UmaKumpatla commited on
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Update src/streamlit_app.py

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  1. src/streamlit_app.py +38 -36
src/streamlit_app.py CHANGED
@@ -1,40 +1,42 @@
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  import altair as alt
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- import numpy as np
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  import pandas as pd
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  import streamlit as st
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- """
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- # Welcome to Streamlit!
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-
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- Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
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- If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
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- forums](https://discuss.streamlit.io).
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-
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- In the meantime, below is an example of what you can do with just a few lines of code:
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- """
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-
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- num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
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- num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
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-
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- indices = np.linspace(0, 1, num_points)
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- theta = 2 * np.pi * num_turns * indices
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- radius = indices
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-
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- x = radius * np.cos(theta)
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- y = radius * np.sin(theta)
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-
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- df = pd.DataFrame({
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- "x": x,
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- "y": y,
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- "idx": indices,
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- "rand": np.random.randn(num_points),
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- })
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-
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- st.altair_chart(alt.Chart(df, height=700, width=700)
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- .mark_point(filled=True)
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- .encode(
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- x=alt.X("x", axis=None),
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- y=alt.Y("y", axis=None),
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- color=alt.Color("idx", legend=None, scale=alt.Scale()),
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- size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
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- ))
 
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  import altair as alt
 
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  import pandas as pd
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  import streamlit as st
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+ from PIL import Image
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+ import cv2
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+ import numpy as np
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+ import io
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+ import zipfile
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+
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+ st.title("Image Augmentation App")
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+ uploaded_image = st.file_uploader("Upload an Image", type=["jpg", "jpeg", "png"])
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+
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+ if uploaded_image:
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+ img = np.array(Image.open(uploaded_image))
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+ st.image(img, caption="Uploaded Image", use_column_width=True)
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+
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+ st.write("**Applying augmentations...**")
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+
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+ transformations = {
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+ "Translated": cv2.warpAffine(img, np.float32([[1, 0, 50], [0, 1, 50]]), (img.shape[1], img.shape[0])),
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+ "Scaled": cv2.resize(img, None, fx=1.2, fy=1.2),
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+ "Rotated": cv2.warpAffine(img, cv2.getRotationMatrix2D((img.shape[1] // 2, img.shape[0] // 2), 45, 1), (img.shape[1], img.shape[0])),
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+ "Cropped": img[50:img.shape[0] - 50, 50:img.shape[1] - 50]
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+ }
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+
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+ augmented_images = []
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+ zip_buffer = io.BytesIO()
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+
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+ with zipfile.ZipFile(zip_buffer, "w") as zipf:
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+ for i, (name, transformed_img) in enumerate(transformations.items()):
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+ img_pil = Image.fromarray(cv2.cvtColor(transformed_img, cv2.COLOR_BGR2RGB))
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+ img_bytes = io.BytesIO()
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+ img_pil.save(img_bytes, format="JPEG")
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+ zipf.writestr(f"aug_{i + 1}_{name}.jpg", img_bytes.getvalue())
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+ augmented_images.append(img_pil)
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+
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+ st.write("**Augmentation completed!**")
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+ st.image(augmented_images, width=150)
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+
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+ zip_buffer.seek(0)
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+ st.download_button("Download Augmented Images", data=zip_buffer, file_name="augmented_images.zip", mime="application/zip")
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