learnopencv/app-seperation-semseg/Background-Blurring.py

from torchvision import models
from PIL import Image, ImageFilter
import matplotlib.pyplot as plt
import torch
import numpy as np
import cv2
# Apply the transformations needed
import torchvision.transforms as T

# Define the helper function
def decode_segmap(image, source, nc=21):
  
  label_colors = np.array([(0, 0, 0),  # 0=background
               # 1=aeroplane, 2=bicycle, 3=bird, 4=boat, 5=bottle
               (128, 0, 0), (0, 128, 0), (128, 128, 0), (0, 0, 128), (128, 0, 128),
               # 6=bus, 7=car, 8=cat, 9=chair, 10=cow
               (0, 128, 128), (128, 128, 128), (64, 0, 0), (192, 0, 0), (64, 128, 0),
               # 11=dining table, 12=dog, 13=horse, 14=motorbike, 15=person
               (192, 128, 0), (64, 0, 128), (192, 0, 128), (64, 128, 128), (192, 128, 128),
               # 16=potted plant, 17=sheep, 18=sofa, 19=train, 20=tv/monitor
               (0, 64, 0), (128, 64, 0), (0, 192, 0), (128, 192, 0), (0, 64, 128)])

  r = np.zeros_like(image).astype(np.uint8)
  g = np.zeros_like(image).astype(np.uint8)
  b = np.zeros_like(image).astype(np.uint8)
  
  for l in range(0, nc):
    idx = image == l
    r[idx] = label_colors[l, 0]
    g[idx] = label_colors[l, 1]
    b[idx] = label_colors[l, 2]
    
    
  rgb = np.stack([r, g, b], axis=2)
  
  # Load the foreground input image 
  foreground = cv2.imread(source)

  # Change the color of foreground image to RGB 
  # and resize image to match shape of R-band in RGB output map  
  foreground = cv2.cvtColor(foreground, cv2.COLOR_BGR2RGB)
  foreground = cv2.resize(foreground,(r.shape[1],r.shape[0]))

  # Create a Gaussian blur of kernel size 7 for the background image
  blurredImage = cv2.GaussianBlur(foreground, (7,7), 0)

  # Convert uint8 to float
  foreground = foreground.astype(float)
  blurredImage = blurredImage.astype(float)

  # Create a binary mask of the RGB output map using the threshold value 0
  th, alpha = cv2.threshold(np.array(rgb),0,255, cv2.THRESH_BINARY)

  # Apply a slight blur to the mask to soften edges
  alpha = cv2.GaussianBlur(alpha, (7,7),0)

  # Normalize the alpha mask to keep intensity between 0 and 1
  alpha = alpha.astype(float)/255

  # Multiply the foreground with the alpha matte
  foreground = cv2.multiply(alpha, foreground)  
  
  # Multiply the background with ( 1 - alpha )
  background = cv2.multiply(1.0 - alpha, blurredImage)  
  
  # Add the masked foreground and background
  outImage = cv2.add(foreground, background)

  # Return a normalized output image for display
  return outImage/255

def segment(net, path, show_orig=True, dev='cuda'):
  img = Image.open(path)
  
  if show_orig: plt.imshow(img); plt.axis('off'); plt.show()
  # Comment the Resize and CenterCrop for better inference results
  trf = T.Compose([T.Resize(450), 
                   #T.CenterCrop(224), 
                   T.ToTensor(), 
                   T.Normalize(mean = [0.485, 0.456, 0.406], 
                               std = [0.229, 0.224, 0.225])])
  inp = trf(img).unsqueeze(0).to(dev)
  out = net.to(dev)(inp)['out']
  om = torch.argmax(out.squeeze(), dim=0).detach().cpu().numpy()
  
  rgb = decode_segmap(om, path)
    
  plt.imshow(rgb); plt.axis('off'); plt.show()
  

dlab = models.segmentation.deeplabv3_resnet101(pretrained=1).eval()

segment(dlab, './images/blur/girl.png', show_orig=False)
segment(dlab, './images/blur/boy.png', show_orig=False)