| """Resize every modality to 224x224 once and store as .npy (uint8) for fast training.""" |
| import os |
|
|
| import numpy as np |
| import tifffile |
| import torch |
| import torch.nn.functional as F |
| from PIL import Image |
| from scipy import ndimage |
|
|
| from common import CACHE, IMG, ORION, load_all_tiles, marker_matrix |
|
|
| os.makedirs(CACHE, exist_ok=True) |
| df = load_all_tiles() |
| N = len(df) |
| he = np.lib.format.open_memmap(os.path.join(CACHE, "he.npy"), "w+", np.uint8, (N, 3, IMG, IMG)) |
| iff = np.lib.format.open_memmap(os.path.join(CACHE, "if.npy"), "w+", np.uint8, (N, 17, IMG, IMG)) |
| nuc = np.lib.format.open_memmap(os.path.join(CACHE, "nuclei.npy"), "w+", np.uint8, (N, 2, IMG, IMG)) |
|
|
| for i, r in df.iterrows(): |
| h = Image.open(os.path.join(ORION, r.image_path)).convert("RGB").resize((IMG, IMG), Image.BICUBIC) |
| he[i] = np.asarray(h).transpose(2, 0, 1) |
| x = torch.from_numpy(tifffile.imread(os.path.join(ORION, r.target_path)).astype(np.float32)).permute(2, 0, 1)[None] |
| iff[i] = F.interpolate(x, size=(IMG, IMG), mode="bilinear", antialias=True, align_corners=False)[0].round().clamp(0, 255).byte().numpy() |
| lab = tifffile.imread(os.path.join(ORION, r.nuclei_path)) |
| |
| bnd = (ndimage.maximum_filter(lab, 3) != ndimage.minimum_filter(lab, 3)) & (lab > 0) |
| m = torch.from_numpy(np.stack([lab > 0, bnd]).astype(np.float32))[None] |
| nuc[i] = (F.interpolate(m, size=(IMG, IMG), mode="area")[0] * 255).round().byte().numpy() |
| if i % 500 == 0: |
| print(i, N, flush=True) |
|
|
| np.save(os.path.join(CACHE, "markers.npy"), marker_matrix(df)) |
| he.flush(); iff.flush(); nuc.flush() |
| print("done") |
|
|
