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@@ -44,3 +44,14 @@ poetry3d/obj/20240827_223159.glb filter=lfs diff=lfs merge=lfs -text
 poetry3d/obj/20240828_192708.glb filter=lfs diff=lfs merge=lfs -text
 poetry3d/obj/20240828_201959.glb filter=lfs diff=lfs merge=lfs -text
 poetry3d/obj/20240829_125414.glb filter=lfs diff=lfs merge=lfs -text
+image/20240828_162621.png filter=lfs diff=lfs merge=lfs -text
+long.png filter=lfs diff=lfs merge=lfs -text
+m1.glb filter=lfs diff=lfs merge=lfs -text
+obj/20240811_015338.glb filter=lfs diff=lfs merge=lfs -text
+obj/20240827_171959.glb filter=lfs diff=lfs merge=lfs -text
+obj/20240827_211152.glb filter=lfs diff=lfs merge=lfs -text
+obj/20240827_214214.glb filter=lfs diff=lfs merge=lfs -text
+obj/20240827_223159.glb filter=lfs diff=lfs merge=lfs -text
+obj/20240828_192708.glb filter=lfs diff=lfs merge=lfs -text
+obj/20240828_201959.glb filter=lfs diff=lfs merge=lfs -text
+obj/20240829_125414.glb filter=lfs diff=lfs merge=lfs -text

2img.py

@@ -0,0 +1,66 @@
+import gradio as gr
+import numpy as np
+import random
+import torch
+import time
+import cv2
+
+def fake_diffusion(prompt):
+    # image = np.ones((1000,1000,3), np.uint8)
+    # image[:] = [255, 124, 0]
+    image=cv2.imread('/home/kun/code/3d/poetry3d/mountain_1.png')
+    yield image
+
+examples = [
+    "千山鸟飞绝，万径人踪灭。孤舟蓑笠翁，独钓寒江雪。【柳宗元 | 江雪】",
+    "空山新雨后，天气晚来秋。明月松间照，清泉石上流。【王维 | 山居秋暝】",
+    "白日依山尽，黄河入海流。欲穷千里目，更上一层楼。【王之涣 | 登鹳雀楼】",
+    "春眠不觉晓，处处闻啼鸟。夜来风雨声，花落知多少。【孟浩然 | 春晓】"
+]
+
+css="""
+#col-container {
+    margin: 0 auto;
+    max-width: 520px;
+}
+"""
+
+if torch.cuda.is_available():
+    power_device = "GPU"
+else:
+    power_device = "CPU"
+
+with gr.Blocks(css=css) as demo:
+    
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(f"""
+        # Poetry3D
+        Currently running on {power_device}.
+        """)
+        
+        with gr.Row():
+            
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt",
+                container=False,
+            )
+            
+            run_button = gr.Button("Run", scale=0)
+        
+        result = gr.Image(label="Result", show_label=False)
+        
+        gr.Examples(
+            examples = examples,
+            inputs = [prompt]
+        )
+
+    run_button.click(
+        fn = fake_diffusion,
+        inputs = [prompt],
+        outputs = [result]
+    )
+
+demo.launch()

3d.py

@@ -0,0 +1,20 @@
+import gradio as gr
+import os
+
+
+def load_mesh(image):
+    return mesh_file_name
+
+
+demo = gr.Interface(
+    fn=load_mesh,
+    inputs=gr.Model3D(),
+    outputs=gr.Model3D(
+            clear_color=[0.0, 0.0, 0.0, 0.0],  label="3D Model"),
+    examples=[
+        ['/home/kun/code/3d/poetry3d/m1.glb'],
+    ],
+)
+
+if __name__ == "__main__":
+    demo.launch()

README.md

@@ -1,12 +1 @@
----
-title: PoeticLandscapes
-emoji: 👁
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 4.44.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# poetry3d

