def icp(d1, d2, max_iterate = 100):
src = np.array([d1.T], copy=True).astype(np.float32)
dst = np.array([d2.T], copy=True).astype(np.float32)
knn = cv2.KNearest()
responses = np.array(range(len(d2[0]))).astype(np.float32)
knn.train(src[0], responses)
Tr = np.array([[np.cos(0), -np.sin(0), 0],
[np.sin(0), np.cos(0), 0],
[0, 0, 1]])
dst = cv2.transform(dst, Tr[0:2])
max_dist = sys.maxint
scale_x = np.max(d1[0]) - np.min(d1[0])
scale_y = np.max(d1[1]) - np.min(d1[1])
scale = max(scale_x, scale_y)
for i in range(max_iterate):
ret, results, neighbours, dist = knn.find_nearest(dst[0], 1)
indeces = results.astype(np.int32).T
indeces = del_miss(indeces, dist, max_dist)
T = cv2.estimateRigidTransform(dst[0, indeces], src[0, indeces], True)
max_dist = np.max(dist)
dst = cv2.transform(dst, T)
Tr = np.dot(np.vstack((T,[0,0,1])), Tr)
if (is_converge(T, scale)):
break
return Tr[0:2]
def merge(self):
min_row, min_col = 0, 0
for image in self.images:
if image.index in self.unique_indices:
print('---finding final panograph size---')
rows, cols = image.img.shape[:2]
box = numpy.array([[0, 0], [cols - 1, 0], [cols - 1, rows - 1],
[0, rows - 1]], dtype=numpy.float32).reshape(-1, 1, 2)
transformed_box = cv2.transform(box, image.M)
_min_col = min(transformed_box[:, :, 0])[0]
_min_row = min(transformed_box[:, :, 1])[0]
if _min_row < min_row:
min_row = _min_row
if _min_col < min_col:
min_col = _min_col
if min_row < 0:
min_row = -min_row
if min_col < 0:
min_col = -min_col
max_row, max_col = 0, 0
for image in self.images:
if image.index in self.unique_indices:
print('---merging---')
image.M[0, 2] += min_col
image.M[1, 2] += min_row
transformed_box = cv2.transform(box, image.M)
_max_col = max(transformed_box[:, :, 0])[0]
_max_row = max(transformed_box[:, :, 1])[0]
if _max_row > max_row:
max_row = _max_row
if _max_col > max_col:
max_col = _max_col
result = numpy.zeros((max_row, max_col, 3), numpy.uint8)
result.fill(255)
result = cv2.cvtColor(result, cv2.COLOR_BGR2BGRA)
for image in self.images:
if image.index in self.unique_indices:
transformed_img = cv2.warpAffine(image.img, image.M, (max_col, max_row), borderMode=cv2.BORDER_TRANSPARENT)
transformed_img = cv2.cvtColor(transformed_img, cv2.COLOR_BGR2BGRA)
numpy.copyto(result, transformed_img, where=numpy.logical_and(result == 255, transformed_img != 255))
result = cv2.cvtColor(result, cv2.COLOR_BGRA2BGR)
return result
def cut_from_rotated_rect(img_data, rot_box, expand_ratio, newH, contour, gt_pt_array):
# extend the bbox width by a percent
if rot_box[1][0] > rot_box[1][1]:
rb1 = (rot_box[1][0] * expand_ratio, rot_box[1][1] * 1.1)
else:
rb1 = (rot_box[1][0] * 1.1, rot_box[1][1] * expand_ratio)
rot_box = (rot_box[0], rb1, rot_box[2])
# Get the 'contour' points
rot_box_pts = cv2.boxPoints(rot_box).reshape((4, 1, 2))
# Get the box width and height
rMinD = min(rot_box[1])
rMaxD = max(rot_box[1])
# New width, the height is constant,setted above
newW = float(newH) / rMinD * rMaxD
# 2,1,0 so the whale is not upside down
pt_orig = rot_box_pts[[2, 1, 0], 0, :]
# find out what is the 2'nd point coordinates
def get_dist(pt1, pt2):
return math.sqrt(math.pow((pt1[0] - pt2[0]), 2) + math.pow((pt1[1] - pt2[1]), 2))
d1 = get_dist(pt_orig[0], pt_orig[1])
d2 = get_dist(pt_orig[1], pt_orig[2])
if d1 < d2:
mid_point = [0, newH]
else:
mid_point = [newW, 0]
# create the destination coordinates
pt_dest = np.array([[0, 0], mid_point, [newW, newH]]).astype(np.float32)
inv_transf = cv2.getAffineTransform(pt_dest, pt_orig)
transf = cv2.getAffineTransform(pt_orig, pt_dest)
x1, y1 = np.meshgrid(np.arange(newW), np.arange(newH), indexing="xy")
coord_trans = np.dstack([x1, y1])
coord_trans2 = cv2.transform(coord_trans, inv_transf).astype(np.float32)
transf_img = cv2.remap(img_data, coord_trans2, None, interpolation=cv2.INTER_CUBIC)
# Transform the contour and the 2 GT points
if contour is not None:
transf_contour = cv2.transform(contour, transf).astype(np.int32)
else:
transf_contour = None
if gt_pt_array is not None:
transf_gt_pts = cv2.transform(gt_pt_array, transf).astype(np.int32).reshape((2, 2)).tolist()
else:
transf_gt_pts = None
return transf_img, rot_box_pts, transf_contour, transf_gt_pts
def getNormalizedLandmarks(img, predictor, d, fronter = None, win2 = None):
shape = predictor(img, d)
landmarks = list(map(lambda p: (p.x, p.y), shape.parts()))
npLandmarks = np.float32(landmarks)
if NORM_MODE == 0:
npLandmarkIndices = np.array(landmarkIndices)
H = cv2.getAffineTransform(npLandmarks[npLandmarkIndices],
MINMAX_TEMPLATE[npLandmarkIndices])
normLM = cv2.transform(np.asarray([npLandmarks]),H)[0,:,:]
return normLM,shape
else:
assert fronter is not None
thumbnail = fronter.frontalizeImage(img,d,npLandmarks)
#thumbnail = imgEnhance(thumbnail)
cut = thumbnail.shape[0]/5
thumbnail = thumbnail[cut+5:thumbnail.shape[0]-cut-5,cut+10:thumbnail.shape[1]-cut-10,:].copy()
newShape = predictor(thumbnail, dlib.rectangle(0,0,thumbnail.shape[0],thumbnail.shape[1]))
if win2 is not None:
win2.clear_overlay()
win2.set_image(thumbnail)
win2.add_overlay(newShape)
#dlib.hit_enter_to_continue()
landmarks = list(map(lambda p: (float(p.x)/thumbnail.shape[0], float(p.y)/thumbnail.shape[1]), newShape.parts()))
npLandmarks = np.float32(landmarks)
normLM = npLandmarks
return normLM,shape,thumbnail
def transform_points(self, points, mat, size, padding=0):
matrix = mat * (size - 2 * padding)
matrix[:,2] += padding
points = np.expand_dims(points, axis=1)
points = cv2.transform(points, matrix, points.shape)
