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| from ultralytics import YOLO | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import gradio as gr | |
| import cv2 | |
| import torch | |
| from PIL import Image | |
| # Load the pre-trained model | |
| model = YOLO('checkpoints/FastSAM.pt') | |
| # Description | |
| title = "<center><strong><font size='8'>🏃 Fast Segment Anything 🤗</font></strong></center>" | |
| description = """This is a demo on Github project 🏃 [Fast Segment Anything Model](https://github.com/CASIA-IVA-Lab/FastSAM). | |
| 🎯 Upload an Image, segment it with Fast Segment Anything (Everything mode). The other modes will come soon. | |
| ⌛️ It takes about 4~ seconds to generate segment results. The concurrency_count of queue is 1, please wait for a moment when it is crowded. | |
| 🚀 To get faster results, you can use a smaller input size and leave high_visual_quality unchecked. | |
| 📣 You can also obtain the segmentation results of any Image through this Colab: [](https://colab.research.google.com/drive/1oX14f6IneGGw612WgVlAiy91UHwFAvr9?usp=sharing) | |
| 😚 A huge thanks goes out to the @HuggingFace Team for supporting us with GPU grant. | |
| 🏠 Check out our [Model Card 🏃](https://huggingface.co/An-619/FastSAM) | |
| """ | |
| examples = [["assets/sa_192.jpg"], ["assets/sa_414.jpg"], | |
| ["assets/sa_561.jpg"], ["assets/sa_862.jpg"], | |
| ["assets/sa_1309.jpg"], ["assets/sa_8776.jpg"], | |
| ["assets/sa_10039.jpg"], ["assets/sa_11025.jpg"]] | |
| default_example = examples[5] | |
| css = "h1 { text-align: center } .about { text-align: justify; padding-left: 10%; padding-right: 10%; }" | |
| def fast_process(annotations, image, high_quality, device, scale): | |
| if isinstance(annotations[0],dict): | |
| annotations = [annotation['segmentation'] for annotation in annotations] | |
| original_h = image.height | |
| original_w = image.width | |
| if high_quality == True: | |
| if isinstance(annotations[0],torch.Tensor): | |
| annotations = np.array(annotations.cpu()) | |
| for i, mask in enumerate(annotations): | |
| mask = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8)) | |
| annotations[i] = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_OPEN, np.ones((8, 8), np.uint8)) | |
| if device == 'cpu': | |
| annotations = np.array(annotations) | |
| inner_mask = fast_show_mask(annotations, | |
| plt.gca(), | |
| bbox=None, | |
| points=None, | |
| pointlabel=None, | |
| retinamask=True, | |
| target_height=original_h, | |
| target_width=original_w) | |
| else: | |
| if isinstance(annotations[0],np.ndarray): | |
| annotations = torch.from_numpy(annotations) | |
| inner_mask = fast_show_mask_gpu(annotations, | |
| plt.gca(), | |
| bbox=None, | |
| points=None, | |
| pointlabel=None) | |
| if isinstance(annotations, torch.Tensor): | |
| annotations = annotations.cpu().numpy() | |
| if high_quality: | |
| contour_all = [] | |
| temp = np.zeros((original_h, original_w,1)) | |
| for i, mask in enumerate(annotations): | |
| if type(mask) == dict: | |
| mask = mask['segmentation'] | |
| annotation = mask.astype(np.uint8) | |
| contours, _ = cv2.findContours(annotation, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) | |
| for contour in contours: | |
| contour_all.append(contour) | |
| cv2.drawContours(temp, contour_all, -1, (255, 255, 255), 2 // scale) | |
| color = np.array([0 / 255, 0 / 255, 255 / 255, 0.9]) | |
| contour_mask = temp / 255 * color.reshape(1, 1, -1) | |
| image = image.convert('RGBA') | |
| overlay_inner = Image.fromarray((inner_mask * 255).astype(np.uint8), 'RGBA') | |
| image.paste(overlay_inner, (0, 0), overlay_inner) | |
| if high_quality: | |
| overlay_contour = Image.fromarray((contour_mask * 255).astype(np.uint8), 'RGBA') | |
