def gen_sample_multi(img, img_mark, rate=0.2, boxsize=10, px_over=5):
"""
Generate samples by splitting the pixel classified image with
provided boxsize
Input
-----
img: np.ndarray
The 2D raw image
img_mark: np.ndarray
The 2D marked image
rate: float
The rate of cavity pixels in the box, belongs to (0,1), default as 0.5
boxsize: integer
Size of the box, default as 10
px_over: integer
Overlapped pixels, default as 5
Output
------
data: np.ndarray
The matrix holding samples, each column represents one sample
label: np.ndarray
Labels with respect to samples, could be 0, 1, and 2.
"""
# Init
rows, cols = img.shape
px_diff = boxsize - px_over
# Number of boxes
box_rows = int(np.round((rows - boxsize - 1) / px_diff)) + 1
box_cols = int(np.round((cols - boxsize - 1) / px_diff)) + 1
# init data and label
data = np.zeros((box_rows * box_cols, boxsize**2))
label = np.zeros((box_rows * box_cols, 1))
# Split
for i in range(box_rows):
for j in range(box_cols):
sample = img[i * px_diff:i * px_diff + boxsize,
j * px_diff:j * px_diff + boxsize]
label_mat = img_mark[i * px_diff:i * px_diff + boxsize,
j * px_diff:j * px_diff + boxsize]
data[i * box_rows + j, :] = sample.reshape((boxsize**2, ))
# get label (modified: 2017/02/22)
mask = label_mat.reshape((boxsize**2,))
mask0 = len(np.where(mask == 0)[0])
mask1 = len(np.where(mask == 127)[0])
mask2 = len(np.where(mask == 255)[0])
try:
r = mask1 / (len(mask))
except ZeroDivisionError:
r = 1
if r >= rate:
label[i * box_rows + j, 0] = 1
else:
mask_mat = np.array([mask0, mask1, mask2])
l = np.where(mask_mat == mask_mat.max())[0][0]
# label[i*box_rows+j,0] = np.where(hist==hist.max())[0][0]
label[i * box_rows + j, 0] = l
return data, label
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