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| # Code credit: [EfficientSAM Demo](https://huggingface.co/spaces/yunyangx/EfficientSAM). | |
| import copy | |
| import os # noqa | |
| import gradio as gr | |
| import numpy as np | |
| import torch | |
| from PIL import ImageDraw | |
| from torchvision.transforms import ToTensor | |
| from utils.tools import format_results, point_prompt | |
| from utils.tools_gradio import fast_process | |
| from tinysam import sam_model_registry, SamPredictor | |
| from huggingface_hub import snapshot_download | |
| import wget | |
| #snapshot_download("merve/tinysam", local_dir="tinysam") | |
| URL = "https://github.com/xinghaochen/TinySAM/releases/download/1.0/tinysam.pth" | |
| response = wget.download(URL, "tinysam.pth") | |
| model_type = "vit_t" | |
| sam = sam_model_registry[model_type](checkpoint="./tinysam.pth") | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| print(device) | |
| sam.to(device=device) | |
| sam.eval() | |
| predictor = SamPredictor(sam) | |
| # Description | |
| title = "<center><strong><font size='8'>TinySAM<font></strong> <a href='https://github.com/xinghaochen/TinySAM'><font size='6'>[GitHub]</font></a> </center>" | |
| description_e = """This is a demo of TinySAM Model](https://github.com/xinghaochen/TinySAM). | |
| """ | |
| description_p = """# Interactive Instance Segmentation | |
| - Point-prompt instruction | |
| <ol> | |
| <li> Click on the left image (point input), visualizing the point on the right image </li> | |
| <li> Click the button of Segment with Point Prompt </li> | |
| </ol> | |
| - Box-prompt instruction | |
| <ol> | |
| <li> Click on the left image (one point input), visualizing the point on the right image </li> | |
| <li> Click on the left image (another point input), visualizing the point and the box on the right image</li> | |
| <li> Click the button of Segment with Box Prompt </li> | |
| </ol> | |
| - Github [link](https://github.com/xinghaochen/TinySAM) | |
| """ | |
| # examples | |
| examples = [ | |
| ["assets/1.jpg"], | |
| ["assets/2.jpg"], | |
| ["assets/3.jpg"], | |
| ["assets/4.jpeg"], | |
| ["assets/5.jpg"], | |
| ["assets/6.jpeg"] | |
| ] | |
| default_example = examples[0] | |
| css = "h1 { text-align: center } .about { text-align: justify; padding-left: 10%; padding-right: 10%; }" | |
| def segment_with_boxs( | |
| image, | |
| seg_image, | |
| global_points, | |
| global_point_label, | |
| input_size=1024, | |
| better_quality=False, | |
| withContours=True, | |
| use_retina=True, | |
| mask_random_color=True, | |
| ): | |
| if len(global_points) < 2: | |
| return seg_image, global_points, global_point_label | |
| print("Original Image : ", image.size) | |
| input_size = int(input_size) | |
| w, h = image.size | |
| scale = input_size / max(w, h) | |
| new_w = int(w * scale) | |
| new_h = int(h * scale) | |
| image = image.resize((new_w, new_h)) | |
| print("Scaled Image : ", image.size) | |
| print("Scale : ", scale) | |
| scaled_points = np.array( | |
| [[int(x * scale) for x in point] for point in global_points] | |
| ) | |
| scaled_points = scaled_points[:2] | |
| scaled_point_label = np.array(global_point_label)[:2] | |
| print(scaled_points, scaled_points is not None) | |
| print(scaled_point_label, scaled_point_label is not None) | |
| if scaled_points.size == 0 and scaled_point_label.size == 0: | |
| print("No points selected") | |
| return image, global_points, global_point_label | |
| nd_image = np.array(image) | |
| img_tensor = ToTensor()(nd_image) | |
| #coord_np = np.array(session_state['coord_list']) | |
| #label_np = np.array(session_state['label_list']) | |
| print(scaled_points, scaled_point_label) | |
| predictor.set_image(np.array(image)) | |
| input_box = scaled_points.reshape([4]) | |
| print('box', input_box) | |
| masks, scores, logits = predictor.predict( | |
| point_coords=None, #scaled_points, | |
| point_labels=None, #scaled_point_label, | |
| box=input_box[None, :] | |
| ) | |
| print(f'scores: {scores}') | |
| area = masks.sum(axis=(1, 2)) | |
| print(f'area: {area}') | |
