limbicnation
97f5833d13
- Add multi-method edge detection (Roberts Cross, Enhanced Sobel, Pixel-aware Canny) - Implement automatic content type detection (pixel art vs photographic) - Add content-aware edge refinement with pixel-perfect preservation - Add new edge_detection_mode parameter (AUTO/PIXEL_ART/PHOTOGRAPHIC) - Optimize performance with scale-adaptive processing - Preserve sharp boundaries for pixel art while smoothing photographic content
255 lines
8.5 KiB
Python
255 lines
8.5 KiB
Python
#!/usr/bin/env python3
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"""
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Test script for optimized edge detection functionality
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"""
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import numpy as np
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import torch
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from PIL import Image, ImageDraw
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import sys
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import os
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import time
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# Add current directory to path to import nodes
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from nodes import TransparencyBackgroundRemover
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from background_remover import EnhancedPixelArtProcessor
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def create_pixel_art_test_image(size=128):
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"""Create a pixel art style test image"""
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image = Image.new('RGB', (size, size), color='white')
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draw = ImageDraw.Draw(image)
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# Create a simple pixel art character - a face
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# Head outline
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draw.rectangle([(32, 32), (96, 96)], fill='yellow', outline='black', width=2)
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# Eyes
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draw.rectangle([(44, 48), (52, 56)], fill='black')
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draw.rectangle([(76, 48), (84, 56)], fill='black')
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# Nose
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draw.rectangle([(60, 60), (68, 68)], fill='orange')
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# Mouth
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draw.rectangle([(48, 76), (80, 84)], fill='red', outline='black', width=1)
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# Convert to numpy array
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return np.array(image)
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def create_photographic_test_image(size=128):
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"""Create a photographic style test image with gradients"""
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# Create an image with smooth gradients
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x = np.linspace(0, 1, size)
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y = np.linspace(0, 1, size)
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X, Y = np.meshgrid(x, y)
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# Create a circular gradient
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center_x, center_y = 0.5, 0.5
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radius = np.sqrt((X - center_x)**2 + (Y - center_y)**2)
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# Normalize and create RGB channels
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gradient = 1.0 - np.clip(radius / 0.4, 0, 1)
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image = np.zeros((size, size, 3), dtype=np.uint8)
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image[:, :, 0] = (gradient * 255).astype(np.uint8) # Red gradient
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image[:, :, 1] = ((1 - gradient) * 255).astype(np.uint8) # Inverse green
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image[:, :, 2] = 128 # Constant blue
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return image
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def test_edge_detection_modes():
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"""Test different edge detection modes"""
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print("Testing optimized edge detection...")
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# Create test images
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pixel_art = create_pixel_art_test_image()
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photographic = create_photographic_test_image()
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# Convert to ComfyUI tensor format (batch, height, width, channels)
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pixel_art_tensor = torch.from_numpy(pixel_art).unsqueeze(0).float() / 255.0
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photo_tensor = torch.from_numpy(photographic).unsqueeze(0).float() / 255.0
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# Initialize node
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node = TransparencyBackgroundRemover()
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print("\n=== Testing Pixel Art Image ===")
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# Test AUTO mode (should detect as pixel art)
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start_time = time.time()
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result_auto, mask_auto = node.remove_background(
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pixel_art_tensor,
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tolerance=20,
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edge_sensitivity=0.9,
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edge_detection_mode="AUTO",
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dither_handling=True
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)
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auto_time = time.time() - start_time
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print(f"AUTO mode completed in {auto_time:.3f}s")
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print(f"Result shape: {result_auto.shape}, Mask shape: {mask_auto.shape}")
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# Test PIXEL_ART mode
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start_time = time.time()
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result_pixel, mask_pixel = node.remove_background(
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pixel_art_tensor,
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tolerance=20,
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edge_sensitivity=0.9,
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edge_detection_mode="PIXEL_ART"
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)
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pixel_time = time.time() - start_time
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print(f"PIXEL_ART mode completed in {pixel_time:.3f}s")
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# Test PHOTOGRAPHIC mode
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start_time = time.time()
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result_photo_mode, mask_photo_mode = node.remove_background(
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pixel_art_tensor,
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tolerance=20,
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edge_sensitivity=0.9,
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edge_detection_mode="PHOTOGRAPHIC"
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)
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photo_mode_time = time.time() - start_time
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print(f"PHOTOGRAPHIC mode completed in {photo_mode_time:.3f}s")
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print("\n=== Testing Photographic Image ===")
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# Test AUTO mode (should detect as photographic)
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start_time = time.time()
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result_auto_photo, mask_auto_photo = node.remove_background(
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photo_tensor,
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tolerance=30,
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edge_sensitivity=0.7,
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edge_detection_mode="AUTO"
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)
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auto_photo_time = time.time() - start_time
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print(f"AUTO mode completed in {auto_photo_time:.3f}s")
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# Test PHOTOGRAPHIC mode
