def predict(mdl, img, patch_size, patch_step, batch_size, dim_img):
"""
the cnn model for image transformation
Parameters
----------
img : array
The image need to be calculated
patch_size : (int, int)
The patches dimension
dim_img : int
The input image dimension
Returns
-------
img_rec
Description.
"""
img = np.float16(utils.nor_data(img))
img_y, img_x = img.shape
x_img = utils.extract_patches(img, patch_size, patch_step)
x_img = np.reshape(x_img, (len(x_img), 1, dim_img, dim_img))
y_img = mdl.predict(x_img, batch_size=batch_size)
del x_img
y_img = np.reshape(y_img, (len(y_img), dim_img, dim_img))
img_rec = utils.reconstruct_patches(y_img, (img_y, img_x), patch_step)
return img_rec
def predict(mdl, img, patch_size, patch_step, batch_size, dim_img):
"""
the cnn model for image transformation
Parameters
----------
img : array
The image need to be calculated
patch_size : (int, int)
The patches dimension
dim_img : int
The input image dimension
Returns
-------
img_rec
Description.
"""
img = np.float16(utils.nor_data(img))
img_y, img_x = img.shape
x_img = utils.extract_patches(img, patch_size, patch_step)
x_img = np.reshape(x_img, (len(x_img), 1, dim_img, dim_img))
y_img = mdl.predict(x_img, batch_size=batch_size)
del x_img
y_img = np.reshape(y_img, (len(y_img), dim_img, dim_img))
img_rec = utils.reconstruct_patches(y_img, (img_y, img_x), patch_step)
return img_rec
def predict(mdl, img, patch_size, patch_step, batch_size, dim_img):
"""
the cnn model for image transformation
Parameters
----------
img : array
The image need to be calculated
patch_size : (int, int)
The patches dimension
dim_img : int
The input image dimension
Returns
-------
img_rec
Description.
"""
img = np.float16(utils.nor_data(img))
img_h, img_w = img.shape
input_img = utils.extract_patches(img, patch_size, patch_step)
input_img = np.reshape(input_img, (input_img.shape[0], dim_img, dim_img, 1))
output_img = mdl.predict(input_img, batch_size=batch_size)
del input_img
output_img = np.reshape(output_img, (output_img.shape[0], dim_img, dim_img))
img_rec = utils.reconstruct_patches(output_img, (img_h, img_w), patch_step)
return img_rec
def pred_single(mdl, predict_x, ih, iw, patch_shape, patch_step, patch_size,
batch_size):
predict_x = extract_3d(predict_x, patch_shape, patch_step)
predict_x = np.reshape(predict_x,
(predict_x.shape[0], patch_size, patch_size, 1))
predict_y = mdl.predict(predict_x, batch_size=batch_size)
predict_y = np.reshape(predict_y,
(predict_y.shape[0], patch_size, patch_size))
predict_y = reconstruct_patches(predict_y, (ih, iw), patch_step)
return predict_y
def seg_predict(img, wpath, patch_size = 32, patch_step = 1,
nb_conv=32, size_conv=3,
batch_size=1000, nb_down=2, nb_gpu = 1):
"""
Function description
Parameters
----------
img : array
The images need to be segmented.
wpath: string
The path where the trained weights of the model can be read.
patch_size: int
The size of the small patches extracted from the input images. This size should be big enough to cover the
features of the segmentation object.
patch_step: int
The pixel steps between neighbour patches. Larger steps leads faster speed, but less quality. I recommend 1
unless you need quick test of the algorithm.
nb_conv: int
Number of the covolutional kernals for the first layer. This number doubles after each downsampling layer.
size_conv: int
Size of the convolutional kernals.
batch_size: int
Batch size for the training. Bigger size leads faster speed. However, it is restricted by the memory size of
the GPU. If the user got the memory error, please decrease the batch size.
nb_epoch: int
Number of the epoches for the training. It can be understand as the number of iterations during the training.
Please define this number as the actual convergence for different data.
nb_down: int
Number of the downsampling for the images in the model.
nb_gpu: int
Number of GPUs you want to use for the training.
Returns
-------
save the segmented images to the spath.
"""
patch_shape = (patch_size, patch_size)
img = np.float32(nor_data(img))
mdl = model_choose(patch_size, patch_size, nb_conv, size_conv, nb_down, nb_gpu)
# print(mdl.summary())
mdl.load_weights(wpath)
if img.ndim == 2:
ih, iw = img.shape
predict_x = extract_3d(img, patch_shape, patch_step)
predict_x = np.reshape(predict_x, (predict_x.shape[0], patch_size, patch_size, 1))
predict_y = mdl.predict(predict_x, batch_size=batch_size)
predict_y = np.reshape(predict_y, (predict_y.shape[0],patch_size, patch_size))
predict_y = reconstruct_patches(predict_y, (ih, iw), patch_step)
return predict_y
else:
pn, ih, iw = img.shape
images = np.empty(pn, dtype=object) # create empty array
for i in range(pn):
print('Processing the %s th image' % i)
tstart = time.time()
predict_x = img[i]
predict_x = extract_3d(predict_x, patch_shape, patch_step)
predict_x = np.reshape(predict_x, (len(predict_x), patch_size, patch_size, 1))
predict_y = mdl.predict(predict_x, batch_size=batch_size)
predict_y = np.reshape(predict_y, (len(predict_y), patch_size, patch_size))
predict_y = reconstruct_patches(predict_y, (ih, iw), patch_step)
images[i]=predict_y
return images
