temp commit

data/dataset/pose_transfer.py

@@ -0,0 +1,122 @@
+from collections import defaultdict
+from itertools import permutations, combinations
+from pathlib import Path
+
+import torch
+from PIL import Image
+from torch.utils.data import Dataset
+from torchvision.transforms import functional as F
+
+from data.registry import DATASET
+from data.transform import transform_pipeline
+from data.util import dlib_landmark
+
+
+def normalize_tensor(tensor):
+    tensor = tensor.float()
+    tensor -= tensor.min()
+    tensor /= tensor.max()
+    return tensor
+
+
+@DATASET.register_module()
+class GenerationUnpairedDatasetWithEdge(Dataset):
+    def __init__(self, root_a, root_b, random_pair, pipeline, edge_type, edges_path, landmarks_path, size=(256, 256),
+                 with_path=False):
+        assert edge_type in ["hed", "canny", "landmark_hed", "landmark_canny"]
+        self.edge_type = edge_type
+        self.size = size
+        self.edges_path = Path(edges_path)
+        self.landmarks_path = Path(landmarks_path)
+        assert self.edges_path.exists()
+        assert self.landmarks_path.exists()
+        self.A = SingleFolderDataset(root_a, pipeline, with_path=True)
+        self.B = SingleFolderDataset(root_b, pipeline, with_path=True)
+        self.random_pair = random_pair
+        self.with_path = with_path
+
+    def get_edge(self, origin_path):
+        op = Path(origin_path)
+        if self.edge_type.startswith("landmark_"):
+            edge_type = self.edge_type.lstrip("landmark_")
+            use_landmark = op.parent.name.endswith("A")
+        else:
+            edge_type = self.edge_type
+            use_landmark = False
+        edge_path = self.edges_path / f"{op.parent.name}/{op.stem}.{edge_type}.png"
+        origin_edge = F.to_tensor(Image.open(edge_path).resize(self.size, Image.BILINEAR))
+        if not use_landmark:
+            return origin_edge
+        else:
+            landmark_path = self.landmarks_path / f"{op.parent.name}/{op.stem}.txt"
+            key_points, part_labels, part_edge = dlib_landmark.read_keypoints(landmark_path, size=self.size)
+
+            dist_tensor = normalize_tensor(torch.from_numpy(dlib_landmark.dist_tensor(key_points, size=self.size)))
+            part_labels = normalize_tensor(torch.from_numpy(part_labels))
+            part_edge = torch.from_numpy(part_edge).unsqueeze(0).float()
+            # edges = origin_edge * (part_labels.sum(0) == 0)  # remove edges within face
+            # edges = part_edge + edges
+            return torch.cat([origin_edge, part_edge, dist_tensor, part_labels])
+
+    def __getitem__(self, idx):
+        a_idx = idx % len(self.A)
+        b_idx = idx % len(self.B) if not self.random_pair else torch.randint(len(self.B), (1,)).item()
+        output = dict(a={}, b={})
+        output["a"]["img"], output["a"]["path"] = self.A[a_idx]
+        output["b"]["img"], output["b"]["path"] = self.B[b_idx]
+        for p in "ab":
+            output[p]["edge"] = self.get_edge(output[p]["path"])
+        return output
+
+    def __len__(self):
+        return max(len(self.A), len(self.B))
+
+    def __repr__(self):
+        return f"<GenerationUnpairedDatasetWithEdge:\n\tA: {self.A}\n\tB: {self.B}>\nPipeline:\n{self.A.pipeline}"
+
+
+@DATASET.register_module()
+class PoseFacesWithSingleAnime(Dataset):
+    def __init__(self, root_face, root_anime, landmark_path, num_face, face_pipeline, anime_pipeline, img_size,
+                 with_order=True):
+        self.num_face = num_face
+        self.landmark_path = Path(landmark_path)
+        self.with_order = with_order
+        self.root_face = Path(root_face)
+        self.root_anime = Path(root_anime)
+        self.img_size = img_size
+        self.face_samples = self.iter_folders()
+        self.face_pipeline = transform_pipeline(face_pipeline)
+        self.B = SingleFolderDataset(root_anime, anime_pipeline, with_path=True)
+
+    def iter_folders(self):
+        pics_per_person = defaultdict(list)
+        for p in self.root_face.glob("*.jpg"):
+            pics_per_person[p.stem[:7]].append(p.stem)
+        data = []
+        for p in pics_per_person:
+            if len(pics_per_person[p]) >= self.num_face:
+                if self.with_order:
+                    data.extend(list(combinations(pics_per_person[p], self.num_face)))
+                else:
+                    data.extend(list(permutations(pics_per_person[p], self.num_face)))
+        return data
+
+    def read_pose(self, pose_txt):
+        key_points, part_labels, part_edge = dlib_landmark.read_keypoints(pose_txt, size=self.img_size)
+        dist_tensor = normalize_tensor(torch.from_numpy(dlib_landmark.dist_tensor(key_points, size=self.img_size)))
+        part_labels = normalize_tensor(torch.from_numpy(part_labels))
+        part_edge = torch.from_numpy(part_edge).unsqueeze(0).float()
+        return torch.cat([part_labels, part_edge, dist_tensor])
+
+    def __len__(self):
+        return len(self.face_samples)
+
+    def __getitem__(self, idx):
+        output = dict()
+        output["anime_img"], output["anime_path"] = self.B[torch.randint(len(self.B), (1,)).item()]
+        for i, f in enumerate(self.face_samples[idx]):
+            output[f"face_{i}"] = self.face_pipeline(self.root_face / f"{f}.jpg")
+            output[f"pose_{i}"] = self.read_pose(self.landmark_path / self.root_face.name / f"{f}.txt")
+            output[f"stem_{i}"] = f
+        return output