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Image based feature alignment
Credits: https://www.learnopencv.com/image-alignment-feature-based-using-opencv-c-python/
"""
import cv2
import numpy as np
from src.processors.interfaces.ImagePreprocessor import ImagePreprocessor
from src.utils.image import ImageUtils
from src.utils.interaction import InteractionUtils
class FeatureBasedAlignment(ImagePreprocessor):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
options = self.options
config = self.tuning_config
# process reference image
self.ref_path = self.relative_dir.joinpath(options["reference"])
ref_img = cv2.imread(str(self.ref_path), cv2.IMREAD_GRAYSCALE)
self.ref_img = ImageUtils.resize_util(
ref_img,
config.dimensions.processing_width,
config.dimensions.processing_height,
)
# get options with defaults
self.max_features = int(options.get("maxFeatures", 500))
self.good_match_percent = options.get("goodMatchPercent", 0.15)
self.transform_2_d = options.get("2d", False)
# Extract keypoints and description of source image
self.orb = cv2.ORB_create(self.max_features)
self.to_keypoints, self.to_descriptors = self.orb.detectAndCompute(
self.ref_img, None
)
def __str__(self):
return self.ref_path.name
def exclude_files(self):
return [self.ref_path]
def apply_filter(self, image, _file_path):
config = self.tuning_config
# Convert images to grayscale
# im1Gray = cv2.cvtColor(im1, cv2.COLOR_BGR2GRAY)
# im2Gray = cv2.cvtColor(im2, cv2.COLOR_BGR2GRAY)
image = cv2.normalize(image, 0, 255, norm_type=cv2.NORM_MINMAX)
# Detect ORB features and compute descriptors.
from_keypoints, from_descriptors = self.orb.detectAndCompute(image, None)
# Match features.
matcher = cv2.DescriptorMatcher_create(
cv2.DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMING
)
# create BFMatcher object (alternate matcher)
# matcher = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
matches = np.array(matcher.match(from_descriptors, self.to_descriptors, None))
# Sort matches by score
matches = sorted(matches, key=lambda x: x.distance, reverse=False)
# Remove not so good matches
num_good_matches = int(len(matches) * self.good_match_percent)
matches = matches[:num_good_matches]
# Draw top matches
if config.outputs.show_image_level > 2:
im_matches = cv2.drawMatches(
image, from_keypoints, self.ref_img, self.to_keypoints, matches, None
)
InteractionUtils.show("Aligning", im_matches, resize=True, config=config)
# Extract location of good matches
points1 = np.zeros((len(matches), 2), dtype=np.float32)
points2 = np.zeros((len(matches), 2), dtype=np.float32)
for i, match in enumerate(matches):
points1[i, :] = from_keypoints[match.queryIdx].pt
points2[i, :] = self.to_keypoints[match.trainIdx].pt
# Find homography
height, width = self.ref_img.shape
if self.transform_2_d:
m, _inliers = cv2.estimateAffine2D(points1, points2)
return cv2.warpAffine(image, m, (width, height))
# Use homography
h, _mask = cv2.findHomography(points1, points2, cv2.RANSAC)
return cv2.warpPerspective(image, h, (width, height))
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