Update app.py

app.py

@@ -1,3 +1,4 @@
+
 import cv2 as cv
 import numpy as np
 import gradio as gr
@@ -6,27 +7,60 @@ from huggingface_hub import hf_hub_download
 
 # Download ONNX model from Hugging Face
 model_path = hf_hub_download(repo_id="opencv/image_classification_mobilenet", filename="image_classification_mobilenetv1_2022apr.onnx")
-top_k = 1
+top_k = 10  # Increased to support variable top_n
 backend_id = cv.dnn.DNN_BACKEND_OPENCV
 target_id = cv.dnn.DNN_TARGET_CPU
 
 # Load MobileNet model
 model = MobileNet(modelPath=model_path, topK=top_k, backendId=backend_id, targetId=target_id)
 
-def classify_image(input_image):
-    image = cv.resize(input_image, (256, 256))
+def add_hsv_noise(image, hue_noise=0, saturation_noise=0, value_noise=0):
+    """Add HSV noise to an image"""
+    if image is None:
+        return None
+
+    # Convert BGR to HSV (OpenCV uses BGR by default)
+    hsv = cv.cvtColor(image, cv.COLOR_BGR2HSV).astype(np.float32)
+
+    # Add noise to each channel
+    hsv[:, :, 0] = np.clip(hsv[:, :, 0] + hue_noise, 0, 179)  # Hue: 0-179
+    hsv[:, :, 1] = np.clip(hsv[:, :, 1] + saturation_noise, 0, 255)  # Saturation: 0-255
+    hsv[:, :, 2] = np.clip(hsv[:, :, 2] + value_noise, 0, 255)  # Value: 0-255
+
+    # Convert back to BGR
+    bgr = cv.cvtColor(hsv.astype(np.uint8), cv.COLOR_HSV2BGR)
+
+    return bgr
+
+def classify_image_with_noise(input_image, top_n, hue_noise, saturation_noise, value_noise):
+    """Classify image with HSV noise applied"""
+    if input_image is None:
+        return None, "Please upload an image first."
+
+    # Apply HSV noise
+    noisy_image = add_hsv_noise(input_image, hue_noise, saturation_noise, value_noise)
+
+    # Resize and crop as in original code
+    image = cv.resize(noisy_image, (256, 256))
     image = image[16:240, 16:240, :]
 
+    # Update model's topK for this inference
+    model.topK = top_n
     result = model.infer(image)
 
-    result_str = "\n".join(f"{label}" for label in result)
-    return result_str
+    # Format results with probabilities if available
+    result_str = "\n".join(f"{i+1}. {label}" for i, label in enumerate(result[:top_n]))
+
+    # Convert BGR to RGB for display in Gradio
+    display_image = cv.cvtColor(noisy_image, cv.COLOR_BGR2RGB)
+
+    return display_image, result_str
 
 def clear_output_on_change(img):
-    return gr.update(value="")
+    return gr.update(value=""), None
 
 def clear_all():
-    return None, None
+    return None, None, ""
 
 with gr.Blocks(css='''.example * {
     font-style: italic;
@@ -34,22 +68,49 @@ with gr.Blocks(css='''.example * {
     color: #0ea5e9 !important;
     }''') as demo:
 
-    gr.Markdown("### Image Classification with MobileNet (OpenCV DNN)")
-    gr.Markdown("Upload an image to classify using a MobileNet model loaded with OpenCV DNN.")
+    gr.Markdown("### Image Classification with MobileNet + HSV Noise Analysis")
+    gr.Markdown("Upload an image and adjust HSV noise sliders to see how it affects MobileNet predictions in real-time.")
 
     with gr.Row():
-        image_input = gr.Image(type="numpy", label="Upload Image")
-        output_box = gr.Textbox(label="Top Prediction(s)")
+        with gr.Column():
+            # Input controls
+            image_input = gr.Image(type="numpy", label="Upload Image")
+
+            gr.Markdown("### Classification Settings")
+            top_n = gr.Slider(minimum=1, maximum=10, value=5, step=1, label="Top N Classes")
+
+            gr.Markdown("### HSV Noise Controls")
+            hue_noise = gr.Slider(minimum=-50, maximum=50, value=0, step=1, label="Hue Noise (-50 to 50)")
+            saturation_noise = gr.Slider(minimum=-100, maximum=100, value=0, step=5, label="Saturation Noise (-100 to 100)")
+            value_noise = gr.Slider(minimum=-100, maximum=100, value=0, step=5, label="Value/Brightness Noise (-100 to 100)")
+
+        with gr.Column():
+            # Output displays
+            noisy_image_output = gr.Image(label="Image with Noise Applied")
+            output_box = gr.Textbox(label="Top Predictions", lines=10, max_lines=15)
 
-    # Clear output when new image is uploaded
-    image_input.change(fn=clear_output_on_change, inputs=image_input, outputs=output_box)
+    # Clear outputs when new image is uploaded
+    image_input.change(fn=clear_output_on_change, inputs=image_input, outputs=[output_box, noisy_image_output])
 
     with gr.Row():
         submit_btn = gr.Button("Submit", variant="primary")
         clear_btn = gr.Button("Clear")
 
-    submit_btn.click(fn=classify_image, inputs=image_input, outputs=output_box)
-    clear_btn.click(fn=clear_all, outputs=[image_input, output_box])
+    # Set up real-time updates for sliders
+    inputs = [image_input, top_n, hue_noise, saturation_noise, value_noise]
+    outputs = [noisy_image_output, output_box]
+
+    # Update predictions when sliders change (real-time)
+    for slider in [top_n, hue_noise, saturation_noise, value_noise]:
+        slider.change(
+            fn=classify_image_with_noise,
+            inputs=inputs,
+            outputs=outputs
+        )
+
+    # Manual submit button
+    submit_btn.click(fn=classify_image_with_noise, inputs=inputs, outputs=outputs)
+    clear_btn.click(fn=clear_all, outputs=[image_input, noisy_image_output, output_box])
 
     gr.Markdown("Click on any example to try it.", elem_classes=["example"])