图像分类 Image Classification

import gradio as gr
import torch
import requests
from torchvision import transformsmodel = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()
response = requests.get("https://git.io/JJkYN")
labels = response.text.split("\n")def predict(inp):inp = transforms.ToTensor()(inp).unsqueeze(0)with torch.no_grad():prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)confidences = {labels[i]: float(prediction[i]) for i in range(1000)}    return confidencesdemo = gr.Interface(fn=predict, inputs=gr.inputs.Image(type="pil"),outputs=gr.outputs.Label(num_top_classes=3),examples=[["cheetah.jpg"]],)demo.launch()

图像分割 Image Segmentation

import gradio as gr
from transformers import pipelinegenerator = pipeline('text-generation', model='gpt2')def generate(text):result = generator(text, max_length=30, num_return_sequences=1)return result[0]["generated_text"]examples = [["The Moon's orbit around Earth has"],["The smooth Borealis basin in the Northern Hemisphere covers 40%"],
]demo = gr.Interface(fn=generate,inputs=gr.inputs.Textbox(lines=5, label="Input Text"),outputs=gr.outputs.Textbox(label="Generated Text"),examples=examples
)demo.launch()

图像风格变换 Image Transformation with AnimeGAN

import gradio as gr
import torchmodel2 = torch.hub.load("AK391/animegan2-pytorch:main","generator",pretrained=True,progress=False
)
model1 = torch.hub.load("AK391/animegan2-pytorch:main", "generator", pretrained="face_paint_512_v1")
face2paint = torch.hub.load('AK391/animegan2-pytorch:main', 'face2paint', size=512,side_by_side=False
)def inference(img, ver):if ver == 'version 2 (🔺 robustness,🔻 stylization)':out = face2paint(model2, img)else:out = face2paint(model1, img)return outtitle = "AnimeGANv2"
description = "Gradio Demo for AnimeGanv2 Face Portrait. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please use a cropped portrait picture for best results similar to the examples below."
article = "<p style='text-align: center'><a href='https://github.com/bryandlee/animegan2-pytorch' target='_blank'>Github Repo Pytorch</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_animegan' alt='visitor badge'></center></p>"
examples=[['groot.jpeg','version 2 (🔺 robustness,🔻 stylization)'],['gongyoo.jpeg','version 1 (🔺 stylization, 🔻 robustness)']]demo = gr.Interface(fn=inference, inputs=[gr.inputs.Image(type="pil"),gr.inputs.Radio(['version 1 (🔺 stylization, 🔻 robustness)','version 2 (🔺 robustness,🔻 stylization)'], type="value", default='version 2 (🔺 robustness,🔻 stylization)', label='version')], outputs=gr.outputs.Image(type="pil"),title=title,description=description,article=article,examples=examples)demo.launch()

3D模型 3D models

import gradio as gr
from transformers import DPTFeatureExtractor, DPTForDepthEstimation
import torch
import numpy as np
from PIL import Image
import open3d as o3d
from pathlib import Pathfeature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large")
model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large")def process_image(image_path):image_path = Path(image_path)image_raw = Image.open(image_path)image = image_raw.resize((800, int(800 * image_raw.size[1] / image_raw.size[0])),Image.Resampling.LANCZOS)# prepare image for the modelencoding = feature_extractor(image, return_tensors="pt")# forward passwith torch.no_grad():outputs = model(**encoding)predicted_depth = outputs.predicted_depth# interpolate to original sizeprediction = torch.nn.functional.interpolate(predicted_depth.unsqueeze(1),size=image.size[::-1],mode="bicubic",align_corners=False,).squeeze()output = prediction.cpu().numpy()depth_image = (output * 255 / np.max(output)).astype('uint8')try:gltf_path = create_3d_obj(np.array(image), depth_image, image_path)img = Image.fromarray(depth_image)return [img, gltf_path, gltf_path]except Exception:gltf_path = create_3d_obj(np.array(image), depth_image, image_path, depth=8)img = Image.fromarray(depth_image)return [img, gltf_path, gltf_path]except:print("Error reconstructing 3D model")raise Exception("Error reconstructing 3D model")def create_3d_obj(rgb_image, depth_image, image_path, depth=10):depth_o3d = o3d.geometry.Image(depth_image)image_o3d = o3d.geometry.Image(rgb_image)rgbd_image = o3d.geometry.RGBDImage.create_from_color_and_depth(image_o3d, depth_o3d, convert_rgb_to_intensity=False)w = int(depth_image.shape[1])h = int(depth_image.shape[0])camera_intrinsic = o3d.camera.PinholeCameraIntrinsic()camera_intrinsic.set_intrinsics(w, h, 500, 500, w/2, h/2)pcd = o3d.geometry.PointCloud.create_from_rgbd_image(rgbd_image, camera_intrinsic)print('normals')pcd.normals = o3d.utility.Vector3dVector(np.zeros((1, 3)))  # invalidate existing normalspcd.estimate_normals(search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.01, max_nn=30))pcd.orient_normals_towards_camera_location(camera_location=np.array([0., 0., 1000.]))pcd.transform([[1, 0, 0, 0],[0, -1, 0, 0],[0, 0, -1, 0],[0, 0, 0, 1]])pcd.transform([[-1, 0, 0, 0],[0, 1, 0, 0],[0, 0, 1, 0],[0, 0, 0, 1]])print('run Poisson surface reconstruction')with o3d.utility.VerbosityContextManager(o3d.utility.VerbosityLevel.Debug):mesh_raw, densities = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(pcd, depth=depth, width=0, scale=1.1, linear_fit=True)voxel_size = max(mesh_raw.get_max_bound() - mesh_raw.get_min_bound()) / 256print(f'voxel_size = {voxel_size:e}')mesh = mesh_raw.simplify_vertex_clustering(voxel_size=voxel_size,contraction=o3d.geometry.SimplificationContraction.Average)# vertices_to_remove = densities < np.quantile(densities, 0.001)# mesh.remove_vertices_by_mask(vertices_to_remove)bbox = pcd.get_axis_aligned_bounding_box()mesh_crop = mesh.crop(bbox)gltf_path = f'./{image_path.stem}.gltf'o3d.io.write_triangle_mesh(gltf_path, mesh_crop, write_triangle_uvs=True)return gltf_pathtitle = "Demo: zero-shot depth estimation with DPT + 3D Point Cloud"
description = "This demo is a variation from the original <a href='https://huggingface.co/spaces/nielsr/dpt-depth-estimation' target='_blank'>DPT Demo</a>. It uses the DPT model to predict the depth of an image and then uses 3D Point Cloud to create a 3D object."
examples = [["examples/1-jonathan-borba-CgWTqYxHEkg-unsplash.jpg"]]iface = gr.Interface(fn=process_image,inputs=[gr.Image(type="filepath", label="Input Image")],outputs=[gr.Image(label="predicted depth", type="pil"),gr.Model3D(label="3d mesh reconstruction", clear_color=[1.0, 1.0, 1.0, 1.0]),gr.File(label="3d gLTF")],title=title,description=description,examples=examples,allow_flagging="never",cache_examples=False)iface.launch(debug=True, enable_queue=False)