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 transform_image(img, ang_range, shear_range, trans_range):
'''
NOTE: Some parts of this method was barrowed from:
https://nbviewer.jupyter.org/github/vxy10/SCND_notebooks/blob/master/preprocessing_stuff/img_transform_NB.ipynb
credit should go to the original author
'''
# Rotation
ang_rot = np.random.uniform(ang_range) - ang_range / 2
rows, cols, ch = img.shape
Rot_M = cv2.getRotationMatrix2D((cols / 2, rows / 2), ang_rot, 1)
# Translation
tr_x = trans_range * np.random.uniform() - trans_range / 2
tr_y = trans_range * np.random.uniform() - trans_range / 2
Trans_M = np.float32([[1, 0, tr_x], [0, 1, tr_y]])
# Shear
pts1 = np.float32([[5, 5], [20, 5], [5, 20]])
pt1 = 5 + shear_range * np.random.uniform() - shear_range / 2
pt2 = 20 + shear_range * np.random.uniform() - shear_range / 2
pts2 = np.float32([[pt1, 5], [pt2, pt1], [5, pt2]])
shear_M = cv2.getAffineTransform(pts1, pts2)
img = cv2.warpAffine(img, Rot_M, (cols, rows))
img = cv2.warpAffine(img, Trans_M, (cols, rows))
img = cv2.warpAffine(img, shear_M, (cols, rows))
return img
def warpImage(original, feats, tri, img_path):
image = cv2.imread(img_path)
white = (255, 255, 255)
rows,cols,ch = image.shape
masked_image = np.zeros(image.shape, dtype=np.uint8)
for t in tri:
old_a = original[t[0]]
old_b = original[t[1]]
old_c = original[t[2]]
new_a = feats[t[0]]
new_b = feats[t[1]]
new_c = feats[t[2]]
pts1 = np.float32([old_a,old_b,old_c])
pts2 = np.float32([new_a,new_b,new_c])
M = cv2.getAffineTransform(pts1,pts2)
dst = cv2.warpAffine(image,M,(cols,rows))
# cv2.imshow('masked image', dst)
mask = np.zeros(image.shape, dtype=np.uint8)
roi_corners = np.array([[new_a, new_b, new_c]], dtype=np.int32)
cv2.fillPoly(mask, roi_corners, white)
masked = cv2.bitwise_and(dst, mask)
masked_image = cv2.bitwise_or(masked_image, masked)
# cv2.imshow('masked image', masked_image)
# cv2.waitKey()
# cv2.destroyAllWindows()
return masked_image
def elastic_transform(image, alpha=2000, sigma=40, alpha_affine=40, random_state=None):
if random_state is None:
random_state = np.random.RandomState(None)
shape = image.shape
shape_size = shape[:2]
# Random affine
center_square = np.float32(shape_size) // 2
square_size = min(shape_size) // 3
pts1 = np.float32([center_square + square_size, [center_square[0]+square_size, center_square[1]-square_size], center_square - square_size])
pts2 = pts1 + random_state.uniform(-alpha_affine, alpha_affine, size=pts1.shape).astype(np.float32)
M = cv2.getAffineTransform(pts1, pts2)
for i in range(shape[2]):
image[:,:,i] = cv2.warpAffine(image[:,:,i], M, shape_size[::-1], borderMode=cv2.BORDER_REFLECT_101)
image = image.reshape(shape)
blur_size = int(4*sigma) | 1
dx = cv2.GaussianBlur((random_state.rand(*shape_size) * 2 - 1), ksize=(blur_size, blur_size), sigmaX=sigma) * alpha
dy = cv2.GaussianBlur((random_state.rand(*shape_size) * 2 - 1), ksize=(blur_size, blur_size), sigmaX=sigma) * alpha
x, y = np.meshgrid(np.arange(shape[1]), np.arange(shape[0]))
indices = np.reshape(y+dy, (-1, 1)), np.reshape(x+dx, (-1, 1))
