app.py

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
import random
import rembg
from typing import Any
import torch.nn.functional as F


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='./', resume_download=True)
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 background_preprocess(input_image, do_remove_background):

    rembg_session = rembg.new_session() if do_remove_background else None

    if do_remove_background:
        input_image = remove_background(input_image, rembg_session)
        input_image = resize_foreground(input_image, 0.85)

    return input_image

def resize_foreground(
    image: Image,
    ratio: float,
) -> Image:
    image = np.array(image)
    assert image.shape[-1] == 4
    alpha = np.where(image[..., 3] > 0)
    y1, y2, x1, x2 = (
        alpha[0].min(),
        alpha[0].max(),
        alpha[1].min(),
        alpha[1].max(),
    )
    # crop the foreground
    fg = image[y1:y2, x1:x2]
    # pad to square
    size = max(fg.shape[0], fg.shape[1])
    ph0, pw0 = (size - fg.shape[0]) // 2, (size - fg.shape[1]) // 2
    ph1, pw1 = size - fg.shape[0] - ph0, size - fg.shape[1] - pw0
    new_image = np.pad(
        fg,
        ((ph0, ph1), (pw0, pw1), (0, 0)),
        mode="constant",
        constant_values=((0, 0), (0, 0), (0, 0)),
    )

    # compute padding according to the ratio
    new_size = int(new_image.shape[0] / ratio)
    # pad to size, double side
    ph0, pw0 = (new_size - size) // 2, (new_size - size) // 2
    ph1, pw1 = new_size - size - ph0, new_size - size - pw0
    new_image = np.pad(
        new_image,
        ((ph0, ph1), (pw0, pw1), (0, 0)),
        mode="constant",
        constant_values=((0, 0), (0, 0), (0, 0)),
    )
    new_image = Image.fromarray(new_image)
    return new_image

def remove_background(image: Image,
    rembg_session: Any = None,
    force: bool = False,
    **rembg_kwargs,
) -> Image:
    do_remove = True
    if image.mode == "RGBA" and image.getextrema()[3][0] < 255:
        do_remove = False
    do_remove = do_remove or force
    if do_remove:
        image = rembg.remove(image, session=rembg_session, **rembg_kwargs)
    return image

def random_crop(image, crop_scale=(0.8, 0.95)):
    """
    随机裁切图片
        image (numpy.ndarray):  (H, W, C)。
        crop_scale (tuple): (min_scale, max_scale)。
    """
    assert isinstance(image, Image.Image), "iput must be PIL.Image.Image"
    assert len(crop_scale) == 2 and 0 < crop_scale[0] <= crop_scale[1] <= 1

    width, height = image.size

    # 计算裁切的高度和宽度
    crop_width = random.randint(int(width * crop_scale[0]), int(width * crop_scale[1]))
    crop_height = random.randint(int(height * crop_scale[0]), int(height * crop_scale[1]))

    # 随机选择裁切的起始点
    left = random.randint(0, width - crop_width)
    top = random.randint(0, height - crop_height)

    # 裁切图片
    cropped_image = image.crop((left, top, left + crop_width, top + crop_height))

    return cropped_image

def get_crop_images(img, num=3):
    cropped_images = []
    for i in range(num):
        cropped_images.append(random_crop(img))
    return cropped_images


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)
    confidence     = F.softmax(dino_pred[:, -2:], dim=-1)[0][0]
    angles = torch.zeros(4)
    angles[0]  = gaus_ax_pred
    angles[1]  = gaus_pl_pred - 90
    angles[2]  = gaus_ro_pred - 30
    angles[3]  = confidence
    return angles

def remove_outliers_and_average(tensor, threshold=1.5):
    assert tensor.dim() == 1, "dimension of input Tensor must equal to 1"

    q1 = torch.quantile(tensor, 0.25)
    q3 = torch.quantile(tensor, 0.75)
    iqr = q3 - q1

    lower_bound = q1 - threshold * iqr
    upper_bound = q3 + threshold * iqr

    non_outliers = tensor[(tensor >= lower_bound) & (tensor <= upper_bound)]

    if len(non_outliers) == 0:
        return tensor.mean().item()

    return non_outliers.mean().item()


def remove_outliers_and_average_circular(tensor, threshold=1.5):
    assert tensor.dim() == 1, "dimension of input Tensor must equal to 1"

    # 将角度转换为二维平面上的点
    radians = tensor * torch.pi / 180.0
    x_coords = torch.cos(radians)
    y_coords = torch.sin(radians)

