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#!/usr/bin/env python3
"""
Fixed SAM 2.1 Interface - Handles negative stride issues properly
"""
import torch
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import gradio as gr
from transformers import Sam2Model, Sam2Processor
import warnings
import io
import base64
import os
from datetime import datetime
warnings.filterwarnings("ignore")
# Global model instance to avoid reloading
MODEL = None
PROCESSOR = None
DEVICE = None
# Global state for saving
CURRENT_MASK = None
CURRENT_IMAGE_NAME = None
CURRENT_POINTS = None
def initialize_sam(model_size="small"):
"""Initialize SAM model once"""
global MODEL, PROCESSOR, DEVICE
if MODEL is None:
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Initializing SAM 2.1 {model_size} on {DEVICE}...")
model_name = f"facebook/sam2-hiera-{model_size}"
MODEL = Sam2Model.from_pretrained(model_name).to(DEVICE)
PROCESSOR = Sam2Processor.from_pretrained(model_name)
print("β Model loaded successfully!")
return MODEL, PROCESSOR, DEVICE
def fix_image_array(image):
"""Fix image input for SAM processing - handles filepath, numpy array, or PIL Image"""
if isinstance(image, str):
# Handle filepath input from Gradio
return Image.open(image).convert("RGB")
elif isinstance(image, np.ndarray):
# Make sure array is contiguous
if not image.flags['C_CONTIGUOUS']:
image = np.ascontiguousarray(image)
# Ensure uint8 dtype
if image.dtype != np.uint8:
if image.max() <= 1.0:
image = (image * 255).astype(np.uint8)
else:
image = image.astype(np.uint8)
# Convert to PIL Image to avoid any stride issues
return Image.fromarray(image).convert("RGB")
elif isinstance(image, Image.Image):
return image.convert("RGB")
else:
raise ValueError(f"Unsupported image type: {type(image)}")
def apply_mask_post_processing(mask, stability_threshold=0.95):
"""Apply post-processing to refine mask size and quality"""
import cv2
# Convert to binary mask
binary_mask = (mask > 0).astype(np.uint8)
# Apply morphological operations to clean up the mask
kernel_size = max(3, int(mask.shape[0] * 0.01)) # Adaptive kernel size
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (kernel_size, kernel_size))
# Close small holes
binary_mask = cv2.morphologyEx(binary_mask, cv2.MORPH_CLOSE, kernel)
# Remove small noise
binary_mask = cv2.morphologyEx(binary_mask, cv2.MORPH_OPEN, kernel)
return binary_mask.astype(np.float32)
def apply_erosion_dilation(mask, erosion_dilation_value):
"""Apply erosion or dilation to adjust mask size"""
import cv2
binary_mask = (mask > 0).astype(np.uint8)
if erosion_dilation_value == 0:
return mask
kernel_size = abs(erosion_dilation_value)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (kernel_size, kernel_size))
if erosion_dilation_value > 0:
# Dilate (make larger)
binary_mask = cv2.dilate(binary_mask, kernel, iterations=1)
else:
# Erode (make smaller)
binary_mask = cv2.erode(binary_mask, kernel, iterations=1)
return binary_mask.astype(np.float32)
def save_binary_mask(mask, image_name, points, mask_threshold, erosion_dilation, save_low_res=False, custom_folder_name=None):
"""Save binary mask to organized folder structure"""
global CURRENT_MASK, CURRENT_IMAGE_NAME, CURRENT_POINTS
try:
# Store current state for saving
CURRENT_MASK = mask
CURRENT_IMAGE_NAME = image_name
CURRENT_POINTS = points
# Extract image name without extension and sanitize
if image_name:
base_name = os.path.splitext(os.path.basename(image_name))[0]
# Remove any path separators and special characters
base_name = base_name.replace('/', '_').replace('\\', '_').replace(':', '_').replace(' ', '_')
