import gradio as gr from paths import * import numpy as np from vision_tower import DINOv2_MLP from transformers import AutoImageProcessor import torch import os import matplotlib.pyplot as plt import io from PIL import Image from huggingface_hub import hf_hub_download ckpt_path = hf_hub_download(repo_id="Viglong/OriNet", filename="celarge/dino_weight.pt", repo_type="model", cache_dir='./') print(ckpt_path) save_path = './' device = 'cpu' dino = DINOv2_MLP( dino_mode = 'large', in_dim = 1024, out_dim = 360+180+60+2, evaluate = True, mask_dino = False, frozen_back = False ).to(device) dino.eval() print('model create') dino.load_state_dict(torch.load(ckpt_path, map_location='cpu')) print('weight loaded') val_preprocess = AutoImageProcessor.from_pretrained(DINO_LARGE, cache_dir='./') def get_3angle(image): # image = Image.open(image_path).convert('RGB') image_inputs = val_preprocess(images = image) image_inputs['pixel_values'] = torch.from_numpy(np.array(image_inputs['pixel_values'])).to(device) with torch.no_grad(): dino_pred = dino(image_inputs) gaus_ax_pred = torch.argmax(dino_pred[:, 0:360], dim=-1) gaus_pl_pred = torch.argmax(dino_pred[:, 360:360+180], dim=-1) gaus_ro_pred = torch.argmax(dino_pred[:, 360+180:360+180+60], dim=-1) angles = torch.zeros(3) angles[0] = gaus_ax_pred angles[1] = gaus_pl_pred - 90 angles[2] = gaus_ro_pred - 30 return angles def scale(x): # print(x) # if abs(x[0])<0.1 and abs(x[1])<0.1: # return x*5 # else: # return x return x*3 def get_proj2D_XYZ(phi, theta, gamma): x = np.array([-1*np.sin(phi)*np.cos(gamma) - np.cos(phi)*np.sin(theta)*np.sin(gamma), np.sin(phi)*np.sin(gamma) - np.cos(phi)*np.sin(theta)*np.cos(gamma)]) y = np.array([-1*np.cos(phi)*np.cos(gamma) + np.sin(phi)*np.sin(theta)*np.sin(gamma), np.cos(phi)*np.sin(gamma) + np.sin(phi)*np.sin(theta)*np.cos(gamma)]) z = np.array([np.cos(theta)*np.sin(gamma), np.cos(theta)*np.cos(gamma)]) x = scale(x) y = scale(y) z = scale(z) return x, y, z # 绘制3D坐标轴 def draw_axis(ax, origin, vector, color, label=None): ax.quiver(origin[0], origin[1], vector[0], vector[1], angles='xy', scale_units='xy', scale=1, color=color) if label!=None: ax.text(origin[0] + vector[0] * 1.1, origin[1] + vector[1] * 1.1, label, color=color, fontsize=12) def figure_to_img(fig): with io.BytesIO() as buf: fig.savefig(buf, format='JPG', bbox_inches='tight') buf.seek(0) image = Image.open(buf).copy() return image # def generate_mutimodal(title, context, img): # return f"Title:{title}\nContext:{context}\n...{img}" def generate_mutimodal(img): angles = get_3angle(img) fig, ax = plt.subplots(figsize=(8, 8)) h, w, c = img.shape if h>w: extent = [-5*w/h, 5*w/h, -5, 5] else: extent = [-5, 5, -5*h/w, 5*h/w] ax.imshow(img, extent=extent, zorder=0, aspect ='auto') # extent 设置图片的显示范围 origin = np.array([0, 0]) # # 设置旋转角度 phi = np.radians(angles[0]) theta = np.radians(angles[1]) gamma = np.radians(-1*angles[2]) # 旋转后的向量 rot_x, rot_y, rot_z = get_proj2D_XYZ(phi, theta, gamma) draw_axis(ax, origin, rot_y, 'g') draw_axis(ax, origin, rot_z, 'b') draw_axis(ax, origin, rot_x, 'r') # 关闭坐标轴和网格 ax.set_axis_off() ax.grid(False) # 设置坐标范围 ax.set_xlim(-5, 5) ax.set_ylim(-5, 5) res_img = figure_to_img(fig) # axis_model = "axis.obj" return [res_img, float(angles[0]), float(angles[1]), float(angles[2])] server = gr.Interface( flagging_mode='never', fn=generate_mutimodal, inputs=[ gr.Image(height=512, width=512, label="upload your image") ], outputs=[ gr.Image(height=512, width=512, label="result image"), # gr.Model3D(clear_color=[0.0, 0.0, 0.0, 0.0], label="3D Model"), gr.Textbox(lines=1, label='Azimuth(0~360°)'), gr.Textbox(lines=1, label='Polar(-90~90°)'), gr.Textbox(lines=1, label='Rotation(-90~90°)') ] ) server.launch()