修改成正确的路径
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@ -11,10 +11,10 @@ from skimage.measure import label
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from skimage.measure import regionprops
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from utils.labelme import save_pred_to_json,save_pred_to_json
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json_path = '/home/gao/mouclear/cc/code_v2/msunet/logs/0/version_3/test.json'
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json_path = './result/predict/test.json'
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# save_path = '/home/gao/mouclear/cc/data/detect_50/替换图像'
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# json_path = '/home/gao/mouclear/cc/code/pd_2/code/egnn/logs/0/version_2/test.json'
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save_path = '/home/gao/mouclear/cc/data_new/analyze_50'
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save_path = './result/post_process/'
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os.makedirs(save_path, exist_ok=True)
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save_pred_to_json(json_path, save_path)
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@ -94,6 +94,7 @@ def process_slide(img_path, save_path):
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img_lst = glob.glob('/home/gao/mouclear/cc/data_new/msunet/train_and_test/test/*.jpg')
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img_lst = glob.glob('./train_and_test/test/*.jpg')
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for item in img_lst:
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process_slide(item, save_path='/home/gao/mouclear/cc/data_new/msunet/train_and_test/test/')
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process_slide(item, save_path='./result/pre_process/')
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@ -2,6 +2,7 @@ import os
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import json
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import glob
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import torch
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torch.set_float32_matmul_precision('high')
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import numpy as np
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@ -43,7 +44,8 @@ EARLY_STOP = 5
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EVERY_N_EPOCHS = 1
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IMAGE_PATH = '../../data/linesv/patch_unet/'
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# IMAGE_PATH2 = '/home/gao/下载/process/test/'
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WEIGHTS = torch.FloatTensor([1./8, 1./4, 1./2, 1.])
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WEIGHTS = torch.FloatTensor([1. / 8, 1. / 4, 1. / 2, 1.])
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# pytorch lightning module
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@ -95,19 +97,21 @@ class FCRN(pl.LightningModule):
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return pred.squeeze(), lbl
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# main
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if __name__ == '__main__':
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train_img_list = np.array(glob.glob('{}/{}/img/*.png'.format(IMAGE_PATH, 'train'))).tolist()
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valid_img_list = np.array(glob.glob('{}/{}/img/*.png'.format(IMAGE_PATH, 'valid'))).tolist()
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test_img_list = np.array(glob.glob('/home/gao/mouclear/cc/data_new/analyze_50/*.jpg')).tolist()
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# train_img_list = np.array(glob.glob('{}/{}/img/*.png'.format(IMAGE_PATH, 'train'))).tolist()
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# valid_img_list = np.array(glob.glob('{}/{}/img/*.png'.format(IMAGE_PATH, 'valid'))).tolist()
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test_img_list = np.array(glob.glob('./result/pre_process/img/*.png')).tolist()
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# test_img_list = np.array(glob.glob('/home/gao/mouclear/detect_40/40/*.jpg')).tolist()
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print('Train nums: {}, Valid nums: {}, Test nums: {}.'.format(len(train_img_list), len(valid_img_list), len(test_img_list)))
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# print('Train nums: {}, Valid nums: {}, Test nums: {}.'.format(len(train_img_list), len(valid_img_list), len(test_img_list)))
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print('Test nums: ', len(test_img_list))
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# train_dataset = MyDataset(train_img_list, dim=DIM, sigma=SIGMA, data_type='train')
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# valid_dataset = MyDataset(valid_img_list, dim=DIM, sigma=SIGMA, data_type='valid')
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test_dataset = MyDatasetSlide_test_uneuqal(test_img_list, ps=patch_size,roi=roi_size)
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test_dataset = MyDatasetSlide_test_uneuqal(test_img_list, ps=patch_size, roi=roi_size)
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# train_loader = DataLoader(
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# dataset = train_dataset,
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@ -125,15 +129,14 @@ if __name__ == '__main__':
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# )
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test_loader = DataLoader(
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dataset = test_dataset,
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batch_size = 1,
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shuffle = False,
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num_workers = 1,
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dataset=test_dataset,
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batch_size=1,
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shuffle=False,
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num_workers=1,
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)
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model = FCRN(IN_CHANNELS)
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# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# model = model.to(device)
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# checkpoint = torch.load('/home/gao/mouclear/cc/code/msunet/logs/0/version_0/checkpoints/last.ckpt', map_location=device)
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@ -153,35 +156,34 @@ if __name__ == '__main__':
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# 现在 new_state_dict 包含了更新后的键，可以安全地加载到模型中
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# model.load_state_dict(state_dict) # 使用 strict=False 以忽略不匹配的键
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logger = TensorBoardLogger(
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name = DATASETS,
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save_dir = 'logs',
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name=DATASETS,
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save_dir='logs',
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)
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checkpoint_callback = ModelCheckpoint(
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every_n_epochs = EVERY_N_EPOCHS,
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save_top_k = SAVE_TOP_K,
