改写战斗中替换group的逻辑
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626
mower/utils/matcher.py
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626
mower/utils/matcher.py
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import lzma
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import pickle
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from typing import Optional, Tuple
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import cv2
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import numpy as np
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import sklearn.pipeline # noqa
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import sklearn.preprocessing
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import sklearn.svm # noqa
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from skimage.metrics import structural_similarity as compare_ssim
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from mower import __rootdir__
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from mower.utils import typealias as tp
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from mower.utils.deprecated import deprecated
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from mower.utils.image import cropimg
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from mower.utils.log import logger
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from mower.utils.vector import va, vs
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GOOD_DISTANCE_LIMIT = 0.7
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ORB = cv2.ORB_create(nfeatures=100000, edgeThreshold=0)
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ORB_no_pyramid = cv2.ORB_create(nfeatures=100000, edgeThreshold=0, nlevels=1)
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def keypoints_scale_invariant(img: tp.GrayImage):
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return ORB.detectAndCompute(img, None)
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def keypoints(img: tp.GrayImage):
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return ORB_no_pyramid.detectAndCompute(img, None)
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with lzma.open(f"{__rootdir__}/models/svm.model", "rb") as f:
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SVC = pickle.loads(f.read())
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# build FlannBasedMatcher
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# FLANN_INDEX_KDTREE = 1
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FLANN_INDEX_LSH = 6
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index_params = dict(
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algorithm=FLANN_INDEX_LSH,
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table_number=1,
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key_size=6,
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multi_probe_level=0,
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)
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search_params = dict(checks=50) # 100
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flann = cv2.FlannBasedMatcher(index_params, search_params)
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def getHash(data: list[float]) -> tp.Hash:
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"""calc image hash"""
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avreage = np.mean(data)
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return np.where(data > avreage, 1, 0)
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def hammingDistance(hash1: tp.Hash, hash2: tp.Hash) -> int:
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"""calc Hamming distance between two hash"""
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return np.count_nonzero(hash1 != hash2)
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def aHash(img1: tp.GrayImage, img2: tp.GrayImage) -> int:
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"""calc image hash"""
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data1 = cv2.resize(img1, (8, 4)).flatten()
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data2 = cv2.resize(img2, (8, 4)).flatten()
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hash1 = getHash(data1)
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hash2 = getHash(data2)
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return hammingDistance(hash1, hash2)
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class Matcher:
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def __init__(self, origin: tp.GrayImage) -> None:
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logger.debug(f"{origin.shape=}")
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self.origin = origin
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self.kp, self.des = keypoints(self.origin)
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def in_scope(self, scope):
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if scope is None:
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return self.kp, self.des
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ori_kp, ori_des = [], []
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for _kp, _des in zip(self.kp, self.des):
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if (
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scope[0][0] <= _kp.pt[0] <= scope[1][0]
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and scope[0][1] <= _kp.pt[1] <= scope[1][1]
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):
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ori_kp.append(_kp)
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ori_des.append(_des)
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logger.debug(f"{scope=}, {len(self.kp)=} -> {len(ori_kp)=}")
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return np.array(ori_kp), np.array(ori_des)
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@deprecated
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def match_old(