points = np.squeeze(points)
return points
def getPano(M, img1, img2):
rows, cols = img2.shape[:2]
# This is the tricky part. For transform, the point is identified as pair
# of (col, row) instead of (row, col)
box = numpy.array([[0, 0], [cols - 1, 0], [cols - 1, rows - 1],
[0, rows - 1]], dtype=numpy.float32).reshape(-1, 1, 2)
transformed_box = cv2.transform(box, M)
min_col = min(transformed_box[:, :, 0])[0]
min_row = min(transformed_box[:, :, 1])[0]
if min_col < 0:
transformed_box[:, :, 0] -= min_col
M[0, 2] -= min_col
if min_row < 0:
transformed_box[:, :, 1] -= min_row
M[1, 2] -= min_row
max_col = max(transformed_box[:, :, 0])[0]
max_row = max(transformed_box[:, :, 1])[0]
I = numpy.array([[1, 0, 0], [0, 1, 0]], dtype=numpy.float)
transformed_img1 = cv2.warpAffine(img1, I, (max_col, max_row))
transformed_img2 = cv2.warpAffine(img2, M, (max_col, max_row))
numpy.copyto(
transformed_img1, transformed_img2, where=transformed_img1 == 0)
return transformed_img1
def coarse_alignment(t_inv):
t_ide = np.identity(3, 'float32')[: 2]
coarse_src_coords = cv2.transform( \
coarse_trg_coords[:, None], t_inv)[:, 0]
# Transform target for coarse grid search
shape = tuple(int(s / d_shift) for s in src_mf.shape)
trg_mf_t = cv2.warpAffine(trg_mf, t_inv / d_shift, shape[::-1])
src_mf_t = cv2.warpAffine(src_mf, t_ide / d_shift, shape[::-1])
# Coarse grid search
t_corr_list = [libreg.affine_registration.match_template_brute( \
get_patch_at(trg_mf_t, \
src_coord / d_shift, patch_size / d_shift), \
scipy.fftpack.fft2(get_patch_at(src_mf_t, \
src_coord / d_shift, shift_space / d_shift)), \
rotation=slice(0, 1, 1) if angle_space is None \
else slice(-angle_space, +angle_space, d_angle), \
logscale_x=slice(0, 1, 1) if scale_space is None \
else slice(-scale_space, +scale_space, d_scale), \
logscale_y=slice(0, 1, 1) if scale_space is None \
else slice(-scale_space, +scale_space, d_scale), \
find_translation=libreg.affine_registration \
.cross_correlation_fft) \
for src_coord in coarse_src_coords]
dx = np.array([np.dot(t[:, :2], \
(patch_size / d_shift, patch_size / d_shift)) \
+ t[:, 2] - (shift_space / d_shift, shift_space / d_shift) \
for t, _ in t_corr_list])
coarse_src_coords += dx * d_shift
corr = np.array([corr for _, corr in t_corr_list], 'float32')
return coarse_src_coords, corr
def get_align_mat(face, size, should_align_eyes):
mat_umeyama = umeyama(numpy.array(face.landmarks_as_xy()[17:]), landmarks_2D, True)[0:2]
if should_align_eyes is False:
return mat_umeyama
mat_umeyama = mat_umeyama * size
# Convert to matrix
landmarks = numpy.matrix(face.landmarks_as_xy())
# cv2 expects points to be in the form np.array([ [[x1, y1]], [[x2, y2]], ... ]), we'll expand the dim
landmarks = numpy.expand_dims(landmarks, axis=1)
# Align the landmarks using umeyama
umeyama_landmarks = cv2.transform(landmarks, mat_umeyama, landmarks.shape)
# Determine a rotation matrix to align eyes horizontally
mat_align_eyes = align_eyes(umeyama_landmarks, size)
# Extend the 2x3 transform matrices to 3x3 so we can multiply them
# and combine them as one
mat_umeyama = numpy.matrix(mat_umeyama)
mat_umeyama.resize((3, 3))
mat_align_eyes = numpy.matrix(mat_align_eyes)
mat_align_eyes.resize((3, 3))
mat_umeyama[2] = mat_align_eyes[2] = [0, 0, 1]
# Combine the umeyama transform with the extra rotation matrix
transform_mat = mat_align_eyes * mat_umeyama
# Remove the extra row added, shape needs to be 2x3
transform_mat = numpy.delete(transform_mat, 2, 0)
transform_mat = transform_mat / size
return transform_mat
def convert_to_sepia(self, frame):
"""Sepia filtras"""
m_sepia = numpy.asarray([[0.393, 0.769, 0.189],
[0.349, 0.686, 0.168],
[0.272, 0.534, 0.131]])
sepia = cv2.transform(frame, m_sepia)
sepia = cv2.cvtColor(sepia, cv2.cv.CV_RGB2BGR)
return sepia
def icp(a, b, init_pose=(0, 0, 0), no_iterations=13):
'''
The Iterative Closest Point estimator.
Takes two cloudpoints a[x,y], b[x,y], an initial estimation of
their relative pose and the number of iterations
Returns the affine transform that transforms
the cloudpoint a to the cloudpoint b.
Note:
(1) This method works for cloudpoints with minor
transformations. Thus, the result depents greatly on
the initial pose estimation.
(2) A large number of iterations does not necessarily
ensure convergence. Contrarily, most of the time it
produces worse results.
'''
src = np.array([a.T], copy=True).astype(np.float32)
dst = np.array([b.T], copy=True).astype(np.float32)
# Initialise with the initial pose estimation
Tr = np.array([[np.cos(init_pose[2]), -np.sin(init_pose[2]), init_pose[0]],
[np.sin(init_pose[2]), np.cos(init_pose[2]), init_pose[1]],
[0, 0, 1]])
src = cv2.transform(src, Tr[0:2])
for i in range(no_iterations):
# Find the nearest neighbours between the current source and the
# destination cloudpoint
nbrs = NearestNeighbors(n_neighbors=1, algorithm='auto',
warn_on_equidistant=False).fit(dst[0])
distances, indices = nbrs.kneighbors(src[0])
# Compute the transformation between the current source
# and destination cloudpoint
T = cv2.estimateRigidTransform(src, dst[0, indices.T], False)
# Transform the previous source and update the
# current source cloudpoint
src = cv2.transform(src, T)
# Save the transformation from the actual source cloudpoint
# to the destination
Tr = np.dot(Tr, np.vstack((T, [0, 0, 1])))
return Tr[0:2]
def frame_edit(self, frame):
"""
Applies sepia filter to the image.
:param frame: image to be changed.
:return: image with sepia filter.
