Update app.py

app.py

@@ -1,114 +1,115 @@
-'''
-author : OzlemAkgunoglu
-github : https://github.com/OzlemAkgunoglu
-Dynamic Photo Filter App 
-This is a Dynamic Photo Filter App that allows you to apply various filters to your images. 
-Adjust brightness, contrast, sharpening, and select a filter for real-time changes.
-And this app is created using OpenCV and Gradio. Thank you for using it. '''
-
-#Let's load the necessary libraries
-import cv2 as cv #OpenCV for image processing
-import numpy as np #Numpy for arrays
-import gradio as gr #Gradio for UI
-
-# Let's define the filter functions
-def apply_grayscale(image):
-    return cv.cvtColor(image, cv.COLOR_BGR2GRAY) #Convert the image to grayscale
-
-#Sepia filter function
-def apply_sepia(image):
-    sepia_filter = np.array([[0.272, 0.534, 0.131], 
-                       [0.349, 0.686, 0.168],
-                       [0.393, 0.769, 0.189]])
-    sepia_image = cv.transform(image, sepia_filter) #Apply the filter
-    return np.clip(sepia_image, 0, 255).astype(np.uint8) #clip to hold values between 0 and 255 prevent excessive brightness or darkening. 
-
-def apply_negative(image):
-    return cv.bitwise_not(image) #Invert the image 
-
-#sketch filter
-'''Apply Gaussian blur to decrease the noise
-      and remove unwanted details in the image for better sketch effect '''
-def apply_sketch(image):
-    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY) #Convert the image to grayscale
-    inv = cv.bitwise_not(gray) #Invert the grayscale image
-    blurred = cv.GaussianBlur(inv, (21, 21), sigmaX=0, sigmaY=0) 
-    sketch_image = cv.divide(gray, 255 - blurred, scale=256)
-    # divide= gray / (255 - blurred) Normalizes the division result to prevent overly high values by scaling with 256
-    return  sketch_image
-
-def apply_sharpen(image, sharpening):
-    sharpening_filter = np.array([[0, -1, 0],
-                       [-1, 5 + sharpening, -1],
-                       [0, -1, 0]])
-    return cv.filter2D(image, -1, sharpening_filter) #Apply the filter each pixel is multiplied by the value in the kernel
-def apply_edge_detection(image):
-    return cv.Canny(image, 100, 200)
-def apply_fall_filter(frame):
-    fall_filter = np.array([[0.393, 0.769, 0.189],
-                            [0.349, 0.686, 0.168],
-                            [0.272, 0.534, 0.131]])
-    return cv.transform(frame, fall_filter)
-
-
-# Dictionary to map filter names to functions
-filter_functions = {
-    "Grayscale": apply_grayscale,
-    "Sepia": apply_sepia,
-    "Negative": apply_negative,
-    "Sketch": apply_sketch,
-    "Sharpen": apply_sharpen,
-    "Edge Detection": apply_edge_detection,
-    "Fall": apply_fall_filter
-}
-
-# Main function to apply selected filters
-def apply_filters(image, filter_type, brightness, contrast, sharpening):
-    if image is None:
-        print("Input image is empty!") #for debugging
-        return None  # Return None if the input image is empty
-    
-    # Adjust brightness and contrast
-    image = cv.convertScaleAbs(image, alpha=contrast, beta=brightness)
-
-    #for debugging
-    #print("Image after brightness and contrast adjustment:", image) 
-    
-
-    # Apply the selected filter from dictionary called filter_functions in line 53
-    if filter_type in filter_functions:
-        if filter_type == "Sharpen":
-            image = filter_functions[filter_type](image, sharpening) # Calls the Sharpen filter with the sharpening parameter for custom sharpening level 
-        else:
-            image = filter_functions[filter_type](image)
-        
-    return image
-
-# Define Interface
-with gr.Blocks() as app:
-    # Title and Description
-    gr.Markdown("<h1 style='text-align: center;'>Dynamic Photo Filter App</h1>")
-    gr.Markdown("This app allows you to apply various filters to your images. Adjust brightness, contrast, sharpening, and select a filter for real-time changes.")
-
-    # Choices and Sliders at the Top
-    with gr.Row():
-        filter_choice = gr.Radio(["Original", "Grayscale", "Sketch", "Sepia", "Negative", "Sharpen", "Edge Detection","Fall"],label="Filter")
-        
-        with gr.Column():
-            brightness_slider = gr.Slider(-100, 100, step=1, label="Brightness", value=0)
-            contrast_slider = gr.Slider(0.5, 3.0, step=0.1, label="Contrast", value=1.0)
-            sharpening_slider = gr.Slider(0, 5, step=0.1, label="Sharpening", value=0) 
-
-    # Horizontal display of the images 
-    with gr.Row():
-        image_input = gr.Image(label="Upload Image", type="numpy")
-        image_output = gr.Image(label="Filtered Image")
-
-    # Link events for real-time updates
-    image_input.change(apply_filters, inputs=[image_input, filter_choice, brightness_slider, contrast_slider, sharpening_slider], outputs=image_output)
-    filter_choice.change(apply_filters, inputs=[image_input, filter_choice, brightness_slider, contrast_slider, sharpening_slider], outputs=image_output)
-    brightness_slider.change(apply_filters, inputs=[image_input, filter_choice, brightness_slider, contrast_slider, sharpening_slider], outputs=image_output)
-    contrast_slider.change(apply_filters, inputs=[image_input, filter_choice, brightness_slider, contrast_slider, sharpening_slider], outputs=image_output)
-    sharpening_slider.change(apply_filters, inputs=[image_input, filter_choice, brightness_slider, contrast_slider, sharpening_slider], outputs=image_output)
-
+'''
+author : OzlemAkgunoglu
+github : https://github.com/OzlemAkgunoglu
+Dynamic Photo Filter App 
+This is a Dynamic Photo Filter App that allows you to apply various filters to your images. 
