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