def cv2_estimateRigidTransform(from_pts, to_pts, full=False):
"""Estimate transforms in OpenCV 3 or OpenCV 4"""
if not from_pts.shape[0] or not to_pts.shape[0]:
return None
if imutils.is_cv4():
transform = cv2.estimateAffinePartial2D(from_pts, to_pts)[0]
else:
transform = cv2.estimateRigidTransform(from_pts, to_pts, full)
return transform
def align(img, landmark):
image_size = (112, 112)
src = np.array([[30.2946, 51.6963], [65.5318, 51.5014], [48.0252, 71.7366],
[33.5493, 92.3655], [62.7299, 92.2041]],
dtype=np.float32)
dst = landmark.astype(np.float32)
M = cv2.estimateAffinePartial2D(dst, src)[0]
warped = cv2.warpAffine(img,
M, (image_size[1], image_size[0]),
borderValue=0.0)
return warped
def rectify(image, endpoints):
""" Rectifies an image such that the ruler(in endpoints) is flat
image: array
Represents an image or image mask
endpoints: array
Represents 2 pair of endpoints for a ruler
"""
dst = np.array([[image.shape[1] * .1, image.shape[0] / 2],
[image.shape[1] * .9, image.shape[0] / 2]])
rt_matrix, _ = cv2.estimateAffinePartial2D(endpoints, dst)
return cv2.warpAffine(image, rt_matrix, (image.shape[1], image.shape[0]))
def get_initial_pos(thumbnail_fixed, thumbnail_float, thumbnail_down_rate):
brisk = cv2.BRISK_create()
(kps_fixed, descs_fixed) = brisk.detectAndCompute(np.array(thumbnail_fixed), None)
(kps_float, descs_float) = brisk.detectAndCompute(np.array(thumbnail_float), None)
if (descs_fixed is None) or (descs_float is None):
reg_status = 0
init_reg_offset = (0, 0)
return init_reg_offset, reg_status
if len(kps_fixed) < 3 or len(kps_float) < 3:
reg_status = 0
init_reg_offset = (0, 0)
return init_reg_offset, reg_status
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=False)
try:
matches = bf.match(descs_fixed, descs_float)
matches = sorted(matches, key=lambda x: x.distance)
if len(matches) < 3: # less than 3 points
reg_status = 0
init_reg_offset = (0, 0)
return init_reg_offset, reg_status
if len(matches) <= 10: # get first 10 points
selected_matches = matches
else:
selected_matches = matches[0:10]
selected_kps_fixed = []
selected_kps_float = []
for m in selected_matches:
selected_kps_float.append(kps_float[m.trainIdx].pt)
selected_kps_fixed.append(kps_fixed[m.queryIdx].pt)
reprojThresh = 3
confidence_ratio = 0.86
(E, status) = cv2.estimateAffinePartial2D(np.float32(selected_kps_fixed), np.float32(selected_kps_float),
ransacReprojThreshold=reprojThresh, confidence=confidence_ratio)
if 0 not in status:
theta = - math.atan2(E[0, 1], E[0, 0]) * 180 / math.pi
if abs(theta) > 1:
reg_status = 0
init_reg_offset = (0, 0)
else:
reg_status = 1
init_reg_offset = (E[0, 2] * thumbnail_down_rate, E[1, 2] * thumbnail_down_rate)
else:
counts = np.count_nonzero(status == 0)
if counts > 5: # if over 50% fails
reg_status = 0
init_reg_offset = (0, 0)
else:
init_reg_offset = (E[0, 2] * thumbnail_down_rate, E[1, 2] * thumbnail_down_rate)
reg_status = 1
return init_reg_offset, reg_status
except:
reg_status = 0
init_reg_offset = (0, 0)
return init_reg_offset, reg_status
def AlignImages_Affine(im1_path, im2_path, factor, MAX_FEATURES = 500, GOOD_MATCH_PERCENT = 0.1):
im1 = Image.open(im1_path)
im2 = Image.open(im2_path)
im1array = np.array(im1)
im2array = np.array(im2)
# Detect ORB features and compute descriptors.
imip_1 = cv2.resize(im1array, dsize = None, fx = factor, fy = factor, interpolation=cv2.INTER_CUBIC)
imip_2 = cv2.resize(im2array, dsize = None, fx = factor, fy = factor, interpolation=cv2.INTER_CUBIC)
orb = cv2.ORB_create(nfeatures = MAX_FEATURES, patchSize = 100)
keypoints1, descriptors1 = orb.detectAndCompute(imip_1, None)
keypoints2, descriptors2 = orb.detectAndCompute(imip_2, None)
