segment-anything-model

# ViT-H (largest, most accurate) - 2.4GB
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth
 
# ViT-L (medium) - 1.2GB
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth
 
# ViT-B (smallest, fastest) - 375MB
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth

import numpy as np
from segment_anything import sam_model_registry, SamPredictor
 
# Load model
sam = sam_model_registry["vit_h"](checkpoint="sam_vit_h_4b8939.pth")
sam.to(device="cuda")
 
# Create predictor
predictor = SamPredictor(sam)
 
# Set image (computes embeddings once)
image = cv2.imread("image.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
predictor.set_image(image)
 
# Predict with point prompts
input_point = np.array([[500, 375]])  # (x, y) coordinates
input_label = np.array([1])  # 1 = foreground, 0 = background
 
masks, scores, logits = predictor.predict(
    point_coords=input_point,
    point_labels=input_label,
    multimask_output=True  # Returns 3 mask options
)
 
# Select best mask
best_mask = masks[np.argmax(scores)]

import torch
from PIL import Image
from transformers import SamModel, SamProcessor
 
# Load model and processor
model = SamModel.from_pretrained("facebook/sam-vit-huge")
processor = SamProcessor.from_pretrained("facebook/sam-vit-huge")
model.to("cuda")
 
# Process image with point prompt
image = Image.open("image.jpg")
input_points = [[[450, 600]]]  # Batch of points
 
inputs = processor(image, input_points=input_points, return_tensors="pt")
inputs = {k: v.to("cuda") for k, v in inputs.items()}
 
# Generate masks
with torch.no_grad():
    outputs = model(**inputs)
 
# Post-process masks to original size
masks = processor.image_processor.post_process_masks(
    outputs.pred_masks.cpu(),
    inputs["original_sizes"].cpu(),
    inputs["reshaped_input_sizes"].cpu()
)

SAM Architecture:
┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│  Image Encoder  │────▶│ Prompt Encoder  │────▶│  Mask Decoder   │
│     (ViT)       │     │ (Points/Boxes)  │     │ (Transformer)   │
└─────────────────┘     └─────────────────┘     └─────────────────┘
        │                       │                       │
   Image Embeddings      Prompt Embeddings         Masks + IoU
   (computed once)       (per prompt)             predictions

# Single foreground point
input_point = np.array([[500, 375]])
input_label = np.array([1])
 
masks, scores, logits = predictor.predict(
    point_coords=input_point,
    point_labels=input_label,
    multimask_output=True
)
 
# Multiple points (foreground + background)
input_points = np.array([[500, 375], [600, 400], [450, 300]])
input_labels = np.array([1, 1, 0])  # 2 foreground, 1 background
 
masks, scores, logits = predictor.predict(
    point_coords=input_points,
    point_labels=input_labels,
    multimask_output=False  # Single mask when prompts are clear
)

# Bounding box [x1, y1, x2, y2]
input_box = np.array([425, 600, 700, 875])
 
masks, scores, logits = predictor.predict(
    box=input_box,
    multimask_output=False
)

# Box + points for precise control
masks, scores, logits = predictor.predict(
    point_coords=np.array([[500, 375]]),
    point_labels=np.array([1]),
    box=np.array([400, 300, 700, 600]),
    multimask_output=False
)

# Initial prediction
masks, scores, logits = predictor.predict(
    point_coords=np.array([[500, 375]]),
    point_labels=np.array([1]),
    multimask_output=True
)
 
# Refine with additional point using previous mask
masks, scores, logits = predictor.predict(
    point_coords=np.array([[500, 375], [550, 400]]),
    point_labels=np.array([1, 0]),  # Add background point
    mask_input=logits[np.argmax(scores)][None, :, :],  # Use best mask
    multimask_output=False
)

from segment_anything import SamAutomaticMaskGenerator
 
# Create generator
mask_generator = SamAutomaticMaskGenerator(sam)
 
# Generate all masks
masks = mask_generator.generate(image)
 
# Each mask contains:
# - segmentation: binary mask
# - bbox: [x, y, w, h]
# - area: pixel count
# - predicted_iou: quality score
# - stability_score: robustness score
# - point_coords: generating point

mask_generator = SamAutomaticMaskGenerator(
    model=sam,
    points_per_side=32,          # Grid density (more = more masks)
    pred_iou_thresh=0.88,        # Quality threshold
    stability_score_thresh=0.95,  # Stability threshold
    crop_n_layers=1,             # Multi-scale crops
    crop_n_points_downscale_factor=2,
    min_mask_region_area=100,    # Remove tiny masks
)
 
masks = mask_generator.generate(image)

