sam2_mask.py

import spaces
import gradio as gr
import os
os.environ["TORCH_CUDNN_SDPA_ENABLED"] = "1"
import torch
import numpy as np
import cv2
import matplotlib.pyplot as plt
from PIL import Image, ImageFilter
from sam2.build_sam import build_sam2
from sam2.sam2_image_predictor import SAM2ImagePredictor
from gradio_image_prompter import ImagePrompter

def preprocess_image(image):
    return image, gr.State([]), gr.State([]), image

def get_point(point_type, tracking_points, trackings_input_label, first_frame_path, evt: gr.SelectData):
    print(f"You selected {evt.value} at {evt.index} from {evt.target}")
    tracking_points.append(evt.index)
    print(f"TRACKING POINTS: {tracking_points}")
    if point_type == "include":
        trackings_input_label.append(1)
    elif point_type == "exclude":
        trackings_input_label.append(0)
    print(f"TRACKING INPUT LABELS: {trackings_input_label}")
    # Open the image and get its dimensions
    transparent_background = Image.open(first_frame_path).convert('RGBA')
    w, h = transparent_background.size
    # Define the circle radius as a fraction of the smaller dimension
    fraction = 0.02  # You can adjust this value as needed
    radius = int(fraction * min(w, h))
    # Create a transparent layer to draw on
    transparent_layer = np.zeros((h, w, 4), dtype=np.uint8)
    for index, track in enumerate(tracking_points):
        if trackings_input_label[index] == 1:
            cv2.circle(transparent_layer, tuple(track), radius, (0, 255, 0, 255), -1)
        else:
            cv2.circle(transparent_layer, tuple(track), radius, (255, 0, 0, 255), -1)
    # Convert the transparent layer back to an image
    transparent_layer = Image.fromarray(transparent_layer, 'RGBA')
    selected_point_map = Image.alpha_composite(transparent_background, transparent_layer)
    return tracking_points, trackings_input_label, selected_point_map
    
# use bfloat16 for the entire notebook
torch.autocast(device_type="cuda", dtype=torch.bfloat16).__enter__()

if torch.cuda.get_device_properties(0).major >= 8:
    # turn on tfloat32 for Ampere GPUs (https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices)
    torch.backends.cuda.matmul.allow_tf32 = True
    torch.backends.cudnn.allow_tf32 = True
    
def show_mask(mask, ax, random_color=False, borders=True):
    if random_color:
        color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)
    else:
        color = np.array([30/255, 144/255, 255/255, 0.6])
    h, w = mask.shape[-2:]
    mask = mask.astype(np.uint8)
    mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
    if borders:
        import cv2
        contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
        # Try to smooth contours
        contours = [cv2.approxPolyDP(contour, epsilon=0.01, closed=True) for contour in contours]
        mask_image = cv2.drawContours(mask_image, contours, -1, (1, 1, 1, 0.5), thickness=2)
    ax.imshow(mask_image)

def show_points(coords, labels, ax, marker_size=375):
    pos_points = coords[labels == 1]
    neg_points = coords[labels == 0]
    ax.scatter(pos_points[:, 0], pos_points[:, 1], color='green', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)
    ax.scatter(neg_points[:, 0], neg_points[:, 1], color='red', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)

def show_box(box, ax):
    x0, y0 = box[0], box[1]
    w, h = box[2] - box[0], box[3] - box[1]
    ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0, 0, 0, 0), lw=2))