# Match features.
matcher = cv2.DescriptorMatcher_create(cv2.DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMING)
matches = matcher.match(descriptors1, descriptors2, None)
# Sort matches by score
matches.sort(key=lambda x: x.distance, reverse=False)
# Remove not so good matches
numGoodMatches = int(len(matches) * GOOD_MATCH_PERCENT)
good = matches[:numGoodMatches]
# Draw top matches
imMatches = cv2.drawMatches(imip_1, keypoints1, imip_2, keypoints2, good, None)
# cv2.imwrite("matches.jpg", imMatches)
# 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, :] = keypoints1[match.queryIdx].pt
points2[i, :] = keypoints2[match.trainIdx].pt
# Find homography
h, mask= cv2.estimateAffinePartial2D(points2, points1, cv2.RANSAC)
# correct the homography matrix with scale factor
new_h = h
new_h[0, 2] = h[0, 2]/factor
new_h[1, 2] = h[1, 2]/factor
print(new_h)
# Use homography
height, width = im1array.shape
# im2Reg = cv2.warpPerspective(im2array, new_h, (width, height))
im2Reg = cv2.warpAffine(im2array, new_h, (width, height))
return im1array, im2array, im2Reg, new_h, imMatches
def findAffine(src, dst, fullAffine=False):
#print("src:", src)
#print("dst:", dst)
if len(src) >= affine_minpts:
# affine = cv2.estimateRigidTransform(np.array([src]), np.array([dst]), fullAffine)
affine, status = \
cv2.estimateAffinePartial2D(np.array([src]).astype(np.float32),
np.array([dst]).astype(np.float32))
else:
affine = None
#print str(affine)
return affine
def track_frame(src, dst, pointsSrcInput): pointsDstOutput = pointsSrcInput try: pointsDstOutput, st, err = cv2.calcOpticalFlowPyrLK(src, dst, pointsSrcInput, None, **lk_params) trs, inliers = cv2.estimateAffinePartial2D(pointsSrcInput, pointsDstOutput) except: trs = transforms_to_matrix([0,0,0]) err = np.array([[100],[100]]) if cv2.mean(err)[0] > 5: print(cv2.mean(err)[0]) trs = transforms_to_matrix([0,0,0]) return(trs, pointsDstOutput, err)
def partialaffine(from_data, to_data, target_data):
"""Compute the partial 2D affine transformation between the data sets via opencv"""
# calculate an approximate affine
affine_matrix, inliers = cv2.estimateAffinePartial2D(from_data, to_data, ransacReprojThreshold=1,
maxIters=20000, confidence=0.95,
refineIters=100, method=cv2.LMEDS)
assert affine_matrix is not None, "Affine transform was not possible"
# print('Percentage inliers used:' + str(np.sum(inliers)*100/from_data.shape[0]))
# make the transformed data homogeneous for multiplication with the affine
transformed_data = np.squeeze(cv2.convertPointsToHomogeneous(target_data))
# apply the affine matrix
return np.matmul(transformed_data, affine_matrix.T)
def track_frame(src, dst, pointsSrcInput, findFeatures): if findFeatures: pointsSrcInput = find_features(src) pointsDstOutput = pointsSrcInput mtrx = np.array([[1, 0, 0], [0, 1, 0]], np.float64) try: pointsDstOutput, st, err = cv2.calcOpticalFlowPyrLK(src, dst, pointsSrcInput, None, **lk_params) mtrx, inliers = cv2.estimateAffinePartial2D(pointsSrcInput, pointsDstOutput) except: mtrx = np.array([[1, 0, 0], [0, 1, 0]], np.float64) err = np.array([[100],[100]]) return(mtrx, pointsDstOutput, err)
def detect_and_align_face(img):
mtcnn = MTCNN(post_process=False, device="cpu")
_, _, landmarks = mtcnn.detect(img, landmarks=True)
assert len(landmarks), "no face detected"
lmk = landmarks[0]
# print(lmk)
src = np.array([lmk[0], lmk[1]], dtype=np.float32)
dst = np.array([(68, 112), (108, 112)], dtype=np.float32)
M, _ = cv2.estimateAffinePartial2D(src, dst)
return cv2.warpAffine(
img, M, (178, 218), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE
)
def face_algin_by_landmark(face_img: np.ndarray,
face_landmark: np.ndarray,
template: np.ndarray = TEMPLATE) -> np.ndarray:
img_dim = face_img.shape[:2][::-1]
M, _ = cv2.estimateAffinePartial2D(face_landmark, img_dim * template)
warped_img = cv2.warpAffine(face_img, M, img_dim)
h_ratio = img_dim[0]
w_ratio = int(h_ratio * FACE_DIMENSION[0] / FACE_DIMENSION[1])
resized = cv2.resize(
warped_img[:,
