| import torch |
| from PIL import Image |
| from utils import * |
| import torch.nn.functional as F |
| import numpy as np |
|
|
| def get_3angle(image, dino, val_preprocess, device): |
| |
| |
| 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+360], 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 - 180 |
| angles[3] = confidence |
| return angles |
|
|
| def get_3angle_infer_aug(origin_img, rm_bkg_img, dino, val_preprocess, device): |
| |
| |
| 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+360], 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 - 180 |
| angles[3] = confidence |
| return angles |