"""
sepia = cv2.transform(frame, self.kernel)
return cv2.cvtColor(sepia, cv2.COLOR_RGB2BGR)
def transform_points(self, points, mat, size, padding=0):
""" Transform points along matrix """
logger.trace("points: %s, matrix: %s, size: %s. padding: %s", points, mat, size, padding)
matrix = self.transform_matrix(mat, size, padding)
points = np.expand_dims(points, axis=1)
points = cv2.transform( # pylint: disable=no-member
points, matrix, points.shape)
retval = np.squeeze(points)
logger.trace("Returning: %s", retval)
return retval
def transform(self, transformMat):
if transformMat is None:
return []
else:
pts = [self.leftTop(), self.rightTop(), self.rightBottom(), self.leftBottom()]
pts = np.float32(pts).reshape(-1,1,2)
transPts = cv2.transform(pts, transformMat)
left, right = min(transPts[:,:,0]), max(transPts[:,:,0])
top, bottom = min(transPts[:,:,1]), max(transPts[:,:,1])
return Bbox(left, top, right, bottom)
def sepia_filter(cv_pic):
#first line apply on output blue (rgb input)
#second line apply on output green
#third line apply on output red
m_sepia = numpy.asarray([[0.131, 0.534, 0.272],
[0.168, 0.686, 0.349],
[0.189, 0.769, 0.393]])
sepia = cv2.transform(cv_pic, m_sepia)
return sepia
def sepia(img):
kernel = np.array(
([0.272, 0.543, 0.131],
[0.349, 0.686, 0.168],
[0.393, 0.769, 0.189])
)
result = cv2.transform(img, kernel)
return result
def warpImages(img1, img2, H):
rows1, cols1 = img1.shape[:2]
rows2, cols2 = img2.shape[:2]
list_of_points_1 = np.float32([[0,0], [0,rows1], [cols1,rows1], [cols1,0]])
temp_points = np.float32([[0,0], [0,rows2], [cols2,rows2], [cols2,0]])
list_of_points_1 = np.array([list_of_points_1])
temp_points = np.array([temp_points])
#H = np.array([[H[0][0], H[0][1], H[0][2]], [H[1][0], H[1][1], H[1][2]]])
#list_of_points_2 = cv2.perspectiveTransform(temp_points, H)
list_of_points_2 = cv2.transform(temp_points, H)
list_of_points = np.concatenate((list_of_points_1, list_of_points_2), axis=0)
[x_min, y_min] = np.min(np.min(list_of_points, axis=1), axis=0)
[x_max, y_max] = np.max(np.max(list_of_points, axis=1), axis=0)
print(list_of_points)
print(x_min, y_min)
translation_dist = [-x_min,-y_min]
H_translation = np.array([[1, 0,translation_dist[0]], [0, 1, translation_dist[1]], [0,0,1]])
#float d = H[0][0], H[0][1], H[0][2];
#H_affine = np.array([[H[0][0], H[0][1], H[0][2]], [H[1][0], H[1][1], H[1][2]]])
#H_translation_affine = np.array([[H_translation[0][0], H_translation[0][1], H_translation[0][2]], [H_translation[1][0], H_translation[1][1], H_translation[1][2]]])
H_translation_affine = np.float32([[1,0,0],[0,1,0]])
#H_affine = np.array([[H[0][0], H[0][1], H[0][2] + H_translation[0][2]], [H[1][0], H[1][1], H[1][2]+ H_translation[1][2]]])
H_affine = np.array([[H[0][0], H[0][1], H[0][2]], [H[1][0], H[1][1], H[1][2]]])
#output_img = cv2.warpPerspective(img2, H_translation.dot(H), (x_max-x_min, y_max-y_min))
#img1_large = cv2.warpPerspective(img1, H_translation, (x_max-x_min, y_max-y_min))
#output_img = cv2.warpPerspective(img2, H_translation.dot(H), (y_max-y_min, x_max-x_min))
#img1_large = cv2.warpPerspective(img1, H_translation, (y_max-y_min, x_max-x_min))
output_img = cv2.warpAffine(img2, H_affine, (y_max-y_min, x_max-x_min))
img1_large = cv2.warpAffine(img1, H_translation_affine, (y_max-y_min, x_max-x_min))
print(output_img.shape[:2])
base_image = np.zeros((x_max-x_min, y_max-y_min, 3), np.uint8)
print(base_image.shape[:2])
(ret,data_map) = cv2.threshold(cv2.cvtColor(output_img, cv2.COLOR_BGR2GRAY),0, 255, cv2.THRESH_BINARY)
base_image = cv2.add(base_image, img1_large, mask=np.bitwise_not(data_map), dtype=cv2.CV_8U)
final_img = cv2.add(base_image, output_img, dtype=cv2.CV_8U)
return final_img
def warp_im(self, im, M, dshape):
output_im = np.ones(dshape, dtype=im.dtype)*255
translationMatrix = np.matrix([0, 0])
moveImageSet = cv2.transform(dshape, translationMatrix)
cv2.warpAffine(im,
M[:2],
(dshape[1], dshape[0]),
dst=output_im,
borderMode=cv2.BORDER_TRANSPARENT,
flags=cv2.WARP_INVERSE_MAP)
return output_im
def applyTransform(points, T):
"""
Apply transform to a list of points
points: list of points
T: rigid transformation matrix (shape 2x3)
"""
dataA = np.array(points)
src = np.array([dataA])
data_dest = cv2.transform(src, T)
a, b, c = data_dest.shape
data_dest = np.reshape(data_dest, (b, c))
return data_dest
def process_coord(coords):
w = h = 600
#eyecornerDst=[80,,(np.int(0.7*w),np.int(h/3))]
eyecornerDst = [(80, 112), (175, 110)]
eyecornerSrc = [coords[36, :], coords[45, :]]
tform = face.similarityTransform(eyecornerSrc, eyecornerDst)
points2 = np.reshape(coords, (68, 1, 2))
points = cv2.transform(points2, tform)
points = np.float32(np.reshape(points, (68, 2)))
return points
def crop_minAreaRect(img, rect): angle = rect[2] rows,cols = img.shape[0], img.shape[1] matrix = cv2.getRotationMatrix2D((cols/2,rows/2),angle,1) img_rot = cv2.warpAffine(img,matrix,(cols,rows)) rect0 = (rect[0], rect[1], 0.0) box = cv2.boxPoints(rect) pts = np.int0(cv2.transform(np.array([box]), matrix))[0] pts[pts < 0] = 0 return img_rot[pts[1][1]:pts[0][1], pts[1][0]:pts[2][0]]
def _preprocess(self, img):
# Convert image to greyscale
gs_luminosities = [0.07, 0.72, 0.21]
grey = cv.transform(img, np.array(gs_luminosities).reshape((1, 3)))
# Blur image
blur = cv.blur(grey, (self.blur, self.blur))
# Get the edges
canny_kernel = np.array([[1, 1, 1], [1, -8, 1], [1, 1, 1]])
edges = cv.filter2D(blur, -1, canny_kernel)
return edges
def get_feature_mask(aligned_landmarks_68, size,
padding=0, dilation=30):
""" Return the face feature mask """
# pylint: disable=no-member
logger.trace("aligned_landmarks_68: %s, size: %s, padding: %s, dilation: %s",
aligned_landmarks_68, size, padding, dilation)
scale = size - 2 * padding
translation = padding
pad_mat = np.matrix([[scale, 0.0, translation],
[0.0, scale, translation]])
aligned_landmarks_68 = np.expand_dims(aligned_landmarks_68, axis=1)
aligned_landmarks_68 = cv2.transform(aligned_landmarks_68,
pad_mat,
aligned_landmarks_68.shape)
aligned_landmarks_68 = np.squeeze(aligned_landmarks_68)
(l_start, l_end) = FACIAL_LANDMARKS_IDXS["left_eye"]
(r_start, r_end) = FACIAL_LANDMARKS_IDXS["right_eye"]
(m_start, m_end) = FACIAL_LANDMARKS_IDXS["mouth"]
(n_start, n_end) = FACIAL_LANDMARKS_IDXS["nose"]
(lb_start, lb_end) = FACIAL_LANDMARKS_IDXS["left_eyebrow"]
(rb_start, rb_end) = FACIAL_LANDMARKS_IDXS["right_eyebrow"]
(c_start, c_end) = FACIAL_LANDMARKS_IDXS["chin"]
l_eye_points = aligned_landmarks_68[l_start:l_end].tolist()
l_brow_points = aligned_landmarks_68[lb_start:lb_end].tolist()
r_eye_points = aligned_landmarks_68[r_start:r_end].tolist()
r_brow_points = aligned_landmarks_68[rb_start:rb_end].tolist()
nose_points = aligned_landmarks_68[n_start:n_end].tolist()
