import osimport torchimport os.path as ospimport moxing as moxpath = osp.join(os.getcwd(),'segment-anything')if not os.path.exists(path):    mox.file.copy_parallel('obs://modelarts-labs-bj4-v2/case_zoo/segment-anything', path)    if os.path.exists(path):        print('Download success')    else:        raise Exception('Download Failed')else:    print("Model Package already exists!") 

%cd segment-anything

!pip install --upgrade pip!pip install torch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1!pip install opencv-python matplotlib!python setup.py install

import numpy as npimport matplotlib.pyplot as pltimport cv2import copyimport torchimport torchvisionprint("PyTorch version:", torch.__version__)print("Torchvision version:", torchvision.__version__)print("CUDA is available:", torch.cuda.is_available())

from segment_anything import sam_model_registry, SamAutomaticMaskGenerator, SamPredictor
sam_checkpoint = "sam_vit_h_4b8939.pth"model_type = "vit_h"
device = "cuda"
sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)sam.to(device=device)
mask_generator = SamAutomaticMaskGenerator(    model=sam,    #points_per_side=32,    #pred_iou_thresh=0.86,    #stability_score_thresh=0.92,    #crop_n_layers=1,    #crop_n_points_downscale_factor=2,    #min_mask_region_area=100,  # Requires open-cv to run post-processing)

def show_anns(anns,image):    segment_image = copy.copy(image)    segment_image.astype("uint8")    if len(anns) == 0:        return    sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)        for ann in sorted_anns:        mask_2d = ann['segmentation']        h,w = mask_2d.shape        mask_3d_color = np.zeros((h,w,3), dtype=np.uint8)        mask = (mask_2d!=0).astype(bool)        rgb = np.random.randint(0, 255, (1, 3), dtype=np.uint8)        mask_3d_color[mask_2d[:, :] == 1] = rgb        segment_image[mask] = segment_image[mask] * 0.5 + mask_3d_color[mask] * 0.5            return segment_imageimage = cv2.imread('images/dog.jpg')image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)masks = mask_generator.generate(image)segment_image = show_anns(masks,image)
fig = plt.figure(figsize=(25, 10))
ax1 = fig.add_subplot(1, 2, 1)plt.title('Original image', fontsize=16)ax1.axis('off')ax1.imshow(image)
ax2 = fig.add_subplot(1, 2, 2)plt.title('Segment image', fontsize=16)ax2.axis('off')ax2.imshow(segment_image)
plt.show()

def apply_mask(image, mask, alpha_channel=True):#应用并且响应mask    if alpha_channel:        alpha = np.zeros_like(image[..., 0])#制作掩体        alpha[mask == 1] = 255#兴趣地方标记为1，且为白色        image = cv2.merge((image[..., 0], image[..., 1], image[..., 2], alpha))#融合图像    else:        image = np.where(mask[..., None] == 1, image, 0)    return image
def mask_image(image, mask, crop_mode_=True):#保存掩盖部分的图像（感兴趣的图像）    if crop_mode_:        y, x = np.where(mask)        y_min, y_max, x_min, x_max = y.min(), y.max(), x.min(), x.max()        cropped_mask = mask[y_min:y_max+1, x_min:x_max+1]        cropped_image = image[y_min:y_max+1, x_min:x_max+1]        masked_image = apply_mask(cropped_image, cropped_mask)    else:        masked_image = apply_mask(image, mask)
    return masked_image
def save_masked_image(image, filepath):    if image.shape[-1] == 4:        cv2.imwrite(filepath, image, [cv2.IMWRITE_PNG_COMPRESSION, 9])    else:        cv2.imwrite(filepath, image)    print(f"Saved as {filepath}")
def save_anns(anns,image,path):    if len(anns) == 0:        return    sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)    index = 1        for ann in sorted_anns:        mask_2d = ann['segmentation']        segment_image = copy.copy(image)        masked_image = mask_image(segment_image, mask_2d)        filename = str(index) + '.png'        filepath = os.path.join(path, filename)        save_masked_image(masked_image, filepath)        index = index + 1

save_path = 'result/'if not os.path.exists(save_path):    os.mkdir(save_path)image = cv2.imread('images/dog.jpg')masks = mask_generator.generate(image)save_anns(masks,image,save_path)

!pip install gradio==3.24.1

def segment_image(image):    masks = mask_generator.generate(image)    return show_anns(masks,image)

def show_image(image):    masks = mask_generator.generate(image)    if len(masks) == 0:        return    sorted_anns = sorted(masks, key=(lambda x: x['area']), reverse=True)    index = 1    image_list = []    for ann in sorted_anns:        mask_2d = ann['segmentation']        segment_image = copy.copy(image)        masked_image = mask_image(segment_image, mask_2d)        image_list.append(masked_image)    return image_list

import gradio as gr
with gr.Blocks() as demo:    with gr.Row():        with gr.Column():            img_in = gr.Image(source='upload')            with gr.Row():                segment_button = gr.Button("segment",variant="primary")                save_button = gr.Button("segment_images",variant="primary")        with gr.Row():            with gr.Column():                img_out = gr.Image()    with gr.Row():        result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=6, height='auto')
    segment_button.click(segment_image,                 inputs= [img_in],                  outputs=[img_out])    save_button.click(show_image,                 inputs= [img_in],                  outputs=[result_gallery])
demo.launch(share=True)