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import torch |
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from PIL import Image |
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from osdsynth.Orient_Anything.utils import * |
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import torch.nn.functional as F |
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import numpy as np |
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def get_3angle(image, dino, val_preprocess, device): |
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image_inputs = val_preprocess(images = image) |
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image_inputs['pixel_values'] = torch.from_numpy(np.array(image_inputs['pixel_values'])).to(device) |
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with torch.no_grad(): |
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dino_pred = dino(image_inputs) |
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gaus_ax_pred = torch.argmax(dino_pred[:, 0:360], dim=-1) |
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gaus_pl_pred = torch.argmax(dino_pred[:, 360:360+180], dim=-1) |
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gaus_ro_pred = torch.argmax(dino_pred[:, 360+180:360+180+180], dim=-1) |
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confidence = F.softmax(dino_pred[:, -2:], dim=-1)[0][0] |
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angles = torch.zeros(4) |
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angles[0] = gaus_ax_pred |
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angles[1] = gaus_pl_pred - 90 |
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angles[2] = gaus_ro_pred - 90 |
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angles[3] = confidence |
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return angles |
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def get_3angle_infer_aug(origin_img, rm_bkg_img, dino, val_preprocess, device): |
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image = get_crop_images(origin_img, num=3) + get_crop_images(rm_bkg_img, num=3) |
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image_inputs = val_preprocess(images = image) |
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image_inputs['pixel_values'] = torch.from_numpy(np.array(image_inputs['pixel_values'])).to(device) |
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with torch.no_grad(): |
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dino_pred = dino(image_inputs) |
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gaus_ax_pred = torch.argmax(dino_pred[:, 0:360], dim=-1).to(torch.float32) |
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gaus_pl_pred = torch.argmax(dino_pred[:, 360:360+180], dim=-1).to(torch.float32) |
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gaus_ro_pred = torch.argmax(dino_pred[:, 360+180:360+180+180], dim=-1).to(torch.float32) |
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gaus_ax_pred = remove_outliers_and_average_circular(gaus_ax_pred) |
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gaus_pl_pred = remove_outliers_and_average(gaus_pl_pred) |
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gaus_ro_pred = remove_outliers_and_average(gaus_ro_pred) |
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confidence = torch.mean(F.softmax(dino_pred[:, -2:], dim=-1), dim=0)[0] |
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angles = torch.zeros(4) |
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angles[0] = gaus_ax_pred |
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angles[1] = gaus_pl_pred - 90 |
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angles[2] = gaus_ro_pred - 90 |
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angles[3] = confidence |
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return angles |