else:
base_name = f"image_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
# Choose folder tag: user-provided name if available, else 'default'
folder_tag = None
if custom_folder_name and str(custom_folder_name).strip():
folder_tag = str(custom_folder_name).strip().replace(' ', '_')
else:
folder_tag = "default"
# Create folder structure: masks/<image_base>/<folder_tag>/
folder_name = f"masks/{base_name}/{folder_tag}"
os.makedirs(folder_name, exist_ok=True)
# Create binary mask (0 and 255 values)
binary_mask = (mask > 0).astype(np.uint8) * 255
# Calculate low resolution dimensions if requested
original_height, original_width = binary_mask.shape
if save_low_res:
# Calculate sqrt-based resolution
sqrt_factor = int(np.sqrt(max(original_width, original_height)))
low_res_width = sqrt_factor
low_res_height = sqrt_factor
print(f"Original mask size: {original_width}x{original_height}")
print(f"Low-res mask size: {low_res_width}x{low_res_height}")
# Save binary mask
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
# Sanitize filename - replace problematic characters
threshold_str = f"{mask_threshold:.2f}".replace('.', 'p') # 0.30 -> 0p30
adj_str = f"{erosion_dilation:+d}".replace('+', 'plus').replace('-', 'minus') # +2 -> plus2, -2 -> minus2
saved_paths = []
# Save full resolution mask as JPEG with a simple filename
mask_filename = "image.jpg"
mask_path = os.path.join(folder_name, mask_filename)
mask_image = Image.fromarray(binary_mask, mode='L')
mask_image.save(mask_path, format="JPEG", quality=95, optimize=True)
saved_paths.append(mask_path)
# Save tensor mask (.pt) as float tensor (0.0/1.0)
tensor_filename = "image.pt"
tensor_path = os.path.join(folder_name, tensor_filename)
torch.save(torch.from_numpy((mask > 0).astype(np.float32)), tensor_path)
saved_paths.append(tensor_path)
# Save low resolution mask if requested
if save_low_res:
# Resize mask to low resolution
low_res_mask = mask_image.resize((low_res_width, low_res_height), Image.Resampling.NEAREST)
low_res_filename = f"mask_lowres_{sqrt_factor}x{sqrt_factor}_t{threshold_str}_adj{adj_str}_{timestamp}.png"
low_res_path = os.path.join(folder_name, low_res_filename)
low_res_mask.save(low_res_path)
saved_paths.append(low_res_path)
# Also save metadata
metadata = {
"timestamp": timestamp,
"points": points,
"mask_threshold": mask_threshold,
"erosion_dilation": erosion_dilation,
"image_name": image_name,
"original_resolution": f"{original_width}x{original_height}",
"saved_paths": saved_paths,
"low_resolution_saved": save_low_res
}
if save_low_res:
metadata["low_resolution"] = f"{low_res_width}x{low_res_height}"
metadata["sqrt_factor"] = sqrt_factor
import json
metadata_path = os.path.join(folder_name, f"metadata_{timestamp}.json")
with open(metadata_path, 'w') as f:
json.dump(metadata, f, indent=2)
# Return appropriate message
if save_low_res:
return f"β
Masks saved:\nπ Full: {os.path.basename(mask_path)}\nπ Low-res: {os.path.basename(low_res_path)}"
else:
return f"β
Mask saved to: {os.path.basename(mask_path)}"
except Exception as e:
return f"β Save failed: {str(e)}"
def process_sam_segmentation(image, points_data, bbox_data, mode, image_name=None, top_k=3, mask_threshold=0.0, stability_score_threshold=0.95, erosion_dilation=0):
"""Main processing function with mask size controls - supports points and bounding boxes"""
global CURRENT_MASK, CURRENT_IMAGE_NAME, CURRENT_POINTS
if image is None:
return None, None, "Please upload an image first."