chin_points = aligned_landmarks_68[c_start:c_end].tolist()
mouth_points = aligned_landmarks_68[m_start:m_end].tolist()
l_eye_points = l_eye_points + l_brow_points
r_eye_points = r_eye_points + r_brow_points
mouth_points = mouth_points + nose_points + chin_points
l_eye_hull = cv2.convexHull(np.array(l_eye_points).reshape(
(-1, 2)).astype(int)).flatten().reshape((-1, 2))
r_eye_hull = cv2.convexHull(np.array(r_eye_points).reshape(
(-1, 2)).astype(int)).flatten().reshape((-1, 2))
mouth_hull = cv2.convexHull(np.array(mouth_points).reshape(
(-1, 2)).astype(int)).flatten().reshape((-1, 2))
mask = np.zeros((size, size, 3), dtype=float)
cv2.fillConvexPoly(mask, l_eye_hull, (1, 1, 1))
cv2.fillConvexPoly(mask, r_eye_hull, (1, 1, 1))
cv2.fillConvexPoly(mask, mouth_hull, (1, 1, 1))
if dilation > 0:
kernel = np.ones((dilation, dilation), np.uint8)
mask = cv2.dilate(mask, kernel, iterations=1)
logger.trace("Returning: %s", mask)
return mask
def get_original_roi(self, mat, size, padding=0):
""" Return the square aligned box location on the original
image """
logger.trace("matrix: %s, size: %s. padding: %s", mat, size, padding)
matrix = self.transform_matrix(mat, size, padding)
points = np.array(
[[0, 0], [0, size - 1], [size - 1, size - 1], [size - 1, 0]],
np.int32)
points = points.reshape((-1, 1, 2))
matrix = cv2.invertAffineTransform(matrix) # pylint: disable=no-member
logger.trace("Returning: (points: %s, matrix: %s", points, matrix)
return cv2.transform(points, matrix) # pylint: disable=no-member
def get_feature_mask(aligned_landmarks_68, size,
padding=0, dilation=30):
""" Return the face feature mask """
# pylint: disable=no-member
logger.trace("aligned_landmarks_68: %s, size: %s, padding: %s, dilation: %s",
aligned_landmarks_68, size, padding, dilation)
scale = size - 2 * padding
translation = padding
pad_mat = np.matrix([[scale, 0.0, translation],
[0.0, scale, translation]])
aligned_landmarks_68 = np.expand_dims(aligned_landmarks_68, axis=1)
aligned_landmarks_68 = cv2.transform(aligned_landmarks_68,
pad_mat,
aligned_landmarks_68.shape)
aligned_landmarks_68 = np.squeeze(aligned_landmarks_68)
(l_start, l_end) = FACIAL_LANDMARKS_IDXS["left_eye"]
(r_start, r_end) = FACIAL_LANDMARKS_IDXS["right_eye"]
(m_start, m_end) = FACIAL_LANDMARKS_IDXS["mouth"]
(n_start, n_end) = FACIAL_LANDMARKS_IDXS["nose"]
(lb_start, lb_end) = FACIAL_LANDMARKS_IDXS["left_eyebrow"]
(rb_start, rb_end) = FACIAL_LANDMARKS_IDXS["right_eyebrow"]
(c_start, c_end) = FACIAL_LANDMARKS_IDXS["chin"]
l_eye_points = aligned_landmarks_68[l_start:l_end].tolist()
l_brow_points = aligned_landmarks_68[lb_start:lb_end].tolist()
r_eye_points = aligned_landmarks_68[r_start:r_end].tolist()
r_brow_points = aligned_landmarks_68[rb_start:rb_end].tolist()
nose_points = aligned_landmarks_68[n_start:n_end].tolist()
chin_points = aligned_landmarks_68[c_start:c_end].tolist()
mouth_points = aligned_landmarks_68[m_start:m_end].tolist()
l_eye_points = l_eye_points + l_brow_points
r_eye_points = r_eye_points + r_brow_points
mouth_points = mouth_points + nose_points + chin_points
l_eye_hull = cv2.convexHull(np.array(l_eye_points).reshape(
(-1, 2)).astype(int)).flatten().reshape((-1, 2))
r_eye_hull = cv2.convexHull(np.array(r_eye_points).reshape(
(-1, 2)).astype(int)).flatten().reshape((-1, 2))
mouth_hull = cv2.convexHull(np.array(mouth_points).reshape(
(-1, 2)).astype(int)).flatten().reshape((-1, 2))
mask = np.zeros((size, size, 3), dtype=float)
cv2.fillConvexPoly(mask, l_eye_hull, (1, 1, 1))
cv2.fillConvexPoly(mask, r_eye_hull, (1, 1, 1))
cv2.fillConvexPoly(mask, mouth_hull, (1, 1, 1))
if dilation > 0:
kernel = np.ones((dilation, dilation), np.uint8)
mask = cv2.dilate(mask, kernel, iterations=1)
logger.trace("Returning: %s", mask)
return mask
def icp(ref_cloud, new_cloud, init_pose=(0, 0, 0), no_iterations=13):
'''
The Iterative Closest Point estimator.
Takes two cloudpoints a[x,y], b[x,y], an initial estimation of
their relative pose and the number of iterations
Returns the affine transform that transforms
the cloudpoint a to the cloudpoint b.
Note:
'''
ref_cloud = np.array([ref_cloud.T], copy=True).astype(np.float32)
new_cloud = np.array([new_cloud.T], copy=True).astype(np.float32)
#Initialise with the initial pose estimation
Transf_mat = np.array(
[[np.cos(init_pose[2]), -np.sin(init_pose[2]), init_pose[0]],
[np.sin(init_pose[2]),
np.cos(init_pose[2]), init_pose[1]], [0, 0, 1]])
ref_cloud_t = cv2.transform(ref_cloud, Transf_mat[0:2])
for i in range(no_iterations):
#Find the nearest neighbours between the current source and the
#destination cloudpoint
nbrs = NearestNeighbors(n_neighbors=1,
algorithm='auto').fit(new_cloud[0])
distances, indices = nbrs.kneighbors(ref_cloud_t[0])
transformation = cv2.estimateAffinePartial2D(ref_cloud_t,
new_cloud[0, indices.T])
#Transform the previous source and update the
#current source cloudpoint
ref_cloud_t = cv2.transform(new_cloud, transformation[0])
#Save the transformation from the actual source cloudpoint
#to the destination
Transf_mat = np.dot(Transf_mat,
np.vstack((transformation[0], [0, 0, 1])))
return Transf_mat[0:2]
def align(self, image, shape):
if len(shape) == 68:
# extract the left and right eye (x, y)-coordinates
(lStart, lEnd) = FACIAL_LANDMARKS_68_IDXS["left_eye"]
(rStart, rEnd) = FACIAL_LANDMARKS_68_IDXS["right_eye"]
leftEyePts = shape[lStart:lEnd]
rightEyePts = shape[rStart:rEnd]
# compute the center of mass for each eye
leftEyeCenter = leftEyePts.mean(axis=0).astype("int")
rightEyeCenter = rightEyePts.mean(axis=0).astype("int")
# compute the angle between the eye centroids
dY = rightEyeCenter[1] - leftEyeCenter[1]
dX = rightEyeCenter[0] - leftEyeCenter[0]
angle = np.degrees(np.arctan2(dY, dX)) - 180
# compute the desired right eye x-coordinate based on the
# desired x-coordinate of the left eye
desiredRightEyeX = 1.0 - self.desiredLeftEye[0]
# determine the scale of the new resulting image by taking
# the ratio of the distance between eyes in the *current*
# image to the ratio of distance between eyes in the
# *desired* image
dist = np.sqrt((dX ** 2) + (dY ** 2))
desiredDist = (desiredRightEyeX - self.desiredLeftEye[0])
desiredDist *= self.desiredFaceWidth
scale = desiredDist / dist
# compute center (x, y)-coordinates (i.e., the median point)
# between the two eyes in the input image
eyesCenter = ((leftEyeCenter[0] + rightEyeCenter[0]) // 2,
(leftEyeCenter[1] + rightEyeCenter[1]) // 2)
# grab the rotation matrix for rotating and scaling the face
M = cv2.getRotationMatrix2D(eyesCenter, angle, scale)
# update the translation component of the matrix
tX = self.desiredFaceWidth * 0.5
tY = self.desiredFaceHeight * self.desiredLeftEye[1]
M[0, 2] += (tX - eyesCenter[0])
