From 275a789feb17bb34da4891938e837e2b08c16e7d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Rafa=C5=82=20T=C5=82uchowski?= <rafal@tluchowski.pl>
Date: Sun, 26 Jan 2025 09:42:32 +0100
Subject: [PATCH] - Face Fit or Restore fixes WIP - base landmarks and obj file

---
 __init__.py                      |    2 +-
 contex_generator.py              |    4 +-
 core/base_landmarks.py           |  288 +++++
 core/image_processor.py          |    2 +-
 core/mediapipe_landmarks_468.obj | 1834 ++++++++++++++++++++++++++++++
 nodes/fit_and_crop.py            |   94 --
 nodes/restore_face.py            |   70 --
 7 files changed, 2126 insertions(+), 168 deletions(-)
 create mode 100644 core/base_landmarks.py
 create mode 100644 core/mediapipe_landmarks_468.obj
 delete mode 100644 nodes/fit_and_crop.py
 delete mode 100644 nodes/restore_face.py

diff --git a/__init__.py b/__init__.py
index 232b3b2..daaa60f 100644
--- a/__init__.py
+++ b/__init__.py
@@ -18,4 +18,4 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "FaceFitAndRestore": "Face Fit or Restore"
 }
 
-__version__ = "1.0.0"
\ No newline at end of file
+__version__ = "1.0.1"
\ No newline at end of file
diff --git a/contex_generator.py b/contex_generator.py
index fb41ba2..4577a03 100644
--- a/contex_generator.py
+++ b/contex_generator.py
@@ -57,10 +57,10 @@ def generate_context_file(
 
 # Example usage
 if __name__ == "__main__":
-    output_file = "D:/code/code_base/face_processor_02.txt"
+    output_file = "D:/code/code_base/face_processor_03.txt"
     generate_context_file(
         root_dir="E:/ComfyUI-DEV/custom_nodes/ComfyUI_FaceProcessor",
         output_file=output_file,
         ignore_dirs={".git", "__pycache__", ".venv", "venv", ".idea", "node_modules", "models"},
-        ignore_files={".gitignore", "contex_generator.py", output_file, "package-lock.json", "TODO.txt"},
+        ignore_files={".gitignore", "contex_generator.py", output_file, "package-lock.json", "TODO.txt", "mediapipe_landmarks_468.obj"},
     )
\ No newline at end of file
diff --git a/core/base_landmarks.py b/core/base_landmarks.py
new file mode 100644
index 0000000..4287cb2
--- /dev/null
+++ b/core/base_landmarks.py
@@ -0,0 +1,288 @@
+import numpy as np
+import os
+
+
+class MediapipeBaseLandmarks:
+    """
+    Class for handling base landmark positions and face topology loaded from OBJ file.
+    Coordinates are stored in normalized 0-1 range.
+    """
+    _base_landmarks = None
+    _faces = None
+    _boundary_faces = None
+    _boundary_landmarks = None
+
+    # Face oval landmarks in clockwise order
+    OVAL_LANDMARKS = [10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,
+                      397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,
+                      172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109]
+
+    @classmethod
+    def _transform_landmarks(cls, landmarks, x_scale=1.0, y_translation=0.0):
+        """
+        Apply transformations to landmarks:
+        - x_scale: horizontal scaling (0.5 to 1.0)
+        - y_translation: vertical translation (-0.5 to 0.5)
+
+        Args:
+            landmarks: numpy array of landmarks
+            x_scale: horizontal scaling factor (default: 1.0)
+            y_translation: vertical translation value (default: 0.0)
+
+        Returns:
+            numpy array: Transformed landmarks
+        """
+        transformed = landmarks.copy()
+
+        # Calculate center point for scaling
+        center_x = (landmarks[:, 0].min() + landmarks[:, 0].max()) / 2
+
+        # Apply horizontal scaling relative to center
+        transformed[:, 0] = center_x + (transformed[:, 0] - center_x) * x_scale
+
+        # Apply vertical translation
+        transformed[:, 1] += y_translation
+
+        return transformed
+
+    @classmethod
+    def _load_obj_data(cls):
+        """
+        Load UV coordinates, vertices and face indices from OBJ file.