# Check input based on mode
if mode == "Points":
if not points_data or len(points_data) == 0:
return None, None, "Please click on the image to select points."
elif mode == "Bounding Box":
if bbox_data is None:
return None, None, "Please click two corners to define a bounding box."
try:
# Initialize model
model, processor, device = initialize_sam()
# Fix image
pil_image = fix_image_array(image)
# Prepare SAM inputs based on mode
input_points = None
input_labels = None
input_boxes = None
points = None
if mode == "Points":
# Extract points with positive/negative labels
points = []
labels = []
for point_info in points_data:
if isinstance(point_info, dict):
points.append([point_info.get("x", 0), point_info.get("y", 0)])
labels.append(1 if point_info.get("positive", True) else 0) # 1 = positive, 0 = negative
elif isinstance(point_info, (list, tuple)) and len(point_info) >= 2:
points.append([point_info[0], point_info[1]])
labels.append(1) # Default to positive for old format
if not points:
return None, "No valid points found."
print(f"Processing {len(points)} points: {points} with labels: {labels}")
input_points = [[points]]
input_labels = [[labels]]
elif mode == "Bounding Box":
# Use bounding box
bbox = bbox_data # [x1, y1, x2, y2]
print(f"Processing bounding box: {bbox}")
input_boxes = [[bbox]]
# For visualization, store the bbox corners as points
points = [[bbox[0], bbox[1]], [bbox[2], bbox[3]]]
# Process with SAM
processor_inputs = {
"images": pil_image,
"return_tensors": "pt"
}
# Add points or boxes based on mode
if mode == "Points":
processor_inputs["input_points"] = input_points
processor_inputs["input_labels"] = input_labels
elif mode == "Bounding Box":
processor_inputs["input_boxes"] = input_boxes
inputs = processor(**processor_inputs).to(device)
# Generate masks with multiple outputs for better control
with torch.no_grad():
outputs = model(**inputs, multimask_output=True)
# Get masks and scores
masks = processor.post_process_masks(
outputs.pred_masks.cpu(),
inputs["original_sizes"]
)[0]
scores = outputs.iou_scores.cpu().numpy().flatten()
# Get top-k masks
top_indices = np.argsort(scores)[::-1][:top_k]
# Apply mask threshold to control size
best_mask = masks[0, top_indices[0]].numpy()
best_score = scores[top_indices[0]]
# Apply threshold to control mask size
if mask_threshold > 0:
best_mask = (best_mask > mask_threshold).astype(np.float32)
# Additional mask processing for size control
best_mask = apply_mask_post_processing(best_mask, stability_score_threshold)
# Apply erosion/dilation for fine size control
if erosion_dilation != 0:
best_mask = apply_erosion_dilation(best_mask, erosion_dilation)
# Store current state for saving
CURRENT_MASK = best_mask
CURRENT_IMAGE_NAME = image_name
CURRENT_POINTS = points
# Create dual visualizations
original_with_input = create_original_with_input_visualization(pil_image, points, bbox_data, mode)
mask_result = create_mask_visualization(pil_image, best_mask, best_score, mask_threshold)
status = f"β Generated mask with score: {float(best_score):.3f}\nπ Ready to save!"
return original_with_input, mask_result, status
except Exception as e:
print(f"Error in processing: {e}")
return None, None, f"Error: {str(e)}"
def create_original_with_input_visualization(pil_image, points, bbox, mode, negative_points=None):
"""Create visualization of original image with input points/bbox overlay"""
# Convert PIL to numpy for matplotlib
img_array = np.array(pil_image)
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
# Show original image only
ax.imshow(img_array)
# Show input visualization based on mode
if mode == "Points":
total_points = 0
# Show positive points (green)
if points:
for point in points:
ax.plot(point[0], point[1], 'go', markersize=12, markeredgewidth=3, markerfacecolor='lime')
total_points += len(points)
# Show negative points (red)
if negative_points:
for point in negative_points:
ax.plot(point[0], point[1], 'ro', markersize=12, markeredgewidth=3, markerfacecolor='red')
total_points += len(negative_points)
pos_count = len(points) if points else 0
neg_count = len(negative_points) if negative_points else 0
title_suffix = f"Points: {pos_count}+ {neg_count}-" if neg_count > 0 else f"Points: {pos_count}"
elif mode == "Bounding Box" and bbox:
# Show bounding box
x1, y1, x2, y2 = bbox
width = x2 - x1
height = y2 - y1
# Draw bounding box rectangle
from matplotlib.patches import Rectangle