M[1, 2] += (tY - eyesCenter[1])
# apply the affine transformation
(w, h) = (self.desiredFaceWidth, self.desiredFaceHeight)
output = cv2.warpAffine(image, M, (w, h),
flags=cv2.INTER_CUBIC)
output_lnd = cv2.transform(shape[np.newaxis], M)
# return the aligned face and lnd
return output, np.squeeze(output_lnd)
def rotate_rotula(img):
"""
Rotates image in a way which transvesal points of Rotula bone are pararel to the margins of the image
:param img: image to be rotated
"""
contours, _ = cv2.findContours(img, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
sorted_contours = sorted(contours, key=cv2.contourArea, reverse=True)
femur = sorted_contours[0]
extTop = tuple(femur[femur[:, :, 1].argmin()][0])
for i in range(img.shape[0] - 1):
for j in range(img.shape[1] - 1):
if extTop[1] < i:
img[i][j] = 0 #remove anything
rotula_contour = sorted_contours[1]
rect = cv2.minAreaRect(rotula_contour)
angle = rect[2]
if angle < -45:
angle = (90 + angle)
# otherwise, just take the inverse of the angle to make
# it positive
else:
angle = -angle
# rotate the image to deskew it
(h, w) = img.shape[:2]
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, angle, 1.0)
rotated = cv2.warpAffine(img,
M, (w, h),
flags=cv2.INTER_CUBIC,
borderMode=cv2.BORDER_REPLICATE)
contours, _ = cv2.findContours(rotated, cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
sorted_contours = sorted(contours, key=cv2.contourArea, reverse=True)
c = sorted_contours[0]
extLeft = tuple(c[c[:, :, 0].argmin()][0])
extRight = tuple(c[c[:, :, 0].argmax()][0])
transform_points = np.array([[[extLeft[0], extLeft[1]]],
[[extRight[0], extRight[1]]]])
M = cv2.getRotationMatrix2D(center, -angle, 1.0)
rotated = cv2.warpAffine(img,
M, (w, h),
flags=cv2.INTER_CUBIC,
borderMode=cv2.BORDER_REPLICATE)
tf2 = cv2.transform(transform_points, M)
return rotated, (tf2[0][0], tf2[1][0])
def cropMinRect(img, rect):
#===expand img
angle = rect[2]
rows, cols = img.shape[0], img.shape[1]
# size fit
theta = np.deg2rad(abs(angle))
phi = np.pi / 2 - theta
cols_new = toInt(cols * np.sin(theta) + rows * np.sin(phi))
rows_new = toInt(cols * np.cos(theta) + rows * np.cos(phi))
cols_add = toInt((cols_new - cols) / 2) #in the border
rows_add = toInt((rows_new - rows) / 2)
#create a Border, for don't crop in rotation
img = cv2.copyMakeBorder(img,
rows_add,
rows_add,
cols_add,
cols_add,
cv2.BORDER_CONSTANT,
value=(255, 255, 255))
# rotate img
M = cv2.getRotationMatrix2D((toInt(cols_new / 2), toInt(rows_new / 2)),
angle, 1.0)
img_rot = cv2.warpAffine(
img, M, (cols_new, rows_new)) ######<<==================FUNCAO LENTA
#show(img_rot)
#===rotate bounding box
rect0 = (rect[0], rect[1], 0.0)
if cv2.__version__[0] is '3':
box = cv2.boxPoints(rect)
else: #version 2.x.x
box = cv2.cv.BoxPoints(rect)
box = np.array(box)
for pt in box: #resize box
pt[0] += cols_add
pt[1] += rows_add
pts = np.int0(cv2.transform(np.array([box]), M))[0]
pts[pts < 0] = 0
# crop
img_crop = img_rot[pts[1][1]:pts[0][1], pts[1][0]:pts[2][0]]
#fix the base to be the largest side, if is not
if img_crop.shape[0] > img_crop.shape[1]:
#for color in img_crop:
img_crop = cv2.transpose(img_crop)
return img_crop
def getSubImage(rect, src):
# Get center, size, and angle from rect
center, size, theta = rect
print(theta)
if theta < -45:
theta = (90 + theta)
# else:
# theta = -theta
center = tuple(map(int, center))
M = cv2.getRotationMatrix2D(center, theta, 1)
out = cv2.transform(src, M)
print("out", len(out))
return out
def map_points(pts, image_size, warpMatrix, distortion_coeffs, camera_matrix,warp_mode=cv2.MOTION_HOMOGRAPHY):
#assert len(affine) == 6, "affine must have len == 6, has len {}".format(len(affine))
# extra dimension makes opencv happy
pts = np.array([pts], dtype=np.float)
new_cam_mat, _ = cv2.getOptimalNewCameraMatrix(camera_matrix, distortion_coeffs, image_size, 1)
new_pts = cv2.undistortPoints(pts, camera_matrix, distortion_coeffs, P=new_cam_mat)
if warp_mode == cv2.MOTION_AFFINE:
new_pts = cv2.transform(new_pts, cv2.invertAffineTransform(warpMatrix))
if warp_mode == cv2.MOTION_HOMOGRAPHY:
new_pts =cv2.perspectiveTransform(new_pts,np.linalg.inv(warpMatrix).astype(np.float32))
return new_pts[0]
def transform_points(self, points, mat, size, padding=0):
""" Transform points along matrix """
logger.trace("points: %s, matrix: %s, size: %s. padding: %s", points,
mat, size, padding)
matrix = self.transform_matrix(mat, size, padding)
points = np.expand_dims(points, axis=1)
points = cv2.transform(
points, # pylint: disable=no-member
matrix,
points.shape)
retval = np.squeeze(points)
logger.trace("Returning: %s", retval)
return retval
def rotate_points(self, pts, angle=None, shape=None):
"""Rotate points in relation to image."""
log.debug('Start rotate points.')
if shape is None:
shape = self.shape
if angle is None:
angle = self.angle
not_cached = self.affine_mat is None or self.size_new is None
changed_val = self.shape != shape or self.angle != angle
if not_cached or changed_val:
self.__get_affine_mat(shape, angle)
return cv2.transform(pts, self.affine_mat[0:2])
def getbbox(self):
mat = self._mat
if mat.dtype == bool:
mat = mat.astype(np.uint8)
_, thim = cv2.threshold(mat, 0, 255, cv2.THRESH_BINARY)
ch = _channels(thim.shape)
if ch > 1:
thim = cv2.transform(thim, np.ones(ch, dtype=np.float32).reshape(1, ch))
x, y, w, h = cv2.boundingRect(thim)
if w == 0 and h == 0:
return None
rect = (x, y, x+w, y+h)
return rect
def keypoints_shift_scale_rotate(keypoints, angle, scale, dx, dy, rows, cols, **params):
target_keypoints = keypoints[:, :2]
meta_inf = keypoints[:, 2:]
height, width = rows, cols
center = (width / 2, height / 2)
matrix = cv2.getRotationMatrix2D(center, angle, scale)
matrix[0, 2] += dx * width
matrix[1, 2] += dy * height
new_keypoints = cv2.transform(target_keypoints[None], matrix).squeeze()
return np.hstack([new_keypoints, meta_inf])
def create_rotation_matrix(self, offset=0):
center = (self.center[0] + offset, self.center[1] + offset)
rm = cv2.getRotationMatrix2D(tuple(center), self.angle, 1)
if self.expand:
# Find the coordinates of the center of rotation in the new image
# The only point for which we know the future coordinates is the center of the image
rot_im_center = cv2.transform(
self.image_center[None, None, :] + offset, rm)[0, 0, :]
new_center = np.array([self.bound_w / 2, self.bound_h / 2
]) + offset - rot_im_center
# shift the rotation center to the new coordinates
rm[:, 2] += new_center
return rm
def crop_minAreaRect(img, rect):
# rotate img
angle = rect[2]
rows,cols = img.shape[0], img.shape[1]
M = cv2.getRotationMatrix2D((cols/2,rows/2),angle,1)
# rotate bounding box
rect0 = (rect[0], rect[1], 0.0)
box = cv2.boxPoints(rect0)
pts = np.int0(cv2.transform(np.array([box]), M))[0]
