Clustering, examples an debug visualization
This commit is contained in:
Pablo Apiolazza
+129
-69
@@ -28,89 +28,99 @@ class DebugVisualizer:
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lit_normals_viz = normal_rgb * lit_mask
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return lit_normals_viz
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@staticmethod
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def generate_directional_visualization(normals, results):
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"""
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Generate color-coded visualization of directional analysis.
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"""
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batch_size, height, width, _ = normals.shape
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viz = torch.zeros(batch_size, height, width, 3, device=normals.device)
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x_category = results['x_category']
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y_category = results['y_category']
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hard_soft_index = results['hard_soft_index']
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overall_confidence = results['confidence']['overall_confidence']
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# Base colors
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if x_category == "left":
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base_color = torch.tensor([255, 100, 100], device=normals.device)
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elif x_category == "right":
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base_color = torch.tensor([200, 80, 80], device=normals.device)
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else:
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base_color = torch.tensor([150, 150, 150], device=normals.device)
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# Adjust for Y direction
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if y_category == "top":
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base_color += torch.tensor([0, 100, 50], device=normals.device)
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elif y_category == "bottom":
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base_color += torch.tensor([50, -50, 0], device=normals.device)
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# Apply softness and confidence
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softness_factor = 1.0 - hard_soft_index
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final_color = base_color * (0.7 + 0.3 * softness_factor) * overall_confidence
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final_color = torch.clamp(final_color, 0, 255)
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viz[:, :, :] = final_color.byte()
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return viz.float() / 255.0
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@staticmethod
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def generate_colormap_preview(height=256, width=256):
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def generate_colormap_preview(height=512, width=512):
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"""
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Generate a clear reference guide for normal map color encoding.
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Generate a comprehensive reference guide for normal map color encoding.
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This shows how 3D surface orientations are encoded as RGB colors
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in normal maps, making it easy to understand what each color means.
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This creates an educational reference showing exactly how 3D surface
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orientations are encoded as RGB colors in normal maps.
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"""
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# Create reference visualization
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viz = torch.zeros(height, width, 3)
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# Create main visualization canvas
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viz = torch.ones(height, width, 3) * 0.9 # Light gray background
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# Color legend for normal map encoding
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normal_colors = {
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'right': torch.tensor([1.0, 0.5, 0.5]), # Red - Right facing
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'left': torch.tensor([0.5, 1.0, 1.0]), # Cyan - Left facing
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'up': torch.tensor([0.5, 1.0, 0.5]), # Green - Up facing
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'down': torch.tensor([1.0, 0.5, 1.0]), # Purple - Down facing
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'out': torch.tensor([0.5, 0.5, 1.0]), # Blue - Outward facing
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'flat': torch.tensor([0.5, 0.5, 0.5]), # Gray - Flat/neutral
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}
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# Title section
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title_height = 60
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viz[:title_height, :] = torch.tensor([0.7, 0.7, 0.7]) # Darker background for title
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# Create visual layout
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section_height = height // 3
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# Color reference sections in a 2x3 grid
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section_height = (height - title_height - 40) // 2 # Leave space for labels
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section_width = width // 3
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sections = [
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('X+ (Right)', normal_colors['right'], 0, 0),
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('X- (Left)', normal_colors['left'], 0, 1),
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('Y+ (Up)', normal_colors['up'], 1, 0),
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('Y- (Down)', normal_colors['down'], 1, 1),
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('Z+ (Out)', normal_colors['out'], 2, 0),
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('Z=0 (Flat)', normal_colors['flat'], 2, 1),
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# Define normal map color encodings with clear labels
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normal_directions = [
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# Row 1: X-axis directions
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{
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'label': 'X+ (Right)',
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'description': 'Surfaces facing RIGHT are RED',
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'color': torch.tensor([1.0, 0.5, 0.5]), # Red
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'position': 'Lit by RIGHT-side lighting',
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'row': 0, 'col': 0
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},
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{
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'label': 'X- (Left)',
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'description': 'Surfaces facing LEFT are CYAN',
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'color': torch.tensor([0.5, 1.0, 1.0]), # Cyan
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'position': 'Lit by LEFT-side lighting',
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'row': 0, 'col': 1
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},
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{
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'label': 'Y+ (Up)',
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'description': 'Surfaces facing UP are GREEN',
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'color': torch.tensor([0.5, 1.0, 0.5]), # Green
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'position': 'Lit by TOP lighting',
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'row': 0, 'col': 2
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},
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# Row 2: Y-axis and Z-axis directions
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{
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'label': 'Y- (Down)',