rect = Rectangle((x1, y1), width, height, linewidth=3, edgecolor='lime', facecolor='none')
ax.add_patch(rect)
# Show corner points
ax.plot([x1, x2], [y1, y2], 'go', markersize=8, markeredgewidth=2, markerfacecolor='lime')
title_suffix = f"BBox: {int(width)}Γ{int(height)}"
else:
title_suffix = "No input"
ax.set_title(f"Input Selection ({title_suffix})", fontsize=14)
ax.axis('off')
# Convert to numpy array
fig.canvas.draw()
buf = fig.canvas.buffer_rgba()
result_array = np.asarray(buf)
# Convert RGBA to RGB
result_array = result_array[:, :, :3]
plt.close(fig)
return result_array
def create_mask_visualization(pil_image, mask, score, mask_threshold=0.0):
"""Create clean mask visualization without input overlays"""
# Convert PIL to numpy for matplotlib
img_array = np.array(pil_image)
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
# Show original image
ax.imshow(img_array)
# Overlay mask in red
mask_overlay = np.zeros((*mask.shape, 4))
mask_overlay[mask > 0] = [1, 0, 0, 0.6] # Red with transparency
ax.imshow(mask_overlay)
ax.set_title(f"Generated Mask (Score: {float(score):.3f}, Threshold: {mask_threshold:.2f})", fontsize=14)
ax.axis('off')
# Convert to numpy array
fig.canvas.draw()
buf = fig.canvas.buffer_rgba()
result_array = np.asarray(buf)
# Convert RGBA to RGB
result_array = result_array[:, :, :3]
plt.close(fig)
return result_array
def create_interface():
"""Create a simplified single-image annotator interface."""
with gr.Blocks(title="SAM 2.1 - Simple Annotator", theme=gr.themes.Soft(), css="""
.negative-mode-checkbox label {
color: #d00000 !important;
font-weight: 800 !important;
font-size: 16px !important;
}
""") as interface:
gr.HTML("""
<div style="text-align: center;">
<h1>π― SAM 2.1 Simple Annotator</h1>
<p>Upload one image, click to add positive/negative points, generate mask, and save.</p>
</div>
""")
# Image input (single image) - directly annotate; this serves as uploader too
# Users can upload by clicking the annotatable image component below.
image_input = gr.Image(
label=None,
type="filepath",
height=0,
visible=False
)
# Main layout: Selected Points on the left, annotatable image in the center, preview on the right
with gr.Row():
with gr.Column(scale=1):
points_display = gr.JSON(label="π Selected Points", value=[], visible=True)
with gr.Column(scale=3):
# Negative mode toggle with clear red styling
negative_point_mode = gr.Checkbox(
label="β NEGATIVE POINT MODE",
value=False,
info="π΄ Enable to add negative points (shown in red)",
interactive=True,
elem_classes="negative-mode-checkbox"
)
original_with_input = gr.Image(
label="π Click to Annotate (toggle negative mode to exclude)",
height=640,
interactive=True
)
with gr.Column(scale=1):
points_overlay = gr.Image(label="π Points Preview (green=positive, red=negative)", height=720, interactive=False)
# Action buttons
with gr.Row():
generate_btn = gr.Button("π― Generate Mask", variant="primary", size="lg")
clear_btn = gr.Button("ποΈ Clear Points", variant="secondary", size="lg")
# Mask result under buttons
with gr.Row():
mask_result = gr.Image(label="π Generated Mask", height=512)
# Save controls under mask
with gr.Row():
mask_name_input = gr.Textbox(label="Folder name (optional)", placeholder="e.g., michael_phelps_bottom_left")
save_btn = gr.Button("πΎ Save Mask", variant="stop", size="lg")
# Status
with gr.Row():
status_text = gr.Textbox(label="π Status", interactive=False, lines=3)
# State to store points only
points_state = gr.State([])
# Event handlers
def on_image_click(image, current_points, negative_mode, evt: gr.SelectData):
"""Handle clicks on the image for point annotations only."""
if evt.index is not None and image is not None:
x, y = evt.index
try:
pil_image = fix_image_array(image)
is_negative = negative_mode
new_point = {"x": int(x), "y": int(y), "positive": not is_negative}
updated_points = current_points + [new_point]
positive_points = [[p["x"], p["y"]] for p in updated_points if p.get("positive", True)]
negative_points = [[p["x"], p["y"]] for p in updated_points if not p.get("positive", True)]
updated_visualization = create_original_with_input_visualization(
pil_image, positive_points, None, "Points", negative_points
)
point_type = "positive" if not is_negative else "negative"
pos_count = len(positive_points)
neg_count = len(negative_points)
return updated_points, updated_points, updated_visualization, (
f"Added {point_type} point at ({x}, {y}). Total: {pos_count} positive, {neg_count} negative points."
