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import gradio as gr
import numpy as np
import io
import PIL.Image as Image
from rembg import remove
from PIL import ImageDraw, ImageFont
import textwrap
# Function to remove background and place text behind person
def text_behind_image(input_image, text, text_color, font_size, text_opacity):
if input_image is None:
return None
# Convert color hex to RGB
try:
r = int(text_color[1:3], 16)
g = int(text_color[3:5], 16)
b = int(text_color[5:7], 16)
text_color_rgb = (r, g, b)
except:
text_color_rgb = (255, 255, 255) # Default to white
try:
# Open the image
img = Image.fromarray(input_image)
# Get dimensions
width, height = img.size
# Create a new image with white background (this will be the canvas)
background = Image.new('RGBA', (width, height), (255, 255, 255, 255))
# Create a drawing context for the text
draw = ImageDraw.Draw(background)
# Determine font size (relative to image size)
font_size = int(min(width, height) * (font_size / 100)) # Convert percentage to actual size
if font_size < 10: # Ensure font is at least 10 pixels
font_size = 10
# Try to load a nice font, fall back to default if not available
try:
# Try different common fonts
font_paths = [
"arial.ttf",
"/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf",
"/usr/share/fonts/truetype/liberation/LiberationSans-Bold.ttf"
]
font = None
for font_path in font_paths:
try:
font = ImageFont.truetype(font_path, font_size)
break
except:
continue
if font is None:
font = ImageFont.load_default()
except:
font = ImageFont.load_default()
# Prepare the text - make it ALL CAPS for better visual impact
text = text.upper()
# Fill the entire background with repeating text pattern
# Word wrap the text to fit within the image width
margin = 20
max_chars = max(10, int(width / (font_size/2)) - 2*margin)
wrapper = textwrap.TextWrapper(width=max_chars)
word_list = wrapper.wrap(text)
# If text is too short, repeat it to fill the background
if len(word_list) == 1 and len(text) < 10:
repeated_text = (text + " ") * 10
word_list = []
for i in range(0, height, font_size + 5):
word_list.append(repeated_text)
# Calculate text block height
line_spacing = int(font_size * 1.2)
text_height = len(word_list) * line_spacing
# Position text in the center
y_text = (height - text_height) // 2
# Draw text with specified opacity
for line in word_list:
# Get line width
try:
line_width = font.getbbox(line)[2] - font.getbbox(line)[0]
except:
# Fallback method to estimate width
line_width = len(line) * (font_size // 2)
# Center the line
x_text = (width - line_width) // 2
# Draw text with specified opacity
text_color_with_opacity = text_color_rgb + (int(text_opacity * 255),)
draw.text((x_text, y_text), line, font=font, fill=text_color_with_opacity)
y_text += line_spacing
# Ensure the text is very visible by drawing it multiple times with different Y positions
if len(word_list) < 5:
for offset in [-line_spacing*2, line_spacing*2]:
y_text = (height - text_height) // 2 + offset
for line in word_list:
try:
line_width = font.getbbox(line)[2] - font.getbbox(line)[0]
except:
line_width = len(line) * (font_size // 2)
x_text = (width - line_width) // 2
draw.text((x_text, y_text), line, font=font, fill=text_color_with_opacity)
y_text += line_spacing
# Remove background from the original image to get the person silhouette
# Use the u2net_human_seg model specifically for better human segmentation
try:
person_img = remove(img, model_name="u2net_human_seg")
except:
# Fallback to default model if human_seg not available
person_img = remove(img)
# Composite the images: text in background, then person on top
final_image = Image.alpha_composite(background.convert('RGBA'), person_img.convert('RGBA'))
# Convert back to RGB for display
return np.array(final_image.convert('RGB'))
except Exception as e:
print(f"Error processing image: {e}")
return input_image # Return original image on error
# Create Gradio interface
with gr.Blocks(title="Text Behind Image") as demo:
gr.Markdown("# Text Behind Image")
gr.Markdown("Upload an image with a person and add text behind them")
with gr.Row():
with gr.Column():
input_image = gr.Image(label="Upload Image", type="numpy")
text_input = gr.Textbox(label="Text to place behind", placeholder="Enter text here...")
with gr.Row():
text_color = gr.ColorPicker(label="Text Color", value="#FFFFFF")
font_size = gr.Slider(label="Font Size (%)", minimum=1, maximum=30, value=10, step=1)
text_opacity = gr.Slider(label="Text Opacity", minimum=0.1, maximum=1.0, value=0.8, step=0.1)
submit_btn = gr.Button("Generate", variant="primary")
with gr.Column():
output_image = gr.Image(label="Result", type="numpy")
submit_btn.click(
fn=text_behind_image,
inputs=[input_image, text_input, text_color, font_size, text_opacity],
outputs=output_image
)
gr.Markdown("## How it works")
gr.Markdown("1. Upload an image with a person")
gr.Markdown("2. Enter the text you want to place behind the person")
gr.Markdown("3. Customize text color, size, and opacity")
gr.Markdown("4. Click 'Generate' to create your image")
# Launch the app
demo.launch() |