115 lines
4.0 KiB
Python
115 lines
4.0 KiB
Python
from dataclasses import dataclass
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import numpy as np
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from PIL import Image
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from skimage import transform, feature, measure
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class ScaleTranslationTransform(transform.SimilarityTransform):
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def estimate(self, src, dst):
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# src and dst are (N, 2) arrays
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if src.shape[0] < 2:
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return False
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# 1. Compute centroids
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src_mean = src.mean(axis=0)
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dst_mean = dst.mean(axis=0)
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# 2. Center the points
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src_centered = src - src_mean
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dst_centered = dst - dst_mean
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# 3. Compute scale s
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# s = sum(src_centered * dst_centered) / sum(src_centered^2)
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# We sum over both dimensions and all points
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num = np.sum(src_centered * dst_centered)
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den = np.sum(src_centered**2)
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if den == 0:
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return False
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s = num / den
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# 4. Compute translation
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# t = dst_mean - s * src_mean
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t = dst_mean - s * src_mean
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# 5. Set parameters
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# Update the matrix
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self.params = np.array([[s, 0, t[0]], [0, s, t[1]], [0, 0, 1]])
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return True
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@dataclass
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class ImageWarper:
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max_keypoints: int = 1000
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min_matches: int = 10
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ransac_residual_threshold: float = 2.0
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def warp(self, original: Image.Image, edited: Image.Image) -> Image.Image:
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"""
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Aligns the edited image to the original image using feature matching and warping.
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If alignment fails, falls back to simple resizing.
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"""
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# Convert to grayscale for feature detection
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img1_gray = np.array(original.convert('L'))
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img2_gray = np.array(edited.convert('L'))
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# Detect ORB features
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orb = feature.ORB(n_keypoints=self.max_keypoints)
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orb.detect_and_extract(img1_gray)
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keypoints1 = orb.keypoints
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descriptors1 = orb.descriptors
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orb.detect_and_extract(img2_gray)
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keypoints2 = orb.keypoints
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descriptors2 = orb.descriptors
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# Check if descriptors were found
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if descriptors1 is None or descriptors2 is None or len(descriptors1) == 0 or len(descriptors2) == 0:
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return edited.resize(original.size, Image.Resampling.LANCZOS)
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# Match descriptors
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matches12 = feature.match_descriptors(descriptors1, descriptors2, cross_check=True)
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if len(matches12) < self.min_matches:
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return edited.resize(original.size, Image.Resampling.LANCZOS)
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# Extract location of good matches
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# keypoints are (row, col)
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src_rc = keypoints2[matches12[:, 1]] # edited
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dst_rc = keypoints1[matches12[:, 0]] # original
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# skimage transforms use (x, y) coordinates, which is (col, row).
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# We need to swap the columns to work with the transform model.
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src = src_rc[:, ::-1]
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dst = dst_rc[:, ::-1]
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# Estimate transform using RANSAC
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# ScaleTranslationTransform handles translation and uniform scaling (no rotation).
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model_robust, inliers = measure.ransac(
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(src, dst), ScaleTranslationTransform, min_samples=3, residual_threshold=self.ransac_residual_threshold, max_trials=100
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)
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if model_robust is None:
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print("RANSAC failed to find a model.")
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return edited.resize(original.size, Image.Resampling.LANCZOS)
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# print(f"Matches: {len(matches12)}")
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# print(f"Inliers: {np.sum(inliers)}")
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# print(f"Scale: {model_robust.scale:.4f}")
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# print(f"Rotation: {model_robust.rotation:.4f} rad")
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# print(f"Translation: {model_robust.translation}")
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# Warp the edited image
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edited_arr = np.array(edited.convert('RGBA'))
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# warp output is float. preserve_range=True keeps values in [0, 255] if input was uint8
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warped_arr = transform.warp(
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edited_arr, model_robust.inverse, output_shape=np.array(original).shape[:2], order=1, mode='constant', cval=0, preserve_range=True
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)
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warped_img = Image.fromarray(warped_arr.astype(np.uint8), 'RGBA')
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return warped_img
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