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import gradio as gr |
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import numpy as np |
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import random |
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import cv2 |
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import torch |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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from transformers import pipeline |
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import requests |
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from PIL import Image |
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pipe = pipeline("image-segmentation", model="briaai/RMBG-1.4", trust_remote_code=True, device = 0) |
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pipe_depth = pipeline(task="depth-estimation", model="depth-anything/Depth-Anything-V2-Base-hf") |
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def lens_blur(pil_img): |
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pillow_mask = pipe(pil_img, return_mask = True) |
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mask_arr = np.array(pillow_mask) |
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mask_bool = mask_arr > 220 |
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mask_filter = ((mask_arr > 220)*255).astype(np.uint8) |
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mask_filter_pillow = Image.fromarray(mask_filter.astype(np.uint8)) |
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res = cv2.bitwise_and(np.array(pil_img),np.array(pil_img),mask = mask_filter) |
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depth = pipe_depth(pil_img)["depth"] |
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depth = np.array(depth) |
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blurred_img_sharp = cv2.GaussianBlur(np.array(pil_img),ksize=(5,5),sigmaX=0.1,sigmaY=0.1) |
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blurred_img_blur = cv2.GaussianBlur(np.array(pil_img),ksize=(5,5),sigmaX=30,sigmaY=30) |
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blurred_with_depth_map = np.expand_dims(depth,-1)/255 * blurred_img_sharp + (255 - np.expand_dims(depth,-1))/255 * blurred_img_blur |
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blurred_with_depth_map = np.clip(blurred_with_depth_map ,0,255) |
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blurred_with_depth_map = blurred_with_depth_map.astype(np.uint8) |
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bg_lens_blur_img = cv2.bitwise_and(blurred_with_depth_map,blurred_with_depth_map,mask = 255 - mask_filter) + res |
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return Image.fromarray(bg_lens_blur_img) |
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css = """ |
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#col-container { |
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margin: 0 auto; |
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max-width: 640px; |
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} |
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""" |
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with gr.Blocks(css=css) as demo: |
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with gr.Column(elem_id="col-container"): |
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gr.Markdown(" # Lens Blur") |
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with gr.Row(): |
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prompt = gr.Image(type="pil") |
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run_button = gr.Button("Run", scale=0, variant="primary") |
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result = gr.Image(label="Result", show_label=False) |
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gr.on( |
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triggers=[run_button.click, prompt.upload], |
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fn=lens_blur, |
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inputs=[ |
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prompt, |
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], |
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outputs=[result], |
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) |
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if __name__ == "__main__": |
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demo.launch(share=True) |
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