demo.py

@@ -0,0 +1,323 @@
+from PIL import Image
+import numpy as np
+from datetime import datetime
+import cv2
+import random
+import torch
+import time
+from requests.exceptions import ConnectionError, Timeout
+import requests
+import json
+import gradio as gr
+import os
+
+def flip_text(x):
+    return x[::-1]
+
+def flip_image(x):
+    return np.fliplr(x)
+
+def meshy(img_url):
+    #YOUR_API_KEY = 'msy_4aO4tF1js0Xsw0Vo3jdLNgh84NLvZrjo8XjQ'
+    YOUR_API_KEY = 'msy_4sAvBmtnJw3PSTWCKaLlFTdPCtcnU2heNZHH'
+
+    headers = {
+        "Authorization": f"Bearer {YOUR_API_KEY}",
+        "Content-Type": "application/json"
+    }
+
+    payload = {
+        "image_url": img_url,  # 替换为实际图片URL
+        "enable_pbr": False, # 可选，是否生成 PBR 贴图
+    }
+    task_id='019192dc-54a7-7621-afae-da16e77b7660'
+    '''ID已知'''
+    # response = requests.post(
+    #     "https://api.meshy.ai/v1/image-to-3d",
+    #     headers=headers,
+    #     json=payload
+    # )
+    max_retries = 5
+    retry_delay = 1  # 每次重试间隔的秒数
+    for attempt in range(max_retries):
+        try:
+            # 尝试发送请求
+            response = requests.post(
+                "https://api.meshy.ai/v1/image-to-3d",
+                headers=headers,
+                json=payload
+            )
+            print(f"请求状态: {response.status_code}")
+            #print(f"任务创建失败，错误信息: {response.text}")
+            data = response.json()
+            print(data)
+            task_id = data['result']
+            print(f"任务创建成功，任务 ID: {task_id}")
+            break
+        except (ConnectionError, Timeout) as e:
+                print(f"网络连接失败，正在重试 {attempt + 1}/{max_retries}...")
+                print(f"错误信息: {e}")
+                time.sleep(retry_delay)
+        except requests.exceptions.RequestException as e:
+                print(f"请求失败: {e}")
+                break  # 如果是其他类型的请求错误，不再重试，直接退出
+
+    # # 检查请求状态
+    # print(f"请求状态: {response.status_code}")
+    # #print(f"任务创建失败，错误信息: {response.text}")
+    # data = response.json()
+    # print(data)
+    # task_id = data['result']
+    # print(f"任务创建成功，任务 ID: {task_id}")
+    ''''''
+    #task_id='01913d5e-f092-7b44-89f2-922406cc3691'
+    # 检索任务状态和结果
+    while True:
+        try:
+            response = requests.get(
+                f"https://api.meshy.ai/v1/image-to-3d/{task_id}",
+                headers=headers,
+                verify=False
+            )
+            response.raise_for_status()  # 检查HTTP请求状态码
+            data = response.json()
+            status=data['status']
+            print(f"任务状态: {status}")
+            if status == 'SUCCEEDED':
+                download_url = data['model_url']    
+                print(f"任务完成，3D 模型下载链接: {download_url}")
+                response = requests.get(download_url,verify=False)
+                return response
+            elif status == 'FAILED':
+                print("任务失败!")
+                return None
+            elif status == 'IN_PROGRESS':
+                print(f"任务正在处理中，进度:{data['progress']}/100")
+                time.sleep(2)  # 等待5秒钟后再次检查状态
+            # else:
+            #     print(f"未知状态: {status}")
+            #     return None
+            # download_url = data['model_url']    
+            # print(f"任务完成，3D 模型下载链接: {download_url}")
+            # response = requests.get(download_url,verify=False)
+            # return response
+        except (ConnectionError, Timeout) as e:
+                print("网络连接失败，正在重试")
+                print(f"错误信息: {e}")
+                time.sleep(1)
+        except requests.exceptions.RequestException as e:
+            print(f"请求失败: {e}")
+            return None
+
+    # # 检查请求状态
+    # print(f"请求状态: {response.status_code}")
+    # #print(f"任务查询失败，错误信息: {response.text}")
+    # data = response.json()
+    # while(data['status'] != 'SUCCEEDED'):
+    #     time.sleep(2)
+    #     print(f"任务状态: {data['status']}")
+    #     data = response.json()
+    # download_url = data['model_url']
+    # print(f"任务完成，3D 模型下载链接: {download_url}")
+    # response = requests.get(download_url,verify=False)
+    # return response
+
+def upload(path):
+    #headers = {'Authorization': 'YxKXccVDyLPhodqaDg4BIYNeJXSuCbkX'}
+    headers = {'Authorization': 'XCcVgVPNgTU4PdDOnRKTHraGTze4LHUB'}
+    files = {'smfile': open(path, 'rb')}
+    url = 'https://sm.ms/api/v2/upload'
+    max_retries = 20
+    retry_delay = 1  # 每次重试间隔的秒数
+    for attempt in range(max_retries):
+        try:
+            # 尝试发送请求
+            res = requests.post(url, files=files, headers=headers,timeout=30)
+            res.raise_for_status() 
+            response_data = res.json()
+            print(response_data)
+            if response_data.get('success'):
+                img_url=res.json()['data']['url']
+                print("请求成功，图片URL为:",img_url)
+                return img_url
+            elif response_data.get('code') == 'image_repeated':
+                print("图片重复上传，已存在的图片URL为:", response_data['images'])
+                return response_data['images']
+        except (ConnectionError, Timeout) as e:
+                print(f"网络连接失败，正在重试 {attempt + 1}/{max_retries}...")
+                print(f"错误信息: {e}")
+                time.sleep(retry_delay)
+        except requests.exceptions.RequestException as e:
+                print(f"请求失败: {e},code:{response_data.get('code')},message:{response_data.get('message')}")
+                time.sleep(retry_delay)
