stl files fun

from PIL import Image
import numpy as np
import trimesh

# --- Step 1: Load and prepare image ---
img_path = "ghostcore_image.bmp"
img = Image.open(img_path).convert("L")  # Convert to grayscale
img = img.resize((256, 256))  # Reduce resolution for mesh simplicity

# --- Step 2: Create height map ---
height_map = np.array(img) / 255.0  # Normalize 0.0–1.0
height_scale = 8.0  # Z-height exaggeration factor

# --- Step 3: Create 3D mesh from height map ---
rows, cols = height_map.shape
vertices = []
faces = []

for y in range(rows):
    for x in range(cols):
        z = height_map[y, x] * height_scale
        vertices.append([x, -y, z])  # Flip Y to make upright

for y in range(rows - 1):
    for x in range(cols - 1):
        i = y * cols + x
        faces.append([i, i + 1, i + cols])
        faces.append([i + 1, i + cols + 1, i + cols])

vertices = np.array(vertices)
faces = np.array(faces)
mesh = trimesh.Trimesh(vertices=vertices, faces=faces)

# --- Step 4: Export STL ---
output_path = "ghostcore_wraith_relief.stl"
mesh.export(output_path)
print(f"✅ STL exported: {output_path}")

import torch
import urllib.request
import cv2
import numpy as np
import os
from PIL import Image
import matplotlib.pyplot as plt
import trimesh

# 1. Load MiDaS model from torch.hub
midas = torch.hub.load("intel-isl/MiDaS", "DPT_Large")
midas.eval()
midas_transforms = torch.hub.load("intel-isl/MiDaS", "transforms").dpt_transform

# 2. Load your image
INPUT_IMAGE_PATH = "input.png"  # Replace this with your image
img = cv2.imread(INPUT_IMAGE_PATH)
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

# 3. Apply MiDaS depth prediction
input_tensor = midas_transforms(img_rgb).unsqueeze(0)
with torch.no_grad():
    prediction = midas(input_tensor)
    depth = prediction.squeeze().cpu().numpy()

# 4. Normalize and resize depth
depth = cv2.resize(depth, (256, 256))
depth_normalized = (depth - depth.min()) / (depth.max() - depth.min())
depth_scaled = depth_normalized * 30  # exaggerate for relief (in mm)

# 5. Convert depth map into mesh
rows, cols = depth_scaled.shape
vertices = []
faces = []

for y in range(rows):
    for x in range(cols):
        z = depth_scaled[y, x]
        vertices.append([x, -y, z])

for y in range(rows - 1):
    for x in range(cols - 1):
        i = y * cols + x
        faces.append([i, i + 1, i + cols])
        faces.append([i + 1, i + cols + 1, i + cols])

vertices = np.array(vertices)
faces = np.array(faces)

mesh = trimesh.Trimesh(vertices=vertices, faces=faces)

# 6. Export as STL
OUTPUT_STL_PATH = "generated_model.stl"
mesh.export(OUTPUT_STL_PATH)

print(f"✅ STL saved to: {OUTPUT_STL_PATH}")