+Adjust brightness, contrast, sharpening, and select a filter for real-time changes.
+And this app is created using OpenCV and Gradio. Thank you for using it. '''
+
+#Let's load the necessary libraries
+import cv2 as cv #OpenCV for image processing
+import numpy as np #Numpy for arrays
+import gradio as gr #Gradio for UI
+
+# Let's define the filter functions
+def apply_grayscale(image):
+    return cv.cvtColor(image, cv.COLOR_BGR2GRAY) #Convert the image to grayscale
+
+#Sepia filter function
+def apply_sepia(image):
+    sepia_filter = np.array([[0.272, 0.534, 0.131], 
+                       [0.349, 0.686, 0.168],
+                       [0.393, 0.769, 0.189]])
+    sepia_image = cv.transform(image, sepia_filter) #Apply the filter
+    return np.clip(sepia_image, 0, 255).astype(np.uint8) #clip to hold values between 0 and 255 prevent excessive brightness or darkening. 
+
+def apply_negative(image):
+    return cv.bitwise_not(image) #Invert the image 
+
+#sketch filter
+'''Apply Gaussian blur to decrease the noise
+      and remove unwanted details in the image for better sketch effect '''
+def apply_sketch(image):
+    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY) #Convert the image to grayscale
+    inv = cv.bitwise_not(gray) #Invert the grayscale image
+    blurred = cv.GaussianBlur(inv, (21, 21), sigmaX=0, sigmaY=0) 
+    sketch_image = cv.divide(gray, 255 - blurred, scale=256)
+    # divide= gray / (255 - blurred) Normalizes the division result to prevent overly high values by scaling with 256
+    return  sketch_image
+
+def apply_sharpen(image, sharpening):
+    sharpening_filter = np.array([[0, -1, 0],
+                       [-1, 5 + sharpening, -1],
+                       [0, -1, 0]])
+    return cv.filter2D(image, -1, sharpening_filter) #Apply the filter each pixel is multiplied by the value in the kernel
+def apply_edge_detection(image):
+    return cv.Canny(image, 100, 200)
+def apply_fall_filter(frame):
+    fall_filter = np.array([[0.393, 0.769, 0.189],
+                            [0.349, 0.686, 0.168],
+                            [0.272, 0.534, 0.131]])
+    return cv.transform(frame, fall_filter)
+
+
+# Dictionary to map filter names to functions
+filter_functions = {
+    "Grayscale": apply_grayscale,
+    "Sepia": apply_sepia,
+    "Negative": apply_negative,
+    "Sketch": apply_sketch,
+    "Sharpen": apply_sharpen,
+    "Edge Detection": apply_edge_detection,
+    "Fall": apply_fall_filter
+}
+
+# Main function to apply selected filters
+def apply_filters(image, filter_type, brightness, contrast, sharpening):
+    if image is None:
+        print("Input image is empty!") #for debugging
+        return None  # Return None if the input image is empty
+    
+    # Adjust brightness and contrast
+    image = cv.convertScaleAbs(image, alpha=contrast, beta=brightness)
+
+    #for debugging
+    #print("Image after brightness and contrast adjustment:", image) 
+    
+
+    # Apply the selected filter from dictionary called filter_functions in line 53
+    if filter_type in filter_functions:
+        if filter_type == "Sharpen":
+            image = filter_functions[filter_type](image, sharpening) # Calls the Sharpen filter with the sharpening parameter for custom sharpening level 
+        else:
+            image = filter_functions[filter_type](image)
+        
+    return image
+
+# Define Interface
+with gr.Blocks(css="""
+    .gradio-container { background-color: #f7f7f7; }""") as app:
+    # Title and Description
+    gr.Markdown("<h1 style='text-align: center; font-family: Arial, sans-serif; color: #333;'>Dynamic Photo Filter App</h1>")
+    gr.Markdown("<p style='text-align: center; color: #666;'>Apply professional photo filters with adjustable brightness, contrast, and sharpness. Perfect your images instantly!</p>")
+
+    # Choices and Sliders at the Top
+    with gr.Row():
+        filter_choice = gr.Radio(["Original", "Grayscale", "Sketch", "Sepia", "Negative", "Sharpen", "Edge Detection","Fall"],label="Filter")
+        
+        with gr.Column():
+            brightness_slider = gr.Slider(-100, 100, step=1, label="Brightness", value=0)
+            contrast_slider = gr.Slider(0.5, 3.0, step=0.1, label="Contrast", value=1.0)
+            sharpening_slider = gr.Slider(0, 5, step=0.1, label="Sharpening", value=0) 
+
+    # Horizontal display of the images 
+    with gr.Row():
+        image_input = gr.Image(label="Upload Image", type="numpy")
+        image_output = gr.Image(label="Filtered Image")
+
+    # Link events for real-time updates
+    image_input.change(apply_filters, inputs=[image_input, filter_choice, brightness_slider, contrast_slider, sharpening_slider], outputs=image_output)
+    filter_choice.change(apply_filters, inputs=[image_input, filter_choice, brightness_slider, contrast_slider, sharpening_slider], outputs=image_output)
+    brightness_slider.change(apply_filters, inputs=[image_input, filter_choice, brightness_slider, contrast_slider, sharpening_slider], outputs=image_output)
+    contrast_slider.change(apply_filters, inputs=[image_input, filter_choice, brightness_slider, contrast_slider, sharpening_slider], outputs=image_output)
+    sharpening_slider.change(apply_filters, inputs=[image_input, filter_choice, brightness_slider, contrast_slider, sharpening_slider], outputs=image_output)
+
 app.launch(share=True)