pts[pts < 0] = 0
return img_crop
def blackAndWhite(img):
res = img.copy()
res = cv2.cvtColor(
res, cv2.COLOR_BGR2RGB) # converting to RGB as sepia matrix is for RGB
res = np.array(res, dtype=np.float64)
res = cv2.transform(
res,
np.matrix([[0.21, 0.72, 0.07], [0.21, 0.72, 0.07], [0.21, 0.72,
0.07]]))
res[np.where(res > 255)] = 255 # clipping values greater than 255 to 255
res = np.array(res, dtype=np.uint8)
res = cv2.cvtColor(res, cv2.COLOR_RGB2BGR)
return res
def get_original_roi(self, mat, size, padding=0):
""" Return the square aligned box location on the original
image """
logger.trace("matrix: %s, size: %s. padding: %s", mat, size, padding)
matrix = self.transform_matrix(mat, size, padding)
points = np.array([[0, 0],
[0, size - 1],
[size - 1, size - 1],
[size - 1, 0]], np.int32)
points = points.reshape((-1, 1, 2))
matrix = cv2.invertAffineTransform(matrix) # pylint: disable=no-member
logger.trace("Returning: (points: %s, matrix: %s", points, matrix)
return cv2.transform(points, matrix) # pylint: disable=no-member
async def process(self):
if not self.source.value:
return
image = self.source.value.image.copy()
edges = cv2.Canny(image, 100, 200)
image[edges > 0] = np.array([200, 200, 200])
red_image = cv2.transform(image, self.TERMINATOR_MAT)
if self.faces.value is not None:
for (x, y, w, h) in self.faces.value:
cv2.rectangle(red_image, (x, y), (x + w, y + h), (255, 255, 255), 2)
frame = Frame(red_image, 'Terminator')
self.output.push(frame)
self.debug_output.push(frame)
def rotate_coords(coords, rotation, center):
"""Rotate coords around given center point
:param coords: points to rotate
:param rotation: rotation angle
:param center: center of rotation
"""
M = cv2.getRotationMatrix2D((center), rotation, 1)
change_coords = [[item[1], item[0]] for item in coords]
coords = np.array([change_coords])
rotated_coords = cv2.transform(coords, M)[0]
out_coords = [[item[1], item[0]] for item in rotated_coords]
return np.asarray(out_coords)
def sepia(img):
res = img.copy()
res = cv2.cvtColor(
res, cv2.COLOR_BGR2RGB) # converting to RGB as sepia matrix is for RGB
res = np.array(res, dtype=np.float64)
res = cv2.transform(
res,
np.matrix([[0.393, 0.769, 0.189], [0.349, 0.686, 0.168],
[0.272, 0.534, 0.131]]))
res[np.where(res > 255)] = 255 # clipping values greater than 255 to 255
res = np.array(res, dtype=np.uint8)
res = cv2.cvtColor(res, cv2.COLOR_RGB2BGR)
return res
def _transform_boundaries(
boundaries: List[np.ndarray],
transformationMatrix: np.ndarray) -> List[np.ndarray]:
transformedList = []
for b in boundaries:
reshapedBoundaries = np.reshape(b, (1, b.shape[0], 2)).astype(
np.float)
transformedBoundaries = cv2.transform(
reshapedBoundaries, transformationMatrix)[0, :, :2]
transformedList.append(transformedBoundaries)
return transformedList
def __call__(self, sample):
image, key_pts = sample['image'], sample['keypoints']
c_x, c_y = int(image.shape[0]/2), int(image.shape[1]/2)
ang = self.max_ang*np.random.rand()-self.max_ang
M = cv2.getRotationMatrix2D((c_x, c_y), ang, 1.0)
image = cv2.warpAffine(image, M, image.shape[:2])
key_pts = np.reshape(np.array(key_pts), (68, 1, 2))
key_pts = cv2.transform(key_pts, M)
key_pts = np.float32(np.reshape(key_pts, (68, 2)))
return {'image': image, 'keypoints': key_pts}
def col_deconvol_blur_clone(img, deconv_mat, size_blur):
# deconvolution
OD_data = transform_OD(img)
deconv_mat = deconv_mat.astype('float32', copy=False)
img_deconv = cv2.transform(OD_data, deconv_mat)
## blur
nucl_blur = cv2.GaussianBlur(img_deconv[:, :, 0], size_blur1, 0)
clone_blur = cv2.GaussianBlur(img_deconv[:, :, 1], size_blur, 0)
## convert to 8 bits
clone_blur = np.clip(clone_blur, 0, 1, out=clone_blur)
clone_blur *= 255
clone_blur = clone_blur.astype('uint8', copy=False)
return clone_blur
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 Normalization(w, h, allPoints, allPoints_json, images):
imagesNorm = []
pointsNorm = [] #the normalized points and images
boundaryPts = np.array([
(0, 0),
(w / 2, 0),
(w - 1, 0),
(w - 1, h / 2),
(w - 1, h - 1),
(w / 2, h - 1),
(0, h - 1),
(0, h / 2)
]
)
pointsAvg = np.array([[0, 0]] * len(allPoints[0])) #an array representing the final average landmarks
eyecorner_chin_Dst = [
[0.3 * w, h / 2],
[0.7 * w, h / 2],
[0.5 * w, h * 0.9]
] #the final locations of eye conners and chin
for i, image in enumerate(images):
points = allPoints[i]
#the two eye corners from the original image
eyecorner_chin_Src = [allPoints_json[i]['left_eye_left_corner'], allPoints_json[i]['right_eye_right_corner'], allPoints_json[i]['contour_chin']]
eyecorner_chin_Src = [[p['x'], p['y']] for p in eyecorner_chin_Src]
tform, img = utils.applyAffineTransform(image, eyecorner_chin_Src, eyecorner_chin_Dst, (w, h)) # transform the original image
points = np.reshape(cv2.transform(np.reshape(np.array(points), (-1, 1, 2)), tform), (-1, 2)) # transform the points
points = np.maximum(points, 0)
points = np.minimum(points, [w - 1, h - 1])
pointsAvg += points # contribute to the average points
pointsNorm.append(np.append(points, boundaryPts, axis = 0))
imagesNorm.append(img)
pointsAvg = pointsAvg / len(images)
return np.append(pointsAvg, boundaryPts, axis = 0), pointsNorm, imagesNorm
def compute_location(self, kp1, des1, kp2, des2):
"""
compute the global location of center of current image
:param kp1: captured keyPoints
:param des1: captured descriptions
:param kp2: map keyPoints
:param des2: map descriptions
:return: global pose
"""
matches = self.matcher.knnMatch(des1, des2, k=2)
good = []
pose = None
for match in matches:
if len(match) > 1 and match[
0].distance < MATCH_RATIO * match[1].distance:
good.append(match[0])
if len(good) > MIN_MATCH_COUNT:
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)
transform = cv2.estimateRigidTransform(src_pts, dst_pts, False)
if transform is not None:
transformed_center = cv2.transform(
CAMERA_CENTER, transform) # get global pixel
transformed_center = [
transformed_center[0][0][0] /
METER_TO_PIXEL, # map to global pose
(MAP_PIXEL_HEIGHT - 1 - transformed_center[0][0][1]) /
METER_TO_PIXEL
]
yaw = np.arctan2(transform[1, 0],
transform[0, 0]) # get global heading
# correct the pose if the drone is not level
z = math.sqrt(self.z**2 / (1 + math.tan(self.angle_x)**2 +
math.tan(self.angle_y)**2))
offset_x = np.tan(self.angle_x) * z
offset_y = np.tan(self.angle_y) * z
global_offset_x = math.cos(yaw) * offset_x + math.sin(
yaw) * offset_y
global_offset_y = math.sin(yaw) * offset_x + math.cos(
yaw) * offset_y
pose = [
transformed_center[0] + global_offset_x,
transformed_center[1] + global_offset_y, z, yaw
]
return pose, len(good)
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 estimateRigidTransform(srcPts, dstPts, inlierThreshold, outlierCoordScale = 1.0, fullAffine = False):
