diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..8a113e3 --- /dev/null +++ b/.gitignore @@ -0,0 +1,15 @@ +# Python-generated files +__pycache__/ +*.py[oc] +build/ +dist/ +wheels/ +*.egg-info + +# Virtual environments +.venv + +# project-specific files +val_log/ +checkpoints/ +predict_out/ diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..6324d40 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.14 diff --git a/README.md b/README.md new file mode 100644 index 0000000..e69de29 --- /dev/null +++ b/README.md diff --git a/dataset.py b/dataset.py new file mode 100644 index 0000000..da2f08a --- /dev/null +++ b/dataset.py @@ -0,0 +1,87 @@ +import os +import xml.etree.ElementTree as ET + +import cv2 +import numpy as np +import torch +from torch.utils.data import Dataset + + +def load_cvat_points(xml_path: str, label_name: str = "tip"): + """ + returns: dict[str, tuple[float,float]] e.g. {"frame_000004.jpg": (143.46, 451.91)} + """ + tree = ET.parse(xml_path) + root = tree.getroot() + + ann = {} + for img in root.findall("image"): + name = img.attrib["name"] + # image内に複数pointsがある可能性もあるのでfindall + pts = img.findall("points") + for p in pts: + if p.attrib.get("label") == label_name: + xy = p.attrib["points"].split(",") + x, y = float(xy[0]), float(xy[1]) + ann[name] = (x, y) + break + return ann + + +def make_gaussian_heatmap(h, w, x, y, sigma=3.0): + """center (x,y) in pixel coords; returns (h,w) float32""" + xx, yy = np.meshgrid(np.arange(w), np.arange(h)) + hm = np.exp(-((xx - x) ** 2 + (yy - y) ** 2) / (2 * sigma**2)).astype(np.float32) + return hm + + +class NeedleTipDataset(Dataset): + def __init__( + self, images_dir, xml_path, img_size=(384, 384), sigma=3.0, label_name="tip" + ): + self.images_dir = images_dir + self.img_w = img_size[0] + self.img_h = img_size[1] + self.sigma = sigma + + self.ann = load_cvat_points(xml_path, label_name=label_name) + + # tipが付いている画像だけを学習に使う（まずはここから） + self.names = sorted(list(self.ann.keys())) + + def __len__(self): + return len(self.names) + + def __getitem__(self, idx): + name = self.names[idx] + path = os.path.join(self.images_dir, name) + img = cv2.imread(path, cv2.IMREAD_COLOR) + if img is None: + raise FileNotFoundError(path) + + h0, w0 = img.shape[:2] + x0, y0 = self.ann[name] # original coords + + # resize + img = cv2.resize(img, (self.img_w, self.img_h), interpolation=cv2.INTER_AREA) + sx = self.img_w / w0 + sy = self.img_h / h0 + x = x0 * sx + y = y0 * sy + + # heatmap + hm = make_gaussian_heatmap( + self.img_h, self.img_w, x, y, sigma=self.sigma + ) # (H,W) + + # to tensor + img = img[:, :, ::-1].copy() # BGR->RGB + img = img.astype(np.float32) / 255.0 + img = np.transpose(img, (2, 0, 1)) # (C,H,W) + + return { + "image": torch.from_numpy(img), # float32, (3,H,W) + "heatmap": torch.from_numpy(hm)[None, ...], # float32, (1,H,W) + "name": name, + "scale": torch.tensor([sx, sy], dtype=torch.float32), + } diff --git a/predict.py b/predict.py new file mode 100644 index 0000000..4594549 --- /dev/null +++ b/predict.py @@ -0,0 +1,78 @@ +import csv +import glob +import os + +import cv2 +import numpy as np +import torch +import torch.nn.functional as F + +from train_val import heatmap_to_xy, write_heatmaps_as_image +from unet import ResNet18UNet + + +@torch.no_grad() +def infer_folder(images_dir, ckpt_path, out_dir="output", img_size=(384, 384)): + device = "cuda" if torch.cuda.is_available() else "cpu" + print("using device:", device) + + model = ResNet18UNet(out_channels=1, pretrained=False).to(device) + state = torch.load(ckpt_path, map_location=device) + model.load_state_dict(state["model"]) + model.eval() + + paths = sorted(glob.glob(os.path.join(images_dir, "*.jpg"))) + + rows = [("name", "x", "y", "conf")] + for p in paths[0:100]: + img0 = cv2.imread(p, cv2.IMREAD_COLOR) + h0, w0 = img0.shape[:2] + img = cv2.resize(img0, img_size, interpolation=cv2.INTER_AREA) + img = img[:, :, ::-1].astype(np.float32) / 255.0 + x = torch.from_numpy(np.transpose(img, (2, 0, 1))).unsqueeze(0).to(device) + + logits = model(x) + hm = torch.sigmoid(logits) + if hm.shape[-2:] != (img_size[1], img_size[0]): + hm = F.interpolate( + hm, + size=(img_size[1], img_size[0]), + mode="bilinear", + align_corners=False, + ) + xy = heatmap_to_xy(hm)[0].cpu().numpy() # in resized coords + conf = float(hm[0, 0, int(xy[1]), int(xy[0])].item()) + + out_path = os.path.join(out_dir, os.path.basename(p).replace("frame", "hmap")) + write_heatmaps_as_image([hm], [f"pred conf={conf:.2f}"], out_path) + + # back to original coords + sx = w0 / img_size[0] + sy = h0 / img_size[1] + x0 = float(xy[0] * sx) + y0 = float(xy[1] * sy) + + cv2.circle(img0, (int(x0), int(y0)), 5, (0, 0, 255), -1) + out_path = os.path.join(out_dir, os.path.basename(p).replace("frame", "pred")) + cv2.imwrite(out_path, img0) + rows.append((os.path.basename(p), x0, y0, conf)) + print(f"processed {p}, conf={conf:.2f}") + + out_csv = os.path.join(out_dir, "pred.csv") + with open(out_csv, "w", newline="", encoding="utf-8") as f: + w = csv.writer(f) + w.writerows(rows) + + print("saved", out_csv) + + +def main(): + images_dir = "../dataset/images" + ckpt_path = "checkpoints/best.pt" + out_dir = "predict_out" + os.makedirs(out_dir, exist_ok=True) + infer_folder(images_dir, ckpt_path, out_dir=out_dir, img_size=(384, 384)) + + +if __name__ == "__main__": + main() diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..a9279c9 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,21 @@ +[project] +name = "code" +version = "0.1.0" +description = "LVA needle tracking using deep learning" +readme = "README.md" +requires-python = ">=3.14" +dependencies = [ + "torch==2.10.0+cu130", + "torchvision==0.25.0+cu130", + "opencv-python", + "matplotlib", +] + +[tool.uv.sources] +torch = { index = "torch-cuda" } +torchvision = { index = "torch-cuda" } + +[[tool.uv.index]] +name = "torch-cuda" +url = "https://download.pytorch.org/whl/cu130" +explicit = true diff --git a/torch_check.py b/torch_check.py new file mode 100644 index 0000000..d991c6d --- /dev/null +++ b/torch_check.py @@ -0,0 +1,10 @@ +import torch + +# Pytorchのバージョン +print(torch.__version__) + +# GPUが利用可能であるか否か +print(torch.cuda.is_available()) + +# 利用可能なGPUの名前 +print(torch.cuda.get_device_name(0)) diff --git a/train.py b/train.py new file mode 100644 index 0000000..f24c2d6 --- /dev/null +++ b/train.py @@ -0,0 +1,58 @@ +import os + +import torch +from torch.utils.data import DataLoader, random_split + +from dataset import NeedleTipDataset +from train_val import train_one_epoch, validate +from unet import ResNet18UNet + + +def main(): + images_dir = "../dataset/images" + xml_path = "../dataset/annotations.xml" + val_log_dir = "val_log" + os.makedirs(val_log_dir, exist_ok=True) + checkpoint_dir = "checkpoints" + os.makedirs(checkpoint_dir, exist_ok=True) + + device = "cuda" if torch.cuda.is_available() else "cpu" + print("using device:", device) + + ds = NeedleTipDataset(images_dir, xml_path, img_size=(384, 