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self,
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query: tp.GrayImage,
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draw: bool = False,
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scope: tp.Scope = None,
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dpi_aware: bool = False,
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prescore: float = 0.0,
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judge: bool = True,
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) -> Optional[tp.Scope]:
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"""check if the image can be matched"""
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rect_score = self.score(
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query,
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draw,
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scope,
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only_score=False,
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dpi_aware=dpi_aware,
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) # get matching score
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if rect_score is None:
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return None # failed in matching
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else:
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rect, score = rect_score
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if prescore > 0:
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if score[3] >= prescore:
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logger.debug(f"{score[3]=} >= {prescore=}")
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return rect
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else:
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logger.debug(f"{score[3]=} < {prescore=}")
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return None
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if judge and not SVC.predict([score])[0]:
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logger.debug(f"{judge=} {SVC.predict([score])[0]=}")
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return None
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logger.debug(f"{rect=}")
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return rect
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def score(
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self,
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query: tp.GrayImage,
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draw: bool = False,
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scope: tp.Scope = None,
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only_score: bool = False,
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dpi_aware: bool = False,
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) -> Optional[Tuple[tp.Scope, tp.Score]]:
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"""scoring of image matching"""
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try:
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# if feature points is empty
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if self.des is None:
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logger.debug(f"{self.des=}")
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return None
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# specify the crop scope
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if scope is not None:
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ori_kp, ori_des = [], []
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for _kp, _des in zip(self.kp, self.des):
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if (
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scope[0][0] <= _kp.pt[0]
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and scope[0][1] <= _kp.pt[1]
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and _kp.pt[0] <= scope[1][0]
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and _kp.pt[1] <= scope[1][1]
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):
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ori_kp.append(_kp)
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ori_des.append(_des)
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logger.debug(f"{scope=}, {len(self.kp)=} -> {len(ori_kp)=}")
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ori_kp, ori_des = np.array(ori_kp), np.array(ori_des)
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else:
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ori_kp, ori_des = self.kp, self.des
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# if feature points is less than 2
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if len(ori_kp) < 2:
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logger.debug(f"{len(ori_kp)=} < 2")
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return None
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# the height & width of query image
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h, w = query.shape
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# the feature point of query image
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if dpi_aware:
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qry_kp, qry_des = keypoints_scale_invariant(query)
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else:
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qry_kp, qry_des = keypoints(query)
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matches = flann.knnMatch(qry_des, ori_des, k=2)
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# store all the good matches as per Lowe's ratio test
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good = []
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for pair in matches:
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if (len_pair := len(pair)) == 2:
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x, y = pair
