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from PIL import Image |
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import numpy as np |
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from rembg import remove |
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import cv2 |
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import os |
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from torchvision.transforms import GaussianBlur |
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import gradio as gr |
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import replicate |
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import requests |
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from io import BytesIO |
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def create_mask(input): |
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input_path = 'input.png' |
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bg_removed_path = 'bg_removed.png' |
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mask_name = 'blured_mask.png' |
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input.save(input_path) |
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bg_removed = remove(input) |
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bg_removed = bg_removed.resize((512, 512)) |
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bg_removed.save(bg_removed_path) |
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img2_grayscale = bg_removed.convert('L') |
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img2_a = np.array(img2_grayscale) |
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mask = np.array(img2_grayscale) |
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threshhold = 0 |
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mask[img2_a==threshhold] = 1 |
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mask[img2_a>threshhold] = 0 |
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strength = 1 |
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d = int(255 * (1-strength)) |
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mask *= 255-d |
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mask += d |
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mask = Image.fromarray(mask) |
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blur = GaussianBlur(11,20) |
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mask = blur(mask) |
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mask = mask.resize((512, 512)) |
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mask.save(mask_name) |
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return Image.open(mask_name) |
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def generate_image(image, product_name, target_name): |
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mask = create_mask(image) |
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image = image.resize((512, 512)) |
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mask = mask.resize((512,512)) |
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guidance_scale=16 |
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num_samples = 1 |
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prompt = 'a photo of a ' + product_name + ' with ' + target_name + ' product photograpy' |
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model = replicate.models.get("cjwbw/stable-diffusion-v2-inpainting") |
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version = model.versions.get("f9bb0632bfdceb83196e85521b9b55895f8ff3d1d3b487fd1973210c0eb30bec") |
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output = version.predict(prompt=prompt, image=open("bg_removed.png", "rb"), mask=open("blured_mask.png", "rb")) |
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response = requests.get(output[0]) |
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return Image.open(BytesIO(response.content)) |
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with gr.Blocks() as demo: |
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gr.Markdown("# Advertise better with AI") |
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with gr.Row(): |
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with gr.Column(): |
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input_image = gr.Image(label = "Upload your product's photo", type = 'pil') |
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product_name = gr.Textbox(label="Describe your product") |
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target_name = gr.Textbox(label="Where do you want to put your product?") |
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image_button = gr.Button("Generate") |
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with gr.Column(): |
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image_output = gr.Image() |
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image_button.click(generate_image, inputs=[input_image, product_name, target_name ], outputs=image_output, api_name='test') |
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demo.launch() |
