質問編集履歴
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コードと3つのnpyファイル、ipynbファイルを添付してます。よろしくお願いします。
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この2つだと実際に見てみると最適でないように感じました。
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縦向きになったとどのように人が認知しているのか知りたいです。最適な角度を算出するアイデアをご教授願います。
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[添付フォルダ(tooth_array.zip内に3つnpyファイル、ipynbファイル)](https://drive.google.com/file/d/1cdY877E7WrsXs9kR9c3p8qI-NqXD7B5X/view?usp=sharing)
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```Python
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#coding:utf-8
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import numpy as np
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import matplotlib.pyplot as plt
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# アフィン変換で画像配列の回転
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def rotate_affine(src, theta):
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# 元画像のサイズを取得
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h, w = src.shape[0], src.shape[1]
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# 出力画像用の配列生成（要素は全て0）
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dst = np.zeros((h,w))
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# degreeかｒradianに変換
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rd = np.radians(theta)
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# アフィン変換
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for y in range(0, h):
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for x in range(0, w):
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xi = int((x - int(w/2))*np.cos(rd) - (y - int(h/2))*np.sin(rd) + int(w/2))
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yi = int((x - int(w/2))*np.sin(rd) + (y - int(h/2))*np.cos(rd) + int(h/2))
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# 変換後の座標が範囲外でなければ出力画像配列に画素値を代入
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if yi < h - 1 and xi < w - 1 and yi > 0 and xi > 0:
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dst[y][x] = src[yi][xi]
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return dst
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def pattern1(src):
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sum_cnt = 1000000
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best_theta = 0
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for theta in range(-90,90+1):
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dst = rotate_affine(src,theta)
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vertical_info = np.count_nonzero(dst ==255, axis=0)
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side_info = np.count_nonzero(dst ==255, axis=1)
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min_flag = False
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for i,num in enumerate(vertical_info):
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if num != 0 and min_flag == False:
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min_flag = True
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min_x = i
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if num == 0 and min_flag == True:
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max_x = i-1
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min_flag = False
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for i,num in enumerate(side_info):
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if num != 0 and min_flag == False:
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min_flag = True
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min_y = i
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if num == 0 and min_flag == True:
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max_y = i-1
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min_flag = False
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tooth_co_info = [min_y,min_x,max_y,max_x]
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ex_dst = dst[tooth_co_info[0]:tooth_co_info[2]+1,tooth_co_info[1]:tooth_co_info[3]+1]
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print("theta,sum:{},{}".format(theta,np.count_nonzero(ex_dst ==0, axis=0).sum()))
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if np.count_nonzero(ex_dst ==0, axis=0).sum() < sum_cnt:
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sum_cnt = np.count_nonzero(ex_dst ==0, axis=0).sum()
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best_theta = theta
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print("best")
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print(best_theta,sum_cnt)
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def pattern2(src):
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max_cnt = 0
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best_theta = 0
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for theta in range(-90,90+1):
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dst = rotate_affine(src,theta)
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print("theta,count:{},{}".format(theta,np.count_nonzero(dst ==255, axis=0).max()))
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if np.count_nonzero(dst ==255, axis=0).max() > max_cnt:
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max_cnt = np.count_nonzero(dst ==255, axis=0).max()
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best_theta = theta
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print("best")
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print("theta,count:{},{}".format(theta,max_cnt))
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if __name__ == '__main__':
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mask = np.load('mask.npy')
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bi_mask = np.where(mask==True,255,0)
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h, w = bi_mask.shape[0], bi_mask.shape[1]
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src = np.zeros((int((w**2 + h**2)**(1/2)), int((w**2 + h**2)**(1/2))))
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dx,dy = int((w**2 + h**2)**(1/2)/2) - int(w/2), int((w**2 + h**2)**(1/2)/2) - int(h/2)
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for y in range(0, h):#平行移動
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for x in range(0, w):
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src[y+dy][x+dx] = bi_mask[y][x]
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theta = 0
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dst = rotate_affine(src,theta)
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plt.imshow(dst)
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plt.show()
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pattern1(src) #切り替え必要
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# pattern2(src)
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```
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