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import streamlit as st
from transformers import MobileViTFeatureExtractor, MobileViTForSemanticSegmentation, pipeline
from PIL import Image, ImageFilter, ImageOps
import numpy as np
# Function to apply Gaussian Blur
def apply_gaussian_blur(image, predicted_mask, sigma=15):
print(f"Predicted Mask: {predicted_mask}")
mask = Image.fromarray(
(predicted_mask.cpu().numpy() * 255).astype(np.uint8)
).resize(image.size).convert('L')
foreground = Image.composite(
image, Image.new('RGB', image.size), mask
)
background = Image.composite(
image.filter(ImageFilter.GaussianBlur(sigma)), image,
ImageOps.invert(mask)
)
final_image = Image.composite(foreground, background, mask)
return final_image
# Function to load and process image for segmentation
def get_segmentation_mask(image):
feature_extractor = MobileViTFeatureExtractor.from_pretrained("apple/mobilevit-small")
model = MobileViTForSemanticSegmentation.from_pretrained("apple/mobilevit-small")
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
# Get segmentation mask
logits = outputs.logits
predicted_mask = logits.argmax(1).squeeze(0)
return predicted_mask
def get_depth_mask(image):
pipe = pipeline(task="depth-estimation", model="Intel/dpt-beit-base-384")
result = pipe(image)
depth_map = result["depth"]
return np.array(depth_map)
def add_depth_based_blur(depth_array, image):
depth_normalized = (depth_array - depth_array.min()) / (depth_array.max() - depth_array.min()) * 15
image_array = np.array(image)
blurred_images = [
np.array(image.filter(ImageFilter.GaussianBlur(radius)))
for radius in range(16)
]
depth_blurred_array = np.zeros_like(image_array)
for i in range(depth_normalized.shape[0]):
for j in range(depth_normalized.shape[1]):
blur_lvl = 15 - int(depth_normalized[i, j])
depth_blurred_array[i, j] = blurred_images[blur_lvl][i, j]
return Image.fromarray(depth_blurred_array.astype(np.uint8))
# Streamlit interface
st.title("Image Segmentation and Blur Effects")
st.write("Upload an image to apply segmentation, Gaussian blur, and depth-based blur.")
uploaded_file = st.file_uploader("Upload an Image (PNG, JPG, JPEG)", type=["png", "jpg", "jpeg"])
if uploaded_file:
image = Image.open(uploaded_file)
image = image.resize((512, 512))
st.image(image, caption="Uploaded Image", use_container_width=True)
predicted_mask = get_segmentation_mask(image)
# Apply Gaussian Blur
sigma = st.slider("Gaussian Blur Intensity", 5, 50, 15)
blurred_image = apply_gaussian_blur(image, predicted_mask, sigma)
st.image(blurred_image, caption="Gaussian Blurred Image", use_container_width=True)
# Perform lens blur
st.write("Calculating depth and applying lens blur...")
with st.spinner("Applying lens blur... This might take a few moments."):
depth_array = get_depth_mask(image)
lens_blurred_img = add_depth_based_blur(depth_array, image)
st.image(lens_blurred_img, caption="Lens Blur Effect Image", use_container_width=True) |