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プログラム全文を掲載しました
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@@ -72,7 +72,116 @@
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### 該当のソースコード
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```analyze.py
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import cv2
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import numpy as np
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import matplotlib.pyplot as plt
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import methods
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# 入力画像読込
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# 画像サイズ（500(width) x 218(height) pix）
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img = cv2.imread('images/madslide12.jpg', cv2.IMREAD_COLOR)
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# オリジナル画像保存
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# import pdb; pdb.set_trace()
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org = img.copy()
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cv2.imwrite('results/original.png', org)
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# 画像ファイル
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# - 画像データを処理プログラムに送る
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methods.image(org,img)
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# PyMeanShift
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# - 第１引数：探索範囲、第２引数：探索色相、第３引数：粗さ
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methods.meanshift(12,3,200)
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# ヒストグラム均一化
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methods.contrast()
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# 類似色統合
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methods.clustering()
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# ブロック分割
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methods.division()
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# カラーラベリング
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methods.labeling()
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```
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```
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```methods.py
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import cv2
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import matplotlib.pyplot as plt
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import matplotlib as mpl
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import numpy as np
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import pymeanshift as pms
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import os
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import sys
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from PIL import Image
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# sys.setrecursionlimit(8000) # 200 x 113 pix
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sys.setrecursionlimit(30000) # 500 x 281 pix
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plt.gray()
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def image(_org,_img):
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global org,img,h,w,c
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global bo,go,ro,al
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org,img = _org,_img
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h,w,c = img.shape
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bo,go,ro = cv2.split(org)
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al = 0.55
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def meanshift(spatial_radius,range_radius,min_density):
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global img
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(img,labels,num) = pms.segment(cv2.cvtColor(img,cv2.COLOR_BGR2Lab),spatial_radius,range_radius,min_density)
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img = cv2.cvtColor(img, cv2.COLOR_Lab2BGR)
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cv2.imwrite('results/meanshift.png',img)
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def contrast():
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global img
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hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
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h,s,v = cv2.split(hsv)
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clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(3, 3))
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result = clahe.apply(v)
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hsv = cv2.merge((h,s,result))
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img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
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cv2.imwrite("results/contrast.png", img)
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def division():
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global block
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hs,ws,cnt = 113,200,1
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if ((h>hs)&(w>ws)):
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for j in range(0,h,hs):
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for i in range(0,w,ws):
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if (((j+hs)<h)&((i+ws)<w)):
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div = np.zeros((hs,ws,c),dtype=int)
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div[0:hs,0:ws] = img[j:j+hs,i:i+ws]
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else:
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if (((i+ws)>w)&((j+hs)>h)):
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div = np.zeros((h-j,w-i,c),dtype=int)
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div[0:h-j,0:w-i] = img[j:h,i:w]
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elif ((j+hs)>h):
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div = np.zeros((h-j,ws,c),dtype=int)
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div[0:h-j,0:ws] = img[j:h,i:i+ws]
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else:
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div = np.zeros((hs,w-i,c),dtype=int)
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div[0:hs,0:w-i] = img[j:j+hs,i:w]
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cv2.imwrite('results/division/division{}.png'.format(cnt),div)
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cnt += 1
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block = cnt
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print('block number :',block)
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def clustering():
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global img
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im = Image.open('results/contrast.png')
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im_q = im.quantize(colors=128, method=0, dither=1)
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im_q.save('results/clustering.png')
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img = cv2.imread('results/clustering.png', cv2.IMREAD_COLOR)
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def approximation(pix1,pix2):
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dif = abs(pix1.astype(np.int8)-pix2.astype(np.int8))
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d1,d2,d3 = dif[0],dif[1],dif[2]
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