LPX commited on
Commit
568299d
·
1 Parent(s): 1260077

feat: enhance forensic analysis by adding additional gradient and MinMax processing variations

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Files changed (1) hide show
  1. app_mcp.py +7 -3
app_mcp.py CHANGED
@@ -361,8 +361,11 @@ def predict_with_ensemble(img, confidence_threshold, augment_methods, rotate_deg
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  # 6. Perform forensic processing
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  gradient_image = gradient_processing(img_np_og) # Added gradient processing
 
 
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  minmax_image = minmax_process(img_np_og) # Added MinMax processing
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- bitplane_image = bit_plane_extractor(img_pil)
 
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  # First pass - standard analysis
@@ -372,7 +375,7 @@ def predict_with_ensemble(img, confidence_threshold, augment_methods, rotate_deg
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  ela2 = ELA(img_np_og, quality=75, scale=75, contrast=25, linear=False, grayscale=True)
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  ela3 = ELA(img_np_og, quality=75, scale=75, contrast=25, linear=False, grayscale=False)
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- forensics_images = [img_pil, ela1, ela2, ela3, gradient_image, minmax_image, bitplane_image]
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  # 7. Generate boilerplate descriptions for forensic outputs for anomaly agent
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  forensic_output_descriptions = [
@@ -381,8 +384,9 @@ def predict_with_ensemble(img, confidence_threshold, augment_methods, rotate_deg
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  "ELA analysis (Pass 2): Grayscale error map, quality 75, enhanced contrast.",
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  "ELA analysis (Pass 3): Color error map, quality 75, enhanced contrast.",
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  "Gradient processing: Highlights edges and transitions.",
 
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  "MinMax processing: Deviations in local pixel values.",
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- "Bit Plane extractor: Visualization of individual bit planes from different color channels."
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  ]
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  # You could also add descriptions for Wavelet and Bit Plane if they were dynamic outputs
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  # For instance, if wavelet_blocking_noise_estimation had parameters that changed and you wanted to describe them.
 
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  # 6. Perform forensic processing
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  gradient_image = gradient_processing(img_np_og) # Added gradient processing
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+ gradient_image2 = gradient_processing(img_np_og, int=45, equalize=True) # Added gradient processing
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+
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  minmax_image = minmax_process(img_np_og) # Added MinMax processing
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+ minmax_image2 = minmax_process(img_np_og, radius=6) # Added MinMax processing
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+ # bitplane_image = bit_plane_extractor(img_pil)
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  # First pass - standard analysis
 
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  ela2 = ELA(img_np_og, quality=75, scale=75, contrast=25, linear=False, grayscale=True)
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  ela3 = ELA(img_np_og, quality=75, scale=75, contrast=25, linear=False, grayscale=False)
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+ forensics_images = [img_pil, ela1, ela2, ela3, gradient_image, gradient_image2, minmax_image, minmax_image2]
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  # 7. Generate boilerplate descriptions for forensic outputs for anomaly agent
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  forensic_output_descriptions = [
 
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  "ELA analysis (Pass 2): Grayscale error map, quality 75, enhanced contrast.",
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  "ELA analysis (Pass 3): Color error map, quality 75, enhanced contrast.",
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  "Gradient processing: Highlights edges and transitions.",
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+ "Gradient processing: Int=45, Equalize=True",
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  "MinMax processing: Deviations in local pixel values.",
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+ "MinMax processing (Radius=6): Deviations in local pixel values.",
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  ]
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  # You could also add descriptions for Wavelet and Bit Plane if they were dynamic outputs
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  # For instance, if wavelet_blocking_noise_estimation had parameters that changed and you wanted to describe them.