384), sigma=8.0) + n = len(ds) + n_train = int(n * 0.8) + n_val = n - n_train + train_ds, val_ds = random_split( + ds, [n_train, n_val], generator=torch.Generator().manual_seed(42) + ) + + train_loader = DataLoader( + train_ds, batch_size=16, shuffle=True, num_workers=4, pin_memory=True + ) + val_loader = DataLoader( + val_ds, batch_size=16, shuffle=False, num_workers=4, pin_memory=True + ) + + model = ResNet18UNet(out_channels=1, pretrained=True).to(device) + optimizer = torch.optim.AdamW(model.parameters(), lr=3e-4, weight_decay=1e-4) + + best = 1e9 + for epoch in range(1, 101): + tr_loss = train_one_epoch(model, train_loader, optimizer, device) + va_loss, va_err = validate(model, val_loader, device, epoch, val_log_dir) + + print( + f"epoch {epoch:03d} | train loss {tr_loss:.6f} | val loss {va_loss:.6f} | val err(px) {va_err:.2f}" + ) + + if va_err < best: + best = va_err + torch.save( + {"model": model.state_dict()}, os.path.join(checkpoint_dir, "best.pt") + ) + print(" saved best.pt") + + +if __name__ == "__main__": + main() diff --git a/train_val.py b/train_val.py new file mode 100644 index 0000000..20358bb --- /dev/null +++ b/train_val.py @@ -0,0 +1,111 @@ +from pathlib import Path +from typing import List + +import matplotlib.pyplot as plt +import torch +import torch.nn as nn +from torch.cuda.amp import GradScaler, autocast + + +def heatmap_to_xy(hm: torch.Tensor): + """ + hm: (B,1,H,W) after sigmoid + returns: (B,2) xy in heatmap coords + """ + B, _, H, W = hm.shape + flat = hm.view(B, -1) + idx = torch.argmax(flat, dim=1) # (B,) + y = (idx // W).float() + x = (idx % W).float() + return torch.stack([x, y], dim=1) + + +# for debug: save heatmaps as images +def write_heatmaps_as_image(heatmaps: List[torch.Tensor], titles: List[str], path: str): + n = len(heatmaps) + fig, axes = plt.subplots(1, n, figsize=(4 * n, 4), tight_layout=True) + if n == 1: + axes = [axes] + for i, hm in enumerate(heatmaps): + img = hm[0, 0].cpu().numpy() + im = axes[i].imshow(img, cmap="gray", vmin=0.0, vmax=1.0) + axes[i].set_title(titles[i]) + axes[i].axis("off") + fig.colorbar(im, ax=axes[i], fraction=0.046, pad=0.04) + fig.savefig(path) + plt.close(fig) + + +def train_one_epoch(model, loader, optimizer, device): + + model.train() + scaler = GradScaler() + loss_fn = nn.MSELoss() + + total = 0.0 + for batch in loader: + img = batch["image"].to(device) + gt = batch["heatmap"].to(device) + + optimizer.zero_grad(set_to_none=True) + + with autocast(): + pred = model(img) + # サイズがズレたらGTへ合わせる + if pred.shape[-2:] != gt.shape[-2:]: + pred = nn.functional.interpolate( + pred, size=gt.shape[-2:], mode="bilinear", align_corners=False + ) + + loss = loss_fn(pred, gt) + + scaler.scale(loss).backward() + scaler.step(optimizer) + scaler.update() + + total += float(loss.item()) + return total / max(1, len(loader)) + + +@torch.no_grad() +def validate(model, loader, device, epoch, log_dir): + model.eval() + loss_fn = nn.MSELoss() + saved_distribution = False + Path(log_dir).mkdir(parents=True, exist_ok=True) + + total_loss = 0.0 + total_err = 0.0 + n = 0 + + for batch in loader: + img = batch["image"].to(device) + gt = batch["heatmap"].to(device) + + pred = model(img) + if pred.shape[-2:] != gt.shape[-2:]: + pred = nn.functional.interpolate( + pred, size=gt.shape[-2:], mode="bilinear", align_corners=False + ) + + loss = loss_fn(pred, gt) + total_loss += float(loss.item()) + + hm = torch.sigmoid(pred) + xy_pred = heatmap_to_xy(hm) # heatmap