def_img = np.zeros_like(image)
for i in range(shape[2]):
def_img[:,:,i] = map_coordinates(image[:,:,i], indices, order=1).reshape(shape_size)
return def_img
def calc_cropping_matrix(width, height, center, joint, sigma=1.0):
jmap = datasets.JOINT_MAP
if 'rhip' in jmap and 'lhip' in jmap:
diam1 = np.sqrt(np.sum((joint[jmap['lsho']] - joint[jmap['rhip']]) ** 2))
diam2 = np.sqrt(np.sum((joint[jmap['rsho']] - joint[jmap['lhip']]) ** 2))
diam = (diam1 + diam2) / 2.0
else:
diam = np.sqrt(np.sum((joint[jmap['lsho']] - joint[jmap['rsho']]) ** 2))
diam *= 2.0
diam *= sigma
half_diam = diam / 2.0
# affine transform
src_triangle = np.array([
[center[0] - half_diam, center[1] - half_diam],
[center[0] + half_diam, center[1] - half_diam],
[center[0] - half_diam, center[1] + half_diam],
], dtype=np.float32)
dst_triangle = np.array([
[0, 0],
[width, 0],
[0, height],
], dtype=np.float32)
affine_mat = cv2.getAffineTransform(src_triangle, dst_triangle) # 2x3
affine_mat = np.vstack((affine_mat, [0, 0, 1])) # 3x3
return affine_mat
def TransformPos():
origP = numpy.float32([[40, 40], [160, 40], [40, 160]])
newP = numpy.float32([[60, 60], [180, 60], [60, 195]])
mat = ii.getAffineTransform(origP, newP)
warpImg = ii.warpAffine(IMG, mat, (w, h))
ii.imshow("image", warpImg)
def modify_affine(new, old, thr, recur):
inlier = range(0,len(old))
last_inlier = []
dist_container = []
count = 0
M = None
while (len(inlier)!=len(last_inlier)):# and (count < recur):
if len(inlier) < 0.8*len(old):
#print len(inlier)
thr = np.mean(dist_container) - np.std(dist_container)
last_inlier = range(0,len(old))
#print thr
else:
last_inlier = inlier
inlier = []
dist_container = []
seeds = range(0,len(last_inlier))
random.shuffle(seeds)
seed = seeds[0:3]
pst_new = np.array([new[last_inlier[j]] for j in seed])
pst_old = np.array([old[last_inlier[j]] for j in seed])
M = cv2.getAffineTransform(pst_new,pst_old)
count = count + 1
for k in range(0,len(last_inlier)):
fone = np.hstack((old[last_inlier[k]],1))
Tfone = np.transpose(fone)
fcomp = np.dot(M,Tfone)
dist = np.linalg.norm(old[last_inlier[k]]-fcomp[0:2])
dist_container.append(dist)
if dist < thr:
inlier.append(last_inlier[k])
return M
def faceclone(src_name, dst_name):
src_img = cv2.imread(src_name)
dst_img = cv2.imread(dst_name)
src_rst = api.detection.detect(img = File(src_name), attribute='pose')
src_img_width = src_rst['img_width']
src_img_height = src_rst['img_height']
src_face = src_rst['face'][0]
dst_rst = api.detection.detect(img = File(dst_name), attribute='pose')
dst_img_width = dst_rst['img_width']
dst_img_height = dst_rst['img_height']
dst_face = dst_rst['face'][0]
ss = np.array(get_feature_points(src_face, src_img_width, src_img_height), dtype=np.float32)
ps = np.array(get_feature_points(dst_face, dst_img_width, dst_img_height), dtype=np.float32)
map_matrix = cv2.getAffineTransform(ps, ss)
#dsize = (300,300)
map_result = cv2.warpAffine(dst_img, map_matrix, dsize=(src_img_width,src_img_height))
extract_mask, center = contour.extract_face_mask(src_face['face_id'], src_img_width, src_img_height, src_name)
