Array.min() #无参，所有中的最小值
Array.min(0) # axis=0; 每列的最小值
Array.min(1) # axis=1；每行的最小值
字符串在输出时的对齐:
S.ljust(width,[fillchar])
#输出width个字符，S左对齐，不足部分用fillchar填充，默认的为空格。
S.rjust(width,[fillchar])
#右对齐
S.center(width, [fillchar])
#中间对齐
S.zfill(width)
#把S变成width长，并在右对齐，不足部分用0补足
def log(text, array=None):
"""Prints a text message. And, optionally, if a Numpy array is provided it
prints it's shape, min, and max values.
"""
if array is not None:
text = text.ljust(25)
text += ("shape: {:20} ".format(str(array.shape)))
if array.size:
text += ("min: {:10.5f} max: {:10.5f}".format(array.min(),array.max()))
#{:10.5f}表示精度
else:
text += ("min: {:10} max: {:10}".format("",""))
#text += " {}".format(array.dtype)
print(text)
从给定的mask中产生bbox
def extract_bboxes(mask):
"""Compute bounding boxes from masks.
mask: [height, width, num_instances]. Mask pixels are either 1 or 0.
Returns: bbox array [num_instances, (y1, x1, y2, x2)].
"""
boxes = np.zeros([mask.shape[-1], 4], dtype=np.int32)
for i in range(mask.shape[-1]):
m = mask[:, :, i]
# Bounding box.
horizontal_indicies = np.where(np.any(m, axis=0))[0]
print("np.any(m, axis=0)",np.any(m, axis=0))
print("p.where(np.any(m, axis=0))",np.where(np.any(m, axis=0)))
vertical_indicies = np.where(np.any(m, axis=1))[0]
if horizontal_indicies.shape[0]:
x1, x2 = horizontal_indicies[[0, -1]]
y1, y2 = vertical_indicies[[0, -1]]
# x2 and y2 should not be part of the box. Increment by 1.
x2 += 1
y2 += 1
else:
# No mask for this instance. Might happen due to
# resizing or cropping. Set bbox to zeros
x1, x2, y1, y2 = 0, 0, 0, 0
boxes[i] = np.array([y1, x1, y2, x2])
return boxes.astype(np.int32)
另外注意：
opencv默认为8位读取，如果该图为16位，则读取为全0，导致程序出错
TensorFlow的函数处理图片后存储的数据是float32格式的，需要转换成uint8才能正确打印图片