srcPts = np.float32(srcPts).reshape(-1,1,2)
dstPts = np.float32(dstPts).reshape(-1,1,2)
M = cv2.estimateRigidTransform(srcPts, dstPts, fullAffine = fullAffine)
print M
if M is None:
inlierMask = np.zeros(len(srcPts))
else:
inlierMask = []
mappedPts = cv2.transform(srcPts, M)
for mappedPt,dstPt in zip(mappedPts, dstPts):
dist = np.linalg.norm(mappedPt/outlierCoordScale - dstPt/outlierCoordScale)
inlierMask.append(int(dist < inlierThreshold))
inlierMask = np.array(inlierMask)
return M, inlierMask
def GET(self):
image_path = os.getcwd() + '/images/'
img_list = os.listdir(image_path)
img = cv2.imread(image_path + img_list[0], cv2.IMREAD_COLOR)
kernel = np.array(
([0.272, 0.543, 0.131],
[0.349, 0.686, 0.168],
[0.393, 0.769, 0.189])
)
result = cv2.transform(img, kernel)
_, data = cv2.imencode('.jpg', result)
jpeg_base64 = base64.b64encode(data.tostring())
return jpeg_base64
def apply_new_face(self, image, new_face, image_mask, mat, image_size, size):
base_image = numpy.copy( image )
new_image = numpy.copy( image )
cv2.warpAffine( new_face, mat, image_size, new_image, cv2.WARP_INVERSE_MAP, cv2.BORDER_TRANSPARENT )
outImage = None
if self.seamless_clone:
masky,maskx = cv2.transform( numpy.array([ size/2,size/2 ]).reshape(1,1,2) ,cv2.invertAffineTransform(mat) ).reshape(2).astype(int)
outimage = cv2.seamlessClone(new_image.astype(numpy.uint8),base_image.astype(numpy.uint8),(image_mask*255).astype(numpy.uint8),(masky,maskx) , cv2.NORMAL_CLONE )
else:
foreground = cv2.multiply(image_mask, new_image.astype(float))
background = cv2.multiply(1.0 - image_mask, base_image.astype(float))
outimage = cv2.add(foreground, background)
return outimage
def get_align_mat(face, size, should_align_eyes):
""" Return the alignment Matrix """
logger.trace("size: %s, should_align_eyes: %s", size, should_align_eyes)
mat_umeyama = umeyama(np.array(face.landmarks_as_xy[17:]), True)[0:2]
if should_align_eyes is False:
return mat_umeyama
mat_umeyama = mat_umeyama * size
# Convert to matrix
landmarks = np.matrix(face.landmarks_as_xy)
# cv2 expects points to be in the form
# np.array([ [[x1, y1]], [[x2, y2]], ... ]), we'll expand the dim
landmarks = np.expand_dims(landmarks, axis=1)
# Align the landmarks using umeyama
umeyama_landmarks = cv2.transform( # pylint: disable=no-member
landmarks,
mat_umeyama,
landmarks.shape)
# Determine a rotation matrix to align eyes horizontally
mat_align_eyes = func_align_eyes(umeyama_landmarks, size)
# Extend the 2x3 transform matrices to 3x3 so we can multiply them
# and combine them as one
mat_umeyama = np.matrix(mat_umeyama)
mat_umeyama.resize((3, 3))
mat_align_eyes = np.matrix(mat_align_eyes)
mat_align_eyes.resize((3, 3))
mat_umeyama[2] = mat_align_eyes[2] = [0, 0, 1]
# Combine the umeyama transform with the extra rotation matrix
transform_mat = mat_align_eyes * mat_umeyama
# Remove the extra row added, shape needs to be 2x3
transform_mat = np.delete(transform_mat, 2, 0)
transform_mat = transform_mat / size
logger.trace("Returning: %s", transform_mat)
return transform_mat
def get_feature_mask(self, aligned_landmarks_68, size, padding=0, dilation=30):
scale = size - 2*padding
translation = padding
pad_mat = np.matrix([[scale, 0.0, translation], [0.0, scale, translation]])
aligned_landmarks_68 = np.expand_dims(aligned_landmarks_68, axis=1)
aligned_landmarks_68 = cv2.transform(aligned_landmarks_68, pad_mat, aligned_landmarks_68.shape)
aligned_landmarks_68 = np.squeeze(aligned_landmarks_68)
(lStart, lEnd) = FACIAL_LANDMARKS_IDXS["left_eye"]
(rStart, rEnd) = FACIAL_LANDMARKS_IDXS["right_eye"]
(mStart, mEnd) = FACIAL_LANDMARKS_IDXS["mouth"]
(nStart, nEnd) = FACIAL_LANDMARKS_IDXS["nose"]
(lbStart, lbEnd) = FACIAL_LANDMARKS_IDXS["left_eyebrow"]
(rbStart, rbEnd) = FACIAL_LANDMARKS_IDXS["right_eyebrow"]
(cStart, cEnd) = FACIAL_LANDMARKS_IDXS["chin"]
l_eye_points = aligned_landmarks_68[lStart:lEnd].tolist()
l_brow_points = aligned_landmarks_68[lbStart:lbEnd].tolist()
r_eye_points = aligned_landmarks_68[rStart:rEnd].tolist()
r_brow_points = aligned_landmarks_68[rbStart:rbEnd].tolist()
nose_points = aligned_landmarks_68[nStart:nEnd].tolist()
chin_points = aligned_landmarks_68[cStart:cEnd].tolist()
mouth_points = aligned_landmarks_68[mStart:mEnd].tolist()
l_eye_points = l_eye_points + l_brow_points
r_eye_points = r_eye_points + r_brow_points
mouth_points = mouth_points + nose_points + chin_points
l_eye_hull = cv2.convexHull(np.array(l_eye_points).reshape((-1, 2)).astype(int)).flatten().reshape((-1, 2))
r_eye_hull = cv2.convexHull(np.array(r_eye_points).reshape((-1, 2)).astype(int)).flatten().reshape((-1, 2))
mouth_hull = cv2.convexHull(np.array(mouth_points).reshape((-1, 2)).astype(int)).flatten().reshape((-1, 2))
mask = np.zeros((size, size, 3), dtype=float)
cv2.fillConvexPoly(mask, l_eye_hull, (1,1,1))
cv2.fillConvexPoly(mask, r_eye_hull, (1,1,1))
cv2.fillConvexPoly(mask, mouth_hull, (1,1,1))
if dilation > 0:
kernel = np.ones((dilation, dilation), np.uint8)
mask = cv2.dilate(mask, kernel, iterations=1)
return mask
def transform_cnt(img, cnt, trfm, anchor_im='centroid', anchor_bg='center'):
"""
Place img on the background canvas, by aligning the anchor_im position on the img
with the anchor_bg position of the background canvas, and then apply trfm
on the image (with rotation centered around centroid of the image). Return the
canvas with transformed image.
"""
if cnt is None:
img = pad_image_cnt(img, cnt, anchor_im, anchor_bg)
else:
img, cnt = pad_image_cnt(img, cnt, anchor_im, anchor_bg)
im_centroid = get_centroid(img)
A = cv2.getRotationMatrix2D(im_centroid, np.rad2deg(trfm[2]), 1)
A[0, 2] = A[0, 2] + trfm[0]
A[1, 2] = A[1, 2] + trfm[1]
img_warp = cv2.warpAffine(img, A, img.shape[0:2])
if cnt is not None:
cnt = cv2.transform(np.resize(cnt, (cnt.shape[0], 1, 2)), A)
cnt = np.squeeze(cnt)
return img_warp, cnt
return img_warp
points1 = allPoints[i];
# Corners of the eye in input image
eyecornerSrc = [ allPoints[i][36], allPoints[i][45] ] ;
# Compute similarity transform
tform = similarityTransform(eyecornerSrc, eyecornerDst);
# Apply similarity transformation
img = cv2.warpAffine(images[i], tform, (w,h));
# Apply similarity transform on points
points2 = np.reshape(np.array(points1), (68,1,2));
points = cv2.transform(points2, tform);
points = np.float32(np.reshape(points, (68, 2)));
# Append boundary points. Will be used in Delaunay Triangulation
points = np.append(points, boundaryPts, axis=0)
# Calculate location of average landmark points.
pointsAvg = pointsAvg + points / numImages;
pointsNorm.append(points);
imagesNorm.append(img);
# Delaunay triangulation
def icp(a, b, init_pose=(0,0,0), no_iterations = 13):
'''
The Iterative Closest Point estimator.