# merge
## first blending the border
extract_alpha = contour.extract_face_alpha(src_face['face_id'], src_img_width, src_img_height, src_name)
center = (map_result.shape[0]/2, map_result.shape[1]/2)
map_result = cv2.seamlessClone(src_img, map_result, extract_mask, center, flags=cv2.NORMAL_CLONE)
imap_matrix = cv2.invertAffineTransform(map_matrix)
final = cv2.warpAffine(map_result, imap_matrix, dsize=(dst_img.shape[0:2]))
return final
def transform_map(self):
self.triangulate()
slam_map = cv2.imread(self.img_2, 0)
rows, cols = cv2.imread(self.img_1, 0).shape
output = np.zeros((rows, cols), np.uint8)
slam_nodes = self.nodes("register/slam.1.node")
semantic_nodes = self.nodes("register/semantic.1.node")
# From the ele file, color triangles
first_line = True
with open("register/slam.1.ele", 'r') as f:
for line in f:
if first_line:
# Do nothing
first_line = False
elif '#' not in line:
# This line is not a comment
s = line.split()
node_index_1 = int(s[1])
node_index_2 = int(s[2])
node_index_3 = int(s[3])
slam_pts = [slam_nodes[node_index_1], slam_nodes[node_index_2], slam_nodes[node_index_3]]
semantic_pts = [semantic_nodes[node_index_1], semantic_nodes[node_index_2], semantic_nodes[node_index_3]]
transform = cv2.getAffineTransform(np.array(slam_pts, dtype='float32'), np.array(semantic_pts, dtype='float32'))
if transform != None:
all_transformed = cv2.warpAffine(slam_map, transform, (cols, rows))
area = np.array(semantic_pts, dtype='int32')
area = area.reshape((-1, 1, 2))
mask = np.zeros((rows, cols), np.uint8)
cv2.fillPoly(mask, [area], 255)
tmp = cv2.bitwise_and(all_transformed, mask)
output = cv2.add(tmp, output)
cv2.imshow('Output', output)
cv2.waitKey(0)
cv2.destroyAllWindows()
def get_subimage(img, slice_x, slice_y, width=None, height=None):
"""
extracts the subimage specified by `slice_x` and `slice_y`.
Optionally, the image can also be resampled, by specifying a different
number of pixels in either direction using the last two arguments
"""
p1_x, p2_x = slice_x[:2]
p1_y, p2_y = slice_y[:2]
if width is None:
width = p2_x - p1_x
if height is None:
height = (p2_y - p1_y) * width / (p2_x - p1_x)
# get corresponding points between the two images
pts1 = np.array(((p1_x, p1_y), (p1_x, p2_y), (p2_x, p1_y)), np.float32)
pts2 = np.array(((0, 0), (height, 0), (0, width)), np.float32)
# determine and apply the affine transformation
matrix = cv2.getAffineTransform(pts1, pts2)
res = cv2.warpAffine(img, matrix, (int(round(height)), int(round(width))))
# return the profile
return res
def align(self, image, box, dimension=96, landmark_indices=None, landmarks=None):
"""
Transform and align a face in an image.
:param image: RGB image to process. Shape: (height, width, 3)
:type image: numpy.ndarray
:param box: Bounding box around the face to align.
:type box: dlib.rectangle
:type dimension: int
:param landmark_indices: The indices to transform to.
:type landmark_indices: list of ints
:param dimension: The edge length in pixels of the square the image is resized to.
:param landmarks: Detected landmark locations. \
Landmarks found on `box` if not provided.
