Add files via upload

Multiangle_Camera.py

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+"""
+SynvowMultiangle Node for ComfyUI
+A 3D camera control node that outputs angle prompts
+"""
+
+import os
+import time
+import numpy as np
+from PIL import Image
+import torch
+import folder_paths
+
+
+class SynvowMultiangleCameraNode:
+    """
+    3D Camera Angle Control Node
+    Provides a 3D scene to adjust camera angles and outputs a formatted prompt string
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "horizontal_angle": ("INT", {
+                    "default": 0, "min": 0, "max": 360, "step": 1, "display": "slider"
+                }),
+                "vertical_angle": ("INT", {
+                    "default": 0, "min": -60, "max": 60, "step": 1, "display": "slider"
+                }),
+                "zoom": ("FLOAT", {
+                    "default": 5.0, "min": 0.0, "max": 10.0, "step": 0.1, "display": "slider"
+                }),
+                "size_mode": (["custom", "original"], {"default": "custom"}),
+                "max_long_edge": ("INT", {
+                    "default": 1024, "min": 64, "max": 8192, "step": 8
+                }),
+            },
+            "optional": {
+                "image": ("IMAGE",),
+            },
+            "hidden": {
+                "uploaded_image": "STRING",
+                "prompt": "PROMPT",
+                "extra_pnginfo": "EXTRA_PNGINFO",
+            }
+        }
+
+    RETURN_TYPES = ("STRING", "IMAGE")
+    RETURN_NAMES = ("prompt", "image")
+    FUNCTION = "generate_prompt"
+    CATEGORY = "💫SynVow/image"
+    DESCRIPTION = "3D camera angle control node for multi-angle prompt generation"
+
+    def generate_prompt(self, horizontal_angle, vertical_angle, zoom,
+                        size_mode="custom", max_long_edge=1024,
+                        image=None, uploaded_image="", prompt=None, extra_pnginfo=None):
+        # Validate input ranges
+        horizontal_angle = max(0, min(360, int(horizontal_angle)))
+        vertical_angle = max(-60, min(60, int(vertical_angle)))
+        zoom = max(0.0, min(10.0, float(zoom)))
+
+        h_angle = horizontal_angle % 360
+
+        # Azimuth (horizontal) - 8 directions
+        h_boundaries = [22.5, 67.5, 112.5, 157.5, 202.5, 247.5, 292.5, 337.5]
+        h_directions = [
+            "front view", "front-right quarter view", "right side view",
+            "back-right quarter view", "back view", "back-left quarter view",
+            "left side view", "front-left quarter view"
+        ]
+        h_idx = 0
+        for i, b in enumerate(h_boundaries):
+            if h_angle < b:
+                h_idx = i
+                break
+        else:
+            h_idx = 0
+        h_direction = h_directions[h_idx]
+
+        # Elevation (vertical) - 5 levels
+        if vertical_angle < -30:
+            v_direction = "extreme low-angle shot"
+        elif vertical_angle < -10:
+            v_direction = "low-angle shot"
+        elif vertical_angle < 10:
+            v_direction = "eye-level shot"
+        elif vertical_angle < 40:
+            v_direction = "elevated shot"
+        else:
+            v_direction = "high-angle shot"
+
+        # Distance - 3 levels
+        if zoom < 2:
+            distance = "wide shot"
+        elif zoom < 6:
+            distance = "medium shot"
+        else:
+            distance = "close-up"
+
+        prompt = f"<sks> {h_direction} {v_direction} {distance}"
+
+        # Get image: prioritize connected IMAGE input, fallback to uploaded file
+        if image is not None:
+            img_tensor = image[0] if len(image.shape) == 4 else image
+        elif uploaded_image:
+            image_path = folder_paths.get_annotated_filepath(uploaded_image)
+            pil_img = Image.open(image_path).convert('RGB')
+            img_np = np.array(pil_img).astype(np.float32) / 255.0
+            img_tensor = torch.from_numpy(img_np)
+        else:
+            raise ValueError("Please provide an image: connect image input or use the load button")
+
+        # Process image based on size mode
+        if size_mode == "custom":
+            h, w = img_tensor.shape[:2]
+            long_edge = max(h, w)
+            if long_edge != max_long_edge:
+                scale = max_long_edge / long_edge
+                new_h, new_w = int(h * scale), int(w * scale)
+                img_np = (img_tensor.cpu().numpy() * 255).astype(np.uint8)
+                pil_image = Image.fromarray(img_np).resize((new_w, new_h), Image.BILINEAR)
+                img_np = np.array(pil_image).astype(np.float32) / 255.0
+                output_image = torch.from_numpy(img_np).unsqueeze(0)
+            else:
+                output_image = img_tensor.unsqueeze(0)
+        else:
+            output_image = img_tensor.unsqueeze(0)
+
+        # Save preview image to temp directory for frontend 3D preview
+        preview_image = None
+        try:
+            temp_dir = folder_paths.get_temp_directory()
+            timestamp = int(time.time() * 1000)
+            filename = f"synvow_preview_{timestamp}.png"
+            filepath = os.path.join(temp_dir, filename)
+
+            img_np = (output_image[0].cpu().numpy() * 255).astype(np.uint8)
+            pil_img = Image.fromarray(img_np)
+            pil_img.save(filepath, compress_level=1)
+
+            preview_image = {"filename": filename, "subfolder": "", "type": "temp"}
+        except Exception as e:
+            print(f"[SynvowMultiangle] Error saving preview: {e}")
+
+        return {
+            "ui": {"preview_image": [preview_image] if preview_image else []},
+            "result": (prompt, output_image)
+        }
+
+
+NODE_CLASS_MAPPINGS = {
+    "SynvowMultiangleCameraNode": SynvowMultiangleCameraNode,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "SynvowMultiangleCameraNode": "Synvow Multiangle Camera",
+}