int((h_ratio - w_ratio) / 2):int((h_ratio + w_ratio) / 2)],
tuple(FACE_DIMENSION))
return resized
def estimate_rigid_transform(self, image1_pts, image2_pts, use_full):
if int(self.OPENCV_MAJOR) < 4: # new in opencv4
affine = cv2.estimateRigidTransform(image1_pts, image2_pts, fullAffine=use_full)
else:
if use_full:
# noinspection PyUnresolvedReferences
# new implementation returns also a vector which indicated which points are inliers
affine = cv2.estimateAffine2D(image1_pts, image2_pts)
else:
# noinspection PyUnresolvedReferences
affine = cv2.estimateAffinePartial2D(image1_pts, image2_pts)
return affine
def align_crop(img,
src_landmarks,
mean_landmarks,
crop_size=256,
face_factor=0.7,
landmark_factor=0.35):
# move
move = np.array([img.shape[1] // 2, img.shape[0] // 2])
# pad border
v_border = img.shape[0] - crop_size
w_border = img.shape[1] - crop_size
if v_border < 0:
v_half = (-v_border + 1) // 2
img = np.pad(img, ((v_half, v_half), (0, 0), (0, 0)), mode='edge')
src_landmarks += np.array([0, v_half])
move += np.array([0, v_half])
if w_border < 0:
w_half = (-w_border + 1) // 2
img = np.pad(img, ((0, 0), (w_half, w_half), (0, 0)), mode='edge')
src_landmarks += np.array([w_half, 0])
move += np.array([w_half, 0])
# estimate transform matrix
mean_landmarks -= np.array([mean_landmarks[0, :] + mean_landmarks[1, :]
]) / 2.0 # middle point of eyes as center
trg_landmarks = mean_landmarks * (crop_size * face_factor *
landmark_factor) + move
tform = cv2.estimateAffinePartial2D(trg_landmarks,
src_landmarks,
ransacReprojThreshold=np.Inf)[0]
# fix the translation to match the middle point of eyes
trg_mid = (trg_landmarks[0, :] + trg_landmarks[1, :]) / 2.0
src_mid = (src_landmarks[0, :] + src_landmarks[1, :]) / 2.0
new_trg_mid = cv2.transform(np.array([[trg_mid]]), tform)[0, 0]
tform[:, 2] += src_mid - new_trg_mid
# warp image by given transform
output_shape = (crop_size // 2 + move[1] + 1, crop_size // 2 + move[0] + 1)
img_align = cv2.warpAffine(img,
tform,
output_shape[::-1],
flags=cv2.WARP_INVERSE_MAP + cv2.INTER_CUBIC,
borderMode=cv2.BORDER_REPLICATE)
# crop
img_crop = img_align[-crop_size:, -crop_size:]
return img_crop
def match_feature(templ,
haystack,
*,
min_match=10,
templ_mask=None,
haystack_mask=None,
limited_transform=False) -> FeatureMatchingResult:
templ = np.asarray(templ.convert('L'))
haystack = np.asarray(haystack.convert('L'))
detector = cv.SIFT_create()
kp1, des1 = detector.detectAndCompute(templ, templ_mask)
kp2, des2 = detector.detectAndCompute(haystack, haystack_mask)
# index_params = dict(algorithm=6,
# table_number=6,
# key_size=12,
# multi_probe_level=2)
index_params = dict(algorithm=0, trees=5) # algorithm=FLANN_INDEX_KDTREE
search_params = {}
flann = cv.FlannBasedMatcher(index_params, search_params)
matches = flann.knnMatch(des1, des2, k=2)
good = []
for group in matches:
if len(group) >= 2 and group[0].distance < 0.75 * group[1].distance:
good.append(group[0])
result = FeatureMatchingResult(len(kp1), len(good))
if len(good) >= min_match:
src_pts = np.float32([kp1[m.queryIdx].pt
for m in good]).reshape(-1, 1, 2)
dst_pts = np.float32([kp2[m.trainIdx].pt
for m in good]).reshape(-1, 1, 2)
if limited_transform:
M, _ = cv.estimateAffinePartial2D(src_pts, dst_pts)
else:
M, mask = cv.findHomography(src_pts, dst_pts, cv.RANSAC, 4.0)
h, w = templ.shape
pts = np.float32([[0, 0], [0, h - 1], [w - 1, h - 1],
[w - 1, 0]]).reshape(-1, 1, 2)
if limited_transform:
dst = cv.transform(pts, M)
else:
dst = cv.perspectiveTransform(pts, M)
result.M = M
result.template_corners = dst.reshape(-1, 2)
# img2 = cv.polylines(haystack, [np.int32(dst)], True, 0, 2, cv.LINE_AA)
return result
def compute_transformation(points: np.ndarray,
reference: np.ndarray) -> np.ndarray:
"""Obtain a tranformation for aligning key points to
reference positions
Arguments
---------
points:
A sequence of points to be mapped onto the reference points,
given as (x,y) coordinates
reference:
A sequence with the same number of points serving as reference
points to which `points` should be moved.
