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import gradio as gr
import cv2
import torch
import numpy as np
from torchvision import transforms
title = "Background Remover"
description = "Automatically remove the image background from a profile photo."
article = "<p style='text-align: center'><a href='https://news.machinelearning.sg/posts/beautiful_profile_pics_remove_background_image_with_deeplabv3/'>Blog</a> | <a href='https://github.com/eugenesiow/practical-ml'>Github Repo</a></p>"
def make_transparent_foreground(pic, mask):
# split the image into channels
b, g, r = cv2.split(np.array(pic).astype('uint8'))
# add an alpha channel with and fill all with transparent pixels (max 255)
a = np.ones(mask.shape, dtype='uint8') * 255
# merge the alpha channel back
alpha_im = cv2.merge([b, g, r, a], 4)
# create a transparent background
bg = np.zeros(alpha_im.shape)
# setup the new mask
new_mask = np.stack([mask, mask, mask, mask], axis=2)
# copy only the foreground color pixels from the original image where mask is set
foreground = np.where(new_mask, alpha_im, bg).astype(np.uint8)
return foreground
def remove_background(input_image):
preprocess = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
input_tensor = preprocess(input_image)
input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model
# move the input and model to GPU for speed if available
if torch.cuda.is_available():
input_batch = input_batch.to('cuda')
model.to('cuda')
with torch.no_grad():
output = model(input_batch)['out'][0]
output_predictions = output.argmax(0)
# create a binary (black and white) mask of the profile foreground
mask = output_predictions.byte().cpu().numpy()
background = np.zeros(mask.shape)
bin_mask = np.where(mask, 255, background).astype(np.uint8)
foreground = make_transparent_foreground(input_image, bin_mask)
return foreground, bin_mask
def inference(img):
foreground, _ = remove_background(img)
return foreground
model = torch.hub.load('pytorch/vision:v0.6.0', 'deeplabv3_resnet101', pretrained=True)
model.eval()
gr.Interface(
inference,
gr.inputs.Image(type="pil", label="Input"),
gr.outputs.Image(type="pil", label="Output"),
title=title,
description=description,
article=article,
examples=[['woman.jpg'], ['man.jpg']],
enable_queue=True
).launch(debug=False) |