File size: 2,778 Bytes
5c75869 3e99e39 5c75869 6e7b1c7 5c75869 6e7b1c7 4feabc5 6e7b1c7 4feabc5 6e7b1c7 b09d090 6bde246 6e7b1c7 4feabc5 6e7b1c7 6bde246 6e7b1c7 6bde246 4feabc5 6e7b1c7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 |
from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
import gradio as gr
from PIL import Image
import torch
import numpy as np
import threading
processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined")
def process_image(image, prompt):
inputs = processor(
text=prompt, images=image, padding="max_length", return_tensors="pt"
)
with torch.no_grad():
outputs = model(**inputs)
preds = outputs.logits
pred = torch.sigmoid(preds)
mat = pred.cpu().numpy()
mask = Image.fromarray(np.uint8(mat * 255), "L")
mask = mask.convert("RGB")
mask = mask.resize(image.size)
mask = np.array(mask)[:, :, 0]
mask_min = mask.min()
mask_max = mask.max()
mask = (mask - mask_min) / (mask_max - mask_min)
return mask
def get_masks(prompts, img, threshold):
prompts = prompts.split(",")
masks = []
for prompt in prompts:
mask = process_image(img, prompt)
mask = mask > threshold
masks.append(mask)
return masks
def extract_image(pos_prompts, neg_prompts, img, threshold):
positive_masks = get_masks(pos_prompts, img, 0.5)
negative_masks = get_masks(neg_prompts, img, 0.5)
pos_mask = np.any(np.stack(positive_masks), axis=0)
neg_mask = np.any(np.stack(negative_masks), axis=0)
final_mask = pos_mask & ~neg_mask
final_mask = Image.fromarray(final_mask.astype(np.uint8) * 255, "L")
output_image = Image.new("RGBA", img.size, (0, 0, 0, 0))
output_image.paste(img, mask=final_mask)
return output_image, final_mask
iface = gr.Interface(
fn=extract_image,
inputs=[
gr.Image(type="pil", label="Input Image"),
gr.Textbox(label="Positive Prompts (comma separated)"),
gr.Textbox(label="Negative Prompts (comma separated)"),
gr.Slider(minimum=0, maximum=1, default=0.4, label="Threshold"),
],
outputs=[
gr.Image(type="pil", label="Output Image"),
gr.Image(type="pil", label="Output Mask"),
],
)
# Launch Gradio UI
iface.launch()
# Define API interface
api_interface = gr.Interface(
fn=extract_image,
inputs=[
gr.Image(type="pil", label="Input Image"),
gr.Textbox(label="Positive Prompts (comma separated)"),
gr.Textbox(label="Negative Prompts (comma separated)"),
gr.Slider(minimum=0, maximum=1, default=0.4, label="Threshold"),
],
outputs=[
gr.Image(type="pil", label="Output Image"),
gr.Image(type="pil", label="Output Mask"),
],
live=True # Setting live to True enables the API endpoint
)
# Launch API
api_interface.launch(share=True) # share=True allows external access to the API
|