+        Returns array of normalized [u,v] coordinates mapped to correct vertex indices
+        and array of face indices.
+        """
+        if cls._base_landmarks is not None and cls._faces is not None:
+            return cls._base_landmarks, cls._faces
+
+        current_dir = os.path.dirname(os.path.realpath(__file__))
+        project_root = os.path.dirname(current_dir)
+        obj_path = os.path.join(project_root, 'models', 'uv_maps', 'mediapipe_landmarks_468.obj')
+
+        vertices = []
+        uvs = []
+        faces = []
+        vertex_to_uv = {}
+
+        print(f"Loading 3D face model from: {obj_path}")
+
+        with open(obj_path, 'r') as f:
+            for line in f:
+                if line.startswith('v '):
+                    parts = line.strip().split()
+                    vertices.append([float(p) for p in parts[1:4]])
+                elif line.startswith('vt '):
+                    parts = line.strip().split()
+                    u = float(parts[1])
+                    v = 1.0 - float(parts[2])
+                    uvs.append([u, v])
+                elif line.startswith('f '):
+                    parts = line.strip().split()[1:]
+                    face_verts = []
+                    for part in parts:
+                        indices = part.split('/')
+                        if len(indices) >= 2:
+                            vertex_idx = int(indices[0]) - 1
+                            uv_idx = int(indices[1]) - 1
+                            vertex_to_uv[vertex_idx] = uv_idx
+                            face_verts.append(vertex_idx)
+                    if len(face_verts) == 3:
+                        faces.append(face_verts)
+
+        print(f"Loaded {len(vertices)} vertices, {len(uvs)} UVs, {len(faces)} faces")
+
+        uvs = np.array(uvs, dtype=np.float32)
+        faces = np.array(faces, dtype=np.int32)
+        landmarks = np.zeros((468, 2), dtype=np.float32)
+
+        for vertex_idx, uv_idx in vertex_to_uv.items():
+            if vertex_idx < len(landmarks):
+                landmarks[vertex_idx] = uvs[uv_idx]
+
+        cls._base_landmarks = landmarks
+        cls._faces = faces
+        return cls._base_landmarks, cls._faces
+
+    @staticmethod
+    def interpolate_edge_points(start_pos, end_pos, n_segments):
+        """
+        Interpolate points along an edge
+        """
+        points = []
+        for i in range(n_segments + 1):
+            t = i / n_segments
+            x = round(start_pos[0] + t * (end_pos[0] - start_pos[0]))
+            y = round(start_pos[1] + t * (end_pos[1] - start_pos[1]))
+            points.append((x, y))
+        return points
+
+    @classmethod
+    def create_boundary_triangles(cls, size):
+        """
+        Create additional triangles between face oval and image boundaries
+        """
+        if isinstance(size, int):
+            width = height = size
+        else:
+            width, height = size
+
+        print(f"Creating boundary triangles for image size: {width}x{height}")
+
+        boundary_points = []
+        boundary_triangles = []
+
+        edges = {
+            'top': {
+                'landmarks': [54, 103, 67, 109, 10, 338, 297, 332, 284],
+                'start': (0, 0),
+                'middle': (width / 2, 0),
+                'end': (width, 0),
+                'segments': 8
+            },
+            'right': {
+                'landmarks': [284, 251, 389, 356, 454, 323, 361, 288, 397, 365],
+                'start': (width, 0),
+                'middle': (width, height / 2),
+                'end': (width, height),
+                'segments': 9
+            },
+            'bottom': {
+                'landmarks': [365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136],
+                'start': (width, height),
+                'middle': (width / 2, height),
+                'end': (0, height),
+                'segments': 10
+            },
+            'left': {
+                'landmarks': [136, 172, 58, 132, 93, 234, 127, 162, 21, 54],
+                'start': (0, height),
+                'middle': (0, height / 2),
+                'end': (0, 0),
+                'segments': 9
+            }
+        }
+
+        current_point_idx = len(cls._base_landmarks)
+
+        for edge_name, edge_info in edges.items():
+            print(f"Processing {edge_name} edge")