:type landmarks: list of (x,y) tuples
:return: The aligned RGB image. Shape: (dimension, dimension, 3)
:rtype: numpy.ndarray
"""
if landmark_indices is None:
landmark_indices = self.OUTER_EYES_AND_NOSE
if landmarks is None:
landmarks = self.find_landmarks(image, box)
landmarks = np.float32(landmarks)
landmark_indices = np.array(landmark_indices)
H = cv2.getAffineTransform(
landmarks[landmark_indices],
dimension * MINMAX_TEMPLATE[landmark_indices])
return cv2.warpAffine(image, H, (dimension, dimension))
def elastic_transform(image, alpha, sigma, alpha_affine, random_state=None):
"""Elastic deformation of images as described in [Simard2003]_ (with modifications).
.. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
Convolutional Neural Networks applied to Visual Document Analysis", in
Proc. of the International Conference on Document Analysis and
Recognition, 2003.
Based on https://gist.github.com/erniejunior/601cdf56d2b424757de5
"""
if random_state is None:
random_state = np.random.RandomState(None)
shape = image.shape
shape_size = shape[:2]
# Random affine
center_square = np.float32(shape_size) // 2
square_size = min(shape_size) // 3
pts1 = np.float32([center_square + square_size, [center_square[0]+square_size, center_square[1]-square_size], center_square - square_size])
pts2 = pts1 + random_state.uniform(-alpha_affine, alpha_affine, size=pts1.shape).astype(np.float32)
M = cv2.getAffineTransform(pts1, pts2)
image = cv2.warpAffine(image, M, shape_size[::-1], borderMode=cv2.BORDER_REFLECT_101)
dx = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma) * alpha
dy = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma) * alpha
dz = np.zeros_like(dx)
x, y, z = np.meshgrid(np.arange(shape[1]), np.arange(shape[0]), np.arange(shape[2]))
indices = np.reshape(y+dy, (-1, 1)), np.reshape(x+dx, (-1, 1)), np.reshape(z, (-1, 1))
return map_coordinates(image, indices, order=1, mode='reflect').reshape(shape)
def affTransform(self,img,operate="",*arg):
rows,cols=img.shape[:2]
points=[]
for ar in arg:
points.append(ar)
print ar
sr1,sr2,sr3=(0,0),(0,0),(0,0)
ds1,ds2,ds3=(0,0),(0,0),(0,0)
if(len(operate)!=0 ):
if(operate=="H_FLIP"):
sr1,sr2,sr3=(0,0),(cols-1,0),(0,rows-1)
ds1,ds2,ds3=(cols-1,0),(0,0),(cols-1,rows-1)
elif(operate=="V_FLIP"):
sr1,sr2,sr3=(0,0),(cols-1,0),(0,rows-1)
ds1,ds2,ds3=(0,rows-1),(cols-1,rows-1),(0,0)
else:
if(len(arg)==6):
sr1,sr2,sr3=points[0],points[1],points[2]
ds1,ds2,ds3=points[3],points[4],points[5]
src_points = np.float32([sr1, sr2, sr3])
dst_points = np.float32([ds1,ds2,ds3])
affine_matrix = cv2.getAffineTransform(src_points, dst_points)
img_output = cv2.warpAffine(img, affine_matrix, (cols,rows))
return img_output
def transform_image(image,ang_range,shear_range,trans_range):
# Rotation
ang_rot = np.random.uniform(ang_range)-ang_range/2
rows,cols,ch = image.shape
Rot_M = cv2.getRotationMatrix2D((cols/2,rows/2),ang_rot,1)
# Translation
tr_x = trans_range*np.random.uniform()-trans_range/2
tr_y = trans_range*np.random.uniform()-trans_range/2
Trans_M = np.float32([[1,0,tr_x],[0,1,tr_y]])
