221 lines
9.0 KiB
Python
221 lines
9.0 KiB
Python
from ultralytics import YOLO
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import numpy as np
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import matplotlib.pyplot as plt
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import gradio as gr
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import cv2
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import torch
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from PIL import Image
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# Load the pre-trained model
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model = YOLO('checkpoints/FastSAM.pt')
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# Description
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title = "<center><strong><font size='8'>快速分割一切</font></strong></center>"
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examples = [["assets/sa_8776.jpg"], ["assets/sa_414.jpg"],
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["assets/sa_1309.jpg"], ["assets/sa_11025.jpg"],
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["assets/sa_561.jpg"], ["assets/sa_192.jpg"],
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["assets/sa_10039.jpg"], ["assets/sa_862.jpg"]]
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default_example = examples[0]
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css = "h1 { text-align: center } .about { text-align: justify; padding-left: 10%; padding-right: 10%; } footer {visibility: hidden}"
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def fast_process(annotations, image, high_quality, device, scale):
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if isinstance(annotations[0],dict):
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annotations = [annotation['segmentation'] for annotation in annotations]
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original_h = image.height
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original_w = image.width
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if high_quality == True:
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if isinstance(annotations[0],torch.Tensor):
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annotations = np.array(annotations.cpu())
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for i, mask in enumerate(annotations):
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mask = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8))
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annotations[i] = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_OPEN, np.ones((8, 8), np.uint8))
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if device == 'cpu':
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annotations = np.array(annotations)
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inner_mask = fast_show_mask(annotations,
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plt.gca(),
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bbox=None,
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points=None,
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pointlabel=None,
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retinamask=True,
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target_height=original_h,
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target_width=original_w)
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else:
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if isinstance(annotations[0],np.ndarray):
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annotations = torch.from_numpy(annotations)
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inner_mask = fast_show_mask_gpu(annotations,
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plt.gca(),
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bbox=None,
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points=None,
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pointlabel=None)
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if isinstance(annotations, torch.Tensor):
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annotations = annotations.cpu().numpy()
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if high_quality:
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contour_all = []
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temp = np.zeros((original_h, original_w,1))
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for i, mask in enumerate(annotations):
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if type(mask) == dict:
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mask = mask['segmentation']
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annotation = mask.astype(np.uint8)
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contours, _ = cv2.findContours(annotation, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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for contour in contours:
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contour_all.append(contour)
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cv2.drawContours(temp, contour_all, -1, (255, 255, 255), 2 // scale)
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color = np.array([0 / 255, 0 / 255, 255 / 255, 0.9])
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contour_mask = temp / 255 * color.reshape(1, 1, -1)
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image = image.convert('RGBA')
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overlay_inner = Image.fromarray((inner_mask * 255).astype(np.uint8), 'RGBA')
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image.paste(overlay_inner, (0, 0), overlay_inner)
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if high_quality:
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overlay_contour = Image.fromarray((contour_mask * 255).astype(np.uint8), 'RGBA')
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image.paste(overlay_contour, (0, 0), overlay_contour)
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return image
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# CPU post process
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def fast_show_mask(annotation, ax, bbox=None,
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points=None, pointlabel=None,
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retinamask=True, target_height=960,
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target_width=960):
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msak_sum = annotation.shape[0]
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height = annotation.shape[1]
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weight = annotation.shape[2]
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# 将annotation 按照面积 排序
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areas = np.sum(annotation, axis=(1, 2))
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sorted_indices = np.argsort(areas)[::1]
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annotation = annotation[sorted_indices]
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index = (annotation != 0).argmax(axis=0)
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color = np.random.random((msak_sum,1,1,3))
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transparency = np.ones((msak_sum,1,1,1)) * 0.6
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visual = np.concatenate([color,transparency],axis=-1)
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mask_image = np.expand_dims(annotation,-1) * visual
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mask = np.zeros((height,weight,4))
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h_indices, w_indices = np.meshgrid(np.arange(height), np.arange(weight), indexing='ij')
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indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None))
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# 使用向量化索引更新show的值
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mask[h_indices, w_indices, :] = mask_image[indices]