+
+            landmarks = edge_info['landmarks']
+            n_segments = edge_info['segments']
+            print(f"Creating {n_segments} segments for {len(landmarks)} landmarks")
+
+            # Generate edge points
+            edge_points = cls.interpolate_edge_points(
+                edge_info['start'],
+                edge_info['end'],
+                n_segments
+            )
+
+            # Create quads and convert to triangles
+            for i in range(n_segments):
+                # Define quad vertices
+                quad = [
+                    landmarks[i],  # Current landmark
+                    landmarks[i + 1],  # Next landmark
+                    current_point_idx + i + 1,  # Next edge point
+                    current_point_idx + i  # Current edge point
+                ]
+
+                # Convert quad to two triangles
+                boundary_triangles.extend([
+                    [quad[0], quad[1], quad[2]],  # First triangle
+                    [quad[0], quad[2], quad[3]]  # Second triangle
+                ])
+
+            # Add edge points to boundary points list
+            boundary_points.extend(edge_points)
+            current_point_idx += len(edge_points)
+
+        print(f"Created {len(boundary_triangles)} triangles from {len(boundary_points)} boundary points")
+
+        cls._boundary_landmarks = np.array(boundary_points)
+        cls._boundary_faces = np.array(boundary_triangles)
+
+        return cls._boundary_faces
+
+    @classmethod
+    def get_base_landmarks(cls, size=None, x_scale=1.0, y_translation=0.0):
+        """
+        Get base landmark positions with optional transformations.
+
+        Args:
+            size: Target size (int or tuple)
+            x_scale: Horizontal scaling factor (0.5 to 1.0)
+            y_translation: Vertical translation (-0.5 to 0.5)
+
+        Returns:
+            numpy array: Base landmarks with applied transformations
+        """
+        landmarks, _ = cls._load_obj_data()
+
+        # Apply transformations before scaling to size
+        landmarks = cls._transform_landmarks(landmarks, x_scale, y_translation)
+
+        if size is not None:
+            if isinstance(size, tuple):
+                landmarks = landmarks * np.array(size)
+            else:
+                landmarks = landmarks * size
+
+        return landmarks
+
+    @classmethod
+    def get_face_triangles(cls, size=None, x_scale=1.0, y_translation=0.0):
+        """
+        Get face triangulation including boundary triangles with optional transformations
+
+        Args:
+            size: Target size (int or tuple)
+            x_scale: Horizontal scaling factor (0.5 to 1.0)
+            y_translation: Vertical translation (-0.5 to 0.5)
+
+        Returns:
+            tuple: (triangles array, landmarks array)
+        """
+        landmarks, faces = cls._load_obj_data()
+
+        # Apply transformations
+        landmarks = cls._transform_landmarks(landmarks, x_scale, y_translation)
+
+        if size is not None:
+            if isinstance(size, tuple):
+                scaled_landmarks = landmarks * np.array(size)
+            else:
+                scaled_landmarks = landmarks * size
+
+            if cls._boundary_faces is None:
+                cls.create_boundary_triangles(size)
+
+            if cls._boundary_faces is not None:
+                # Transform boundary landmarks if they exist
+                if cls._boundary_landmarks is not None:
+                    boundary_landmarks = cls._boundary_landmarks
+                    # Note: We don't apply x_scale to boundary landmarks as they should
+                    # stay at the image edges, but we do apply y_translation
+                    if y_translation != 0:
+                        boundary_landmarks = boundary_landmarks.copy()
+                        boundary_landmarks[:, 1] += y_translation * (size if isinstance(size, int) else size[1])
+
+                    all_points = np.vstack([scaled_landmarks, boundary_landmarks])
+                    all_triangles = np.vstack([faces, cls._boundary_faces])
+                    return all_triangles, all_points
+
+        return faces, landmarks
+
+    @staticmethod
+    def validate_size(size):
+        """
+        Validate size parameter
+        """
+        if isinstance(size, (int, tuple)):
