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import streamlit as st
from utils import transcribe_audio_segments, create_video_segments, generate_speech
from PIL import Image
import os
import cv2
import numpy as np
from io import BytesIO
import tempfile
from moviepy.editor import ImageSequenceClip
from rembg import remove
from basicsr.archs.rrdbnet_arch import RRDBNet
from realesrgan import RealESRGANer
# Helper function to apply cartoon effect
def cartoonize(img):
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = cv2.medianBlur(gray, 5)
edges = cv2.adaptiveThreshold(
gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY, 9, 9
)
color = cv2.bilateralFilter(img, 9, 300, 300)
cartoon = cv2.bitwise_and(color, color, mask=edges)
return cartoon
# Helper function to apply edge detection
def edge_detect(img):
return cv2.Canny(img, 100, 200)
# Helper function to enhance face (upscale image)
def enhance_image(img):
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64,
num_block=23, num_grow_ch=32, scale=4)
upsampler = RealESRGANer(
scale=4, model_path='realesrgan-x4plus.pth', model=model,
tile=0, tile_pad=10, pre_pad=0, half=False
)
_, _, output = upsampler.enhance(np.array(img), outscale=4)
return output
# Save processed images
def save_image(img, filename):
cv2.imwrite(filename, img)
# Convert images to slideshow video
def create_slideshow(images, out_path="slideshow.mp4"):
clip = ImageSequenceClip(images, fps=1)
clip.write_videofile(out_path, codec='libx264')
# Streamlit UI
st.set_page_config(layout="wide")
st.title("🎨 Interactive Image Effects Studio")
uploaded_files = st.file_uploader("Upload images", type=["jpg", "jpeg", "png"], accept_multiple_files=True)
effects = st.multiselect("Select effects to apply", ["Cartoonize", "Edge Detection", "Background Removal", "Enhance/ Upscale"])
effect_images = []
if uploaded_files:
for uploaded_file in uploaded_files:
image = Image.open(uploaded_file).convert("RGB")
img_array = np.array(image)
original_img = cv2.cvtColor(img_array, cv2.COLOR_RGB2BGR)
processed_img = original_img.copy()
if "Cartoonize" in effects:
processed_img = cartoonize(processed_img)
if "Edge Detection" in effects:
processed_img = cv2.cvtColor(edge_detect(processed_img), cv2.COLOR_GRAY2BGR)
if "Background Removal" in effects:
processed_img = remove(Image.fromarray(cv2.cvtColor(processed_img, cv2.COLOR_BGR2RGB)))
processed_img = cv2.cvtColor(np.array(processed_img), cv2.COLOR_RGB2BGR)
if "Enhance/ Upscale" in effects:
processed_img = enhance_image(processed_img)
st.image(cv2.cvtColor(processed_img, cv2.COLOR_BGR2RGB), caption="Processed Image", use_column_width=True)
# Save processed
effect_images.append(cv2.cvtColor(processed_img, cv2.COLOR_BGR2RGB))
with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp_file:
save_image(processed_img, tmp_file.name)
with open(tmp_file.name, "rb") as file:
btn = st.download_button(
label="Download Processed Image",
data=file,
file_name=os.path.basename(uploaded_file.name),
mime="image/jpeg"
)
if st.button("Create Slideshow from Images"):
with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as tmp_video:
create_slideshow(effect_images, tmp_video.name)
with open(tmp_video.name, "rb") as file:
st.video(file)
st.download_button("Download Slideshow Video", file, file_name="slideshow.mp4")
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