# Shear
pts1 = np.float32([[5,5],[20,5],[5,20]])
pt1 = 5+shear_range*np.random.uniform()-shear_range/2
pt2 = 20+shear_range*np.random.uniform()-shear_range/2
pts2 = np.float32([[pt1,5],[pt2,pt1],[5,pt2]])
shear_M = cv2.getAffineTransform(pts1,pts2)
image = cv2.warpAffine(image,Rot_M,(cols,rows))
image = cv2.warpAffine(image,Trans_M,(cols,rows))
image = cv2.warpAffine(image,shear_M,(cols,rows))
return image
def get_warped(img):
rows,cols,_ = img.shape
# random scaling coefficients
rndx = np.random.rand(3) - 0.5
rndx *= cols * 0.06 # this coefficient determines the degree of warping
rndy = np.random.rand(3) - 0.5
rndy *= rows * 0.06
# 3 starting points for transform, 1/4 way from edges
x1 = cols/4
x2 = 3*cols/4
y1 = rows/4
y2 = 3*rows/4
pts1 = np.float32([[y1,x1],
[y2,x1],
[y1,x2]])
pts2 = np.float32([[y1+rndy[0],x1+rndx[0]],
[y2+rndy[1],x1+rndx[1]],
[y1+rndy[2],x2+rndx[2]]])
M = cv2.getAffineTransform(pts1,pts2)
dst = cv2.warpAffine(img,M,(cols,rows))
dst = dst[:,:,np.newaxis]
return dst
def warp_image(img, triangulation, base_points, coord):
"""
Realize the mesh warping phase
triangulation is the Delaunay triangulation of the base points
base_points are the coordinates of the landmark poitns of the reference image
code inspired from http://www.learnopencv.com/warp-one-triangle-to-another-using-opencv-c-python/
"""
all_points, coordinates = preprocess_image_before_triangulation(img)
img_out = 255 * np.ones(img.shape, dtype=img.dtype)
for t in triangulation:
# triangles to map one another
src_tri = np.array([[all_points[x][0], all_points[x][1]] for x in t]).astype(np.float32)
dest_tri = np.array([[base_points[x][0], base_points[x][1]] for x in t]).astype(np.float32)
# bounding boxes
src_rect = cv2.boundingRect(np.array([src_tri]))
dest_rect = cv2.boundingRect(np.array([dest_tri]))
# crop images
src_crop_tri = np.zeros((3, 2), dtype=np.float32)
dest_crop_tri = np.zeros((3, 2))
for k in range(0, 3):
for dim in range(0, 2):
src_crop_tri[k][dim] = src_tri[k][dim] - src_rect[dim]
dest_crop_tri[k][dim] = dest_tri[k][dim] - dest_rect[dim]
src_crop_img = img[src_rect[1]:src_rect[1] + src_rect[3], src_rect[0]:src_rect[0] + src_rect[2]]
# affine transformation estimation
mat = cv2.getAffineTransform(
np.float32(src_crop_tri),
np.float32(dest_crop_tri)
)
dest_crop_img = cv2.warpAffine(
src_crop_img,
mat,
(dest_rect[2], dest_rect[3]),
None,
flags=cv2.INTER_LINEAR,
borderMode=cv2.BORDER_REFLECT_101
)
# Use a mask to keep only the triangle pixels
# Get mask by filling triangle
mask = np.zeros((dest_rect[3], dest_rect[2], 3), dtype=np.float32)
cv2.fillConvexPoly(mask, np.int32(dest_crop_tri), (1.0, 1.0, 1.0), 16, 0)
# Apply mask to cropped region
dest_crop_img = dest_crop_img * mask
# Copy triangular region of the rectangular patch to the output image
img_out[dest_rect[1]:dest_rect[1] + dest_rect[3], dest_rect[0]:dest_rect[0] + dest_rect[2]] = \
img_out[dest_rect[1]:dest_rect[1] + dest_rect[3], dest_rect[0]:dest_rect[0] + dest_rect[2]] * (
(1.0, 1.0, 1.0) - mask)
img_out[dest_rect[1]:dest_rect[1] + dest_rect[3], dest_rect[0]:dest_rect[0] + dest_rect[2]] = \
