我正在為顯微鏡影像堆疊進行語意分割。我的代碼作業正常，但問題是它只使用了我的 CPU 的一個核心，這讓我等待很長時間才能獲得分段影像。
我最近知道有一些方法可以將多核處理與其他 python 庫一起使用，但我不知道如何實作它。
那么有人可以幫助我使用其中一個多處理庫來編輯我的代碼嗎？我的代碼在下面。
import numpy as np
from patchify import patchify, unpatchify
import os
import cv2
from tqdm import tqdm
from tensorflow import keras
from tensorflow.keras.utils import normalize
import natsort
model = keras.models.load_model("C:/mymodel.h5", compile=False)
#creating recon image directory
recon_image_directory = "C:/Users/recon"
if not os.path.exists(recon_image_directory):
os.makedirs(recon_image_directory)
large_image_path = "C:/original_images/"
check_images = natsort.natsorted(os.listdir(large_image_path))
for num, large_image_name in tqdm(enumerate(check_images), total=len(check_images)):
if (large_image_name.split('.')[1] == "tif"):
img = cv2.imread(large_image_path large_image_name, 0)
patches = patchify(img, (256, 256), step=256)
predicted_patches = []
for i in range(patches.shape[0]):
for j in range(patches.shape[1]):
single_patch = patches[i,j,:,:] #(256, 256)
single_patch_norm = normalize(np.array(single_patch), axis=1)
single_patch_input = np.stack((single_patch_norm,)*3, axis=-1) # (256, 256, 3)
single_patch_input = np.expand_dims(single_patch_input, 0) #(1,256,256,3)
single_patch_prediction = (model.predict(single_patch_input)[0,:,:,0]>0.5).astype(np.uint8)
predicted_patches.append(single_patch_prediction)
predicted_patches = np.array(predicted_patches)
predicted_patches_reshaped = np.reshape(predicted_patches, (patches.shape[0], patches.shape[1], 256,256) )
reconstructed_image = unpatchify(predicted_patches_reshaped, img.shape)
cv2.imwrite(recon_image_directory "/recon" '_' str(num) ".tif", reconstructed_image)
uj5u.com熱心網友回復：
這個片段有用嗎？它應該在單獨的行程中運行每個預測。
@ray.remote
def predict(large_image_name: str) -> None:
img = cv2.imread(large_image_path large_image_name, 0)
patches = patchify(img, (256, 256), step=256)
predicted_patches = []
for i in range(patches.shape[0]):
for j in range(patches.shape[1]):
single_patch = patches[i,j,:,:] #(256, 256)
single_patch_norm = normalize(np.array(single_patch), axis=1)
single_patch_input = np.stack((single_patch_norm,)*3, axis=-1) # (256, 256, 3)
single_patch_input = np.expand_dims(single_patch_input, 0) #(1,256,256,3)
single_patch_prediction = (model.predict(single_patch_input)[0,:,:,0]>0.5).astype(np.uint8)
predicted_patches.append(single_patch_prediction)
predicted_patches = np.array(predicted_patches)
predicted_patches_reshaped = np.reshape(predicted_patches, (patches.shape[0], patches.shape[1], 256,256) )
reconstructed_image = unpatchify(predicted_patches_reshaped, img.shape)
cv2.imwrite(recon_image_directory "/recon" '_' str(num) ".tif", reconstructed_image)
futures = []
for num, large_image_name in tqdm(enumerate(check_images), total=len(check_images)):
if (large_image_name.split('.')[1] == "tif"):
futures.append(predict.remote(large_image_name))
ray.get(futures)
我們也有一個高級抽象來做這種事情。如果您有興趣，您應該查看Ray AI Runtime (AIR)。
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