def predict_and_show(self, image, show_output_channels):
"""
Args:
img: str(image path) or numpy array(b=1, h=576, w=576, c=1)
show_output_channels: 1 or 2
Returns:
"""
if isinstance(image, str):
images_src = self.read_images([image])
else:
images_src = image
img = DataSet.preprocess(images_src, mode="image")
predict_mask = self.predict(img, 1, use_channels=show_output_channels)
predict_mask = np.squeeze(predict_mask, axis=0)
predict_mask = self.postprocess(predict_mask)
predict_mask = DataSet.de_preprocess(predict_mask, mode="mask")
if show_output_channels == 2:
mask0 = predict_mask[..., 0]
mask1 = predict_mask[..., 1]
image_c3 = np.concatenate([np.squeeze(images_src, axis=0) for i in range(3)], axis=-1)
image_mask0 = apply_mask(image_c3, mask0, color=[255, 106, 106], alpha=0.5)
# result = np.concatenate((np.squeeze(images_src, axis=[0, -1]), mask0, mask1, image_mask0), axis=1)
plt.imshow(image_mask0)
else:
result = np.concatenate((np.squeeze(images_src, axis=[0, -1]), predict_mask), axis=1)
plt.imshow(result, cmap="gray")
plt.show()
def do_predict_custom():
model = get_dilated_unet(
input_shape=(None, None, 1),
mode='cascade',
filters=32,
n_class=1
)
model_weights = "/home/topsky/helloworld/study/njai_challenge/cbct/func/others_try/model_weights.hdf5"
img_path = "/media/topsky/HHH/jzhang_root/data/njai/cbct/CBCT_testingset/CBCT_testingset/04+246ori.tif"
img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
img = np.expand_dims(img, axis=-1)
img = np.expand_dims(img, axis=0)
img = DataSet.preprocess(img, mode="image")
# print(img.shape)
# exit()
model.load_weights(model_weights)
pred = model.predict(img, batch_size=1)
pred_img = np.squeeze(pred[0], -1)
pred_img = DataSet.de_preprocess(pred_img, mode="image")
plt.imshow(pred_img, "gray")
plt.show()
def save_mask(pred, mask_file_lst, mask_nb, result_save_dir):
"""
Args:
pred: 4-d numpy array, (b, h, w, c)
mask_file_lst:
mask_nb:
result_save_dir:
Returns:
"""
if not os.path.isdir(result_save_dir):
os.makedirs(result_save_dir)
masks = pred[..., mask_nb]
mask_file_path_lst = [os.path.join(result_save_dir, x) for x in mask_file_lst]
# 将预测结果转换为0-1数组。
masks = ModelDeployment.postprocess(masks)
# 将0-1数组转换为0-255数组。
masks = DataSet.de_preprocess(masks, mode="mask")
for i in range(len(pred)):
cv2.imwrite(mask_file_path_lst[i], masks[i])
def inference_2stages_from_files(model_def_stage1, model_weights_stage1, model_def_stage2, model_weights_stage2,
file_dir, pred_save_dir):
if not os.path.isdir(pred_save_dir):
os.makedirs(pred_save_dir)
model_obj = ModelDeployment(model_def_stage1, model_weights_stage1)
file_path_lst = get_file_path_list(file_dir, ext=".tif")
dst_file_path_lst = [os.path.join(pred_save_dir, os.path.basename(x)) for x in file_path_lst]
imgs_src = model_obj.read_images(file_path_lst)
imgs = DataSet.preprocess(imgs_src, mode="image")
pred_stage1 = model_obj.predict(imgs, batch_size=5, use_channels=1)
pred_stage1 = np.expand_dims(pred_stage1, axis=-1)
input_stage2 = np.concatenate([imgs_src, pred_stage1], axis=-1)
del model_obj
print(pred_stage1.shape)
print(input_stage2.shape)
model_obj = ModelDeployment(model_def_stage2, model_weights_stage2)
pred = model_obj.predict(input_stage2, batch_size=5, use_channels=1)
pred = model_obj.postprocess(pred)
pred = DataSet.de_preprocess(pred, mode="mask")
for i in range(len(pred)):
cv2.imwrite(dst_file_path_lst[i], pred[i])
def predict_from_h5data_old(self, h5_data_path, val_fold_nb, is_train=False, save_dir=None,
color_lst=None):
dataset = DataSet(h5_data_path, val_fold_nb)
images = dataset.get_images(is_train=is_train)
imgs_src = np.concatenate([images for i in range(3)], axis=-1)
masks = dataset.get_masks(is_train=is_train, mask_nb=0)
masks = np.squeeze(masks, axis=-1)
print("predicting ...")
y_pred = self.predict(dataset.preprocess(images, mode="image"), batch_size=4, use_channels=1)
y_pred = self.postprocess(y_pred)
y_pred = DataSet.de_preprocess(y_pred, mode="mask")
print(y_pred.shape)
if save_dir:
keys = dataset.get_keys(is_train)
if color_lst is None:
color_gt = [255, 106, 106]
color_pred = [0, 191, 255]
# color_pred = [255, 255, 0]
else:
color_gt = color_lst[0]
color_pred = color_lst[1]
# BGR to RGB
imgs_src = imgs_src[..., ::-1]
image_masks = [apply_mask(image, mask, color_gt, alpha=0.5) for image, mask in zip(imgs_src, masks)]
image_preds = [apply_mask(image, mask, color_pred, alpha=0.5) for image, mask in zip(imgs_src, y_pred)]
dst_image_path_lst = [os.path.join(save_dir, "{:03}.tif".format(int(key))) for key in keys]
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
image_mask_preds = np.concatenate([imgs_src, image_masks, image_preds], axis=2)
for i in range(len(image_masks)):
cv2.imwrite(dst_image_path_lst[i], image_mask_preds[i])
print("Done.")
else:
return y_pred