+    '''ID已知'''
+    # res = requests.post(url, files=files, headers=headers,timeout=30).json()
+    # img_url=res['data']['url']
+    ''''''
+    #img_url=5
+    print("未得到url")
+    return 'https://s2.loli.net/2024/08/27/HAmJRGznS3ldvrI.png'                                                                                       
+
+def gen3d(image):
+    #print(image.shape)
+    #print(image.type)
+    #image = Image.fromarray(np.uint8(image))
+    current_time = datetime.now()
+    file_name = current_time.strftime("%Y%m%d_%H%M%S") + ".png"
+    current_directory = os.getcwd()
+    file_path = os.path.join(current_directory, 'image',file_name)
+    #print(file_path)
+    image = image[:, :, [2,1,0]]
+    cv2.imwrite(file_path, image)
+    #image.save(file_path)
+    #print(success)u
+    img_url=upload(file_path)
+    response=meshy(img_url)
+    file_name = file_name.replace(".png", ".glb")
+    file_path = os.path.join(current_directory, 'obj',file_name)
+    with open(file_path, "wb") as file:
+        file.write(response.content)
+    return file_path
+
+def predict(im):
+    return im["composite"]
+
+def display_images():
+    img_dir='/home/kun/code/3d/poetry3d/image'
+    imgs_List=[ os.path.join(img_dir,name) 
+               for name in sorted(os.listdir(img_dir)) 
+               if  name.endswith(('.png','.jpg','.webp','.tif','.jpeg'))]
+    return imgs_List
+
+def display_models():
+    model_dir='/home/kun/code/3d/poetry3d/obj'
+    models_List=[ os.path.join(model_dir,name) 
+               for name in sorted(os.listdir(model_dir))
+               if  name.endswith(('.glb'))]
+    return models_List[0]
+
+def fake_diffusion(prompt):
+    # image = np.ones((1000,1000,3), np.uint8)
+    # image[:] = [255, 124, 0]
+    time.sleep(1)
+    image=cv2.imread('/home/kun/code/3d/poetry3d/long.png')
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    yield image
+
+poetry_examples = [
+    "千山鸟飞绝，万径人踪灭。孤舟蓑笠翁，独钓寒江雪。【柳宗元 | 江雪】",
+    "空山新雨后，天气晚来秋。明月松间照，清泉石上流。【王维 | 山居秋暝】",
+    "白日依山尽，黄河入海流。欲穷千里目，更上一层楼。【王之涣 | 登鹳雀楼】",
+    "春眠不觉晓，处处闻啼鸟。夜来风雨声，花落知多少。【孟浩然 | 春晓】"
+]
+
+css="""
+#col-container {
+    margin: 0 auto;
+    max-width: 520px;
+}
+"""
+js_func = """
+function refresh() {
+    const url = new URL(window.location);
+
+    if (url.searchParams.get('__theme') !== 'dark') {
+        url.searchParams.set('__theme', 'dark');
+        window.location.href = url.href;
+    }
+}
+"""
+
+if torch.cuda.is_available():
+    power_device = "GPU"
+else:
+    power_device = "CPU"
+
+with gr.Blocks(css=css,js=js_func,fill_width=True) as demo:
+    with gr.Row():
+        with gr.Column(elem_id="col-container",scale=20):
+            gr.Markdown(f"""
+            # Poetic Landscapes
+            Currently running on {power_device}.
+            """)
+            
+            with gr.Row():
+                
+                prompt = gr.Text(
+                    label="Prompt",
+                    show_label=False,
+                    max_lines=1,
+                    #placeholder="Enter your prompt",
+                    placeholder="输入诗句",
+                    container=False,
+                )
+                
+                run_button = gr.Button("生成图像", scale=1)
+            
+            result = gr.Image(label="Result", show_label=False)
+            
+            gr.Examples(
+                label="示例诗句",
+                examples = poetry_examples,
+                inputs = [prompt]
+            )
+            
+            # with gr.Row():
+            #     im = gr.ImageEditor()
+            #     im_preview = gr.Image()
+            im = gr.ImageEditor(label="编辑图像",
+                                #placeholder="将上一步生成图像拖到此处",
+                                )
+
+
+            
+            im_result=gr.Image(label="图像预览",scale=10)
+            run_button_2 = gr.Button("生���模型")
+            
+        with gr.Column(scale=10):
+            model=gr.Model3D(label="模型",scale=10)
+        
+        with gr.Column(scale=2):
+            gallery=gr.Gallery(label="历史图像")
+            btn = gr.Button("刷新")
+            btn.click(display_images, None, gallery)
+            model2=gr.Model3D(label="历史模型",scale=10)
+            btn2 = gr.Button("刷新")
+            btn2.click(display_models, None, model2)
+
+            # with gr.Tab("历史图像"):
+            #     gallery=gr.Gallery(show_label=False)
+            #     btn = gr.Button("刷新")
+            #     btn.click(display_images, None, gallery)
+            # with gr.Tab("历史模型"):
+            #     with gr.Row():
+            #         # model1=gr.Model3D(label="模型")
+            #         model2=gr.Model3D(label="模型")
+            #         # model3=gr.Model3D(label="模型")
+            #         # model4=gr.Model3D(label="模型")
+            #         btn = gr.Button("刷新")
+            #         btn.click(display_models, None, model2)
+
+    run_button.click(
+        fn = fake_diffusion,
+        inputs = [prompt],
+        outputs = [result]
+    )
+    run_button_2.click(
+        fn = gen3d,
+        inputs = im_result,
+        outputs = model
+    )
+    
+
+    
+    # with gr.Column():
+    #     gr.Interface(
+    #         fn=gen3d,
+    #         inputs=gr.Image(),
+    #         outputs=gr.Model3D(label="3D Model")
+    #         )
+    im.change(predict, outputs=im_result, inputs=im, show_progress="hidden")
+
+if __name__ == "__main__":
+    demo.launch(share=True)

