ansal commited on
Commit
b064fda
·
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1 Parent(s): 709a98a

Update app.py

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Files changed (1) hide show
  1. app.py +11 -11
app.py CHANGED
@@ -1,13 +1,18 @@
 
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  import tempfile
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- from PIL import Image
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  from pathlib import Path
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- from shiny import App, Inputs, Outputs, Session, reactive, render, ui
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- from shiny.types import FileInfo
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  import torch
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  import numpy as np
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- import os
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- from transformers import SamModel
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  import torchvision.transforms as transforms
 
 
 
 
 
 
 
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  image_resize_transform = transforms.Compose([
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  transforms.Resize((1024, 1024)),
@@ -37,12 +42,10 @@ def server(input: Inputs, output: Outputs, session: Session):
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  transform = image_resize_transform
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  image_tensor = transform(image).to(device)
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-
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  with torch.no_grad():
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- outputs = model2(pixel_values=image_tensor.unsqueeze(0),multimask_output=False)
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  predicted_masks = outputs.pred_masks.squeeze(1)
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  predicted_masks = predicted_masks[:, 0, :, :]
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-
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  mask_tensor = predicted_masks.cpu().detach().squeeze()
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  mask_array = mask_tensor.numpy()
@@ -50,19 +53,16 @@ def server(input: Inputs, output: Outputs, session: Session):
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  mask = Image.fromarray(mask_array)
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  mask = mask.resize((1024, 1024), Image.LANCZOS)
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  mask = mask.convert('RGBA')
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-
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  alpha = Image.new('L', mask.size, 128)
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  mask.putalpha(alpha)
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-
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  image = Image.open(file[0]["datapath"]).convert('RGB')
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  image = image.resize((1024, 1024), Image.LANCZOS)
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  image = image.convert('RGBA')
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  combined = Image.alpha_composite(image, mask)
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-
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  combined_file = tempfile.NamedTemporaryFile(delete=False, suffix='.png')
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  original_file = tempfile.NamedTemporaryFile(delete=False, suffix='.png')
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  image.save(original_file.name, "PNG", quality=100)
 
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+ import os
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  import tempfile
 
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  from pathlib import Path
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+ import pandas as pd
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+ from PIL import Image
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  import torch
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  import numpy as np
 
 
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  import torchvision.transforms as transforms
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+ import matplotlib.pyplot as plt
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+ from shiny import App, Inputs, Outputs, Session, reactive, render, ui
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+ from shiny.types import FileInfo
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+
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+ os.environ["TRANSFORMERS_CACHE"] = "/tmp/transformers_cache/"
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+
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+ from transformers import SamModel
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  image_resize_transform = transforms.Compose([
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  transforms.Resize((1024, 1024)),
 
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  transform = image_resize_transform
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  image_tensor = transform(image).to(device)
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  with torch.no_grad():
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+ outputs = model2(pixel_values=image_tensor.unsqueeze(0), multimask_output=False)
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  predicted_masks = outputs.pred_masks.squeeze(1)
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  predicted_masks = predicted_masks[:, 0, :, :]
 
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  mask_tensor = predicted_masks.cpu().detach().squeeze()
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  mask_array = mask_tensor.numpy()
 
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  mask = Image.fromarray(mask_array)
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  mask = mask.resize((1024, 1024), Image.LANCZOS)
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  mask = mask.convert('RGBA')
 
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  alpha = Image.new('L', mask.size, 128)
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  mask.putalpha(alpha)
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  image = Image.open(file[0]["datapath"]).convert('RGB')
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  image = image.resize((1024, 1024), Image.LANCZOS)
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  image = image.convert('RGBA')
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  combined = Image.alpha_composite(image, mask)
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  combined_file = tempfile.NamedTemporaryFile(delete=False, suffix='.png')
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  original_file = tempfile.NamedTemporaryFile(delete=False, suffix='.png')
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  image.save(original_file.name, "PNG", quality=100)