Update app.py

app.py

@@ -20,31 +20,29 @@ def extract_frames(gif_path):
     except Exception as e:
         return None, f"Error loading GIF: {str(e)}"
 
-def preprocess_frame(frame):
+def preprocess_frame(frame, lower_bound, upper_bound, morph_iterations):
     """Preprocess a frame: isolate mid-to-light pixels and enhance circular patterns."""
     # Apply Gaussian blur to reduce noise
     blurred = cv2.GaussianBlur(frame, (9, 9), 0)
 
-    # Isolate mid-to-light pixels (intensity range 100–200 in grayscale)
-    lower_bound = 150
-    upper_bound = 255
+    # Isolate mid-to-light pixels using user-defined intensity range
     mask = cv2.inRange(blurred, lower_bound, upper_bound)
 
     # Apply morphological operation to enhance circular patterns
     kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
-    enhanced = cv2.dilate(mask, kernel, iterations=2)
+    enhanced = cv2.dilate(mask, kernel, iterations=morph_iterations)
 
     return enhanced
 
-def detect_circles(frame_diff, image_center, min_radius=20, max_radius=200):
+def detect_circles(frame_diff, image_center, center_tolerance, param1, param2, min_radius=20, max_radius=200):
     """Detect circles in a frame difference image, centered at the Sun."""
     circles = cv2.HoughCircles(
         frame_diff,
         cv2.HOUGH_GRADIENT,
         dp=1.5,  # Resolution for better detection
         minDist=100,  # Prevent overlapping circles
-        param1=80,  # Lower edge threshold to capture fainter edges
-        param2=15,  # Lower accumulator threshold to detect faint circles
+        param1=param1,  # User-defined edge threshold
+        param2=param2,  # User-defined accumulator threshold
         minRadius=min_radius,
         maxRadius=max_radius
     )
@@ -53,7 +51,6 @@ def detect_circles(frame_diff, image_center, min_radius=20, max_radius=200):
         circles = np.round(circles[0, :]).astype("int")
         # Filter circles: only keep those centered near the image center
         filtered_circles = []
-        center_tolerance = 30  # Allow 30-pixel deviation from the center
         for (x, y, r) in circles:
             if (abs(x - image_center[0]) < center_tolerance and 
                 abs(y - image_center[1]) < center_tolerance):
@@ -61,7 +58,7 @@ def detect_circles(frame_diff, image_center, min_radius=20, max_radius=200):
         return filtered_circles if filtered_circles else None
     return None
 
-def analyze_gif(gif_file):
+def analyze_gif(gif_file, lower_bound, upper_bound, param1, param2, center_tolerance, morph_iterations):
     """Analyze a GIF for growing concentric circles of mid-to-light pixels."""
     try:
         # Handle Gradio file input
@@ -86,8 +83,8 @@ def analyze_gif(gif_file):
 
         # Process frames and detect circles
         for i in range(len(frames) - 1):
-            frame1 = preprocess_frame(frames[i])
-            frame2 = preprocess_frame(frames[i + 1])
+            frame1 = preprocess_frame(frames[i], lower_bound, upper_bound, morph_iterations)
+            frame2 = preprocess_frame(frames[i + 1], lower_bound, upper_bound, morph_iterations)
 
             # Compute absolute difference between consecutive frames
             frame_diff = cv2.absdiff(frame2, frame1)
@@ -95,7 +92,7 @@ def analyze_gif(gif_file):
             frame_diff = cv2.convertScaleAbs(frame_diff, alpha=3.0, beta=0)
 
             # Detect circles centered at the Sun
-            circles = detect_circles(frame_diff, image_center, min_radius, max_radius)
+            circles = detect_circles(frame_diff, image_center, center_tolerance, param1, param2, min_radius, max_radius)
 
             if circles:
                 # Take the largest circle (most prominent CME feature)
@@ -120,7 +117,7 @@ def analyze_gif(gif_file):
 
         # Generate output frames and report
         results = []
-        report = "Analysis Report (as of 07:34 PM PDT, May 24, 2025):\n"
+        report = "Analysis Report (as of 07:44 PM PDT, May 24, 2025):\n"
         if growing_circle_data:
             report += f"Detected {len(growing_circle_data)} frames with growing concentric circles of mid-to-light pixels:\n"
             for c in growing_circle_data:
@@ -140,16 +137,24 @@ def analyze_gif(gif_file):
     except Exception as e:
         return f"Error during analysis: {str(e)}", []
 
-# Gradio interface
+# Gradio interface with user controls
 iface = gr.Interface(
     fn=analyze_gif,
-    inputs=gr.File(label="Upload Solar GIF", file_types=[".gif"]),
+    inputs=[
+        gr.File(label="Upload Solar GIF", file_types=[".gif"]),
+        gr.Slider(minimum=0, maximum=255, value=100, step=1, label="Lower Intensity Bound (0-255)"),
+        gr.Slider(minimum=0, maximum=255, value=200, step=1, label="Upper Intensity Bound (0-255)"),
+        gr.Slider(minimum=10, maximum=200, value=80, step=1, label="Hough Param1 (Edge Threshold)"),
+        gr.Slider(minimum=1, maximum=50, value=15, step=1, label="Hough Param2 (Accumulator Threshold)"),
+        gr.Slider(minimum=10, maximum=100, value=30, step=1, label="Center Tolerance (Pixels)"),
+        gr.Slider(minimum=1, maximum=5, value=2, step=1, label="Morphological Dilation Iterations")
+    ],
     outputs=[
         gr.Textbox(label="Analysis Report"),
         gr.Gallery(label="Frames with Growing Circles")
     ],
     title="Solar CME Detection",
-    description="Upload a GIF of solar images to detect growing concentric circles of mid-to-light pixels indicative of Earth-directed coronal mass ejections (CMEs)."
+    description="Upload a GIF of solar images to detect growing concentric circles of mid-to-light pixels indicative of Earth-directed coronal mass ejections (CMEs). Adjust the sliders to fine-tune detection."
 )
 
 if __name__ == "__main__":