deploy

README.md

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+---
+title: Image Processing Streamlit
+emoji: 🏆
+colorFrom: blue
+colorTo: blue
+sdk: streamlit
+sdk_version: 1.42.2
+app_file: app.py
+pinned: false
+---

app.py

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+import streamlit as st
+import cv2
+import numpy as np
+from PIL import Image
+
+# Function to process the image
+def process_image(image, operation, value=None):
+    if operation == "🖼 Original":
+        return image
+    elif operation == "⚫ Convert to Grayscale":
+        return cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+    elif operation == "🌈 Convert to Color":
+        return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    elif operation == "🔄 Rotate":
+        if value is not None:
+            return rotate_image_without_cropping(image, value)
+    elif operation == "🌀 Blur":
+        if value is not None:
+            kernel_size = (value, value)
+            return cv2.GaussianBlur(image, kernel_size, 0)
+    elif operation == "✂ Edge Detection":
+        return cv2.Canny(image, 100, 200)
+    elif operation == "☀ Adjust Brightness":
+        if value is not None:
+            hsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
+            h, s, v = cv2.split(hsv)
+            v = np.clip(v.astype(np.int16) + value, 0, 255).astype(np.uint8)
+            final_hsv = cv2.merge((h, s, v))
+            return cv2.cvtColor(final_hsv, cv2.COLOR_HSV2RGB)
+    return image
+
+# Fix rotation issue (avoids cropping & mirroring)
+def rotate_image_without_cropping(image, angle):
+    (h, w) = image.shape[:2]
+    center = (w // 2, h // 2)
+
+    # Compute the rotation matrix
+    M = cv2.getRotationMatrix2D(center, angle, 1.0)
+
+    # Compute the new bounding box size
+    cos = np.abs(M[0, 0])
+    sin = np.abs(M[0, 1])
+    new_w = int((h * sin) + (w * cos))
+    new_h = int((h * cos) + (w * sin))
+
+    # Adjust transformation matrix to fit in new bounds
+    M[0, 2] += (new_w / 2) - center[0]
+    M[1, 2] += (new_h / 2) - center[1]
+
+    # Apply rotation with expanded borders
+    rotated = cv2.warpAffine(image, M, (new_w, new_h), borderMode=cv2.BORDER_CONSTANT, borderValue=(0, 0, 0))
+
+    return rotated
+
+# Streamlit app
+def main():
+    st.title(" Image Processing App ")
+    st.write("📂 Upload an image and apply various transformations below! ✨")
+
+    uploaded_file = st.file_uploader("📤 Choose an image...", type=["jpg", "jpeg", "png"])
+
+    if uploaded_file is not None:
+        # Read image
+        image = Image.open(uploaded_file)
+        img_array = np.array(image)
+        img_bgr = cv2.cvtColor(img_array, cv2.COLOR_RGB2BGR)
+
+        col1, col2 = st.columns(2)
+
+        with col1:
+            st.subheader("📌 Original Image")
+            st.image(image, use_container_width=True)
+
+        with col2:
+            st.subheader("🖌 Processed Image")
+
+            operation = st.selectbox(
+                "🎛 Select an Operation",
+                ["🖼 Original", "⚫ Convert to Grayscale", "🌈 Convert to Color", 
+                 "🔄 Rotate", "🌀 Blur", "✂ Edge Detection", "☀ Adjust Brightness"]
+            )
+
+            processed_img = img_bgr.copy()
+
+            if operation == "🔄 Rotate":
+                angle = st.slider("↩ Rotation Angle", -180, 180, 0)
+                processed_img = process_image(img_bgr, operation, angle)
+            elif operation == "☀ Adjust Brightness":
+                brightness = st.slider("🔆 Brightness Level", -100, 100, 0)
+                processed_img = process_image(img_bgr, operation, brightness)
+            elif operation == "🌀 Blur":
+                blur_level = st.slider("🎭 Blur Intensity", 1, 25, 5, step=2)  # Ensures odd kernel size
+                processed_img = process_image(img_bgr, operation, blur_level)
+            else:
+                processed_img = process_image(img_bgr, operation)
+
+            # Convert back to RGB
+            if len(processed_img.shape) == 2:
+                processed_img_rgb = cv2.cvtColor(processed_img, cv2.COLOR_GRAY2RGB)
+            else:
+                processed_img_rgb = cv2.cvtColor(processed_img, cv2.COLOR_BGR2RGB)
+
+            st.image(processed_img_rgb, use_container_width=True)
+
+        # Ensure grayscale images are properly handled before download
+        if len(processed_img.shape) == 2:
+            processed_img = cv2.cvtColor(processed_img, cv2.COLOR_GRAY2BGR)
+
+        is_success, buffer = cv2.imencode(".png", processed_img)
+        if is_success:
+            st.download_button(
+                label="📥 Download Processed Image",
+                data=buffer.tobytes(),
+                file_name="processed_image.png",
+                mime="image/png"
+            )
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+streamlit>=1.0.0
+opencv-python>=4.5.0
+numpy>=1.19.0
+Pillow>=8.0.0