pyplot as plt
+import numpy as np
+import cv2
+import glob
+
+method_distance=[cv2.HISTCMP_CORREL,
+				 cv2.HISTCMP_CHISQR,
+			  	 cv2.HISTCMP_INTERSECT,
+				 cv2.HISTCMP_BHATTACHARYYA,
+				 cv2.HISTCMP_HELLINGER,
+				 cv2.HISTCMP_CHISQR_ALT,
+				 cv2.HISTCMP_KL_DIV]
+
+method_lbp=['default',
+			'ror',
+			'uniform',
+			'var']
+
+numPoints=8
+radius=3
+id_method_distance=6
+id_method_lbp=2
+
+textures=['gazon', 'gravier', 'bois']
+
+tab_images=[]
+tab_hist=[]
+for texture in textures:
+	print(">>>", "{}.jpg".format(texture))
+	image=cv2.imread("{}.jpg".format(texture), 0)
+	if image is None:
+		quit("Probleme image...")
+	lbp=feature.local_binary_pattern(image, numPoints, radius, method=method_lbp[id_method_lbp])
+	hist_ref, _=np.histogram(lbp, normed=True, density=True, bins=2**numPoints, range=(0, 2**numPoints))
+	tab_images.append(image)
+	tab_hist.append(hist_ref)
+
+for fichier in glob.glob("textures/*.jpg"):
+	print("Lecture de", fichier)
+	frame=cv2.imread(fichier)
+
+	if frame is None:
+		print("Probleme image ...")
+		continue
+
+	cv2.imshow("Image", frame)
+
+	gray=cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+	tab_distance=[]
+	for id in range(len(tab_hist)):
+		h=tab_hist[id]
+		lbp=feature.local_binary_pattern(gray, numPoints, radius, method=method_lbp[id_method_lbp])
+		hist, _=np.histogram(lbp, normed=True, density=True, bins=2**numPoints, range=(0, 2**numPoints))
+		score=cv2.compareHist(hist.astype(np.float32), h.astype(np.float32), method_distance[id_method_distance])
+		tab_distance.append(score)
+		print("  score {:10}: {:2.6f}".format(textures[id], score))
+
+	tab_distance=np.array(tab_distance)
+	print("  -> texture:", textures[np.argmin(tab_distance)])
+
+	key=cv2.waitKey()&0xFF
+	if key==ord('q'):
+		quit()