    # 计算平均向量
    mean_x = torch.mean(x_coords)
    mean_y = torch.mean(y_coords)

    differences = torch.sqrt((x_coords - mean_x) * (x_coords - mean_x) + (y_coords - mean_y) * (y_coords - mean_y))

    # 计算四分位数和 IQR
    q1 = torch.quantile(differences, 0.25)
    q3 = torch.quantile(differences, 0.75)
    iqr = q3 - q1

    # 计算上下限
    lower_bound = q1 - threshold * iqr
    upper_bound = q3 + threshold * iqr

    # 筛选非离群点
    non_outliers = tensor[(differences >= lower_bound) & (differences <= upper_bound)]

    if len(non_outliers) == 0:
        mean_angle = torch.atan2(mean_y, mean_x) * 180.0 / torch.pi
        mean_angle = (mean_angle + 360) % 360
        return mean_angle  # 如果没有非离群点，返回 None

    # 对非离群点再次计算平均向量
    radians = non_outliers * torch.pi / 180.0
    x_coords = torch.cos(radians)
    y_coords = torch.sin(radians)

    mean_x = torch.mean(x_coords)
    mean_y = torch.mean(y_coords)

    mean_angle = torch.atan2(mean_y, mean_x) * 180.0 / torch.pi
    mean_angle = (mean_angle + 360) % 360

    return mean_angle

def get_3angle_infer_aug(origin_img, rm_bkg_img):
    
    # image = Image.open(image_path).convert('RGB')
    image = get_crop_images(origin_img, num=3) + get_crop_images(rm_bkg_img, num=3)
    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).to(torch.float32)
    gaus_pl_pred   = torch.argmax(dino_pred[:, 360:360+180], dim=-1).to(torch.float32)
    gaus_ro_pred   = torch.argmax(dino_pred[:, 360+180:360+180+60], dim=-1).to(torch.float32)
    
    gaus_ax_pred   = remove_outliers_and_average_circular(gaus_ax_pred)
    gaus_pl_pred   = remove_outliers_and_average(gaus_pl_pred)
    gaus_ro_pred   = remove_outliers_and_average(gaus_ro_pred)
    
    confidence     = torch.mean(F.softmax(dino_pred[:, -2:], dim=-1), dim=0)[0]
    angles = torch.zeros(4)
    angles[0]  = gaus_ax_pred
    angles[1]  = gaus_pl_pred - 90
    angles[2]  = gaus_ro_pred - 30
    angles[3]  = confidence
    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 infer_func(img, do_rm_bkg, do_infer_aug):
    origin_img = Image.fromarray(img)
    if do_infer_aug:
        rm_bkg_img = background_preprocess(origin_img, True)
        angles = get_3angle_infer_aug(origin_img, rm_bkg_img)
    else:
        rm_bkg_img = background_preprocess(origin_img, do_rm_bkg)
        angles = get_3angle(rm_bkg_img)
    
    fig, ax = plt.subplots(figsize=(8, 8))

    w, h = rm_bkg_img.size
    if h>w:
        extent = [-5*w/h, 5*w/h, -5, 5]
    else:
        extent = [-5, 5, -5*h/w, 5*h/w]
    ax.imshow(rm_bkg_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 arrow
    arrow_attr = [{'point':rot_x, 'color':'r', 'label':'front'}, 
                  {'point':rot_y, 'color':'g', 'label':'right'}, 
                  {'point':rot_z, 'color':'b', 'label':'top'}]
    
    if phi> 45 and phi<=225:
        order = [0,1,2]
    elif phi > 225 and phi < 315:
        order = [2,0,1]
    else:
        order = [2,1,0]
    
    for i in range(3):
        draw_axis(ax, origin, arrow_attr[order[i]]['point'], arrow_attr[order[i]]['color'], arrow_attr[order[i]]['label'])
        # draw_axis(ax, origin, rot_y, 'g', label='right')
        # draw_axis(ax, origin, rot_z, 'b', label='top')
        # draw_axis(ax, origin, rot_x, 'r', label='front')

    # 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, round(float(angles[0]), 2), round(float(angles[1]), 2), round(float(angles[2]), 2), round(float(angles[3]), 2)]

server = gr.Interface(
    flagging_mode='never',
    fn=infer_func, 
    inputs=[
        gr.Image(height=512, width=512, label="upload your image"),
        gr.Checkbox(label="Remove Background", value=True),
        gr.Checkbox(label="Inference time augmentation", value=False)
    ], 
    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°)'),
        gr.Textbox(lines=1, label='Confidence(0~1)')
    ]
)

server.launch()