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monitor = 'val_dice',
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mode = 'max',
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save_last = True,
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filename = '{epoch}-{val_loss:.2f}-{val_dice:.2f}'
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every_n_epochs=EVERY_N_EPOCHS,
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save_top_k=SAVE_TOP_K,
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monitor='val_dice',
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mode='max',
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save_last=True,
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filename='{epoch}-{val_loss:.2f}-{val_dice:.2f}'
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)
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earlystop_callback = EarlyStopping(
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monitor = "val_dice",
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mode = "max",
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min_delta = 0.00,
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patience = EARLY_STOP,
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monitor="val_dice",
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mode="max",
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min_delta=0.00,
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patience=EARLY_STOP,
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)
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# training
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trainer = pl.Trainer(
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accelerator = 'gpu',
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devices = GPUS,
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max_epochs = EPOCHS,
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logger = logger,
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callbacks = [checkpoint_callback, earlystop_callback],
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accelerator='gpu',
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devices=GPUS,
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max_epochs=EPOCHS,
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logger=logger,
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callbacks=[checkpoint_callback, earlystop_callback],
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)
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# trainer.fit(
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@ -192,24 +194,25 @@ if __name__ == '__main__':
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# inference
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predictions = trainer.predict(
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model = model,
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dataloaders = test_loader,
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ckpt_path = '/home/gao/mouclear/cc/code/msunet/logs/0/version_1/checkpoints/last.ckpt'
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model=model,
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dataloaders=test_loader,
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ckpt_path='./result/train/last.ckpt'
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)
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# pre = = spliter.recover(patches, item[1].shape[1], item[1].shape[2], ps=patch_size,roi=roi_size)
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preds = np.concatenate([test_dataset.spliter.recover(item[0], item[1].shape[1], item[1].shape[2], ps=patch_size,roi=roi_size)[np.newaxis, :, :] for item in predictions]).tolist()
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preds = np.concatenate([test_dataset.spliter.recover(item[0], item[1].shape[1], item[1].shape[2], ps=patch_size,
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roi=roi_size)[np.newaxis, :, :] for item in
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predictions]).tolist()
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labels = torch.squeeze(torch.concat([item[1] for item in predictions])).numpy().tolist()
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results ={
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results = {
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'img_path': test_img_list,
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'pred': preds,
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'label': labels,
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}
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predict_dir= './result/predict/'
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results_json = json.dumps(results)
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with open(os.path.join(trainer.log_dir, 'test.json'), 'w+') as f:
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with open(os.path.join(predict_dir, 'test.json'), 'w+') as f:
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f.write(results_json)
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@ -27,6 +27,7 @@ class_dict_rev = {
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}
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def get_json(img_path, lbl):
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print("get json image path:", img_path)
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img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
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w, h = np.where(lbl != 0)
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@ -34,7 +35,7 @@ def get_json(img_path, lbl):
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points = np.array(points, np.int16).tolist()
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shapes = [{"label": class_dict[lbl[item[1], item[0]]], "points": [item], "group_id": None, "shape_type": "point", "flags": {}} for item in points]
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imagePath = img_path.split('/')[-1]
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imageData = utils.img_arr_to_b64(img).decode('utf-8')
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imageData = utils.img_arr_to_b64(img) #.decode('utf-8')
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imageHeight, imageWidth = img.shape
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json_data = {
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@ -168,11 +169,12 @@ def save_pred_to_json(json_path, save_path):
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pred = np.array(data['pred'])
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lb = np.array(data['label'], dtype=np.uint8)
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img_path = np.array(data['img_path'])
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print("image path: ", img_path)
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nums = len(img_path)
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for i in range(nums):
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name = img_path[i].split('/')[-1].split('.')[0]
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print("name: ", name)
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# gt
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Image.fromarray(lb[i]).save('{}/{}.png'.format(save_path, name))
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Image.open(img_path[i]).save('{}/{}.jpg'.format(save_path, name))
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