remove outdated top-level scripts

mu_map/compute_mu_maps.py

-import os
-import time
-
-import numpy as np
-import pydicom
-import torch
-
-from mu_map.data.prepare import headers
-from mu_map.data.remove_bed import add_bed
-from mu_map.dataset.default import MuMapDataset
-from mu_map.dataset.normalization import GaussianNormTransform
-from mu_map.dataset.transform import SequenceTransform, PadCropTranform
-from mu_map.file.dicom import load_dcm, update_dcm, change_uid
-from mu_map.models.unet import UNet
-
-torch.set_grad_enabled(False)
-
-device = torch.device("cuda")
-split_name = "test"
-
-dir_train = "results/cgan_random_search_02/03"
-dir_data = "data/second/"
-dir_out = "results/mu_map_syn/"
-
-file_weights = os.path.join(dir_train, "snapshots", "val_min_generator.pth")
-
-model = UNet()
-model.load_state_dict(torch.load(file_weights, map_location=device))
-model = model.to(device)
-model = model.eval()
-
-transform_normalization = SequenceTransform([GaussianNormTransform(), PadCropTranform(dim=3, size=32)])
-dataset = MuMapDataset(dir_data, transform_normalization=transform_normalization, split_name=split_name)
-dataset_with_bed = MuMapDataset(dir_data, transform_normalization=PadCropTranform(dim=3, size=32), split_name=split_name, bed_contours_file=None)
-
-for i, ((recon_nac, _), (_, mu_map)) in enumerate(zip(dataset, dataset_with_bed)):
-    row = dataset.table.iloc[i]
-    _id = row[headers.id]
-    print(f"Process {_id} ...")
-
-    recon_nac = recon_nac.to(device)
-    mu_map_syn = model(recon_nac.unsqueeze(dim=0))
-
-    mu_map = mu_map.squeeze().cpu().numpy()
-
-    mu_map_syn = mu_map_syn.squeeze().cpu().numpy()
-    mu_map_syn = np.where(mu_map_syn < 0.0, 0.0, mu_map_syn)
-    mu_map_syn = add_bed(mu_map_syn, mu_map, dataset.bed_contours[_id])
-
-    file_mu_map = os.path.join(dir_data, "images", row[headers.file_mu_map])
-    dcm_mu_map = pydicom.dcmread(file_mu_map)
-
-    dcm_mu_map_syn = update_dcm(dcm_mu_map, mu_map_syn)
-    dcm_mu_map_syn = change_uid(dcm_mu_map_syn)
-
-    base, ext = os.path.splitext(os.path.basename(file_mu_map))
-    file_mu_map_syn = os.path.join(dir_out, f"{base}_syn{ext}")
-    pydicom.dcmwrite(file_mu_map_syn, dcm_mu_map_syn)
-

mu_map/test.py

-import cv2 as cv
-import matplotlib.pyplot as plt
-import numpy as np
-import torch
-
-from mu_map.dataset.default import MuMapDataset
-from mu_map.dataset.normalization import MeanNormTransform
-from mu_map.dataset.transform import PadCropTranform, SequenceTransform
-from mu_map.models.unet import UNet
-from mu_map.util import to_grayscale, COLOR_WHITE
-
-torch.set_grad_enabled(False)
-
-dataset = MuMapDataset(
-    "data/second/",
-    transform_normalization=SequenceTransform([
-        MeanNormTransform(),
-        PadCropTranform(dim=3, size=32)
-    ]),
-)
-
-model = UNet(in_channels=1)
-device = torch.device("cpu")
-# weights = torch.load("train_data/snapshots/val_min_Model.pth", map_location=device)
-# model.load_state_dict(weights)
-model = model.eval()
-
-recon, mu_map = dataset[0]
-recon = recon.unsqueeze(dim=0)
-
-output = model(recon)
-
-diff = ((mu_map - output) ** 2).mean()
-print(f"Diff: {diff:.5f}")
-
-output = output.squeeze().numpy()
-mu_map = mu_map.squeeze().numpy()
-
-assert output.shape[0] == mu_map.shape[0]
-
-wname = "Dataset"
-cv.namedWindow(wname, cv.WINDOW_NORMAL)
-cv.resizeWindow(wname, 1600, 900)
-space = np.full((1024, 10), 239, np.uint8)
-
-def to_display_image(image, _slice, _min=None, _max=None):
-    _max = _max if _max is not None else image.max()
-    _min = _min if _min is not None else image.min()
-
-    _image = to_grayscale(image[_slice], min_val=_min, max_val=_max)
-    _image = cv.resize(_image, (1024, 1024), cv.INTER_AREA)
-    _text = f"{str(_slice):>{len(str(image.shape[0]))}}/{str(image.shape[0])}"
-    _image = cv.putText(
-        _image, _text, (0, 30), cv.FONT_HERSHEY_SIMPLEX, 1, COLOR_WHITE, 3
-    )
-    return _image
-
-def com(image1, image2, _slice):
-    image1 = to_display_image(image1, _slice)
-    image2 = to_display_image(image2, _slice)
-    space = np.full((image1.shape[0], 10), 239, np.uint8)
-    return np.hstack((image1, space, image2))
-
-output = np.clip(output, 0, mu_map.max())
-
-i = 0
-while True:
-    x = com(output, mu_map, i)
-    cv.imshow(wname, x)
-    key = cv.waitKey(100)
-
-    if key == ord("q"):
-        break
-
-    i = (i + 1) % output.shape[0]
-