"""
# Obtain point and reference coordinates in a format suitable
# for OpenCV, that can be numpy arrays of shape (N, 2), with
# N being the number of points, and each point is described
# by 2-dimensional cartesian coordinates (x, y).
dst = points.reshape(1, -1, 2)
src = reference.reshape(1, -1, 2)
if src.shape != dst.shape:
raise ValueError("Incompatible shapes: "
f"points {src.shape} vs. reference {dst.shape}")
# fullAffine=False
transformation, _ = cv2.estimateAffinePartial2D(dst, src)
# fullAffine=True
# transformation, _ = cv2.estimateAffine2D(dst, src)
# There are also function to estimate a similarity transform
# (not a general affine transformation):
# * opencv 3.2 and newer (including 4.x):
# cv2.estimateAffinePartial2D()
#
# * before opencv 3.2:
# cv2.estimateRigidTransform()
# https://docs.opencv.org/3.4/dc/d6b/group__video__track.html#
# ga762cbe5efd52cf078950196f3c616d48
# transformation = \
# cv2.estimateRigidTransform(dst, src, fullAffine=False)
#
# M, inliers = cv2.estimateAffinePartial2D(pts1, pts2)
#
# There is also cv2.getAffineTransform in case there
# are only three points, which allows to calculate an exact
# solution.
return transformation
def callback2(self, msg):
timestamp = time()
# to skip first frame
if self.prev_gray == []:
curr_img = self.bridge.imgmsg_to_cv2(
msg, desired_encoding='passthrough')
curr_gray = cv.cvtColor(curr_img, cv.COLOR_BGR2GRAY)
self.prev_gray = curr_gray
else:
# Detect features to track
prev_pts = cv.goodFeaturesToTrack(self.prev_gray,
maxCorners=1000,
qualityLevel=0.1,
minDistance=30)
# Get the current img
curr_img = self.bridge.imgmsg_to_cv2(
msg, desired_encoding='passthrough')
# Convert to gray scales
curr_gray = cv.cvtColor(curr_img, cv.COLOR_BGR2GRAY)
# Track feature points
curr_pts, status, err = cv.calcOpticalFlowPyrLK(
self.prev_gray, curr_gray, prev_pts, None)
# Sanity check
assert prev_pts.shape == curr_pts.shape
# Filter only valid points
idx = np.where(status == 1)[0]
prev_pts = prev_pts[idx]
curr_pts = curr_pts[idx]
# Save raw image
cv.imwrite(
'../raw/' + self.session_name + '/' + str(self.j).zfill(10) +
'.jpg', curr_img)
for i in curr_pts:
x, y = i.ravel()
cv.circle(curr_img, (x, y), 3, 255, -1)
self.j += 1
# Save annotated image
cv.imwrite(
'../temp/' + self.session_name + '/' + str(self.j).zfill(10) +
'.jpg', curr_img)
m, _ = cv.estimateAffinePartial2D(prev_pts, curr_pts)
dx = m[0][2]
dy = m[1][2]
# Rotation angle
da = np.arctan2(m[1][0], m[0][0])
# Store transformation
self.transforms.append([timestamp, dx, dy, da])
self.prev_gray = curr_gray
def get_image_homography_SIFT(im1, im2, mask=None, filename=''):
# Convert images to grayscale
if len(im1.shape) == 3:
im1 = cv2.cvtColor(im1, cv2.COLOR_BGR2GRAY)
im2 = cv2.cvtColor(im2, cv2.COLOR_BGR2GRAY)
orb = True
sift = False #True
# Detect features and compute descriptors.
feature_detector = cv2.SIFT_create(nfeatures=MAX_FEATURES,
nOctaveLayers=3,
contrastThreshold=0.04,
edgeThreshold=5,
sigma=1.6)
keypoints1, descriptors1 = feature_detector.detectAndCompute(im1, mask)
keypoints2, descriptors2 = feature_detector.detectAndCompute(im2, mask)
# Match features.
FLANN_INDEX_KDTREE = 1
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)
flann = cv2.FlannBasedMatcher(index_params, search_params)
matches = flann.knnMatch(descriptors1, descriptors2, k=2)
matcher = cv2.BFMatcher()
matches = matcher.knnMatch(descriptors1, descriptors2, k=2)
# Apply ratio test
good = []
for m, n in matches:
if m.distance < 0.7 * n.distance:
good.append(m)
points1 = np.float32([keypoints1[m.queryIdx].pt
for m in good]).reshape(-1, 1, 2)
points2 = np.float32([keypoints2[m.trainIdx].pt
for m in good]).reshape(-1, 1, 2)
# Find homography
if len(points1) == 0 or len(points2) == 0:
print("ERROR: no points found!!!")