| image.paste(overlay_contour, (0, 0), overlay_contour) | |
| return image | |
| # CPU post process | |
| def fast_show_mask(annotation, ax, bbox=None, | |
| points=None, pointlabel=None, | |
| retinamask=True, target_height=960, | |
| target_width=960): | |
| msak_sum = annotation.shape[0] | |
| height = annotation.shape[1] | |
| weight = annotation.shape[2] | |
| # 将annotation 按照面积 排序 | |
| areas = np.sum(annotation, axis=(1, 2)) | |
| sorted_indices = np.argsort(areas)[::1] | |
| annotation = annotation[sorted_indices] | |
| index = (annotation != 0).argmax(axis=0) | |
| color = np.random.random((msak_sum,1,1,3)) | |
| transparency = np.ones((msak_sum,1,1,1)) * 0.6 | |
| visual = np.concatenate([color,transparency],axis=-1) | |
| mask_image = np.expand_dims(annotation,-1) * visual | |
| mask = np.zeros((height,weight,4)) | |
| h_indices, w_indices = np.meshgrid(np.arange(height), np.arange(weight), indexing='ij') | |
| indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None)) | |
| # 使用向量化索引更新show的值 | |
| mask[h_indices, w_indices, :] = mask_image[indices] | |
| if bbox is not None: | |
| x1, y1, x2, y2 = bbox | |
| ax.add_patch(plt.Rectangle((x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor='b', linewidth=1)) | |
| # draw point | |
| if points is not None: | |
| plt.scatter([point[0] for i, point in enumerate(points) if pointlabel[i]==1], [point[1] for i, point in enumerate(points) if pointlabel[i]==1], s=20, c='y') | |
| plt.scatter([point[0] for i, point in enumerate(points) if pointlabel[i]==0], [point[1] for i, point in enumerate(points) if pointlabel[i]==0], s=20, c='m') | |
| if retinamask==False: | |
| mask = cv2.resize(mask, (target_width, target_height), interpolation=cv2.INTER_NEAREST) | |
| return mask | |
| def fast_show_mask_gpu(annotation, ax, | |
| bbox=None, points=None, | |
| pointlabel=None): | |
| msak_sum = annotation.shape[0] | |
| height = annotation.shape[1] | |
| weight = annotation.shape[2] | |
| areas = torch.sum(annotation, dim=(1, 2)) | |
| sorted_indices = torch.argsort(areas, descending=False) | |
| annotation = annotation[sorted_indices] | |
| # 找每个位置第一个非零值下标 | |
| index = (annotation != 0).to(torch.long).argmax(dim=0) | |
| color = torch.rand((msak_sum,1,1,3)).to(annotation.device) | |
| transparency = torch.ones((msak_sum,1,1,1)).to(annotation.device) * 0.6 | |
| visual = torch.cat([color,transparency],dim=-1) | |
| mask_image = torch.unsqueeze(annotation,-1) * visual | |
| # 按index取数，index指每个位置选哪个batch的数，把mask_image转成一个batch的形式 | |
| mask = torch.zeros((height,weight,4)).to(annotation.device) | |
| h_indices, w_indices = torch.meshgrid(torch.arange(height), torch.arange(weight)) | |
| indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None)) | |
| # 使用向量化索引更新show的值 | |
| mask[h_indices, w_indices, :] = mask_image[indices] | |
| mask_cpu = mask.cpu().numpy() | |
| if bbox is not None: | |
| x1, y1, x2, y2 = bbox | |
| ax.add_patch(plt.Rectangle((x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor='b', linewidth=1)) | |
| # draw point | |
| if points is not None: | |
| plt.scatter([point[0] for i, point in enumerate(points) if pointlabel[i]==1], [point[1] for i, point in enumerate(points) if pointlabel[i]==1], s=20, c='y') | |
| plt.scatter([point[0] for i, point in enumerate(points) if pointlabel[i]==0], [point[1] for i, point in enumerate(points) if pointlabel[i]==0], s=20, c='m') | |
| return mask_cpu | |
| device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
| def segment_image(input, input_size=1024, high_visual_quality=True, iou_threshold=0.7, conf_threshold=0.25): | |
| input_size = int(input_size) # 确保 imgsz 是整数 | |
| # Thanks for the suggestion by hysts in HuggingFace. | |
| w, h = input.size | |
| scale = input_size / max(w, h) | |