# Sort by area (largest first)
masks = sorted(masks, key=lambda x: x['area'], reverse=True)
 
# Filter by predicted IoU
high_quality = [m for m in masks if m['predicted_iou'] > 0.9]
 
# Filter by stability score
stable_masks = [m for m in masks if m['stability_score'] > 0.95]

# Process multiple images efficiently
images = [cv2.imread(f"image_{i}.jpg") for i in range(10)]
 
all_masks = []
for image in images:
    predictor.set_image(image)
    masks, _, _ = predictor.predict(
        point_coords=np.array([[500, 375]]),
        point_labels=np.array([1]),
        multimask_output=True
    )
    all_masks.append(masks)

# Process multiple prompts efficiently (one image encoding)
predictor.set_image(image)
 
# Batch of point prompts
points = [
    np.array([[100, 100]]),
    np.array([[200, 200]]),
    np.array([[300, 300]])
]
 
all_masks = []
for point in points:
    masks, scores, _ = predictor.predict(
        point_coords=point,
        point_labels=np.array([1]),
        multimask_output=True
    )
    all_masks.append(masks[np.argmax(scores)])

python scripts/export_onnx_model.py \
    --checkpoint sam_vit_h_4b8939.pth \
    --model-type vit_h \
    --output sam_onnx.onnx \
    --return-single-mask

import onnxruntime
 
# Load ONNX model
ort_session = onnxruntime.InferenceSession("sam_onnx.onnx")
 
# Run inference (image embeddings computed separately)
masks = ort_session.run(
    None,
    {
        "image_embeddings": image_embeddings,
        "point_coords": point_coords,
        "point_labels": point_labels,
        "mask_input": np.zeros((1, 1, 256, 256), dtype=np.float32),
        "has_mask_input": np.array([0], dtype=np.float32),
        "orig_im_size": np.array([h, w], dtype=np.float32)
    }
)

import cv2
 
# Load model
predictor = SamPredictor(sam)
predictor.set_image(image)
 
def on_click(event, x, y, flags, param):
    if event == cv2.EVENT_LBUTTONDOWN:
        # Foreground point
        masks, scores, _ = predictor.predict(
            point_coords=np.array([[x, y]]),
            point_labels=np.array([1]),
            multimask_output=True
        )
        # Display best mask
        display_mask(masks[np.argmax(scores)])

def extract_object(image, point):
    """Extract object at point with transparent background."""
    predictor.set_image(image)
 
    masks, scores, _ = predictor.predict(
        point_coords=np.array([point]),
        point_labels=np.array([1]),
        multimask_output=True
    )
 
    best_mask = masks[np.argmax(scores)]
 
    # Create RGBA output
    rgba = np.zeros((image.shape[0], image.shape[1], 4), dtype=np.uint8)
    rgba[:, :, :3] = image
    rgba[:, :, 3] = best_mask * 255
 
    return rgba

# Process medical images (grayscale to RGB)
medical_image = cv2.imread("scan.png", cv2.IMREAD_GRAYSCALE)
rgb_image = cv2.cvtColor(medical_image, cv2.COLOR_GRAY2RGB)
 
predictor.set_image(rgb_image)
 
# Segment region of interest
masks, scores, _ = predictor.predict(
    box=np.array([x1, y1, x2, y2]),  # ROI bounding box
    multimask_output=True
)

# SamAutomaticMaskGenerator output
{
    "segmentation": np.ndarray,  # H×W binary mask
    "bbox": [x, y, w, h],        # Bounding box
    "area": int,                 # Pixel count
    "predicted_iou": float,      # 0-1 quality score
    "stability_score": float,    # 0-1 robustness score
    "crop_box": [x, y, w, h],    # Generation crop region
    "point_coords": [[x, y]],    # Input point
}

from pycocotools import mask as mask_utils
 
# Encode mask to RLE
rle = mask_utils.encode(np.asfortranarray(mask.astype(np.uint8)))
rle["counts"] = rle["counts"].decode("utf-8")
 
# Decode RLE to mask
decoded_mask = mask_utils.decode(rle)

# Use smaller model for limited VRAM
sam = sam_model_registry["vit_b"](checkpoint="sam_vit_b_01ec64.pth")
 
# Process images in batches
# Clear CUDA cache between large batches
torch.cuda.empty_cache()

# Use half precision
sam = sam.half()
 
# Reduce points for automatic generation
mask_generator = SamAutomaticMaskGenerator(
    model=sam,
    points_per_side=16,  # Default is 32
)
 
# Use ONNX for deployment
# Export with --return-single-mask for faster inference