+            if isinstance(size, int) and size <= 0:
+                raise ValueError("Size must be a positive integer")
+            if isinstance(size, tuple) and (len(size) != 2 or size[0] <= 0 or size[1] <= 0):
+                raise ValueError("Size tuple must contain two positive integers")
+            return size
+        raise ValueError("Size must be an integer or tuple of two integers")
\ No newline at end of file
diff --git a/core/image_processor.py b/core/image_processor.py
index 53b25ac..817b608 100644
--- a/core/image_processor.py
+++ b/core/image_processor.py
@@ -167,4 +167,4 @@ class ImageProcessor:
         cropped_face = image[y1:y2, x1:x2]
 
         # Return the cropped face and the crop bounding box
-        return cropped_face, (x1, y1, x2 - x1, y2 - y1)
\ No newline at end of file
+        return cropped_face, (x1, y1, x2 - x1, y2 - y1)
diff --git a/core/mediapipe_landmarks_468.obj b/core/mediapipe_landmarks_468.obj
new file mode 100644
index 0000000..0e666d1
--- /dev/null
+++ b/core/mediapipe_landmarks_468.obj
@@ -0,0 +1,1834 @@
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diff --git a/nodes/fit_and_crop.py b/nodes/fit_and_crop.py
deleted file mode 100644
index 7b67679..0000000
--- a/nodes/fit_and_crop.py
+++ /dev/null
@@ -1,94 +0,0 @@
-import numpy as np
-import torch
-import cv2
-import pandas as pd
-from typing import Union, Optional, Tuple
-
-from core.face_detector import FaceDetector
-from core.image_processor import ImageProcessor
-
-
-class FitAndCrop:
-    """ComfyUI node for detecting, cropping, and fitting faces to a square bbox."""
-
-    def __init__(self):
-        self.face_detector = FaceDetector()
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "image": ("IMAGE",),
-                "padding_percent": ("FLOAT", {
-                    "default": 0.0,
-                    "min": 0.0,
-                    "max": 1.0,
-                    "step": 0.05
-                }),
-                "bbox_size": (["512", "1024", "2048"], {
-                    "default": "1024"
-                })
-            }
-        }
-
-    RETURN_TYPES = ("IMAGE", "FACE_SETTINGS")
-    RETURN_NAMES = ("image", "face_settings")
-    FUNCTION = "process_image"
-    CATEGORY = "Face Processor"
-
-    def process_image(self, image, padding_percent=0.0, bbox_size="1024"):
-        # Convert tensor to numpy
-        image_np = self._convert_to_numpy(image)
-        if image_np is None:
-            return (image, {})
-
-        # Detect facial landmarks
-        landmarks_df = self.face_detector.detect_landmarks(image_np)
-        if landmarks_df is None:
-            print("No face detected, returning original image")
-            return (image, {})
-
-        # Calculate rotation angle and rotate the image
-        rotation_angle = ImageProcessor.calculate_rotation_angle(landmarks_df)
-        rotated_image, updated_landmarks = ImageProcessor.rotate_image(image_np, landmarks_df)
-        if rotated_image is None:
-            print("Failed to rotate image, returning original")
-            return (image, {})
-
-        # Crop face region to a square (1:1)
-        cropped_face, crop_bbox = ImageProcessor.crop_face_to_square(rotated_image, updated_landmarks, padding_percent)
-        if cropped_face is None:
-            print("Failed to crop face, returning original image")
-            return (image, {})
-
-        # Resize to target size
-        target_size = int(bbox_size)
-        final_image = ImageProcessor.resize_image(cropped_face, target_size)
-        if final_image is None:
-            print("Failed to resize image, returning original")
-            return (image, {})
-
-        # Convert back to float32 format expected by ComfyUI
-        final_image = final_image.astype(np.float32) / 255.0
-        final_image = torch.from_numpy(final_image).unsqueeze(0)
-
-        # Save face settings for restoration
-        face_settings = {
-            "original_image_shape": image_np.shape,
-            "rotation_angle": rotation_angle,
-            "crop_bbox": crop_bbox,  # (x, y, w, h)
-            "padding_percent": padding_percent,
-            "bbox_size": target_size,
-        }
-
-        return (final_image, face_settings)
-
-    def _convert_to_numpy(self, image: torch.Tensor) -> Optional[np.ndarray]:
-        """Convert tensor to numpy array."""
