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Update app.py
Browse files
app.py
CHANGED
@@ -21,14 +21,20 @@ def extract_frames(gif_path):
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return None, f"Error loading GIF: {str(e)}"
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def preprocess_frame(frame):
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"""Preprocess a frame:
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# Apply Gaussian blur to reduce noise
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blurred = cv2.GaussianBlur(frame, (9, 9), 0)
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def detect_circles(frame_diff, image_center, min_radius=20, max_radius=200):
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"""Detect circles in a frame difference image, centered at the Sun."""
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@@ -37,8 +43,8 @@ def detect_circles(frame_diff, image_center, min_radius=20, max_radius=200):
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cv2.HOUGH_GRADIENT,
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dp=1.5, # Resolution for better detection
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minDist=100, # Prevent overlapping circles
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param1=
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param2=
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minRadius=min_radius,
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maxRadius=max_radius
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)
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@@ -56,7 +62,7 @@ def detect_circles(frame_diff, image_center, min_radius=20, max_radius=200):
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return None
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def analyze_gif(gif_file):
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"""Analyze a GIF for growing concentric circles
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try:
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# Handle Gradio file input
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gif_path = gif_file.name if hasattr(gif_file, 'name') else gif_file
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@@ -85,7 +91,7 @@ def analyze_gif(gif_file):
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# Compute absolute difference between consecutive frames
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frame_diff = cv2.absdiff(frame2, frame1)
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# Enhance contrast for
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frame_diff = cv2.convertScaleAbs(frame_diff, alpha=3.0, beta=0)
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# Detect circles centered at the Sun
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@@ -114,9 +120,9 @@ def analyze_gif(gif_file):
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# Generate output frames and report
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results = []
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report = "Analysis Report:\n"
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if growing_circle_data:
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report += f"Detected {len(growing_circle_data)} frames with growing concentric circles:\n"
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for c in growing_circle_data:
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# Visualize the frame with detected circle
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output_frame = cv2.cvtColor(c["output_frame"], cv2.COLOR_GRAY2RGB)
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@@ -126,9 +132,9 @@ def analyze_gif(gif_file):
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results.append(output_frame)
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report += f"Frame {c['frame']}: Center at {c['center']}, Radius {c['radius']} pixels\n"
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report += "\nConclusion: Growing concentric circles detected, indicative of a potential Earth-directed CME."
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else:
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report += "No growing concentric circles detected. CME may not be Earth-directed."
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return report, results
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except Exception as e:
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@@ -143,7 +149,7 @@ iface = gr.Interface(
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gr.Gallery(label="Frames with Growing Circles")
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],
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title="Solar CME Detection",
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description="Upload a GIF of solar images to detect growing concentric circles indicative of Earth-directed coronal mass ejections (CMEs)."
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)
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if __name__ == "__main__":
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return None, f"Error loading GIF: {str(e)}"
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def preprocess_frame(frame):
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"""Preprocess a frame: isolate mid-to-light pixels and enhance circular patterns."""
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# Apply Gaussian blur to reduce noise
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blurred = cv2.GaussianBlur(frame, (9, 9), 0)
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# Isolate mid-to-light pixels (intensity range 100–200 in grayscale)
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lower_bound = 100
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upper_bound = 200
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mask = cv2.inRange(blurred, lower_bound, upper_bound)
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# Apply morphological operation to enhance circular patterns
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kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
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enhanced = cv2.dilate(mask, kernel, iterations=2)
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return enhanced
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def detect_circles(frame_diff, image_center, min_radius=20, max_radius=200):
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"""Detect circles in a frame difference image, centered at the Sun."""
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cv2.HOUGH_GRADIENT,
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dp=1.5, # Resolution for better detection
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minDist=100, # Prevent overlapping circles
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param1=80, # Lower edge threshold to capture fainter edges
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param2=15, # Lower accumulator threshold to detect faint circles
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minRadius=min_radius,
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maxRadius=max_radius
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)
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return None
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def analyze_gif(gif_file):
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"""Analyze a GIF for growing concentric circles of mid-to-light pixels."""
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try:
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# Handle Gradio file input
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gif_path = gif_file.name if hasattr(gif_file, 'name') else gif_file
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# Compute absolute difference between consecutive frames
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frame_diff = cv2.absdiff(frame2, frame1)
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# Enhance contrast for the difference image
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frame_diff = cv2.convertScaleAbs(frame_diff, alpha=3.0, beta=0)
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# Detect circles centered at the Sun
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# Generate output frames and report
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results = []
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report = "Analysis Report (as of 07:34 PM PDT, May 24, 2025):\n"
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if growing_circle_data:
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report += f"Detected {len(growing_circle_data)} frames with growing concentric circles of mid-to-light pixels:\n"
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for c in growing_circle_data:
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# Visualize the frame with detected circle
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output_frame = cv2.cvtColor(c["output_frame"], cv2.COLOR_GRAY2RGB)
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results.append(output_frame)
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report += f"Frame {c['frame']}: Center at {c['center']}, Radius {c['radius']} pixels\n"
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report += "\nConclusion: Growing concentric circles of mid-to-light pixels detected, indicative of a potential Earth-directed CME."
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else:
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report += "No growing concentric circles of mid-to-light pixels detected. CME may not be Earth-directed."
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return report, results
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except Exception as e:
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gr.Gallery(label="Frames with Growing Circles")
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],
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title="Solar CME Detection",
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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)."
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)
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if __name__ == "__main__":
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