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import os
import cv2
import numpy as np
import streamlit as st
import tensorflow as tf
def process_image(uploaded_file):
file_content = uploaded_file.read()
nparr = np.frombuffer(file_content, np.uint8)
image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
return image
model = tf.saved_model.load("model")
def perform_segmentation(image):
resized_image = cv2.resize(image, (128, 128))
resized_image = cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB)
normalized_image = resized_image / 255.0
input_array = np.expand_dims(normalized_image, axis=0).astype(np.float32)
segmented_mask = model(tf.constant(input_array))[0]
threshold = 0.5
binary_mask = (segmented_mask > threshold).numpy().astype(np.uint8)
# Resize the binary mask to match the size of the input image
binary_mask_resized = cv2.resize(binary_mask, (image.shape[1], image.shape[0]))
segmented_image = cv2.bitwise_and(image, image, mask=binary_mask_resized)
return segmented_image
def main():
st.title("Solution Challenge")
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
image = process_image(uploaded_file)
segmented_image = perform_segmentation(image)
# Create two columns to display images side by side
col1, col2 = st.columns(2)
# Display original image in the first column
col1.image(image, caption="Original Image", use_column_width=True)
# Display segmented image in the second column
col2.image(
segmented_image, caption="Segmentation Results", use_column_width=True
)
if __name__ == "__main__":
main()
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