#print(points1, points2)
#h, mask = cv2.findHomography(points1, points2, cv2.RANSAC, 5.0) # <- wtf is the 5.0 - some method??
h, mask = cv2.estimateAffinePartial2D(points1, points2) # cv2.RANSAC)
if not homography_is_translation(h) and filename != '':
print("ERROR: the homography found is no translation!", h)
# Draw top matches
#imMatches = cv2.drawMatches(im1, keypoints1, im2, keypoints2, matches, None)
#cv2.imwrite(os.path.dirname(os.path.realpath(__file__)) + os.sep + filename + "_matches.tif", imMatches)
return h, mask
def find_drift_poi(points1, points2):
""" Find the best fit for the rotation and translation that map points1 to points2 using
the RANSAC algorithm (fitting robust to outliers).
Args:
points1 (numpy.ndarray, int): list of points (n x 2)
points2 (numpy.ndarray, int): list of points (n x 2)
Returns:
array: x and y translation
"""
# Find affine transformation matrix from points1 to points2 (i.e. Ax1 = x2)
h, _ = cv2.estimateAffinePartial2D(points1,points2)
# Extract x and y translation
return np.array([h[0,2], h[1,2]])
def stabilize_frame(self, previousBwFrame, currentBwFrame):
# Detect feature points in previous frame
t0 = time.perf_counter()
if not self.flagPts:
self.prev_pts = cv2.goodFeaturesToTrack(
previousBwFrame,
maxCorners=self.maxGoodPoints,
qualityLevel=0.01,
minDistance=30,
blockSize=3)
self.flagPts = True
t1 = time.perf_counter()
t2 = time.perf_counter()
# Calculate optical flow (i.e. track feature points) using the same pts, as they should not have moved after image stabilization.
curr_pts, status, err = cv2.calcOpticalFlowPyrLK(
previousBwFrame,
currentBwFrame,
self.prev_pts,
None,
maxLevel=self.maxLevel,
winSize=(self.winSize, self.winSize))
t3 = time.perf_counter()
# Sanity check
assert self.prev_pts.shape == curr_pts.shape
#print(status)
# Keep only pts for which the flow was found
idx = np.where(status == 1)[0]
self.prev_pts = self.prev_pts[idx]
curr_pts = curr_pts[idx]
if len(self.prev_pts) < self.maxGoodPoints / 2:
self.flagPts = False # We have lost too many points, need to redo
#Find transformation matrix to go from curr_pts -> prev_pts
t4 = time.perf_counter()
m, _ = cv2.estimateAffinePartial2D(curr_pts, self.prev_pts)
t5 = time.perf_counter()
if m is not None:
# stabilize the frame
t6 = time.perf_counter()
frameStabilized = cv2.warpAffine(
currentBwFrame, m, (self.frameWidth, self.frameHeight))
t7 = time.perf_counter()
#frameStabilized = self.fixBorder(frameStabilized)
self.timing.append([t1 - t0, t3 - t2, t5 - t4, t7 - t6])
return m, frameStabilized
def findAffine(src, dst, fullAffine=False):
#print("src:", src)
#print("dst:", dst)
if len(src) >= affine_minpts:
# affine = cv2.estimateRigidTransform(np.array([src]), np.array([dst]), fullAffine)
affine, status = \
cv2.estimateAffinePartial2D(np.array([src]).astype(np.float32),
np.array([dst]).astype(np.float32))
else:
affine = None
status = None
print("num pts:", len(src), "used:", np.count_nonzero(status), "affine:\n", affine)
#print str(affine)
return affine, status
def get_alignment_img(img, kp2, des2):
height, width = img.shape[:2]
# 対応点を探索
apt1, apt2 = get_matcher(img, kp2, des2)
# アフィン行列の推定
mtx = cv2.estimateAffinePartial2D(apt1, apt2)[0]
# アフィン変換
if mtx is not None:
return cv2.warpAffine(img, mtx, (width, height))
else:
return None
def align(self, image: np.ndarray, marks: np.ndarray):
"""Aligns a face image.
Parameters
----------
image : array_like
The image face to be aligned.
marks : array_like of shape = [5, 2]
The landmark set of the input image face. Each entry in `marks`
must contains the (x, y) locations of the face landmarks in the
following order: (0) left eye center, (1) right eye center, (2)
nose center, (3) mouth left corner and (4) mouth right corner.
Returns
-------
aligned_image : array_like of shape = [out_size, out_size, 3]
The resulting aligned face image. Alignment is made by computing
the optimal limited affine transformation with 4 degrees of freedom
between the set of input face landmarks and a set of reference
points that represents a reference front-aligned face.
nose_deviation : tuple of length = 2
The nose deviation of the input face image. The nose deviation is a
tuple where the first and second elements represents the horizontal
and vertical deviation of nose landmark, respectively For a
deviation lower than (0.4, 0.3), the face is approximately frontal
in both planes.