| annotations = np.expand_dims(masks[scores.argmax()], axis=0) | |
| print(annotations) | |
| fig = fast_process( | |
| annotations=annotations, | |
| image=image, | |
| device=device, | |
| scale=(1024 // input_size), | |
| better_quality=better_quality, | |
| mask_random_color=mask_random_color, | |
| use_retina=use_retina, | |
| bbox = scaled_points.reshape([4]), | |
| withContours=withContours, | |
| ) | |
| global_points = [] | |
| global_point_label = [] | |
| # return fig, None | |
| return fig, global_points, global_point_label | |
| def segment_with_points( | |
| image, | |
| global_points, | |
| global_point_label, | |
| input_size=1024, | |
| better_quality=False, | |
| withContours=True, | |
| use_retina=True, | |
| mask_random_color=True, | |
| ): | |
| print("Original Image : ", image.size) | |
| input_size = int(input_size) | |
| w, h = image.size | |
| scale = input_size / max(w, h) | |
| new_w = int(w * scale) | |
| new_h = int(h * scale) | |
| image = image.resize((new_w, new_h)) | |
| print("Scaled Image : ", image.size) | |
| print("Scale : ", scale) | |
| if global_points is None: | |
| return image, global_points, global_point_label | |
| if len(global_points) < 1: | |
| return image, global_points, global_point_label | |
| scaled_points = np.array( | |
| [[int(x * scale) for x in point] for point in global_points] | |
| ) | |
| scaled_point_label = np.array(global_point_label) | |
| print(scaled_points, scaled_points is not None) | |
| print(scaled_point_label, scaled_point_label is not None) | |
| if scaled_points.size == 0 and scaled_point_label.size == 0: | |
| print("No points selected") | |
| return image, global_points, global_point_label | |
| nd_image = np.array(image) | |
| img_tensor = ToTensor()(nd_image) | |
| print(img_tensor.shape) | |
| predictor.set_image(nd_image) | |
| masks, scores, logits = predictor.predict( | |
| point_coords=scaled_points, | |
| point_labels=global_point_label, | |
| ) | |
| print(f'scores: {scores}') | |
| area = masks.sum(axis=(1, 2)) | |
| print(f'area: {area}') | |
| annotations = np.expand_dims(masks[scores.argmax()], axis=0) | |
| fig = fast_process( | |
| annotations=annotations, | |
| image=image, | |
| device=device, | |
| scale=(1024 // input_size), | |
| better_quality=better_quality, | |
| mask_random_color=mask_random_color, | |
| points = scaled_points, | |
| bbox=None, | |
| use_retina=use_retina, | |
| withContours=withContours, | |
| ) | |
| global_points = [] | |
| global_point_label = [] | |
| # return fig, None | |
| return fig, global_points, global_point_label | |
| def get_points_with_draw(image, cond_image, global_points, global_point_label, evt: gr.SelectData): | |
| print("Starting functioning") | |
| if len(global_points) == 0: | |
| image = copy.deepcopy(cond_image) | |
| x, y = evt.index[0], evt.index[1] | |
| label = "Add Mask" | |
| point_radius, point_color = 8, (255, 255, 0) if label == "Add Mask" else ( | |
| 255, | |
| 0, | |
| 255, | |
| ) | |
| global_points.append([x, y]) | |
| global_point_label.append(1 if label == "Add Mask" else 0) | |
| print(x, y, label == "Add Mask") | |
| if image is not None: | |
| draw = ImageDraw.Draw(image) | |
| draw.ellipse( | |
| [(x - point_radius, y - point_radius), (x + point_radius, y + point_radius)], | |
| fill=point_color, | |
| ) | |
| return image, global_points, global_point_label | |
| def get_points_with_draw_(image, cond_image, global_points, global_point_label, evt: gr.SelectData): | |
| if len(global_points) == 0: | |
| image = copy.deepcopy(cond_image) | |
| if len(global_points) > 2: | |
| return image, global_points, global_point_label | |
| x, y = evt.index[0], evt.index[1] | |
| label = "Add Mask" | |
| point_radius, point_color = 8, (255, 255, 0) if label == "Add Mask" else ( | |
| 255, | |
| 0, | |
| 255, | |
| ) | |
| global_points.append([x, y]) | |
| global_point_label.append(1 if label == "Add Mask" else 0) | |
| print(x, y, label == "Add Mask") | |
| if image is not None: | |
| draw = ImageDraw.Draw(image) | |
| draw.ellipse( | |
| [(x - point_radius, y - point_radius), (x + point_radius, y + point_radius)], | |