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if x.distance < GOOD_DISTANCE_LIMIT * y.distance:
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good.append(x)
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elif len_pair == 1:
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good.append(pair[0])
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good_matches_rate = len(good) / len(qry_des)
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# draw all the good matches, for debug
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if draw:
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result = cv2.drawMatches(query, qry_kp, self.origin, ori_kp, good, None)
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from matplotlib import pyplot as plt
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plt.imshow(result)
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plt.show()
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# if the number of good matches no more than 4
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if len(good) <= 4:
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logger.debug(f"{len(good)=} <= 4, {len(qry_des)=}")
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return None
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# get the coordinates of good matches
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qry_pts = np.int32([qry_kp[m.queryIdx].pt for m in good]).reshape(-1, 1, 2)
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ori_pts = np.int32([ori_kp[m.trainIdx].pt for m in good]).reshape(-1, 1, 2)
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# calculated transformation matrix and the mask
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M, mask = cv2.estimateAffine2D(qry_pts, ori_pts, None, cv2.RANSAC)
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# if transformation matrix is None
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if M is None:
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logger.debug("M is None")
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return None
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else:
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logger.debug(f"M={M.tolist()}")
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M[0][1] = 0
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M[1][0] = 0
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avg = (M[0][0] + M[1][1]) / 2
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M[0][0] = avg
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M[1][1] = avg
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# calc the location of the query image
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# quad = np.float32([[[0, 0]], [[0, h-1]], [[w-1, h-1]], [[w-1, 0]]])
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quad = np.int32([[[0, 0]], [[w, h]]])
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quad = cv2.transform(quad, M) # quadrangle
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rect = quad.reshape(2, 2).tolist()
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# draw the result, for debug
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if draw:
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matchesMask = mask.ravel().tolist()
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origin_copy = cv2.cvtColor(self.origin, cv2.COLOR_GRAY2RGB)
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cv2.rectangle(origin_copy, rect[0], rect[1], (255, 0, 0), 3)
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draw_params = dict(
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matchColor=(0, 255, 0),
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singlePointColor=None,
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matchesMask=matchesMask,
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flags=2,
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)
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result = cv2.drawMatches(
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query, qry_kp, origin_copy, ori_kp, good, None, **draw_params
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)
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plt.imshow(result)
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plt.show()
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min_width = max(10, 0 if dpi_aware else w * 0.8)
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min_height = max(10, 0 if dpi_aware else h * 0.8)
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rect_w = rect[1][0] - rect[0][0]
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rect_h = rect[1][1] - rect[0][1]
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if rect_w < min_width or rect_h < min_height:
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logger.debug(f"{rect_w=}x{rect_h=} < {min_width=}x{min_height=}")
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return None
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if not dpi_aware:
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max_width = w * 1.25
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max_height = h * 1.25
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if rect_w > max_width or rect_h > max_height:
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logger.debug(f"{rect_w=}x{rect_h=} > {max_width=}x{max_height=}")
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return None
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# measure the rate of good match within the rectangle (x-axis)
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better = filter(
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lambda m: rect[0][0] < ori_kp[m.trainIdx].pt[0] < rect[1][0]
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and rect[0][1] < ori_kp[m.trainIdx].pt[1] < rect[1][1],
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good,
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)
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better_kp_x = [qry_kp[m.queryIdx].pt[0] for m in better]