"""
if marks.dtype != np.float32:
marks = np.float32(marks)
tfm, _ = cv.estimateAffinePartial2D(marks,
self._reference.copy(),
method=cv.LMEDS)
aligned_image = cv.warpAffine(image, tfm,
(self.out_size, self.out_size))
aligned_marks = (np.matmul(tfm[:, 0:2], marks.T) + tfm[:, 2].reshape(
(-1, 1))).T
face_center = np.mean(aligned_marks[[0, 1, 3, 4]], axis=0)
eye_dist = abs(aligned_marks[0][0] - aligned_marks[1][0])
nose_x, nose_y = aligned_marks[2]
nose_deviation = (
abs(face_center[0] - nose_x) / eye_dist,
abs(face_center[1] - nose_y) / eye_dist,
)
return aligned_image, nose_deviation
def PerformFeatureMatching(grayBlur1, grayBlur2):
# create orb object
orb = cv.ORB_create()
try:
# find key points and descriptors
kp1, des1 = orb.detectAndCompute(grayBlur1, None)
kp2, des2 = orb.detectAndCompute(grayBlur2, None)
# initialise brute force matcher and match descriptors
matcher = cv.BFMatcher(cv.NORM_HAMMING, crossCheck=True)
rawMatches = matcher.match(des1, des2)
# sort matches based on distance
dmatches = sorted(rawMatches, key=lambda x: x.distance)
# draw matches
matches = cv.drawMatches(grayBlur1,
kp1,
grayBlur2,
kp2,
dmatches[:10],
None,
flags=2)
rsmatches = cv.resize(matches, (1280, 720))
cv.imshow('matches', rsmatches)
# find key point matrixes
p1 = np.float32([kp1[m.queryIdx].pt
for m in dmatches]).reshape(-1, 1, 2)
p2 = np.float32([kp2[m.trainIdx].pt
for m in dmatches]).reshape(-1, 1, 2)
# if key point matrixes are not null fetch and estimate affine transformation matrix
if (p1.size != 0 and p2.size != 0):
H, _ = cv.estimateAffinePartial2D(p2,
p1,
cv.RANSAC,
ransacReprojThreshold=5.0)
# slice to find translation matrix only
H2 = H[:, 2]
M = np.float32([[1, 0, H2[0]], [0, 1, H2[1]]])
# return translation matrix
return M
else:
print(
"Please check that the input video exists within the project."
)
return None
except:
print(
"Please check that the input video exists within the project.")
return None
def _getAffine(self, old_gray, frame_gray, p0):
# === calculate optical flow
p1, st, err = cv2.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None,
**self.lk_params)
# Select good points
good_old = p0[st == 1].reshape((-1, 1, 2))
good_new = p1[st == 1].reshape((-1, 1, 2))
nCor = len(p1)
self.corDetected.append(nCor)
self.qLvlList.append(self.qLvl)
self.pid_qLvl() #pid
retval, inliers = cv2.estimateAffinePartial2D(good_new, good_old)
return retval
def add(self, image, mask, name, frame_idx):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
key_points, descriptors = self.orb.detectAndCompute(gray, mask)
if self.key_points is not None:
matches = self.matcher.match(self.desc, descriptors, None)
matches.sort(key=lambda x: x.distance, reverse=False)
count = int(len(matches) * GOOD_MATCH_PERCENT)
matches = matches[:count]
# matches_image = cv2.drawMatches(self.image, self.key_points, image, key_points, matches, None)
# cv2.imshow("matches", matches_image)
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, :] = self.key_points[match.queryIdx].pt
points2[i, :] = key_points[match.trainIdx].pt
# mat = cv2.estimateRigidTransform(points2, points1, False)
h, m = cv2.estimateAffinePartial2D(points2, points1)
# h, m = cv2.findHomography(points2, points1, cv2.RANSAC)
self.mat = h if self.mat is None else self.mul_2d(h, self.mat)
with open(self.file_name, 'a', newline='') as file:
writer = csv.writer(file)
writer.writerow([name, frame_idx, ' '.join(map(str, h.flatten().tolist()))])
if self.with_gui:
height, width, channels = image.shape
image = np.copy(image)
# image[~mask.astype(bool)] = 0
mask[0] = 0
mask[-1] = 0
mask[:, 0] = 0
mask[:, -1] = 0
mask = cv2.warpAffine(mask, self.mat, (width, height * 2)).astype(bool)
res = cv2.warpAffine(image, self.mat, (width, height * 2)) # ,flags=cv2.WARP_INVERSE_MAP)
# mask = cv2.warpPerspective(mask, self.mat, (width, height * 2)).astype(bool)
# res = cv2.warpPerspective(image, self.mat, (width, height * 2)) # ,flags=cv2.WARP_INVERSE_MAP)
self.image[mask] = res[mask]
