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 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_path, tracking_points, trackings_input_label, 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