img_out[dest_rect[1]:dest_rect[1] + dest_rect[3], dest_rect[0]:dest_rect[0] + dest_rect[2]] + dest_crop_img
return img_out[coord[2]:coord[3], coord[0]:coord[1]]
def extract_time_window(contour_list, hierarchy_list, original_image, \
max_error):
time_handles = get_time_handles(contour_list, hierarchy_list, max_error)
if time_handles == None:
return((1, None, 'No time handle detected'))
time_handle_centres = [get_centre_of_contour(contour_list[handle[2]]) \
for handle in time_handles]
distance = abs(time_handle_centres[1][0] - time_handle_centres[0][0])
one_unit = (distance / (81.5/2.3))
total_vertical_distance = int(numpy.ceil(17.0 * one_unit))
total_horizontal_distance = int(numpy.ceil((11.0+(81.5/2.3)) * one_unit))
short_horizontal_distance = 5.5 * one_unit
long_vertical_distance = 12.5 * one_unit
long_horizontal_distance = (5.5+(81.5/2.3)) * one_unit
model_points = numpy.array([ [short_horizontal_distance, \
short_horizontal_distance], [long_horizontal_distance, \
short_horizontal_distance], [(short_horizontal_distance + \
long_horizontal_distance) / 2, long_vertical_distance] ], \
dtype='float32')
time_handle_points = numpy.array(time_handle_centres, dtype='float32')
M = cv2.getAffineTransform(time_handle_points, model_points)
rotated_cropped_img = cv2.warpAffine( numpy.copy(original_image), M, \
(total_horizontal_distance, total_vertical_distance) )
return((0, rotated_cropped_img, 'Success'))
def _process(self):
w = self.display.widget
img = w.image
out = []
v = self.pRef.value()
if v == 'Reference points':
r0, r1 = self._refPn
pts0 = np.array([(h['pos'].y(), h['pos'].x())
for h in r0.handles]) + r0.pos()
pts1 = np.array([(h['pos'].y(), h['pos'].x())
for h in r1.handles]) + r1.pos()
#TODO: embed in PyerspectiveCorrection
M = cv2.getAffineTransform(
pts0.astype(
np.float32), pts1.astype(
np.float32))
for n, i in enumerate(img):
out.append(
# TODO: allow different image shapes
cv2.warpAffine(i, M, w.image.shape[1:3],
borderValue=0))
print(out[-1].shape)
else:
r = v == 'Reference image'
e = self.pExecOn.value()
for n, i in enumerate(img):
if (e == 'all images'
or (e == 'current image' and n == w.currentIndex)
or (e == 'last image' and n == len(img) - 1)):
if not (r and n == self._refImg_from_own_display):
out.append(self.pc.correct(i))
return out
def extract_algae_window(contour_list, hierarchy_list, original_image, \
max_error):
corners = get_corner_handles(contour_list, hierarchy_list, max_error)
if corners == None:
return((1, None, 'No corners detected'))
corners = sort_corner_handles(corners, contour_list)
corner_centres = [get_centre_of_contour(contour_list[corner[2]]) \
for corner in corners]
distance = max(
((corner_centres[0][0] - corner_centres[1][0])**2 +
(corner_centres[0][1] - corner_centres[1][1])**2)**0.5,
((corner_centres[2][0] - corner_centres[1][0])**2 +
(corner_centres[2][1] - corner_centres[1][1])**2)**0.5)
short_distance = 5.5*(distance/32.0)
long_distance = 37.5*(distance/32.0)
total_distance = int(numpy.ceil(43.0*(distance/32.0)))
