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import numpy as np
from PIL import Image, ImageDraw
import cv2
import os
import tempfile
def create_demo_mask_from_image(image_shape, center_x=0.5, center_y=0.5, radius=0.3):
"""Create a circular mask in the center of the image"""
height, width = image_shape[:2]
center_x = int(width * center_x)
center_y = int(height * center_y)
radius = int(min(width, height) * radius)
# Create mask
mask = np.zeros((height, width), dtype=np.uint8)
y, x = np.ogrid[:height, :width]
mask_area = (x - center_x) ** 2 + (y - center_y) ** 2 <= radius ** 2
mask[mask_area] = 255
return mask
def validate_image_dimensions(image, max_size=2048):
"""Validate and resize image if needed"""
height, width = image.shape[:2]
if max(height, width) > max_size:
scale = max_size / max(height, width)
new_height = int(height * scale)
new_width = int(width * scale)
resized = cv2.resize(image, (new_width, new_height), interpolation=cv2.INTER_AREA)
print(f"Image resized from {width}x{height} to {new_width}x{new_height}")
return resized
return image
def prepare_image_for_inference(image):
"""Prepare image for inference pipeline"""
if len(image.shape) == 3 and image.shape[2] == 3:
# Ensure RGB format
if image.dtype != np.uint8:
image = (image * 255).astype(np.uint8)
return image
else:
raise ValueError("Image must be RGB format")
def save_temporary_file(data, suffix=".png"):
"""Save data to a temporary file"""
if isinstance(data, np.ndarray):
if len(data.shape) == 2:
# Grayscale image
img = Image.fromarray(data, mode='L')
else:
# RGB image
img = Image.fromarray(data)
else:
img = data
with tempfile.NamedTemporaryFile(suffix=suffix, delete=False) as temp_file:
img.save(temp_file.name)
return temp_file.name
def cleanup_temp_files(temp_paths):
"""Clean up temporary files"""
for path in temp_paths:
try:
if os.path.exists(path):
os.unlink(path)
except Exception as e:
print(f"Warning: Could not delete temporary file {path}: {e}")
def get_inference_status():
"""Check if inference modules are available"""
try:
from inference import Inference, load_image, load_single_mask
return True, "Inference modules available"
except ImportError as e:
return False, f"Inference modules not available: {e}"
def format_file_size(size_bytes):
"""Format file size in human readable format"""
if size_bytes < 1024:
return f"{size_bytes} B"
elif size_bytes < 1024**2:
return f"{size_bytes/1024:.1f} KB"
elif size_bytes < 1024**3:
return f"{size_bytes/(1024**2):.1f} MB"
else:
return f"{size_bytes/(1024**3):.1f} GB"
def create_sample_mask_options():
"""Create sample mask creation options"""
return [
("No mask", None),
("Center circle", "circle_center"),
("Center ellipse", "ellipse_center"),
("Full image", "full"),
]
This Gradio application provides:
## Key Features:
1. **Professional UI**: Modern interface with gradient header and clear sections
2. **Image Upload**: Drag-and-drop or click to upload images
3. **Optional Mask Upload**: Upload segmentation masks for focused processing
4. **Configuration Options**: Adjustable random seed and model selection
5. **Real-time Status**: Progress updates and error handling
6. **Download Functionality**: Direct download of generated 3D models
7. **Demo Mode**: Works even without the inference module installed
8. **Error Handling**: Robust error management with user-friendly messages
## Usage Instructions:
1. **Setup**: Make sure to clone the SAM-3D-objects repository and install dependencies
2. **Image Upload**: Upload the image you want to convert to 3D
3. **Mask (Optional)**: Upload a mask for better segmentation results
4. **Configure**: Adjust the random seed if needed
5. **Generate**: Click the generate button to create your 3D model
6. **Download**: Save the generated PLY file when complete
The app includes a "Built with anycoder" link as requested and provides a complete working interface for image-to-3D conversion using the SAM-3D-objects inference pipeline.
**Important**: Before running, make sure to:
1. Clone the repository: `git clone https://github.com/facebookresearch/sam-3d-objects`
2. Install dependencies as per the repository requirements
3. Ensure the model checkpoints are available in the `checkpoints/` directory |