关于numpy:numpy-卷积核可视化

1. 高斯卷积核可视化

   #设置卷积核大小 K_size, 像素长度 l
   K_size = 3
   l = 50
   pad = K_size // 2

   img = np.zeros([K_size * 50, K_size * 50, 3], dtype=np.uint8)
   for i in range(K_size):
      for j in range(K_size):
        #绘制卷积两头地位
         if i == pad and j == pad:
            x = i * l
            y = j * l
            img[x: x + 47, y: y + 47] = (0, 0, 100)
            cv2.putText(img, str(0.16), (j * l + 5, (i + 1) * l - 25), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (200, 200, 0), 2)
        #绘制最靠近核心的地位
         elif i + j == 3 or i + j == 1:
            x = i * l
            y = j * l
            img[x: x + 47, y: y + 47] = (50, 0, 0)
            cv2.putText(img, str(0.12), (j * l + 5, (i + 1) * l - 25), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (200, 200, 0), 2)
        #绘制角落
         else:
            x = i * l
            y = j * l
            img[x: x + 47, y: y + 47] = (100, 0, 0)
            cv2.putText(img, str(0.09), (j * l + 5, (i + 1) * l - 25), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (200, 200, 0), 2)

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