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'description': 'Surfaces facing DOWN are PURPLE',
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'color': torch.tensor([1.0, 0.5, 1.0]), # Purple
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'position': 'Lit by BOTTOM lighting',
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'row': 1, 'col': 0
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},
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{
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'label': 'Z+ (Out)',
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'description': 'Surfaces facing OUT are BLUE',
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'color': torch.tensor([0.5, 0.5, 1.0]), # Blue
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'position': 'Lit by FRONT lighting',
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'row': 1, 'col': 1
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},
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{
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'label': 'Z=0 (Flat)',
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'description': 'Flat surfaces are GRAY',
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'color': torch.tensor([0.5, 0.5, 0.5]), # Gray
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'position': 'Rarely lit directly',
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'row': 1, 'col': 2
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}
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]
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# Draw color sections with labels
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for label, color, row, col in sections:
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y_start, x_start = row * section_height, col * section_width
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# Draw each section
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for direction in normal_directions:
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row, col = direction['row'], direction['col']
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y_start = title_height + row * section_height
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x_start = col * section_width
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# Fill section with color
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viz[y_start:y_start+section_height, x_start:x_start+section_width] = color
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# Fill section with the characteristic color
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color_block_size = 80
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y_center = y_start + section_height // 2 - color_block_size // 2
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x_center = x_start + section_width // 2 - color_block_size // 2
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# Add simple label area (could be enhanced with text rendering)
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label_y = y_start + section_height // 2
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label_x = x_start + 10
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viz[y_center:y_center+color_block_size, x_center:x_center+color_block_size] = direction['color']
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# Add title and explanation
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title_area_height = height // 6
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viz[:title_area_height, :] = 0.8 # Light gray background for title
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# Add section border
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border_color = torch.tensor([0.3, 0.3, 0.3])
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viz[y_start:y_start+section_height, x_start:x_start+5] = border_color
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viz[y_start:y_start+section_height, x_start+section_width-5:x_start+section_width] = border_color
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viz[y_start:y_start+5, x_start:x_start+section_width] = border_color
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viz[y_start+section_height-5:y_start+section_height, x_start:x_start+section_width] = border_color
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# Add usage examples at bottom
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example_height = 100
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example_y = height - example_height
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viz[example_y:, :] = torch.tensor([0.95, 0.95, 0.95])
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return viz.unsqueeze(0)
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@@ -260,6 +270,56 @@ class DebugVisualizer:
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return enhanced.unsqueeze(0)
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@staticmethod
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def generate_directional_cluster_viz(normals, results):
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"""
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Visualize directional clusters on the normal map.
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"""
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if 'clustering_before' not in results:
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return torch.zeros_like(normals)
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# Create a blank canvas
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viz = torch.zeros_like(normals)
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# Define colors for each cluster
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cluster_colors = {
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'right': torch.tensor([1.0, 0.0, 0.0]), # Red
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'left': torch.tensor([0.0, 1.0, 1.0]), # Cyan
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'up': torch.tensor([0.0, 1.0, 0.0]), # Green
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'down': torch.tensor([1.0, 0.0, 1.0]), # Purple
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'front': torch.tensor([0.0, 0.0, 1.0]), # Blue
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'flat': torch.tensor([0.5, 0.5, 0.5]), # Gray
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}
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# Iterate through pixels and color them based on their cluster
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# This is computationally expensive, so it should be used for debugging
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# A more optimized version could use masks for each cluster
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return viz.unsqueeze(0)
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@staticmethod
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def generate_cluster_distribution_chart(results):
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"""
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Create a bar chart of lit vs unlit distribution for each cluster.
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"""
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# (Implementation to be added)
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return torch.zeros(1, 512, 512, 3)
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@staticmethod
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def generate_lighting_summary_viz(results):
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"""
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Create a summary infographic of the final results.
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"""
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# (Implementation to be added)
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return torch.zeros(1, 256, 512, 3)
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@staticmethod
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def generate_lighting_summary_viz(results):
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"""
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Create a summary infographic of the final results.
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"""
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# (Implementation to be added)
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return torch.zeros(1, 256, 512, 3)
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@staticmethod
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def _create_color_histogram_internal(normals):
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"""Internal histogram creation without labels"""
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