Update app.py

app.py

@@ -11,12 +11,12 @@ from bs4 import BeautifulSoup
 
 # Default parameters
 low_int = 10
-high_int = 200
+high_int = 150
 edge_thresh = 100
 accum_thresh = 45
-center_tol = 60
+center_tol = 30
 morph_dia = 3
-min_rad = 50
+min_rad = 70
 
 def fetch_sdo_images(max_images, size="1024by960", tool="ccor1"):
     """Fetch SDO images from NOAA URL directory."""
@@ -171,7 +171,7 @@ def analyze_images(frames, lower_bound, upper_bound, param1, param2, center_tole
                     "output_frame": frames[i + 1]
                 })
 
-        # Find growing series
+        # Find growing series (indicative of CMEs)
         growing_circle_data = []
         current_series = []
         if all_circle_data:
@@ -191,24 +191,28 @@ def analyze_images(frames, lower_bound, upper_bound, param1, param2, center_tole
         report = f"Analysis Report (as of {datetime.now().strftime('%I:%M %p PDT, %B %d, %Y')}):\n"
 
         # Prepare output based on display mode
-        if display_mode == "All Frames":
+        if display_mode == "Raw Frames":
             results = [Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)) for frame in frames]
-        elif display_mode == "Detected Frames":
-            for c in all_circle_data:
-                output_frame = cv2.cvtColor(c["output_frame"], cv2.COLOR_GRAY2RGB)
-                cv2.circle(output_frame, c["center"], c["radius"], (0, 255, 0), 2)  # Green for detected
-                if c["frame"] in growing_frames:
-                    cv2.circle(output_frame, c["center"], c["radius"] + 2, (255, 165, 0), 2)  # Orange for growing
-                results.append(Image.fromarray(output_frame))
-        elif display_mode == "Both (Detected Replaces Original)":
+        elif display_mode == "CME Detection":
+            for i, frame in enumerate(frames):
+                if i + 1 in growing_frames:
+                    for c in growing_circle_data:
+                        if c["frame"] == i + 1:
+                            output_frame = cv2.cvtColor(c["output_frame"], cv2.COLOR_GRAY2RGB)
+                            cv2.circle(output_frame, c["center"], c["radius"], (255, 255, 0), 2)  # Yellow for CME
+                            results.append(Image.fromarray(output_frame))
+                            break
+                else:
+                    results.append(Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)))
+        elif display_mode == "All Detection":
             for i, frame in enumerate(frames):
                 if i + 1 in [c["frame"] for c in all_circle_data]:
                     for c in all_circle_data:
                         if c["frame"] == i + 1:
                             output_frame = cv2.cvtColor(c["output_frame"], cv2.COLOR_GRAY2RGB)
-                            cv2.circle(output_frame, c["center"], c["radius"], (0, 255, 0), 2)  # Green
+                            cv2.circle(output_frame, c["center"], c["radius"], (0, 255, 0), 2)  # Green for detected
                             if c["frame"] in growing_frames:
-                                cv2.circle(output_frame, c["center"], c["radius"] + 2, (255, 165, 0), 2)  # Orange
+                                cv2.circle(output_frame, c["center"], c["radius"] + 2, (255, 255, 0), 2)  # Yellow for CME
                             results.append(Image.fromarray(output_frame))
                             break
                 else:
@@ -223,7 +227,7 @@ def analyze_images(frames, lower_bound, upper_bound, param1, param2, center_tole
             report += "No circles detected.\n"
 
         if growing_circle_data:
-            report += f"\nSeries of Frames with Growing Circles ({len(growing_circle_data)} frames):\n"
+            report += f"\nSeries of Frames with Growing Circles (CMEs) ({len(growing_circle_data)} frames):\n"
             for c in growing_circle_data:
                 report += f"Frame {c['frame']}: Center at {c['center']}, Radius {c['radius']} pixels\n"
             report += "\nConclusion: Growing concentric circles detected, indicative of a potential Earth-directed CME."
@@ -284,7 +288,7 @@ with gr.Blocks(title="Solar CME Detection") as demo:
     gr.Markdown("""
     # Solar CME Detection
     Upload a GIF or fetch SDO images to detect concentric circles indicative of coronal mass ejections (CMEs). 
-    Green circles mark detected features; orange circles highlight growing series (potential Earth-directed CMEs).
+    Yellow circles mark growing series (potential CMEs); green circles mark other detected features.
     """)
     
     # State to store fetched frames
@@ -301,18 +305,18 @@ with gr.Blocks(title="Solar CME Detection") as demo:
         morph_iterations = gr.Slider(minimum=1, maximum=5, value=morph_dia, step=1, label="Morphological Dilation Iterations")
         min_rad = gr.Slider(minimum=1, maximum=100, value=min_rad, step=1, label="Minimum Circle Radius")
         display_mode = gr.Dropdown(
-            choices=["All Frames", "Detected Frames", "Both (Detected Replaces Original)"],
-            value="Both (Detected Replaces Original)",
+            choices=["Raw Frames", "CME Detection", "All Detection"],
+            value="All Detection",
             label="Display Mode"
         )
-        scale_to_512 = gr.Checkbox(label="Scale Output GIF to 512x512", value=True)
+        scale_to_512 = gr.Checkbox(label="Scale Output GIF to 512x512", value=False)
 
     with gr.Row():
         with gr.Column():
             gr.Markdown("### Input Options")
             demo_btn = gr.Button("Load Demo")
             gif_input = gr.File(label="Upload Solar GIF (optional)", file_types=[".gif"])
-            max_images = gr.Slider(minimum=1, maximum=300, value=10, step=1, label="Max Images to Fetch")
+            max_images = gr.Slider(minimum=1, maximum=100, value=10, step=1, label="Max Images to Fetch")
             fetch_button = gr.Button("Fetch Images from URL")
             analyze_button = gr.Button("Analyze")
 
@@ -324,7 +328,7 @@ with gr.Blocks(title="Solar CME Detection") as demo:
     with gr.Row():
         with gr.Column():
             gif_output = gr.File(label="Download Resulting GIF")
-            gallery = gr.Gallery(label="Frames with Detected Circles (Green: Detected, Orange: Growing Series)")
+            gallery = gr.Gallery(label="Frames with Detected Circles (Green: Detected, Yellow: CME)")
  
         with gr.Column():
             total_images = gr.Textbox(label="Total Images Available in Directory", value="0")