-        if torch.is_tensor(image):
-            image = image.detach().cpu().numpy()
-            if len(image.shape) == 4:
-                image = image[0]
-            image = (image * 255).astype(np.uint8)
-            return image
-        return None
\ No newline at end of file
diff --git a/nodes/restore_face.py b/nodes/restore_face.py
deleted file mode 100644
index 30c68ec..0000000
--- a/nodes/restore_face.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import numpy as np
-import torch
-import cv2
-from typing import Union, Optional
-
-class RestoreFace:
-    """ComfyUI node to restore a processed face back to its original position in the image."""
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "processed_face": ("IMAGE",),
-                "face_settings": ("FACE_SETTINGS",),
-            }
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "restore_face"
-    CATEGORY = "Face Processor"
-
-    def restore_face(self, processed_face, face_settings):
-        # Convert processed face to numpy
-        processed_face_np = self._convert_to_numpy(processed_face)
-        if processed_face_np is None:
-            return (processed_face,)
-
-        # Extract face settings
-        original_image_shape = face_settings.get("original_image_shape")
-        rotation_angle = face_settings.get("rotation_angle")
-        crop_bbox = face_settings.get("crop_bbox")
-        padding_percent = face_settings.get("padding_percent")
-        bbox_size = face_settings.get("bbox_size")
-
-        if not all([original_image_shape, crop_bbox]):
-            print("Invalid face settings, returning processed face")
-            return (processed_face,)
-
-        # Resize the processed face back to the original crop size
-        x1, y1, w, h = crop_bbox
-        resized_face = cv2.resize(processed_face_np, (w, h), interpolation=cv2.INTER_LANCZOS4)
-
-        # Create a blank image with the original size
-        restored_image = np.zeros(original_image_shape, dtype=np.uint8)
-
-        # Place the resized face back into the rotated image
-        restored_image[y1:y1 + h, x1:x1 + w] = resized_face
-
-        # Rotate the image back to the original orientation (reverse the rotation)
-        if rotation_angle != 0:
-            height, width = original_image_shape[:2]
-            center = (width // 2, height // 2)
-            rotation_matrix = cv2.getRotationMatrix2D(center, -rotation_angle, 1.0)  # Reverse the rotation
-            restored_image = cv2.warpAffine(restored_image, rotation_matrix, (width, height), flags=cv2.INTER_LANCZOS4)
-
-        # Convert back to float32 format expected by ComfyUI
-        restored_image = restored_image.astype(np.float32) / 255.0
-        restored_image = torch.from_numpy(restored_image).unsqueeze(0)
-
-        return (restored_image,)
-
-    def _convert_to_numpy(self, image: torch.Tensor) -> Optional[np.ndarray]:
-        """Convert tensor to numpy array."""
-        if torch.is_tensor(image):
-            image = image.detach().cpu().numpy()
-            if len(image.shape) == 4:
-                image = image[0]
-            image = (image * 255).astype(np.uint8)
-            return image
-        return None
\ No newline at end of file