init app

.gitignore

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+.idea/
+ __pycache__/
+

app.py

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+import os
+import cv2
+import numpy as np
+import torch
+import gradio as gr
+import torchvision.transforms as transforms
+
+os.system("pip freeze")
+
+model = torch.hub.load('pytorch/vision:v0.6.0', 'deeplabv3_resnet101', weights='DEFAULT')
+model.eval()
+
+
+def image_to_tensor(image):
+    return transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
+    ])(image)
+
+
+def make_transparent_foreground(image, mask):
+    # split the image into channels
+    b, g, r = cv2.split(np.array(image).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)
+    # set up 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
+    return np.where(new_mask, alpha_im, bg).astype(np.uint8)
+
+
+def predict(image):
+    input_tensor = image_to_tensor(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)
+    return np.where(mask, 255, background).astype(np.uint8)
+
+
+def inference(image):
+    mask = predict(image)
+    return make_transparent_foreground(image, mask)
+
+
+title = "Zero Background"
+description = r"""
+## Remove image background
+
+This is another implementation of <a href='https://github.com/eugenesiow/practical-ml/blob/master/notebooks/Remove_Image_Background_DeepLabV3.ipynb' target='_blank'>eugenesiow</a>.
+It has no any particular purpose than start research on AI models.
+
+"""
+
+article = r"""
+Questions, doubts, comments, please email 📧 `[email protected]`
+
+This demo is running on a CPU, if you like this project please make us a donation to run on a GPU or just give us a <a href='https://github.com/leonelhs/face-shine' target='_blank'>Github ⭐</a>
+
+<a href="https://www.buymeacoffee.com/leonelhs"><img src="https://img.buymeacoffee.com/button-api/?text=Buy me a coffee&emoji=&slug=leonelhs&button_colour=FFDD00&font_colour=000000&font_family=Cookie&outline_colour=000000&coffee_colour=ffffff" /></a>
+
+<center><img src='https://visitor-badge.glitch.me/badge?page_id=deoldify.visitor-badge' alt='visitor badge'></center>
+"""
+
+demo = gr.Interface(
+    inference, [
+        gr.Image(type="pil", label="Image"),
+    ], [
+        gr.Image(type="pil", label="Image alpha background")
+    ],
+    title=title,
+    description=description,
+    article=article)
+
+demo.queue().launch()

requirements.txt

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+torch>=2.0.1
+opencv-python
+
+
+