model_points = numpy.array([ [short_distance,long_distance], \
[short_distance,short_distance], [long_distance,short_distance] ], \
dtype='float32')
corner_points = numpy.array(corner_centres, dtype='float32')
M = cv2.getAffineTransform(corner_points, model_points)
rotated_cropped_img = cv2.warpAffine( numpy.copy(original_image), M, \
(total_distance, total_distance) )
return((0, rotated_cropped_img, 'Success'))
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 get_map_to_predict(src_locs, src_x_axiss, src_y_axiss, map, map_size,
interpolation=cv2.INTER_LINEAR):
fss = []
valids = []
center = (map_size-1.0)/2.0
dst_theta = np.pi/2.0
dst_loc = np.array([center, center])
dst_x_axis = np.array([np.cos(dst_theta), np.sin(dst_theta)])
dst_y_axis = np.array([np.cos(dst_theta+np.pi/2), np.sin(dst_theta+np.pi/2)])
def compute_points(center, x_axis, y_axis):
points = np.zeros((3,2),dtype=np.float32)
points[0,:] = center
points[1,:] = center + x_axis
points[2,:] = center + y_axis
return points
dst_points = compute_points(dst_loc, dst_x_axis, dst_y_axis)
for i in range(src_locs.shape[0]):
src_loc = src_locs[i,:]
src_x_axis = src_x_axiss[i,:]
src_y_axis = src_y_axiss[i,:]
src_points = compute_points(src_loc, src_x_axis, src_y_axis)
M = cv2.getAffineTransform(src_points, dst_points)
fs = cv2.warpAffine(map, M, (map_size, map_size), None, flags=interpolation,
borderValue=np.NaN)
valid = np.invert(np.isnan(fs))
valids.append(valid)
fss.append(fs)
return fss, valids
def register(fid1, fid2, fid3, image, blank):
numRows, numCols, numBands, dtype = ipcv.dimensions(blank)
blank1row, blank1col = ipcv.fftCorrelation2(fid1,blank)
blank2row, blank2col = ipcv.fftCorrelation2(fid2,blank)
blank3row, blank3col = ipcv.fftCorrelation2(fid3,blank)
print 'blank1row', blank1row
print 'blank1col', blank1col
print 'blank2row', blank2row
print 'blank2col', blank2col
print 'blank3row', blank3row
print 'blank3col', blank3col
#blankfid1 = numpy.array([738,60])
#blankfid2 = numpy.array([738,542])
#blankfid3 = numpy.array([53,556])
#print blankfid2.shape
fid1row, fid1col = ipcv.fftCorrelation2(fid1,image)
fid2row, fid2col = ipcv.fftCorrelation2(fid2,image)
fid3row, fid3col = ipcv.fftCorrelation2(fid3,image)
print 'fid1row', fid1row
print 'fid1col', fid1col
print 'fid2row', fid2row
print 'fid2col', fid2col
print 'fid3row', fid3row
print 'fid3col', fid3col
numRowsIm, numColsIm, numBandsIm, dataTypeIm = ipcv.dimensions(image)
#Rotated 180 degrees
if fid2row - 25 < fid1row < fid2row + 25 and fid3col - 25 < fid2col < fid3col + 25 and fid2row < numRowsIm/2 and fid3col < numColsIm/2:
print 'hello'
image = numpy.rot90(image, k=2)
cv2.namedWindow('image', cv2.WINDOW_AUTOSIZE)
cv2.imshow('image', image)
fid1row, fid1col = ipcv.fftCorrelation2(fid1,image)
fid2row, fid2col = ipcv.fftCorrelation2(fid2,image)
fid3row, fid3col = ipcv.fftCorrelation2(fid3,image)
blankpts = numpy.array([[blank1row,blank1col],[blank2row,blank2col],[blank3row,blank3col]]).astype(numpy.float32)
#blankpts = numpy.array([blankfid1,blankfid2,blankfid3]).astype(numpy.float32)
#print blankpts.shape