# cv2.addWeighted(self.image, 0.5, res, 0.5, 0.0)
cv2.imshow('res', self.image)
else:
height, width, channels = image.shape
self.image = np.zeros((height * 2, width, channels), dtype=image.dtype)
self.image[:height] = image
self.image[:height][~mask.astype(bool)] = 0
self.key_points = key_points
self.desc = descriptors
def matchMaps(feat1,
feat2,
cent1,
cent2,
dir1,
dir2,
shape1,
lowes_ratio=0.85,
threshold=200):
(h, w) = shape1
src_pts_list, dst_pts_list = [], []
scale_ratio_vector, rotation_vector = [], []
# Match keypoints
for i in range(4):
for j in range(4):
src_pts, dst_pts, dir1_, dir2_ = matchKeypoints(
feat1[i], feat2[j], cent1, cent2, dir1[i].copy(),
dir2[j].copy(), lowes_ratio)
src_pts_list.append(src_pts)
dst_pts_list.append(dst_pts)
scale_ratio_vector.append(
(np.ones(len(src_pts)) * i - j).astype('int8'))
rotation_vector.append(dir1_ - dir2_)
# Merge both and compute transformation
src_pts = np.concatenate(src_pts_list)
dst_pts = np.concatenate(dst_pts_list)
M, mask = cv2.estimateAffinePartial2D(src_pts,
dst_pts,
cv2.RANSAC,
ransacReprojThreshold=threshold)
mask = np.ravel(mask)
pts = np.float32([[0, 0], [0, h - 1], [w - 1, h - 1],
[w - 1, 0]]).reshape(-1, 1, 2)
dst = (pts @ M[:, :2]) + M[:, 2:3].transpose()
coherence_score, scale_coherence, rot_coherence, regularizer, rotation_vector, angle = computeMatchScore(
rotation_vector, scale_ratio_vector, mask, dst, h, w,
feat1.shape[-1])
return coherence_score, scale_coherence, rot_coherence, regularizer, dst, M, src_pts, dst_pts, rotation_vector, angle
def alignImages(im1, im2):
# Convert images to grayscale
im1Gray = cv2.cvtColor(im1, cv2.COLOR_BGR2GRAY)
im2Gray = cv2.cvtColor(im2, cv2.COLOR_BGR2GRAY)
# Detect ORB features and compute descriptors.
orb = cv2.ORB_create(MAX_FEATURES)
keypoints1, descriptors1 = orb.detectAndCompute(im1Gray, None)
keypoints2, descriptors2 = orb.detectAndCompute(im2Gray, None)
# Match features.
matcher = cv2.DescriptorMatcher_create(
cv2.DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMING)
matches = matcher.match(descriptors1, descriptors2, None)
# Sort matches by score
matches.sort(key=lambda x: x.distance, reverse=False)
# Remove not so good matches
numGoodMatches = int(len(matches) * GOOD_MATCH_PERCENT)
matches = matches[:numGoodMatches]
# Draw top matches
imMatches = cv2.drawMatches(im1, keypoints1, im2, keypoints2, matches,
None)
cv2.imwrite("matches.jpg", imMatches)
# 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, :] = keypoints1[match.queryIdx].pt
points2[i, :] = keypoints2[match.trainIdx].pt
# Find homography
# h, mask = cv2.findHomography(points1, points2, cv2.RANSAC)
# M = cv2.getAffineTransform(points1, points2) # 点对数为3
M = cv2.estimateAffinePartial2D(points1, points2)
# Use homography
height, width, channels = im2.shape
# im1Reg = cv2.warpPerspective(im1, h, (width, height))
im1Reg = cv2.warpAffine(im1, M[0], (width, height))
# return im1Reg, h
return im1Reg, M
def similarity_transform(inPoints, outPoints):
""" similarity_transform takes in a set of input points
and a set of output points and finds an affine transformation
between the two. cv2.estimateAffinePartial2D requires
a two sets of three coordinates in order to find an
affine transformation between them. similarity_transform will
take two sets of two coordinates and find the corresponding
third coordinate by finding a point that forms an equilateral
triangle with the original two points.
For more information look at:
https://docs.opencv.org/3.4/d4/d61/tutorial_warp_affine.html
Source:
https://github.com/spmallick/learnopencv/tree/master/FaceAverage
**Parameters**
inPoints: list
A list of tuples, in this case corresponding to specific
image coordinate pairs
outPoints: list
A list of tuples, in this case corresponding to specific
image coordinate pairs
**Returns**
tform[0]: array
A 2 x 3 matrix corresponding to the optimal affine
transformation between the two sets of points.