| new_w = int(w * scale) | |
| new_h = int(h * scale) | |
| input = input.resize((new_w, new_h)) | |
| results = model(input, device=device, retina_masks=True, iou=iou_threshold, conf=conf_threshold, imgsz=input_size) | |
| fig = fast_process(annotations=results[0].masks.data, | |
| image=input, high_quality=high_visual_quality, | |
| device=device, scale=(1024 // input_size)) | |
| return fig | |
| # input_size=1024 | |
| # high_quality_visual=True | |
| # inp = 'assets/sa_192.jpg' | |
| # input = Image.open(inp) | |
| # device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
| # input_size = int(input_size) # 确保 imgsz 是整数 | |
| # results = model(input, device=device, retina_masks=True, iou=0.7, conf=0.25, imgsz=input_size) | |
| # pil_image = fast_process(annotations=results[0].masks.data, | |
| # image=input, high_quality=high_quality_visual, device=device) | |
| cond_img = gr.Image(label="Input", value=default_example[0], type='pil') | |
| segm_img = gr.Image(label="Segmented Image", interactive=False, type='pil') | |
| input_size_slider = gr.components.Slider(minimum=512, maximum=1024, value=1024, step=64, label='input_size') | |
| with gr.Blocks(css=css, title='Fast Segment Anything') as demo: | |
| with gr.Row(): | |
| # Title | |
| gr.Markdown(title) | |
| # # # Description | |
| # # gr.Markdown(description) | |
| # Images | |
| with gr.Row(variant="panel"): | |
| with gr.Column(scale=1): | |
| cond_img.render() | |
| with gr.Column(scale=1): | |
| segm_img.render() | |
| # Submit & Clear | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_size_slider.render() | |
| with gr.Row(): | |
| vis_check = gr.Checkbox(value=True, label='high_visual_quality') | |
| with gr.Column(): | |
| segment_btn = gr.Button("Segment Anything", variant='primary') | |
| # with gr.Column(): | |
| # clear_btn = gr.Button("Clear", variant="primary") | |
| gr.Markdown("Try some of the examples below ⬇️") | |
| gr.Examples(examples=examples, | |
| inputs=[cond_img], | |
| outputs=segm_img, | |
| fn=segment_image, | |
| cache_examples=True, | |
| examples_per_page=4) | |
| # gr.Markdown("Try some of the examples below ⬇️") | |
| # gr.Examples(examples=examples, | |
| # inputs=[cond_img, input_size_slider, vis_check, iou_threshold, conf_threshold], | |
| # outputs=output, | |
| # fn=segment_image, | |
| # examples_per_page=4) | |
| with gr.Column(): | |
| with gr.Accordion("Advanced options", open=False): | |
| iou_threshold = gr.Slider(0.1, 0.9, 0.7, step=0.1, label='iou_threshold') | |
| conf_threshold = gr.Slider(0.1, 0.9, 0.25, step=0.05, label='conf_threshold') | |
| # Description | |
| gr.Markdown(description) | |
| segment_btn.click(segment_image, | |
| inputs=[cond_img, input_size_slider, vis_check, iou_threshold, conf_threshold], | |
| outputs=segm_img) | |
| # def clear(): | |
| # return None, None | |
| # clear_btn.click(fn=clear, inputs=None, outputs=None) | |
| demo.queue() | |
| demo.launch() | |
| # app_interface = gr.Interface(fn=predict, | |
| # inputs=[gr.Image(type='pil'), | |
| # gr.components.Slider(minimum=512, maximum=1024, value=1024, step=64, label='input_size'), | |
| # gr.components.Checkbox(value=True, label='high_visual_quality')], | |
| # # outputs=['plot'], | |
| # outputs=gr.Image(type='pil'), | |
| # # examples=[["assets/sa_8776.jpg"]], | |
| # # # ["assets/sa_1309.jpg", 1024]], | |
| # examples=[["assets/sa_192.jpg"], ["assets/sa_414.jpg"], | |
| # ["assets/sa_561.jpg"], ["assets/sa_862.jpg"], | |
| # ["assets/sa_1309.jpg"], ["assets/sa_8776.jpg"], | |
| # ["assets/sa_10039.jpg"], ["assets/sa_11025.jpg"],], | |
| # cache_examples=True, | |
| # title="Fast Segment Anything (Everything mode)" | |
| # ) | |
| # app_interface.queue(concurrency_count=1, max_size=20) | |
| # app_interface.launch() |