coords + + if not saved_distribution: + out_path = Path(log_dir) / f"val_map_ep{epoch:03d}.png" + write_heatmaps_as_image( + [pred, hm, gt], ["pred", "heatmap", "gt"], path=out_path + ) + saved_distribution = True + + # GT点（ヒートマップの最大点）でも良いが、ここではGTヒートマップのargmaxを使う + xy_gt = heatmap_to_xy(gt) + + err = torch.norm(xy_pred - xy_gt, dim=1).sum().item() # pixel in resized space + total_err += err + n += img.size(0) + + return total_loss / max(1, len(loader)), total_err / max(1, n) diff --git a/unet.py b/unet.py new file mode 100644 index 0000000..89a0768 --- /dev/null +++ b/unet.py @@ -0,0 +1,76 @@ +import torch +import torch.nn as nn +import torchvision.models as models + + +class ConvBNReLU(nn.Module): + def __init__(self, in_ch, out_ch): + super().__init__() + self.net = nn.Sequential( + nn.Conv2d(in_ch, out_ch, 3, padding=1, bias=False), + nn.BatchNorm2d(out_ch), + nn.ReLU(inplace=True), + nn.Conv2d(out_ch, out_ch, 3, padding=1, bias=False), + nn.BatchNorm2d(out_ch), + nn.ReLU(inplace=True), + ) + + def forward(self, x): + return self.net(x) + + +class UpBlock(nn.Module): + def __init__(self, in_ch, skip_ch, out_ch): + super().__init__() + self.up = nn.Upsample(scale_factor=2, mode="bilinear", align_corners=False) + self.conv = ConvBNReLU(in_ch + skip_ch, out_ch) + + def forward(self, x, skip): + x = self.up(x) + # サイズ差が出た時の安全策 + if x.shape[-2:] != skip.shape[-2:]: + x = nn.functional.interpolate( + x, size=skip.shape[-2:], mode="bilinear", align_corners=False + ) + x = torch.cat([x, skip], dim=1) + return self.conv(x) + + +class ResNet18UNet(nn.Module): + def __init__(self, out_channels=1, pretrained=True): + super().__init__() + base = models.resnet18( + weights=models.ResNet18_Weights.DEFAULT if pretrained else None + ) + + self.stem = nn.Sequential(base.conv1, base.bn1, base.relu) # /2 + self.pool = base.maxpool # /4 + self.enc1 = base.layer1 # /4 + self.enc2 = base.layer2 # /8 + self.enc3 = base.layer3 # /16 + self.enc4 = base.layer4 # /32 + + self.center = ConvBNReLU(512, 512) + + self.up4 = UpBlock(512, 256, 256) + self.up3 = UpBlock(256, 128, 128) + self.up2 = UpBlock(128, 64, 64) + self.up1 = UpBlock(64, 64, 64) + + self.head = nn.Conv2d(64, out_channels, kernel_size=1) + + def forward(self, x): + s1 = self.stem(x) # (64, H/2, W/2) + s2 = self.pool(s1) # (64, H/4, W/4) + s2 = self.enc1(s2) # (64, H/4, W/4) + s3 = self.enc2(s2) # (128, H/8, W/8) + s4 = self.enc3(s3) # (256, H/16, W/16) + s5 = self.enc4(s4) # (512, H/32, W/32) + + x = self.center(s5) + x = self.up4(x, s4) + x = self.up3(x, s3) + x = self.up2(x, s2) + x = self.up1(x, s1) + + return self.head(x) # logits (1, H/2?,W/2?) -> 実際はstemが/2なのでH/2 diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..46703e0 --- /dev/null +++ b/uv.lock @@ -0,0 +1,622 @@ +version = 1 +revision = 2 +requires-python = ">=3.14" + +[[package]] +name = "code" +version = "0.1.0" +source = { virtual = "." } +dependencies = [ + { name = "matplotlib" }, + { name = "opencv-python" }, + { name = "torch" }, + { name = "torchvision" }, +] + +[package.metadata] +requires-dist = [ + { name = "matplotlib" }, + { name = "opencv-python" }, + { name = "torch", specifier = "==2.10.0+cu130", index = "https://download.pytorch.org/whl/cu130" }, + { name = "torchvision", specifier = "==0.25.0+cu130", index = "https://download.pytorch.org/whl/cu130" }, +] + +[[package]] +name = "contourpy" +version = "1.3.3" +source = { registry 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