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if bbox is not None:
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x1, y1, x2, y2 = bbox
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ax.add_patch(plt.Rectangle((x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor='b', linewidth=1))
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# draw point
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if points is not None:
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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')
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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')
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if retinamask==False:
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mask = cv2.resize(mask, (target_width, target_height), interpolation=cv2.INTER_NEAREST)
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return mask
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def fast_show_mask_gpu(annotation, ax,
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bbox=None, points=None,
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pointlabel=None):
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msak_sum = annotation.shape[0]
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height = annotation.shape[1]
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weight = annotation.shape[2]
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areas = torch.sum(annotation, dim=(1, 2))
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sorted_indices = torch.argsort(areas, descending=False)
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annotation = annotation[sorted_indices]
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# 找每个位置第一个非零值下标
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index = (annotation != 0).to(torch.long).argmax(dim=0)
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color = torch.rand((msak_sum,1,1,3)).to(annotation.device)
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transparency = torch.ones((msak_sum,1,1,1)).to(annotation.device) * 0.6
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visual = torch.cat([color,transparency],dim=-1)
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mask_image = torch.unsqueeze(annotation,-1) * visual
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# 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
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mask = torch.zeros((height,weight,4)).to(annotation.device)
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h_indices, w_indices = torch.meshgrid(torch.arange(height), torch.arange(weight))
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indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None))
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# 使用向量化索引更新show的值
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mask[h_indices, w_indices, :] = mask_image[indices]
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mask_cpu = mask.cpu().numpy()
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if bbox is not None:
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x1, y1, x2, y2 = bbox
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ax.add_patch(plt.Rectangle((x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor='b', linewidth=1))
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# draw point
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if points is not None:
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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')
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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')
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return mask_cpu
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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def segment_image(input, input_size=1024, high_visual_quality=True, iou_threshold=0.7, conf_threshold=0.25):
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input_size = int(input_size) # 确保 imgsz 是整数
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# Thanks for the suggestion by hysts in HuggingFace.
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w, h = input.size
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scale = input_size / max(w, h)
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new_w = int(w * scale)
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new_h = int(h * scale)
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input = input.resize((new_w, new_h))
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results = model(input, device=device, retina_masks=True, iou=iou_threshold, conf=conf_threshold, imgsz=input_size)
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fig = fast_process(annotations=results[0].masks.data,
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image=input, high_quality=high_visual_quality,
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device=device, scale=(1024 // input_size))
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return fig
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cond_img = gr.Image(label="输入", value=default_example[0], type='pil')
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segm_img = gr.Image(label="分割后的图片", interactive=False, type='pil')
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input_size_slider = gr.components.Slider(minimum=512, maximum=1024, value=1024, step=64, label='输入尺寸')
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with gr.Blocks(css=css, title='快速分割一切') as demo:
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with gr.Row():
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gr.Markdown(title)
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# Images
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with gr.Row(variant="panel"):
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with gr.Column(scale=1):
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cond_img.render()
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with gr.Column(scale=1):
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segm_img.render()
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# Submit & Clear
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with gr.Row():
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with gr.Column():
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input_size_slider.render()
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with gr.Row():
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vis_check = gr.Checkbox(value=True, label='高质量')
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with gr.Column():
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segment_btn = gr.Button("分割一切", variant='primary')
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gr.Examples(examples=examples,
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inputs=[cond_img],
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outputs=segm_img,
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fn=segment_image,
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cache_examples=True,
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examples_per_page=4, label="示例图片")
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with gr.Column():
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with gr.Accordion("高级选项", open=False):
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iou_threshold = gr.Slider(0.1, 0.9, 0.7, step=0.1, label='iou_threshold')
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conf_threshold = gr.Slider(0.1, 0.9, 0.25, step=0.05, label='conf_threshold')
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segment_btn.click(segment_image,
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inputs=[cond_img, input_size_slider, vis_check, iou_threshold, conf_threshold],
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outputs=segm_img)
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demo.queue().launch(server_name="0.0.0.0")
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