diffusion.py

@@ -0,0 +1,153 @@
+import gradio as gr
+import numpy as np
+import random
+from diffusers import DiffusionPipeline
+import torch
+import time
+
+# device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# if torch.cuda.is_available():
+#     torch.cuda.max_memory_allocated(device=device)
+#     pipe = DiffusionPipeline.from_pretrained("/home/kun/code/3d", torch_dtype=torch.float16) 
+#     #pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
+#     pipe.enable_xformers_memory_efficient_attention()
+#     pipe = pipe.to(device)
+# else: 
+#     pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
+#     pipe = pipe.to(device)
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1024
+
+def fake_diffusion(steps):
+    image = np.ones((1000,1000,3), np.uint8)
+    image[:] = [255, 124, 0]
+    yield image
+
+# def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
+
+#     if randomize_seed:
+#         seed = random.randint(0, MAX_SEED)
+        
+#     generator = torch.Generator().manual_seed(seed)
+    
+#     image = pipe(
+#         prompt = prompt, 
+#         negative_prompt = negative_prompt,
+#         guidance_scale = guidance_scale, 
+#         num_inference_steps = num_inference_steps, 
+#         width = width, 
+#         height = height,
+#         generator = generator
+#     ).images[0] 
+    
+#     return image
+
+examples = [
+    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
+    "An astronaut riding a green horse",
+    "A delicious ceviche cheesecake slice",
+]
+
+css="""
+#col-container {
+    margin: 0 auto;
+    max-width: 520px;
+}
+"""
+
+if torch.cuda.is_available():
+    power_device = "GPU"
+else:
+    power_device = "CPU"
+
+with gr.Blocks(css=css) as demo:
+    
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(f"""
+        # Text-to-Image Gradio Template
+        Currently running on {power_device}.
+        """)
+        
+        with gr.Row():
+            
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt",
+                container=False,
+            )
+            
+            run_button = gr.Button("Run", scale=0)
+        
+        result = gr.Image(label="Result", show_label=False)
+
+        # with gr.Accordion("Advanced Settings", open=False):
+            
+        #     negative_prompt = gr.Text(
+        #         label="Negative prompt",
+        #         max_lines=1,
+        #         placeholder="Enter a negative prompt",
+        #         visible=False,
+        #     )
+            
+        #     seed = gr.Slider(
+        #         label="Seed",
+        #         minimum=0,
+        #         maximum=MAX_SEED,
+        #         step=1,
+        #         value=0,
+        #     )
+            
+        #     randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+            
+        #     with gr.Row():
+                
+        #         width = gr.Slider(
+        #             label="Width",
+        #             minimum=256,
+        #             maximum=MAX_IMAGE_SIZE,
+        #             step=32,
+        #             value=512,
+        #         )
+                
+        #         height = gr.Slider(
+        #             label="Height",
+        #             minimum=256,
+        #             maximum=MAX_IMAGE_SIZE,
+        #             step=32,
+        #             value=512,
+        #         )
+            
+        #     with gr.Row():
+                
+        #         guidance_scale = gr.Slider(
+        #             label="Guidance scale",
+        #             minimum=0.0,
+        #             maximum=10.0,
+        #             step=0.1,
+        #             value=0.0,
+        #         )
+                
+        #         num_inference_steps = gr.Slider(
+        #             label="Number of inference steps",
+        #             minimum=1,
+        #             maximum=12,
+        #             step=1,
+        #             value=2,
+        #         )
+        
+        gr.Examples(
+            examples = examples,
+            inputs = [prompt]
+        )
+
+    run_button.click(
+        fn = fake_diffusion,
+        inputs = [prompt],#, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        outputs = [result]
+    )
+
+demo.queue().launch()