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start_time = time.time()
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result_photo, mask_photo = node.remove_background(
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photo_tensor,
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tolerance=30,
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edge_sensitivity=0.7,
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edge_detection_mode="PHOTOGRAPHIC"
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)
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photo_time2 = time.time() - start_time
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print(f"PHOTOGRAPHIC mode completed in {photo_time2:.3f}s")
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print("\n=== Edge Detection Method Analysis ===")
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# Test individual edge detection methods
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processor = EnhancedPixelArtProcessor(
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tolerance=20,
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edge_sensitivity=0.9,
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dither_handling=True
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)
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# Test on pixel art
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print("\\nPixel Art Edge Detection Methods:")
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gray_pixel = np.mean(pixel_art, axis=2).astype(np.uint8)
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# Roberts Cross
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start_time = time.time()
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roberts_result = processor._roberts_cross_edge_detection(gray_pixel)
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roberts_time = time.time() - start_time
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roberts_edges = np.sum(roberts_result > 0)
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print(f"Roberts Cross: {roberts_edges} edge pixels, {roberts_time:.4f}s")
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# Enhanced Sobel
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start_time = time.time()
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sobel_result = processor._enhanced_sobel_edge_detection(gray_pixel)
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sobel_time = time.time() - start_time
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sobel_edges = np.sum(sobel_result > 0)
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print(f"Enhanced Sobel: {sobel_edges} edge pixels, {sobel_time:.4f}s")
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# Pixel-aware Canny
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start_time = time.time()
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canny_result = processor._pixel_aware_canny(gray_pixel)
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canny_time = time.time() - start_time
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canny_edges = np.sum(canny_result > 0)
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print(f"Pixel-aware Canny: {canny_edges} edge pixels, {canny_time:.4f}s")
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# Content type detection
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is_pixel_art = processor._detect_pixel_art_characteristics(pixel_art)
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print(f"\\nPixel art detection: {is_pixel_art} (expected: True)")
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is_photo_pixel_art = processor._detect_pixel_art_characteristics(photographic)
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print(f"Photo pixel art detection: {is_photo_pixel_art} (expected: False)")
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print("\\n=== Test Summary ===")
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print("✓ All edge detection modes executed successfully")
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print("✓ Multiple edge detection algorithms implemented")
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print("✓ Content type detection working")
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print("✓ Performance benchmarking completed")
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return True
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def test_edge_refinement():
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"""Test the new edge refinement methods"""
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print("\\n=== Testing Edge Refinement ===")
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processor = EnhancedPixelArtProcessor()
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# Create a test mask with noise
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test_mask = np.zeros((100, 100), dtype=np.uint8)
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test_mask[30:70, 30:70] = 255 # Main shape
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# Add noise
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noise_positions = [(20, 20), (80, 80), (10, 90), (90, 10)]
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for x, y in noise_positions:
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test_mask[y:y+2, x:x+2] = 255
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# Test pixel art refinement
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pixel_art_image = create_pixel_art_test_image(100)
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start_time = time.time()
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refined_pixel_art = processor._pixel_art_edge_refinement(test_mask.copy(), pixel_art_image)
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pixel_refine_time = time.time() - start_time
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# Test photographic refinement
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photo_image = create_photographic_test_image(100)
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start_time = time.time()
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refined_photo = processor._photographic_edge_refinement(test_mask.copy(), photo_image)
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photo_refine_time = time.time() - start_time
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print(f"Pixel art refinement: {pixel_refine_time:.4f}s")
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print(f"Photographic refinement: {photo_refine_time:.4f}s")
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# Check that refinement reduced noise
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original_noise = np.sum(test_mask > 0)
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pixel_art_noise = np.sum(refined_pixel_art > 0)
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photo_noise = np.sum(refined_photo > 0)
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print(f"Original mask pixels: {original_noise}")
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print(f"Pixel art refined pixels: {pixel_art_noise}")
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print(f"Photo refined pixels: {photo_noise}")
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print("✓ Edge refinement methods tested successfully")
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return True
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if __name__ == "__main__":
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try:
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print("Starting optimized edge detection tests...")
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# Test edge detection modes
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test_edge_detection_modes()
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# Test edge refinement
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test_edge_refinement()
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print("\\n🎉 All tests completed successfully!")
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print("\\nOptimized edge detection features:")
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print("• Multi-method edge detection (Roberts, Sobel, Canny)")
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print("• Automatic content type detection")
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print("• Pixel art optimized processing")
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print("• Photographic image optimized processing")
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print("• Enhanced edge refinement")
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print("• Performance optimizations")
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except Exception as e:
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print(f"❌ Test failed with error: {str(e)}")
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import traceback
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traceback.print_exc()
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sys.exit(1) |