| fill=point_color, | |
| ) | |
| if len(global_points) == 2: | |
| x1, y1 = global_points[0] | |
| x2, y2 = global_points[1] | |
| if x1 < x2 and y1 < y2: | |
| draw.rectangle([x1, y1, x2, y2], outline="red", width=5) | |
| elif x1 < x2 and y1 >= y2: | |
| draw.rectangle([x1, y2, x2, y1], outline="red", width=5) | |
| global_points[0][0] = x1 | |
| global_points[0][1] = y2 | |
| global_points[1][0] = x2 | |
| global_points[1][1] = y1 | |
| elif x1 >= x2 and y1 < y2: | |
| draw.rectangle([x2, y1, x1, y2], outline="red", width=5) | |
| global_points[0][0] = x2 | |
| global_points[0][1] = y1 | |
| global_points[1][0] = x1 | |
| global_points[1][1] = y2 | |
| elif x1 >= x2 and y1 >= y2: | |
| draw.rectangle([x2, y2, x1, y1], outline="red", width=5) | |
| global_points[0][0] = x2 | |
| global_points[0][1] = y2 | |
| global_points[1][0] = x1 | |
| global_points[1][1] = y1 | |
| return image, global_points, global_point_label | |
| cond_img_p = gr.Image(label="Input with Point", value=default_example[0], type="pil") | |
| cond_img_b = gr.Image(label="Input with Box", value=default_example[0], type="pil") | |
| segm_img_p = gr.Image( | |
| label="Segmented Image with Point-Prompt", interactive=False, type="pil" | |
| ) | |
| segm_img_b = gr.Image( | |
| label="Segmented Image with Box-Prompt", interactive=False, type="pil" | |
| ) | |
| input_size_slider = gr.components.Slider( | |
| minimum=512, | |
| maximum=1024, | |
| value=1024, | |
| step=64, | |
| label="Input_size", | |
| info="Our model was trained on a size of 1024", | |
| ) | |
| with gr.Blocks(css=css, title="TinySAM") as demo: | |
| global_points = gr.State([]) | |
| global_point_label = gr.State([]) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| # Title | |
| gr.Markdown(title) | |
| with gr.Tab("Point mode"): | |
| # Images | |
| with gr.Row(variant="panel"): | |
| with gr.Column(scale=1): | |
| cond_img_p.render() | |
| with gr.Column(scale=1): | |
| segm_img_p.render() | |
| # Submit & Clear | |
| # ### | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Column(): | |
| segment_btn_p = gr.Button( | |
| "Segment with Point Prompt", variant="primary" | |
| ) | |
| clear_btn_p = gr.Button("Clear", variant="secondary") | |
| gr.Markdown("Try some of the examples below ⬇️") | |
| gr.Examples( | |
| examples=examples, | |
| inputs=[cond_img_p], | |
| examples_per_page=6, | |
| ) | |
| with gr.Column(): | |
| # Description | |
| gr.Markdown(description_p) | |
| with gr.Tab("Box mode"): | |
| # Images | |
| with gr.Row(variant="panel"): | |
| with gr.Column(scale=1): | |
| cond_img_b.render() | |
| with gr.Column(scale=1): | |
| segm_img_b.render() | |
| # Submit & Clear | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Column(): | |
| segment_btn_b = gr.Button( | |
| "Segment with Box Prompt", variant="primary" | |
| ) | |
| clear_btn_b = gr.Button("Clear", variant="secondary") | |
| gr.Markdown("Try some of the examples below ⬇️") | |
| gr.Examples( | |
| examples=examples, | |
| inputs=[cond_img_b], | |
| examples_per_page=6, | |
| ) | |
| with gr.Column(): | |
| # Description | |
| gr.Markdown(description_p) | |
| cond_img_p.select(get_points_with_draw, inputs = [segm_img_p, cond_img_p, global_points, global_point_label], outputs = [segm_img_p, global_points, global_point_label]) | |
| cond_img_b.select(get_points_with_draw_, [segm_img_b, cond_img_b, global_points, global_point_label], [segm_img_b, global_points, global_point_label]) | |
| segment_btn_p.click( | |
| segment_with_points, inputs=[cond_img_p, global_points, global_point_label], outputs=[segm_img_p, global_points, global_point_label] | |
| ) | |
| segment_btn_b.click( | |
| segment_with_boxs, inputs=[cond_img_b, segm_img_b, global_points, global_point_label], outputs=[segm_img_b,global_points, global_point_label] | |
| ) | |
| def clear(): | |
| return None, None, [], [] | |
| clear_btn_p.click(clear, outputs=[cond_img_p, segm_img_p, global_points, global_point_label]) | |
| clear_btn_b.click(clear, outputs=[cond_img_b, segm_img_b, global_points, global_point_label]) | |
| demo.queue() | |
| demo.launch() |