download.py

@@ -0,0 +1,21 @@
+from pathlib import Path
+import gradio as gr
+
+def upload_file(filepath):
+    name = Path(filepath).name
+    return [gr.UploadButton(visible=False), gr.DownloadButton(label=f"Download {name}", value=filepath, visible=True)]
+
+def download_file():
+    return [gr.UploadButton(visible=True), gr.DownloadButton(visible=False)]
+
+with gr.Blocks() as demo:
+    gr.Markdown("First upload a file and and then you'll be able download it (but only once!)")
+    with gr.Row():
+        u = gr.UploadButton("Upload a file", file_count="single")
+        d = gr.DownloadButton("Download the file", visible=False)
+
+    u.upload(upload_file, u, [u, d])
+    d.click(download_file, None, [u, d])
+
+if __name__ == "__main__":
+    demo.launch()

edit.py

@@ -0,0 +1,17 @@
+import gradio as gr
+
+def predict(im):
+    return im["composite"]
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        im = gr.ImageEditor(
+            type="numpy",
+            #crop_size="1:1",
+        )
+        im_preview = gr.Image()
+
+    im.change(predict, outputs=im_preview, inputs=im, show_progress="hidden")
+
+if __name__ == "__main__":
+    demo.launch()

gallery.py

@@ -0,0 +1,24 @@
+# -*- coding: utf-8 -*-
+# @Time : 2023/10/12 13:48
+# @Author : XyZeng
+
+import os
+import gradio as gr
+
+def display_images():
+    img_dir='/home/kun/code/3d/poetry3d/image'
+    imgs_List=[ os.path.join(img_dir,name) 
+               for name in sorted(os.listdir(img_dir)) 
+               if  name.endswith(('.png','.jpg','.webp','.tif','.jpeg'))]
+    return imgs_List
+
+with gr.Blocks() as demo:
+    
+    gallery=gr.Gallery(show_label=False)
+    btn = gr.Button("刷新画廊", scale=0)
+    btn.click(display_images, None, gallery)
+
+if __name__ == "__main__":
+
+    demo.launch()
+

image.py

@@ -0,0 +1,14 @@
+import requests
+import json
+
+
+def upload(path):
+    headers = {'Authorization': 'YxKXccVDyLPhodqaDg4BIYNeJXSuCbkX'}
+    files = {'smfile': open(path, 'rb')}
+    url = 'https://sm.ms/api/v2/upload'
+    res = requests.post(url, files=files, headers=headers).json()
+    print(res['data']['url'])
+
+
+if __name__ == "__main__":
+    upload('/home/kun/code/3d/poetry3d/mountain_1.png')