✨ feat(ui): add sharpen and noise augmentation

- add new sharpen sliders for image processing
- add new noise sliders for image processing

🐛 fix(utils): correct augmentation method assignment

- correctly set default augmentations methods
- allow Dynamic settings for sharpen

📝 docs(ui): update Image UI with new Augmnents

- add new augment methods UI
- current weights of image slider
- display of image prediction timestamp

app.py

@@ -216,21 +216,27 @@ with gr.Blocks() as iface:
         with gr.Column(scale=1):
             image_input = gr.Image(label="Upload Image to Analyze", sources=['upload'], type='pil')
             with gr.Accordion("Settings", open=False, elem_id="settings_accordion"):
-                confidence_slider = gr.Slider(0.0, 1.0, value=0.5, step=0.01, label="Confidence Threshold")
                 augment_checkboxgroup = gr.CheckboxGroup(["rotate", "add_noise", "sharpen"], label="Augmentation Methods")
                 rotate_slider = gr.Slider(0, 360, value=0, step=1, label="Rotate Degrees", visible=False)
-            inputs = [image_input, confidence_slider, augment_checkboxgroup, rotate_slider]
+                noise_slider = gr.Slider(0, 100, value=0, step=1, label="Noise Level", visible=False)
+                sharpen_slider = gr.Slider(0, 100, value=1, step=1, label="Sharpen Strength", visible=False)
+                confidence_slider = gr.Slider(0.0, 1.0, value=0.5, step=0.01, label="Confidence Threshold")
+            inputs = [image_input, confidence_slider, augment_checkboxgroup, rotate_slider, noise_slider, sharpen_slider]
             predict_button = gr.Button("Predict")
+            image_output = gr.Image(label="Processed Image", visible=True)
+
         with gr.Column(scale=2):
             with gr.Accordion("Project OpenSight - Model Evaluations & Playground", open=False, elem_id="project_accordion"):
                 gr.Markdown("## OpenSight is a SOTA gen. image detection model, in pre-release prep.\n\nThis HF Space is a temporary home for us and the public to evaluate the shortcomings of current open source models.\n\n<-- Feel free to play around by starting with an image as we prepare our formal announcement.")
-            image_output = gr.Image(label="Processed Image", visible=False)
             # Custom HTML component to display results in 5 columns
             results_html = gr.HTML(label="Model Predictions")
             outputs = [image_output, results_html]
     
+    # Show/hide rotate slider based on selected augmentation method
     augment_checkboxgroup.change(lambda methods: gr.update(visible="rotate" in methods), inputs=[augment_checkboxgroup], outputs=[rotate_slider])
-
+    augment_checkboxgroup.change(lambda methods: gr.update(visible="add_noise" in methods), inputs=[augment_checkboxgroup], outputs=[noise_slider])
+    augment_checkboxgroup.change(lambda methods: gr.update(visible="sharpen" in methods), inputs=[augment_checkboxgroup], outputs=[sharpen_slider])
+    
     predict_button.click(
         fn=predict_image_with_html, 
         inputs=inputs, 

utils/utils.py

@@ -7,15 +7,15 @@ def softmax(vector):
     e = np.exp(vector - np.max(vector))  # for numerical stability
     return e / e.sum()
 
-def augment_image(img_pil, methods, rotate_degrees=0):
+def augment_image(img_pil, methods, rotate_degrees=0, noise_level=0, sharpen_strength=1):
     for method in methods:
         if method == "rotate":
             img_pil = img_pil.rotate(rotate_degrees)
         elif method == "add_noise":
-            noise = np.random.normal(0, 25, img_pil.size[::-1] + (3,)).astype(np.uint8)
+            noise = np.random.normal(0, noise_level, img_pil.size[::-1] + (3,)).astype(np.uint8)
             img_pil = Image.fromarray(np.clip(np.array(img_pil) + noise, 0, 255).astype(np.uint8))
         elif method == "sharpen":
-            img_pil = img_pil.filter(ImageFilter.SHARPEN)
+            img_pil = img_pil.filter(ImageFilter.UnsharpMask(radius=2, percent=sharpen_strength, threshold=3))
     return img_pil, img_pil
 
 def convert_pil_to_bytes(image, format='JPEG'):