Duplicate from taesiri/CLIPSeg

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README.md

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+---
+title: CLIPSeg
+emoji: 🦀
+colorFrom: indigo
+colorTo: purple
+sdk: gradio
+sdk_version: 3.16.2
+app_file: app.py
+pinned: false
+duplicated_from: taesiri/CLIPSeg
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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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()
+
+    bmask = Image.fromarray(bmask.astype(np.uint8) * 255, "L")
+    output_image = Image.new("RGBA", image.size, (0, 0, 0, 0))
+    output_image.paste(image, mask=bmask)
+
+    return fig, mask, output_image
+
+
+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_mask = gr.Image(label="Mask")
+            output_image = gr.Image(label="Output Image")
+
+    btn_process.click(
+        process_image,
+        inputs=[
+            input_image,
+            input_prompt,
+            input_slider_T,
+            input_slider_A,
+            draw_rectangles,
+        ],
+        outputs=[output_plot, output_mask, 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()

packages.txt

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+python3-opencv

requirements.txt

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+git+https://github.com/huggingface/transformers.git
+torch
+opencv-python