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app.py

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+import streamlit as st
+import cv2
+import numpy as np
+from PIL import Image
+import io
+import base64
+
+st.set_page_config(page_title="Image Augmentation Tool 🧰", page_icon="🖼️", layout="centered")
+
+
+def get_base64_image(image_path):
+    with open(image_path, "rb") as f:
+        data = f.read()
+    return base64.b64encode(data).decode()
+
+image_base64 = get_base64_image("innomatics-footer-logo.png")
+
+st.markdown(
+    f"""
+    <div style='text-align: center;'>
+        <img src="data:image/png;base64,{image_base64}" width="600" height="100">
+    </div>
+    """,
+    unsafe_allow_html=True
+)
+
+st.markdown("<h1 style='text-align: center;'>🖼️ Image Augmentation Tool 🎨</h1>", unsafe_allow_html=True)
+st.markdown("<h4 style='text-align: center;'>✨ Apply real-time transformations to your images</h4>", unsafe_allow_html=True)
+st.markdown("---")
+
+# Upload section
+st.subheader("📤 Upload Your Image")
+uploaded_file = st.file_uploader("Choose an image file (jpg, jpeg, png)", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
+    image = cv2.imdecode(file_bytes, 1)
+    original_image = image.copy()
+
+    # Sidebar options
+    st.sidebar.title("🛠️ Transformations Panel")
+    st.sidebar.markdown("🔄 Choose your desired transformations below:")
+
+    grayscale = st.sidebar.checkbox("🖤 Convert to Grayscale")
+    rotate_angle = st.sidebar.slider("🌀 Rotate", -180, 180, 0)
+    flip_horizontal = st.sidebar.checkbox("↔️ Flip Horizontally")
+    flip_vertical = st.sidebar.checkbox("↕️ Flip Vertically")
+    shear_factor = st.sidebar.slider("📐 Shearing", -0.5, 0.5, 0.0, step=0.01)
+    translate_x = st.sidebar.slider("➡️ Translate X", -100, 100, 0)
+    translate_y = st.sidebar.slider("⬇️ Translate Y", -100, 100, 0)
+    crop = st.sidebar.checkbox("✂️ Crop Image")
+
+    if crop:
+        crop_x = st.sidebar.slider("🔲 Crop Start X", 0, image.shape[1] - 1, 0)
+        crop_y = st.sidebar.slider("🔳 Crop Start Y", 0, image.shape[0] - 1, 0)
+        crop_w = st.sidebar.slider("🔲 Crop Width", 1, image.shape[1] - crop_x, image.shape[1] - crop_x)
+        crop_h = st.sidebar.slider("🔳 Crop Height", 1, image.shape[0] - crop_y, image.shape[0] - crop_y)
+
+    # Apply transformations
+    try:
+        if grayscale:
+            image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+            image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
+
+        if rotate_angle != 0:
+            (h, w) = image.shape[:2]
+            M = cv2.getRotationMatrix2D((w // 2, h // 2), rotate_angle, 1.0)
+            image = cv2.warpAffine(image, M, (w, h))
+
+        if flip_horizontal:
+            image = cv2.flip(image, 1)
+
+        if flip_vertical:
+            image = cv2.flip(image, 0)
+
+        if shear_factor != 0.0:
+            rows, cols, _ = image.shape
+            M = np.float32([
+                [1, shear_factor, 0],
+                [0, 1, 0]
+            ])
+            image = cv2.warpAffine(image, M, (cols, rows))
+
+        if translate_x != 0 or translate_y != 0:
+            M = np.float32([
+                [1, 0, translate_x],
+                [0, 1, translate_y]
+            ])
+            image = cv2.warpAffine(image, M, (image.shape[1], image.shape[0]))
+
+        if crop:
+            image = image[crop_y:crop_y + crop_h, crop_x:crop_x + crop_w]
+
+        # Display results
+        st.subheader("🔍 Preview")
+        col1, col2 = st.columns(2)
+        with col1:
+            st.image(original_image, caption="🖼️ Original Image", use_container_width=True, channels="BGR")
+        with col2:
+            st.image(image, caption="✨ Transformed Image", use_container_width=True, channels="BGR")
+
+        # Download button
+        img_pil = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+        buf = io.BytesIO()
+        img_pil.save(buf, format="PNG")
+        byte_im = buf.getvalue()
+
+        st.download_button(
+            label="📥 Download Transformed Image",
+            data=byte_im,
+            file_name="transformed_image.png",
+            mime="image/png"
+        )
+
+    except Exception as e:
+        st.error(f"🚨 Error: {e}")
+
+else:
+    st.info("📎 Please upload an image to begin.")
+

requirements.txt

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+Pillow
+streamlit
+opencv-python
+numpy
+base64