Update app.py

app.py

@@ -29,7 +29,7 @@ if uploaded_file is not None:
 
     # Display original image
     st.subheader("Original Image")
-    st.image(image, caption="Uploaded Image", use_column_width=True)
+    st.image(image, caption="Uploaded Image", use_container_width=True)
 
     # Enhance Options
     st.subheader("Enhancement Options")
@@ -38,32 +38,53 @@ if uploaded_file is not None:
         "Contrast Enhancement",
         "Sharpening",
         "Denoising",
-        "Edge Detection"
+        "Edge Detection",
+        "Apply All Enhancements"
     ])
 
+    enhanced_image = image_np
+
     if enhancement == "Brightness Adjustment":
         brightness = st.slider("Adjust Brightness", -100, 100, 0)
-        enhanced_image = cv2.convertScaleAbs(image_np, beta=brightness)
+        enhanced_image = cv2.convertScaleAbs(enhanced_image, beta=brightness)
     elif enhancement == "Contrast Enhancement":
         contrast = st.slider("Adjust Contrast", 1.0, 3.0, 1.0)
         alpha = contrast  # Simple contrast control
         beta = 0          # Simple brightness control
-        enhanced_image = cv2.convertScaleAbs(image_np, alpha=alpha, beta=beta)
+        enhanced_image = cv2.convertScaleAbs(enhanced_image, alpha=alpha, beta=beta)
     elif enhancement == "Sharpening":
         kernel = np.array([[0, -1, 0],
                            [-1, 5, -1],
                            [0, -1, 0]])
-        enhanced_image = cv2.filter2D(image_np, -1, kernel)
+        enhanced_image = cv2.filter2D(enhanced_image, -1, kernel)
     elif enhancement == "Denoising":
-        enhanced_image = cv2.fastNlMeansDenoisingColored(image_np, None, 10, 10, 7, 21)
+        h = st.slider("Denoising Strength", 1, 20, 10)
+        enhanced_image = cv2.fastNlMeansDenoisingColored(enhanced_image, None, h, h, 7, 21)
     elif enhancement == "Edge Detection":
-        gray_image = cv2.cvtColor(image_np, cv2.COLOR_BGR2GRAY)
-        edges = cv2.Canny(gray_image, 100, 200)
+        threshold1 = st.slider("Threshold 1", 50, 200, 100)
+        threshold2 = st.slider("Threshold 2", 50, 300, 200)
+        gray_image = cv2.cvtColor(enhanced_image, cv2.COLOR_BGR2GRAY)
+        edges = cv2.Canny(gray_image, threshold1, threshold2)
         enhanced_image = cv2.cvtColor(edges, cv2.COLOR_GRAY2BGR)
+    elif enhancement == "Apply All Enhancements":
+        # Apply all filters sequentially
+        brightness = 50
+        contrast = 1.5
+        h = 10
+        kernel = np.array([[0, -1, 0],
+                           [-1, 5, -1],
+                           [0, -1, 0]])
+        gray_image = cv2.cvtColor(enhanced_image, cv2.COLOR_BGR2GRAY)
+        edges = cv2.Canny(gray_image, 100, 200)
+        enhanced_image = cv2.convertScaleAbs(enhanced_image, beta=brightness)
+        enhanced_image = cv2.convertScaleAbs(enhanced_image, alpha=contrast, beta=0)
+        enhanced_image = cv2.filter2D(enhanced_image, -1, kernel)
+        enhanced_image = cv2.fastNlMeansDenoisingColored(enhanced_image, None, h, h, 7, 21)
+        edges = cv2.cvtColor(edges, cv2.COLOR_GRAY2BGR)
 
     # Display Enhanced Image
     st.subheader("Enhanced Image")
-    st.image(enhanced_image, caption="Enhanced Image", use_column_width=True)
+    st.image(enhanced_image, caption="Enhanced Image", use_container_width=True)
 
     # Download Option
     st.subheader("Download Enhanced Image")
@@ -74,6 +95,3 @@ if uploaded_file is not None:
         file_name="enhanced_image.png",
         mime="image/png"
     )
-
-# Hugging Face Integration
-st.markdown("**Note:** Ensure this app is running on Hugging Face Spaces with the required dependencies listed in the `requirements.txt` file.")