Spaces:
Runtime error
Runtime error
File size: 5,301 Bytes
b52ce7d e0446fc b52ce7d d1323b2 b52ce7d e0446fc 28c8a2d e0446fc 28c8a2d e0446fc 28c8a2d e0446fc 28c8a2d e0446fc 28c8a2d e0446fc b52ce7d e0446fc b52ce7d 28c8a2d e0446fc b52ce7d d1323b2 b52ce7d e0446fc b52ce7d e0446fc b52ce7d 0d7ecfe b52ce7d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 |
import streamlit as st
import cv2
import numpy as np
from PIL import Image
import io
# Function to convert image to sketch with adjustable outline thickness
def image_to_sketch(image, kernel_size=1):
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
inverted_image = 255 - gray_image
blurred_image = cv2.GaussianBlur(inverted_image, (21, 21), 0)
inverted_blurred = 255 - blurred_image
sketch = cv2.divide(gray_image, inverted_blurred, scale=256.0)
# Apply adaptive thresholding to enhance edges
adaptive_thresh = cv2.adaptiveThreshold(sketch, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 9, 2)
# Apply morphological operation to thicken the outlines
kernel = np.ones((kernel_size, kernel_size), np.uint8)
sketch = cv2.dilate(adaptive_thresh, kernel, iterations=1)
return sketch
# Function to blur the background of the image
def blur_background(image, blur_strength=31):
# # Convert the image to grayscale
# gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# # Use thresholding to create a binary mask
# _, mask = cv2.threshold(gray, 120, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
# # Create the inverse mask
# mask_inv = cv2.bitwise_not(mask)
# # Blur the entire image
# blurred = cv2.GaussianBlur(image, (blur_strength, blur_strength), 0)
# # Use the mask to combine the original image with the blurred image
# background = cv2.bitwise_and(blurred, blurred, mask=mask)
# foreground = cv2.bitwise_and(image, image, mask=mask_inv)
# combined = cv2.add(background, foreground)
ksize = (10, 10)
# Using cv2.blur() method
combined = cv2.blur(image, ksize)
return combined
# Streamlit app layout
st.set_page_config(page_title="Image to Sketch Converter", page_icon="🎨", layout="centered")
# Custom CSS for heading color and footer positioning
st.markdown("""
<style>
.title {
color: blue;
font-size: 2.5em;
font-weight: bold;
text-align: center;
}
.footer {
position: relative;
bottom: 0;
width: 100%;
background-color: orange;
text-align: center;
color: black;
padding: 10px;
font-weight: bold;
margin-top: 50px;
}
.content {
margin-bottom: 70px;
}
.spacing {
margin: 10px 10px;
}
.centered-button {
display: flex;
justify-content: center;
align-items: center;
gap: 10px;
}
</style>
""", unsafe_allow_html=True)
# Title and description
st.markdown('<p class="title">🎨 Image to Sketch Converter</p>', unsafe_allow_html=True)
st.markdown("""
Convert your images into beautiful sketches with this simple app.
Upload an image, and get the sketch version instantly! You can even download the sketch.
""")
# Example conversions
st.subheader("Example Conversions")
# Load and display example image
example_image_path = 'Dog.jpg'
example_image = cv2.imread(example_image_path)
if example_image is not None:
# Convert BGR to RGB for correct color display
example_image_rgb = cv2.cvtColor(example_image, cv2.COLOR_BGR2RGB)
example_image_blurred = blur_background(example_image)
example_sketch = image_to_sketch(example_image_blurred)
col1, col2 = st.columns(2)
with col1:
st.image(example_image_rgb, caption='Original Image', use_column_width=True)
with col2:
st.image(example_image_blurred, caption='Sketch Image', use_column_width=True)
else:
st.error(f"Failed to load example image from path: {example_image_path}")
# User upload section
st.subheader("Upload Your Image")
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
# Load the image
image = Image.open(uploaded_file)
image_np = np.array(image)
# Determine the format of the uploaded image
image_format = image.format.lower()
st.write("Converting...")
# Blur the background
image_blurred = blur_background(image_np)
# Convert the image to a sketch
sketch = image_to_sketch(image_blurred)
col3, col4 = st.columns(2)
with col3:
st.image(image, caption='Uploaded Image', use_column_width=True)
with col4:
st.image(image_blurred, caption='Sketch', use_column_width=True)
# Add some space before the button
st.markdown('<div class="spacing"></div>', unsafe_allow_html=True)
# Convert the sketch to an image and save to an in-memory file object
sketch_image = Image.fromarray(sketch)
buf = io.BytesIO()
sketch_image.save(buf, format=image_format.upper())
byte_im = buf.getvalue()
# Provide a download link for the sketch image in the center
st.markdown('<div class="centered-button">', unsafe_allow_html=True)
btn = st.download_button(
label="Download Sketch",
data=byte_im,
file_name=f"sketch.{image_format}",
mime=f"image/{image_format}"
)
st.markdown('</div>', unsafe_allow_html=True)
else:
st.info("Please upload an image to convert.")
# Footer
st.markdown("""
<div class="footer">
Made by Mallela Preethi
</div>
""", unsafe_allow_html=True)
|