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from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
import gradio as gr
from PIL import Image
import torch
import matplotlib.pyplot as plt
import cv2
import torch
import numpy as np
processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined")
def process_image(image, prompt, threhsold, alpha_value, draw_rectangles):
inputs = processor(
text=prompt, images=image, padding="max_length", return_tensors="pt"
)
# predict
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]
# normalize the mask
mask_min = mask.min()
mask_max = mask.max()
mask = (mask - mask_min) / (mask_max - mask_min)
# threshold the mask
bmask = mask > threhsold
# zero out values below the threshold
mask[mask < threhsold] = 0
fig, ax = plt.subplots()
ax.imshow(image)
ax.imshow(mask, alpha=alpha_value, cmap="jet")
if draw_rectangles:
contours, hierarchy = cv2.findContours(
bmask.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE
)
for contour in contours:
x, y, w, h = cv2.boundingRect(contour)
rect = plt.Rectangle(
(x, y), w, h, fill=False, edgecolor="yellow", linewidth=2
)
ax.add_patch(rect)
ax.axis("off")
plt.tight_layout()
return fig, mask
title = "Interactive demo: zero-shot image segmentation with CLIPSeg"
description = "Demo for using CLIPSeg, a CLIP-based model for zero- and one-shot image segmentation. To use it, simply upload an image and add a text to mask (identify in the image), or use one of the examples below and click 'submit'. Results will show up in a few seconds."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2112.10003'>CLIPSeg: Image Segmentation Using Text and Image Prompts</a> | <a href='https://huggingface.co/docs/transformers/main/en/model_doc/clipseg'>HuggingFace docs</a></p>"
with gr.Blocks() as demo:
gr.Markdown("# CLIPSeg: Image Segmentation Using Text and Image Prompts")
gr.Markdown(article)
gr.Markdown(description)
gr.Markdown(
"*Example images are taken from the [ImageNet-A](https://paperswithcode.com/dataset/imagenet-a) dataset*"
)
with gr.Row():
with gr.Column():
input_image = gr.Image(type="pil")
input_prompt = gr.Textbox(label="Please describe what you want to identify")
input_slider_T = gr.Slider(
minimum=0, maximum=1, value=0.4, label="Threshold"
)
input_slider_A = gr.Slider(minimum=0, maximum=1, value=0.5, label="Alpha")
draw_rectangles = gr.Checkbox(label="Draw rectangles")
btn_process = gr.Button(label="Process")
with gr.Column():
output_plot = gr.Plot(label="Segmentation Result")
output_image = gr.Image(label="Mask")
btn_process.click(
process_image,
inputs=[
input_image,
input_prompt,
input_slider_T,
input_slider_A,
draw_rectangles,
],
outputs=[output_plot, output_image],
)
gr.Examples(
[
["0.003473_cliff _ cliff_0.51112.jpg", "dog", 0.5, 0.5, True],
["0.001861_submarine _ submarine_0.9862991.jpg", "beacon", 0.55, 0.4, True],
["0.004658_spatula _ spatula_0.35416836.jpg", "banana", 0.4, 0.5, True],
],
inputs=[
input_image,
input_prompt,
input_slider_T,
input_slider_A,
draw_rectangles,
],
)
demo.launch()
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