def show_masks(image, masks, scores, point_coords=None, box_coords=None, input_labels=None, borders=True):
    combined_images = []  # List to store filenames of images with masks overlaid
    mask_images = []      # List to store filenames of separate mask images
    for i, (mask, score) in enumerate(zip(masks, scores)):
        # ---- Original Image with Mask Overlaid ----
        plt.figure(figsize=(10, 10))
        plt.imshow(image)
        show_mask(mask, plt.gca(), borders=borders)  # Draw the mask with borders
        if box_coords is not None:
            show_box(box_coords, plt.gca())
        if len(scores) > 1:
            plt.title(f"Mask {i+1}, Score: {score:.3f}", fontsize=18)
        plt.axis('off')
        # Save the figure as a JPG file
        combined_filename = f"combined_image_{i+1}.jpg"
        plt.savefig(combined_filename, format='jpg', bbox_inches='tight')
        combined_images.append(combined_filename)
        plt.close()  # Close the figure to free up memory
        # ---- Separate Mask Image (White Mask on Black Background) ----
        # Create a black image
        mask_image = np.zeros_like(image, dtype=np.uint8)
        # The mask is a binary array where the masked area is 1, else 0.
        # Convert the mask to a white color in the mask_image
        mask_layer = (mask > 0).astype(np.uint8) * 255
        for c in range(3):  # Assuming RGB, repeat mask for all channels
            mask_image[:, :, c] = mask_layer
        # Save the mask image
        mask_filename = f"mask_image_{i+1}.png"
        Image.fromarray(mask_image).save(mask_filename)
        mask_images.append(mask_filename)
        plt.close()  # Close the figure to free up memory
    return combined_images, mask_images

@spaces.GPU()
def sam_process(original_image, points, labels):
    
    print(f"Points: {points}")
    print(f"Labels: {labels}")
    image = Image.open(original_image)
    image = np.array(image.convert("RGB"))
    
    if not points or not labels:
        print("No points or labels provided, returning None")
        return None
    # Convert image to numpy array for SAM2 processing
    # image = np.array(original_image)
    predictor = SAM2ImagePredictor.from_pretrained("facebook/sam2.1-hiera-large")
    predictor.set_image(image)
    input_point = np.array(points.value)
    input_label = np.array(labels.value)
    
    print(predictor._features["image_embed"].shape, predictor._features["image_embed"][-1].shape)

    masks, scores, logits = predictor.predict(
        point_coords=input_point,
        point_labels=input_label,
        multimask_output=False,
    )
    sorted_indices = np.argsort(scores)[::-1]
    masks = masks[sorted_indices]
    scores = scores[sorted_indices]
    logits = logits[sorted_indices]
    print(masks.shape)

    results, mask_results = show_masks(image, masks, scores, point_coords=input_point, input_labels=input_label, borders=True)
    print(results)
    
    return results[0], mask_results[0]

def create_sam2_tab():
    first_frame = gr.State()  # Tracks original image
    tracking_points = gr.State([])
    trackings_input_label = gr.State([])
    
    with gr.Column():
        with gr.Row():
            with gr.Column():
                sam_input_image = gr.Image(label="input image", interactive=False, type="filepath", visible=False)                 
                points_map = gr.Image(
                    label="points map", 
                    type="filepath",
                    interactive=True
                )
                with gr.Row():
                    point_type = gr.Radio(["include", "exclude"], value="include", label="Point Type")
                    clear_button = gr.Button("Clear Points")
                submit_button = gr.Button("Submit")

            with gr.Column():
                output_image = gr.Image("Segmented Output")
                output_result_mask = gr.Image()

    # Event handlers
    points_map.upload(
        fn = preprocess_image,
        inputs = [points_map], 
        # outputs=[sam_input_image, first_frame, tracking_points, trackings_input_label],
        outputs = [first_frame, tracking_points, trackings_input_label, sam_input_image],
        queue=False
    )
    
    clear_button.click(
        lambda img: ([], [], img),
        inputs=first_frame,
        outputs=[tracking_points, trackings_input_label, points_map],
        queue=False
    )
    
    points_map.select(
        get_point,
        inputs=[point_type, tracking_points, trackings_input_label, first_frame],
        outputs=[tracking_points, trackings_input_label, points_map],
        queue = False
    )
    
    submit_button.click(
        sam_process,
        inputs=[sam_input_image, tracking_points, trackings_input_label],
        outputs = [output_image, output_result_mask]
    )
    
    return sam_input_image, points_map, output_image