)
except Exception as e:
print(f"Error in visualization: {e}")
return current_points, current_points, None, f"Error updating visualization: {str(e)}"
return current_points, current_points, None, "Click on the image to add points."
def on_image_upload(image):
"""Handle image upload and show it for annotation."""
if image is not None:
try:
pil_image = fix_image_array(image)
img_array = np.array(pil_image)
# Populate both the annotation image (left) and the points preview (right)
return img_array, img_array, [], [], "Image uploaded. Click on the left image to add points (enable negative mode for exclusion)."
except Exception as e:
return None, None, [], [], f"Error loading image: {str(e)}"
return None, None, [], [], "No image uploaded."
def clear_all_points(image):
"""Clear points and keep the image visible for annotation."""
try:
if image is not None:
pil_image = fix_image_array(image)
img_array = np.array(pil_image)
return [], [], img_array, img_array, None, "All points cleared. You can continue annotating."
except Exception:
pass
return [], [], None, None, None, "All points cleared."
def generate_segmentation(image, points):
"""Generate a single segmentation mask using points only."""
# Determine image name
if isinstance(image, str):
image_name = os.path.basename(image)
else:
# Prefer an explicit friendly default if metadata lacks a good name
image_name = None
if hasattr(image, 'orig_name'):
image_name = image.orig_name
elif isinstance(image, dict) and 'orig_name' in image:
image_name = image['orig_name']
elif hasattr(image, 'name'):
image_name = image.name
if not image_name or 'tmp' in str(image_name).lower() or 'uploaded_image' in str(image_name).lower():
image_name = "michael_phelps_bottom_left.jpg"
# Run segmentation (points mode)
_, mask_img, status = process_sam_segmentation(
image, points, None, "Points", image_name, 1, 0.0, 0.95, 0
)
if mask_img is not None:
status += f"\nπ Image: {os.path.basename(image_name)}"
return mask_img, status
def save_current_mask(custom_folder_name):
"""Save the currently generated mask."""
global CURRENT_MASK, CURRENT_IMAGE_NAME, CURRENT_POINTS
if CURRENT_MASK is None:
return "β No mask to save. Generate a mask first."
if CURRENT_POINTS is None:
return "β No points available. Generate a mask first."
return save_binary_mask(CURRENT_MASK, CURRENT_IMAGE_NAME, CURRENT_POINTS, 0.0, 0, False, custom_folder_name=(custom_folder_name or None))
# Wire events
# Let the annotatable image also handle image uploads (drag & drop / click upload)
original_with_input.upload(
on_image_upload,
inputs=[original_with_input],
outputs=[original_with_input, points_overlay, points_state, points_display, status_text]
)
original_with_input.select(
on_image_click,
inputs=[original_with_input, points_state, negative_point_mode],
outputs=[points_state, points_display, points_overlay, status_text]
)
generate_btn.click(
generate_segmentation,
inputs=[original_with_input, points_state],
outputs=[mask_result, status_text]
)
clear_btn.click(
clear_all_points,
inputs=[original_with_input],
outputs=[points_state, points_display, points_overlay, original_with_input, mask_result, status_text]
)
save_btn.click(
save_current_mask,
inputs=[mask_name_input],
outputs=[status_text]
)
return interface
def main():
"""Main function"""
print("π Starting Fixed SAM 2.1 Interface...")
interface = create_interface()
print("π Launching web interface...")
print("π Click on objects in images to segment them!")
interface.launch(
server_port=int(os.environ.get("GRADIO_SERVER_PORT", 7860)),
share=False,
inbrowser=False, # Don't auto-open browser in server environment
show_error=True
)
if __name__ == "__main__":
main() |