Update app.py

app.py

@@ -35,10 +35,10 @@ def detect_circles(frame_diff, image_center, min_radius=20, max_radius=200):
     circles = cv2.HoughCircles(
         frame_diff,
         cv2.HOUGH_GRADIENT,
-        dp=1.5,  # Increase resolution for better detection
+        dp=1.5,  # Resolution for better detection
         minDist=100,  # Prevent overlapping circles
-        param1=100,  # Higher edge threshold to reduce noise
-        param2=20,  # Lower accumulator threshold to detect faint circles
+        param1=100,  # Edge threshold to reduce noise
+        param2=20,  # Accumulator threshold to detect faint circles
         minRadius=min_radius,
         maxRadius=max_radius
     )
@@ -74,12 +74,11 @@ def analyze_gif(gif_file):
         image_center = (width // 2, height // 2)  # Assume Sun is at the center
 
         # Initialize results
-        results = []
-        circle_data = []
+        all_circle_data = []  # Store all detected circles
         min_radius = 20
         max_radius = min(height, width) // 2  # Limit max radius to half the image size
 
-        # Process frames
+        # Process frames and detect circles
         for i in range(len(frames) - 1):
             frame1 = preprocess_frame(frames[i])
             frame2 = preprocess_frame(frames[i + 1])
@@ -96,45 +95,40 @@ def analyze_gif(gif_file):
                 # Take the largest circle (most prominent CME feature)
                 largest_circle = max(circles, key=lambda c: c[2])  # Sort by radius
                 x, y, r = largest_circle
-                circle_data.append({
+                all_circle_data.append({
                     "frame": i + 1,
                     "center": (x, y),
-                    "radius": r
+                    "radius": r,
+                    "output_frame": frames[i + 1]  # Store the frame for visualization
                 })
 
-            # Visualize the frame with detected circle
-            output_frame = cv2.cvtColor(frames[i + 1], cv2.COLOR_GRAY2RGB)
-            if circles:
-                x, y, r = largest_circle
-                cv2.circle(output_frame, (x, y), r, (0, 255, 0), 2)
-            # Convert to PIL Image for Gradio
-            output_frame = Image.fromarray(output_frame)
-            results.append(output_frame)
-
-        # Analyze circle data for growth
+        # Filter frames where the circle is growing
+        growing_circle_data = []
+        if all_circle_data:
+            # Start with the first detection
+            growing_circle_data.append(all_circle_data[0])
+            for i in range(1, len(all_circle_data)):
+                # Compare radius with the last growing circle
+                if all_circle_data[i]["radius"] > growing_circle_data[-1]["radius"]:
+                    growing_circle_data.append(all_circle_data[i])
+
+        # Generate output frames and report
+        results = []
         report = "Analysis Report:\n"
-        if circle_data:
-            radii = [c["radius"] for c in circle_data]
-            centers = [c["center"] for c in circle_data]
-            frames_with_circles = [c["frame"] for c in circle_data]
-
-            # Check if radii are increasing over frames
-            is_growing = all(radii[i] < radii[i + 1] for i in range(len(radii) - 1)) if len(radii) > 1 else False
-            center_consistent = all(
-                abs(centers[i][0] - centers[0][0]) < 20 and
-                abs(centers[i][1] - centers[0][1]) < 20
-                for i in range(1, len(centers))
-            ) if len(centers) > 1 else False
-
-            report += f"Detected {len(circle_data)} circles across frames.\n"
-            for c in circle_data:
+        if growing_circle_data:
+            report += f"Detected {len(growing_circle_data)} frames with growing concentric circles:\n"
+            for c in growing_circle_data:
+                # Visualize the frame with detected circle
+                output_frame = cv2.cvtColor(c["output_frame"], cv2.COLOR_GRAY2RGB)
+                cv2.circle(output_frame, (c["center"][0], c["center"][1]), c["radius"], (0, 255, 0), 2)
+                # Convert to PIL Image for Gradio
+                output_frame = Image.fromarray(output_frame)
+                results.append(output_frame)
+
                 report += f"Frame {c['frame']}: Center at {c['center']}, Radius {c['radius']} pixels\n"
-            if is_growing and center_consistent:
-                report += "\nConclusion: Growing concentric circles detected, indicative of a potential Earth-directed CME."
-            else:
-                report += "\nConclusion: Detected circles, but growth pattern or center consistency does not confirm a clear CME."
+            report += "\nConclusion: Growing concentric circles detected, indicative of a potential Earth-directed CME."
         else:
-            report += "No concentric circles detected."
+            report += "No growing concentric circles detected. CME may not be Earth-directed."
 
         return report, results
     except Exception as e:
@@ -146,7 +140,7 @@ iface = gr.Interface(
     inputs=gr.File(label="Upload Solar GIF", file_types=[".gif"]),
     outputs=[
         gr.Textbox(label="Analysis Report"),
-        gr.Gallery(label="Frames with Detected Circles")
+        gr.Gallery(label="Frames with Growing Circles")
     ],
     title="Solar CME Detection",
     description="Upload a GIF of solar images to detect growing concentric circles indicative of Earth-directed coronal mass ejections (CMEs)."