"""
s60 = math.sin(60 * math.pi / 180)
c60 = math.cos(60 * math.pi / 180)
inPts = np.copy(inPoints).tolist()
outPts = np.copy(outPoints).tolist()
xin = c60 * (inPts[0][0] - inPts[1][0]) - s60 * (inPts[0][1] -
inPts[1][1]) + inPts[1][0]
yin = s60 * (inPts[0][0] - inPts[1][0]) + c60 * (inPts[0][1] -
inPts[1][1]) + inPts[1][1]
inPts.append([np.int(xin), np.int(yin)])
xout = c60 * (outPts[0][0] - outPts[1][0]) - s60 * (
outPts[0][1] - outPts[1][1]) + outPts[1][0]
yout = s60 * (outPts[0][0] - outPts[1][0]) + c60 * (
outPts[0][1] - outPts[1][1]) + outPts[1][1]
outPts.append([np.int(xout), np.int(yout)])
tform = cv2.estimateAffinePartial2D(np.array([inPts]), np.array([outPts]))
return tform[0]
def getHomography(matches, kp1, kp2):
points1 = np.zeros(
(len(matches), 2),
dtype=np.float32) #Prints empty array of size equal to (matches, 2)
points2 = np.zeros((len(matches), 2), dtype=np.float32)
for i, match in enumerate(matches):
points1[i, :] = kp1[
match.
queryIdx].pt #gives index of the descriptor in the list of query descriptors
points2[i, :] = kp2[
match.
trainIdx].pt #gives index of the descriptor in the list of train descriptors
#h, mask = cv2.findHomography(points2, points1, cv2.RANSAC)
h, mask = cv2.estimateAffinePartial2D(points2, points1)
return h
def get_image_homography_ORB(im1, im2, mask=None, filename=''):
# Convert images to grayscale
if len(im1.shape) == 3:
im1 = cv2.cvtColor(im1, cv2.COLOR_BGR2GRAY)
im2 = cv2.cvtColor(im2, cv2.COLOR_BGR2GRAY)
# Detect features and compute descriptors.
#feature_detector = cv2.SURF_create()
feature_detector = cv2.ORB_create(nfeatures=MAX_FEATURES)
keypoints1, descriptors1 = feature_detector.detectAndCompute(im1, mask)
keypoints2, descriptors2 = feature_detector.detectAndCompute(im2, mask)
# Match features.
matcher = cv2.DescriptorMatcher_create(
cv2.DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMING)
matches = matcher.match(descriptors1, descriptors2, None)
# Sort matches by score
matches = sorted(matches, key=lambda x: x.distance, reverse=False)
#matches.sort(key=lambda x: x.distance, reverse=False)
# Remove not so good matches
numGoodMatches = int(len(matches) * GOOD_MATCH_PERCENT)
matches = matches[:numGoodMatches]
# 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, :] = keypoints1[match.queryIdx].pt
points2[i, :] = keypoints2[match.trainIdx].pt
# Find homography
if len(points1) == 0 or len(points2) == 0:
print("ERROR: no points found!!!")
#h, mask = cv2.findHomography(points1, points2, cv2.RANSAC, 5.0) # <- wtf is the 5.0 - some method??
h, mask = cv2.estimateAffinePartial2D(points1, points2) # cv2.RANSAC)
if not homography_is_translation(h) and filename != '':
print("ERROR: the homography found is no translation!", h)
# Draw top matches
#imMatches = cv2.drawMatches(im1, keypoints1, im2, keypoints2, matches, None)
#cv2.imwrite(os.path.dirname(os.path.realpath(__file__)) + os.sep + filename + "_matches.tif", imMatches)
return h, mask
def similarityTransform(inPoints, outPoints) :
s60 = math.sin(60*math.pi/180)
c60 = math.cos(60*math.pi/180)
inPts = np.copy(inPoints).tolist()
outPts = np.copy(outPoints).tolist()
xin = c60*(inPts[0][0] - inPts[1][0]) - s60*(inPts[0][1] - inPts[1][1]) + inPts[1][0]
yin = s60*(inPts[0][0] - inPts[1][0]) + c60*(inPts[0][1] - inPts[1][1]) + inPts[1][1]
inPts.append([np.int(xin), np.int(yin)])
xout = c60*(outPts[0][0] - outPts[1][0]) - s60*(outPts[0][1] - outPts[1][1]) + outPts[1][0]
yout = s60*(outPts[0][0] - outPts[1][0]) + c60*(outPts[0][1] - outPts[1][1]) + outPts[1][1]
outPts.append([np.int(xout), np.int(yout)])
tform = cv2.estimateAffinePartial2D(np.array([inPts]), np.array([outPts]))
return tform[0]