Takes two cloudpoints a[x,y], b[x,y], an initial estimation of
their relative pose and the number of iterations
Returns the affine transform that transforms
the cloudpoint a to the cloudpoint b.
Note:
(1) This method works for cloudpoints with minor
transformations. Thus, the result depents greatly on
the initial pose estimation.
(2) A large number of iterations does not necessarily
ensure convergence. Contrarily, most of the time it
produces worse results.
'''
src = np.array([a.T], copy=True).astype(np.float32)
dst = np.array([b.T], copy=True).astype(np.float32)
#Initialise with the initial pose estimation
Tr = np.array([[np.cos(init_pose[2]),-np.sin(init_pose[2]),init_pose[0]],
[np.sin(init_pose[2]), np.cos(init_pose[2]),init_pose[1]],
[0, 0, 1 ]])
#import pdb; pdb.set_trace()
src = cv2.transform(src, Tr[0:2])
#empty = np.zeros((730, 1300, 3))
for i in range(no_iterations):
empty = np.zeros((720, 1280, 3))
#Find the nearest neighbours between the current source and the
#destination cloudpoint
nbrs = NearestNeighbors(n_neighbors=1, algorithm='auto').fit(dst[0])
distances, indices = nbrs.kneighbors(src[0])
qualified = (distances<5).ravel()
indices = indices[qualified, :]
#Compute the transformation between the current source
#and destination cloudpoint
T = cv2.estimateRigidTransform(src[:, qualified, :], dst[0, indices.T], True)
#T = cv2.getPerspectiveTransform(src, dst[0, indices.T])
#Transform the previous source and update the
#current source cloudpoint
src = cv2.transform(src, T)
#empty[a[0,:], a[1,:], :] = (255, 0, 0)
empty[b[0,:], b[1,:], :] = (255, 0, 0)
idx = src[:, qualified, :].astype(int)
idx2 = dst[0, indices.T].astype(int)
#import pdb; pdb.set_trace()
for (y, x) in idx[0, :]:
cv2.circle(empty, (x, y), 8, (0,255,0), 1)
for (y, x) in idx2[0, :]:
cv2.circle(empty, (x, y), 3, (0,0,255), 1)
cv2.imshow('icp', empty)
k = cv2.waitKey(50) & 0xFF
if k == 27: break
#Save the transformation from the actual source cloudpoint
#to the destination
Tr = np.dot(Tr, np.vstack((T,[0,0,1])))
print T
#import pdb; pdb.set_trace()
#import pdb; pdb.set_trace()
return Tr, (0, Tr[0,2], Tr[1, 2])
cv2.circle(empty, (x, y), 3, (0,0,255), 1)
cv2.imshow('icp', empty)
k = cv2.waitKey(50) & 0xFF
if k == 27: break
#Save the transformation from the actual source cloudpoint
#to the destination
Tr = np.dot(Tr, np.vstack((T,[0,0,1])))
print T
#import pdb; pdb.set_trace()
#import pdb; pdb.set_trace()
return Tr, (0, Tr[0,2], Tr[1, 2])
if __name__ == '__main__':
ang = np.linspace(-np.pi/2, np.pi/2, 320)
a = np.array([ang, np.sin(ang)])
th = np.pi/2
rot = np.array([[np.cos(th), -np.sin(th)],[np.sin(th), np.cos(th)]])
b = np.dot(rot, a) + np.array([[0.2], [0.3]])
#Run the icp
M2 = icp(a, b, [0.1, 0.33, np.pi/2.2], 30)
#Plot the result
src = np.array([a.T]).astype(np.float32)
res = cv2.transform(src, M2)
plt.figure()
plt.plot(b[0],b[1])
plt.plot(res[0].T[0], res[0].T[1], 'r.')
plt.plot(a[0], a[1])
plt.show()
def rotate_landmarks(face, rotation_matrix):
# pylint: disable=c-extension-no-member
""" Rotate the landmarks and bounding box for faces
found in rotated images.
Pass in a DetectedFace object, Alignments dict or BoundingBox"""
logger.trace("Rotating landmarks: (rotation_matrix: %s, type(face): %s",
rotation_matrix, type(face))
if isinstance(face, DetectedFace):
bounding_box = [[face.x, face.y],
[face.x + face.w, face.y],
[face.x + face.w, face.y + face.h],
[face.x, face.y + face.h]]
landmarks = face.landmarksXY
elif isinstance(face, dict):
bounding_box = [[face.get("x", 0), face.get("y", 0)],
[face.get("x", 0) + face.get("w", 0),
face.get("y", 0)],
[face.get("x", 0) + face.get("w", 0),
face.get("y", 0) + face.get("h", 0)],
[face.get("x", 0),
face.get("y", 0) + face.get("h", 0)]]
landmarks = face.get("landmarksXY", list())
elif isinstance(face, BoundingBox):
bounding_box = [[face.left, face.top],
[face.right, face.top],
[face.right, face.bottom],
[face.left, face.bottom]]
landmarks = list()
else:
raise ValueError("Unsupported face type")
logger.trace("Original landmarks: %s", landmarks)
rotation_matrix = cv2.invertAffineTransform( # pylint: disable=no-member
rotation_matrix)
rotated = list()
for item in (bounding_box, landmarks):
if not item:
continue
points = np.array(item, np.int32)
points = np.expand_dims(points, axis=0)
transformed = cv2.transform(points, # pylint: disable=no-member
rotation_matrix).astype(np.int32)
rotated.append(transformed.squeeze())
# Bounding box should follow x, y planes, so get min/max
# for non-90 degree rotations
pt_x = min([pnt[0] for pnt in rotated[0]])
pt_y = min([pnt[1] for pnt in rotated[0]])
pt_x1 = max([pnt[0] for pnt in rotated[0]])
pt_y1 = max([pnt[1] for pnt in rotated[0]])
if isinstance(face, DetectedFace):
face.x = int(pt_x)
face.y = int(pt_y)
face.w = int(pt_x1 - pt_x)
face.h = int(pt_y1 - pt_y)
face.r = 0
if len(rotated) > 1:
rotated_landmarks = [tuple(point) for point in rotated[1].tolist()]
face.landmarksXY = rotated_landmarks
elif isinstance(face, dict):
face["x"] = int(pt_x)
face["y"] = int(pt_y)
face["w"] = int(pt_x1 - pt_x)
face["h"] = int(pt_y1 - pt_y)
face["r"] = 0
if len(rotated) > 1:
rotated_landmarks = [tuple(point) for point in rotated[1].tolist()]
face["landmarksXY"] = rotated_landmarks
else:
rotated_landmarks = BoundingBox(pt_x, pt_y, pt_x1, pt_y1)
face = rotated_landmarks
logger.trace("Rotated landmarks: %s", rotated_landmarks)
return face
def red_filter(cv_pic):
m_red = numpy.asarray([[0.0, 0.0, 0.0],
[0.0, 0.0, 0.0],
[0.0, 0.0, 1.0]])
red = cv2.transform(cv_pic, m_red)
return red
def green_filter(cv_pic):
m_green = numpy.asarray([[0.0, 0.0, 0.0],
[0.0, 1.0, 0.0],
[0.0, 0.0, 0.0]])
green = cv2.transform(cv_pic, m_green)
return green