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if len(better_kp_x):
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good_area_rate = np.ptp(better_kp_x) / w
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else:
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good_area_rate = 0
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# rectangle: float -> int
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rect = np.array(rect, dtype=int).tolist()
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rect_img = cropimg(self.origin, rect)
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# if rect_img is too small
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if rect_img.shape[0] < min_height or rect_img.shape[1] < min_width:
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logger.debug(f"{rect_img.shape=} < {min_width=}x{min_height=}")
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return None
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# transpose rect_img
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rect_img = cv2.resize(rect_img, query.shape[::-1])
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# draw the result
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if draw:
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plt.subplot(1, 2, 1)
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plt.imshow(query, cmap="gray", vmin=0, vmax=255)
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plt.subplot(1, 2, 2)
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plt.imshow(rect_img, cmap="gray", vmin=0, vmax=255)
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plt.show()
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# calc aHash between query image and rect_img
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hash = 1 - (aHash(query, rect_img) / 16)
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# calc ssim between query image and rect_img
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ssim = compare_ssim(query, rect_img, multichannel=True)
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# return final rectangle and four dimensions of scoring
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result = good_matches_rate, good_area_rate, hash, ssim
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if not only_score:
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result = rect, result
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logger.debug(result)
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return result
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except Exception as e:
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logger.exception(e)
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def match(
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self,
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query: tp.GrayImage,
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draw: bool = False,
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scope: tp.Scope | None = None,
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) -> tuple[float, tp.Scope | None]:
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if self.des is None:
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logger.debug(f"{self.des=}")
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return -1, None
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ori_kp, ori_des = self.in_scope(scope)
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if len(ori_kp) < 2:
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logger.debug(f"{len(ori_kp)=} < 2")
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return -1, None
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qry_kp, qry_des = keypoints(query)
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matches = flann.knnMatch(qry_des, ori_des, k=2)
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good = []
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for pair in matches:
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if (len_pair := len(pair)) == 2:
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x, y = pair
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if x.distance < GOOD_DISTANCE_LIMIT * y.distance:
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good.append(x)
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elif len_pair == 1:
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good.append(pair[0])
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if draw:
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from matplotlib import pyplot as plt
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result = cv2.drawMatches(query, qry_kp, self.origin, ori_kp, good, None)
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plt.imshow(result)
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plt.show()
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if len(good) <= 4:
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logger.debug(f"{len(good)=} <= 4, {len(qry_des)=}")
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return -1, None
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qry_pts = np.float32([qry_kp[m.queryIdx].pt for m in good]).reshape(-1, 1, 2)
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ori_pts = np.float32([ori_kp[m.trainIdx].pt for m in good]).reshape(-1, 1, 2)
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M, mask = cv2.estimateAffine2D(qry_pts, ori_pts, None, cv2.RANSAC)
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if M is None:
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logger.debug("M is None")
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return -1, None
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else:
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logger.debug(f"M={M.tolist()}")
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M[0][1] = M[1][0] = 0
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M[0][0] = M[1][1] = 1
