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Update app.py
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app.py
CHANGED
@@ -1,6 +1,5 @@
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import gradio as gr
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import numpy as np
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import io
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import PIL.Image as Image
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from rembg import remove
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from PIL import ImageDraw, ImageFont
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@@ -27,16 +26,30 @@ def text_behind_image(input_image, text, text_color, font_size, text_opacity):
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# Get dimensions
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width, height = img.size
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#
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try:
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except:
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# Fallback to default model if human_seg not available
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#
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# Prepare the text
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text = text.strip().upper()
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@@ -113,9 +126,13 @@ def text_behind_image(input_image, text, text_color, font_size, text_opacity):
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# Draw the text with opacity
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draw.text((x, y), line, font=font, fill=text_color_rgb + (int(text_opacity * 255),))
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# Convert to RGB for display
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return np.array(final_image.convert('RGB'))
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import gradio as gr
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import numpy as np
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import PIL.Image as Image
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from rembg import remove
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from PIL import ImageDraw, ImageFont
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# Get dimensions
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width, height = img.size
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# Step 1: Extract the person from the image
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try:
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# Try to use the human segmentation model first
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person_only = remove(img, model_name="u2net_human_seg")
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except:
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# Fallback to default model if human_seg not available
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person_only = remove(img)
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# Step 2: Create a mask from the person cutout
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person_mask = Image.new('RGBA', (width, height), (0, 0, 0, 0))
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person_mask.paste(person_only, (0, 0), person_only)
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# Step 3: Extract the background (original image without the person)
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# Create inverted mask (where the person is black, background is white)
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inverted_mask = Image.new('L', (width, height), 255)
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inverted_mask.paste(0, (0, 0), person_only)
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# Extract just the background
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background = img.copy()
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background.putalpha(inverted_mask)
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# Step 4: Create the text layer
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text_layer = Image.new('RGBA', (width, height), (0, 0, 0, 0))
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draw = ImageDraw.Draw(text_layer)
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# Prepare the text
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text = text.strip().upper()
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# Draw the text with opacity
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draw.text((x, y), line, font=font, fill=text_color_rgb + (int(text_opacity * 255),))
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# Step 5: Composite all layers together
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# First, composite the text on top of the background
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# We use the inverted mask so text only shows where the person isn't
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background_with_text = Image.alpha_composite(background, text_layer)
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# Then, composite the person on top
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final_image = Image.alpha_composite(background_with_text, person_mask)
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# Convert to RGB for display
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return np.array(final_image.convert('RGB'))
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