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.value.append(evt.index) print(f"TRACKING POINT: {tracking_points.value}") if point_type == "include": trackings_input_label.value.append(1) elif point_type == "exclude": trackings_input_label.value.append(0) print(f"TRACKING INPUT LABEL: {trackings_input_label.value}") transparent_background = Image.open(first_frame_path).convert('RGBA') w, h = transparent_background.size transparent_layer = np.zeros((h, w, 4)) for index, track in enumerate(tracking_points.value): if trackings_input_label.value[index] == 1: cv2.circle(transparent_layer, track, 5, (0, 0, 255, 255), -1) else: cv2.circle(transparent_layer, track, 5, (255, 0, 0, 255), -1) transparent_layer = Image.fromarray(transparent_layer.astype(np.uint8)) 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): masks_store = [] for i, (mask, score) in enumerate(zip(masks, scores)): plt.figure(figsize=(10, 10)) plt.imshow(image) show_mask(mask, plt.gca(), borders=borders) if point_coords is not None: assert input_labels is not None show_points(point_coords, input_labels, plt.gca()) if box_coords is not None: # boxes show_box(box_coords, plt.gca()) if len(scores) > 1: plt.title(f"Mask {i+1}, Score: {score:.3f}", fontsize=18) plt.axis('off') # plt.show() # Save the figure as a JPG file filename = f"masked_image_{i+1}.jpg" plt.savefig(filename, format='jpg', bbox_inches='tight') masks_store.append(filename) # Close the figure to free up memory plt.close() return masks_store def sam_process(input_image, tracking_points, trackings_input_label): image = Image.open(input_image) image = np.array(image.convert("RGB")) sam2_checkpoint = "./checkpoints/sam2_hiera_tiny.pt" model_cfg = "sam2_hiera_t.yaml" sam2_model = build_sam2(model_cfg, sam2_checkpoint, device="cuda") predictor = SAM2ImagePredictor(sam2_model) predictor.set_image(image) input_point = np.array(tracking_points.value) input_label = np.array(trackings_input_label.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_ind = np.argsort(scores)[::-1] masks = masks[sorted_ind] scores = scores[sorted_ind] logits = logits[sorted_ind] print(masks.shape) results = show_masks(image, masks, scores, point_coords=input_point, input_labels=input_label, borders=True) print(results) return results[0] with gr.Blocks() as demo: first_frame_path = gr.State() tracking_points = gr.State([]) trackings_input_label = gr.State([]) with gr.Column(): gr.Markdown("# SAM2 Image Predictor") with gr.Row(): input_image = gr.Image(label="input image", interactive=True, type="filepath") with gr.Column(): point_type = gr.Radio(label="point type", choices=["include", "exclude"], value="include") points_map = gr.Image(label="points map", interactive=False) submit_btn = gr.Button("Submit") output_result = gr.Image() input_image.upload(preprocess_image, input_image, [first_frame_path, tracking_points, trackings_input_label, points_map]) points_map.select(get_point, [point_type, tracking_points, trackings_input_label, first_frame_path], [tracking_points, trackings_input_label, points_map]) submit_btn.click( fn = sam_process, inputs = [input_image, tracking_points, trackings_input_label], outputs = [output_result] ) demo.launch()