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h, w = query.shape
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if draw:
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matches_mask = mask.ravel().tolist()
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pts = np.int32([[[0, 0]], [[w - 1, h - 1]]])
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dst = np.int32(cv2.transform(pts, M)).tolist()
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dst = [i[0] for i in dst]
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dst = np.int32(
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[
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[dst[0]],
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[[dst[0][0], dst[1][1]]],
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[dst[1]],
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[[dst[1][0], dst[0][1]]],
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]
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)
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disp = cv2.cvtColor(self.origin, cv2.COLOR_GRAY2RGB)
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disp = cv2.polylines(disp, [dst], True, (255, 0, 0), 2, cv2.LINE_AA)
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disp = cv2.drawMatches(
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query,
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qry_kp,
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disp,
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ori_kp,
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good,
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None,
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(0, 255, 0),
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matchesMask=matches_mask,
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flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS,
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)
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plt.imshow(disp)
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plt.show()
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pts = np.int32([[[-20, -20]], [[w + 19, h + 19]]])
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dst = cv2.transform(pts, M)
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rect = dst.reshape(2, 2).tolist()
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rect = np.array(rect, dtype=int).tolist()
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disp = cropimg(self.origin, rect)
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rh, rw = disp.shape
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if rh < h or rw < w:
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logger.debug(f"{rect=} {rh=} {h=} {rw=} {w=}")
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return -1, None
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result = cv2.matchTemplate(disp, query, cv2.TM_CCOEFF_NORMED)
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min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
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if draw:
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disp = cropimg(disp, (max_loc, va(max_loc, (w, h))))
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plt.subplot(1, 2, 1)
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plt.imshow(query, cmap="gray", vmin=0, vmax=255)
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plt.subplot(1, 2, 2)
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plt.imshow(disp, cmap="gray", vmin=0, vmax=255)
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plt.show()
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top_left = va(rect[0], max_loc)
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scope = top_left, va(top_left, (w, h))
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logger.debug(f"{max_val=} {scope=}")
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return max_val, scope
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def match2d(
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self,
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query: tp.GrayImage,
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draw: bool = False,
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scope: tp.Scope | None = None,
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) -> tuple[float, tp.Scope | None]:
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if self.des is None:
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logger.debug(f"{self.des=}")
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return -1, None
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ori_kp, ori_des = self.in_scope(scope)
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if len(ori_kp) < 2:
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logger.debug(f"{len(ori_kp)=} < 2")
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return -1, None
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qry_kp, qry_des = keypoints_scale_invariant(query)
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matches = flann.knnMatch(qry_des, ori_des, k=2)
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good = []
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for pair in matches:
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if (len_pair := len(pair)) == 2:
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x, y = pair
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if x.distance < GOOD_DISTANCE_LIMIT * y.distance:
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good.append(x)
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elif len_pair == 1:
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good.append(pair[0])
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if draw:
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from matplotlib import pyplot as plt
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result = cv2.drawMatches(query, qry_kp, self.origin, ori_kp, good, None)
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plt.imshow(result)
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plt.show()
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if len(good) <= 4:
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logger.debug(f"{len(good)=} <= 4, {len(qry_des)=}")
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return -1, None
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qry_pts = np.float32([qry_kp[m.queryIdx].pt for m in good]).reshape(-1, 1, 2)
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ori_pts = np.float32([ori_kp[m.trainIdx].pt for m in good]).reshape(-1, 1, 2)
|
||||
M, mask = cv2.estimateAffine2D(qry_pts, ori_pts, None, cv2.RANSAC)
|
||||
|
||||
if M is None:
|
||||
logger.debug("M is None")
|
||||
return -1, None
|
||||
else:
|
||||
logger.debug(f"M={M.tolist()}")
|
||||
|
||||
M[0][1] = M[1][0] = 0
|
||||
M[0][0] = M[1][1] = scale = (M[0][0] + M[1][1]) / 2
|
||||
|
||||
logger.debug(f"{scale=}")
|
||||
|
||||
h, w = query.shape
|
||||
|
||||
if draw:
|
||||
matches_mask = mask.ravel().tolist()
|
||||
pts = np.int32([[[0, 0]], [[w - 1, h - 1]]])
|
||||
dst = np.int32(cv2.transform(pts, M)).tolist()
|
||||
dst = [i[0] for i in dst]
|
||||
dst = np.int32(
|
||||
[
|
||||
[dst[0]],
|
||||
[[dst[0][0], dst[1][1]]],
|
||||
[dst[1]],
|
||||
[[dst[1][0], dst[0][1]]],
|
||||
]
|
||||
)
|
||||
disp = cv2.cvtColor(self.origin, cv2.COLOR_GRAY2RGB)
|
||||
disp = cv2.polylines(disp, [dst], True, (255, 0, 0), 2, cv2.LINE_AA)
|
||||
disp = cv2.drawMatches(
|
||||
query,
|
||||
qry_kp,
|
||||
disp,
|
||||
ori_kp,
|
||||
good,
|
||||
None,
|
||||
(0, 255, 0),
|
||||
matchesMask=matches_mask,
|
||||
flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS,
|
||||
)
|
||||
plt.imshow(disp)
|
||||
plt.show()
|
||||
|
||||
pts = np.int32([[[-20, -20]], [[w + 19, h + 19]]])
|
||||
dst = cv2.transform(pts, M)
|
||||
rect = dst.reshape(2, 2).tolist()
|
||||
rect = np.array(rect, dtype=int).tolist()
|
||||
rect_img = cropimg(self.origin, rect)
|
||||
disp = cv2.resize(rect_img, dsize=None, fx=1 / scale, fy=1 / scale)
|
||||
result = cv2.matchTemplate(disp, query, cv2.TM_CCOEFF_NORMED)
|
||||
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
|
||||
|
||||
if draw:
|
||||
disp = cropimg(disp, (max_loc, va(max_loc, (w, h))))
|
||||
plt.subplot(1, 2, 1)
|
||||
plt.imshow(query, cmap="gray", vmin=0, vmax=255)
|
||||
plt.subplot(1, 2, 2)
|
||||
plt.imshow(disp, cmap="gray", vmin=0, vmax=255)
|
||||
plt.show()
|
||||
|
||||
top_left = vs(max_loc, (20, 20))
|
||||
scope = top_left, va(top_left, (w, h))
|
||||
scope = np.float32(scope).reshape(-1, 1, 2)
|
||||
scope = cv2.transform(scope, M)
|
||||
scope = np.int32(scope).reshape(2, 2).tolist()
|
||||
logger.debug(f"{max_val=} {scope=}")
|
||||
return max_val, scope
|
||||
|
||||
def match3d(
|
||||
self,
|
||||
query: tp.GrayImage,
|
||||
draw: bool = False,
|
||||
scope: tp.Scope | None = None,
|
||||
) -> tuple[float, tp.Scope | None]:
|
||||
if self.des is None:
|
||||
logger.debug(f"{self.des=}")
|
||||
return -1, None
|
||||
ori_kp, ori_des = self.in_scope(scope)
|
||||
if len(ori_kp) < 2:
|
||||
logger.debug(f"{len(ori_kp)=} < 2")
|
||||
return -1, None
|
||||
|
||||
qry_kp, qry_des = keypoints_scale_invariant(query)
|
||||
matches = flann.knnMatch(qry_des, ori_des, k=2)
|
||||
good = []
|
||||
for pair in matches:
|
||||
if (len_pair := len(pair)) == 2:
|
||||
x, y = pair
|
||||
if x.distance < GOOD_DISTANCE_LIMIT * y.distance:
|
||||
good.append(x)
|
||||
elif len_pair == 1:
|
||||
good.append(pair[0])
|
||||
|
||||
if draw:
|
||||
from matplotlib import pyplot as plt
|
||||
|
||||
result = cv2.drawMatches(query, qry_kp, self.origin, ori_kp, good, None)
|
||||
plt.imshow(result)
|
||||
plt.show()
|
||||
|
||||
if len(good) <= 4:
|
||||
logger.debug(f"{len(good)=} <= 4, {len(qry_des)=}")
|
||||
return -1, None
|
||||
|
||||
qry_pts = np.float32([qry_kp[m.queryIdx].pt for m in good]).reshape(-1, 1, 2)
|
||||
ori_pts = np.float32([ori_kp[m.trainIdx].pt for m in good]).reshape(-1, 1, 2)
|
||||
M, mask = cv2.findHomography(qry_pts, ori_pts, cv2.RANSAC)
|
||||
|
||||
if M is None:
|
||||
logger.debug("M is None")
|
||||
return -1, None
|
||||
else:
|
||||
logger.debug(f"M={M.tolist()}")
|
||||
|
||||
h, w = query.shape
|
||||
|
||||
if draw:
|
||||
matches_mask = mask.ravel().tolist()
|
||||
pts = np.float32(
|
||||
[
|
||||
[0, 0],
|
||||
[0, h - 1],
|
||||
[w - 1, h - 1],
|
||||
[w - 1, 0],
|
||||
]
|
||||
).reshape(-1, 1, 2)
|
||||
dst = cv2.perspectiveTransform(pts, M)
|
||||
disp = cv2.cvtColor(self.origin, cv2.COLOR_GRAY2RGB)
|
||||
disp = cv2.polylines(
|
||||
disp, [np.int32(dst)], True, (255, 0, 0), 2, cv2.LINE_AA
|
||||
)
|
||||
disp = cv2.drawMatches(
|
||||
query,
|
||||
qry_kp,
|
||||
disp,
|
||||
ori_kp,
|
||||
good,
|
||||
None,
|
||||
(0, 255, 0),
|
||||
matchesMask=matches_mask,
|
||||
flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS,
|
||||
)
|
||||
plt.imshow(disp)
|
||||
plt.show()
|
||||
|
||||
offset = (20, 20)
|
||||
A = np.array(
|
||||
[
|
||||
[1, 0, -offset[0]],
|
||||
[0, 1, -offset[1]],
|
||||
[0, 0, 1],
|
||||
]
|
||||
)
|
||||
|
||||
disp = cv2.warpPerspective(
|
||||
self.origin, M.dot(A), va((w, h), (40, 40)), None, cv2.WARP_INVERSE_MAP
|
||||
)
|
||||
result = cv2.matchTemplate(disp, query, cv2.TM_CCOEFF_NORMED)
|
||||
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
|
||||
|
||||
if draw:
|
||||
disp = cropimg(disp, (max_loc, va(max_loc, (w, h))))
|
||||
plt.subplot(1, 2, 1)
|
||||
plt.imshow(query, cmap="gray", vmin=0, vmax=255)
|
||||
plt.subplot(1, 2, 2)
|
||||
plt.imshow(disp, cmap="gray", vmin=0, vmax=255)
|
||||
plt.show()
|
||||
|
||||
top_left = vs(max_loc, offset)
|
||||
scope = top_left, va(top_left, (w, h))
|
||||
scope = np.float32(scope).reshape(-1, 1, 2)
|
||||
scope = cv2.perspectiveTransform(scope, M)
|
||||
scope = np.int32(scope).reshape(2, 2).tolist()
|
||||
logger.debug(f"{max_val=} {scope=}")
|
||||
return max_val, scope
|
||||
Loading…
Add table
Add a link
Reference in a new issue