fidpts = numpy.array([[fid1row,fid1col],[fid2row,fid2col],[fid3row,fid3col]]).astype(numpy.float32)
#print fidpts.shape
M = cv2.getAffineTransform(blankpts,fidpts)
regIm = cv2.warpAffine(image,M,(numCols,numRows), borderMode = cv2.BORDER_TRANSPARENT)
cv2.namedWindow('rot',cv2.WINDOW_AUTOSIZE)
cv2.imshow('rot',regIm.astype(numpy.uint8))
cv2.waitKey()
cv2.imwrite('new0001.tif', regIm.astype(numpy.uint8))
return regIm
def draw(photo_file, cloud, alfa, betta, gamma):#Функция, собирающая всё вместе
corners = getCorners(photo_file)#взяли углы с фотки
cloud = rotateCloud(cloud, alfa, betta, gamma)#повернули облако на тот угол, с которого делалась фотография
pr=[]
for i in range(len(cloud)):#берём проекцию
p = Point2(cloud[i].x+270,cloud[i].z+300)#числа взяты так, чтобы проекция рисовалась примерно по центру
pr.append(p)
conf_pr = getConformity(pr, corners)#сопоставляем проекцию и углы
triangles = []
meshes = getMeshes('cube.jpg', corners, 10, 10)#запоминаем нужные грани
for i in range(len(meshes)):#сопоставляем треугольники с проекции облака и треугольники с фотографии,
#чтобы удобнее было копировать кусочки изображения
trngl=[]
for j in range(3):
for k in range(len(corners)):
if meshes[i][j].x == corners[k].x and meshes[i][j].y == corners[k].y:
trngl.append(conf_pr[k])
triangles.append(trngl)
#наложение текстур
image = cv2.imread(photo_file)
rows,cols,ch = image.shape
new_image = numpy.zeros(image.shape, numpy.uint8)
new_image = cv2.bitwise_not(new_image)
for i in range(len(meshes)):
#точки треугольника с фотографии
x1 = meshes[i][0].x
y1 = meshes[i][0].y
x2 = meshes[i][1].x
y2 = meshes[i][1].y
x3 = meshes[i][2].x
y3 = meshes[i][2].y
pts1 = numpy.float32([[x1,y1],[x2,y2],[x3,y3]])
roi_corners = numpy.array([[(x1,y1), (x2,y2), (x3,y3)]], dtype=numpy.int32)
mask = numpy.zeros(image.shape, dtype=numpy.uint8)#маска для фотографии
#точки треугольника проекции облака
X1 = triangles[i][0].x
Y1 = triangles[i][0].y
X2 = triangles[i][1].x
Y2 = triangles[i][1].y
X3 = triangles[i][2].x
Y3 = triangles[i][2].y
pts2 = numpy.float32([[X1,Y1],[X2,Y2],[X3,Y3]])
roi2_corners = numpy.array([[(X1,Y1), (X2,Y2), (X3,Y3)]], dtype=numpy.int32)
mask2 = numpy.zeros(new_image.shape, dtype=numpy.uint8)#маска для места, куда вставим изображение
cv2.fillPoly(mask, roi_corners, (255,255,255))#создаём маску
masked_image = cv2.bitwise_and(image, mask)#применяем маску к фотографии
M = cv2.getAffineTransform(pts1,pts2)#применяем аффинные преобразования
warp_affin_img = cv2.warpAffine(masked_image,M,(cols,rows))
cv2.fillPoly(mask2, roi2_corners, (255,255,255))#создаём вторую маску
mask2 = cv2.bitwise_not(mask2)#инвентируем для обратного эффекта (заполнять нужно то, что вне треугольника)
new_image = cv2.bitwise_and(new_image, mask2)#применяем маску к прекции
new_image = cv2.bitwise_or(new_image, warp_affin_img)#объединяем изображения
cv2.imshow('result',new_image)
def affine(im):
height,width=im.shape
angle=getAngle(im)
pts1=np.float32([[width/2,height/2],[width/2,0],[0,height/2]])
pts2=np.float32([[width/2,height/2],[width/2+height/2/math.tan(math.radians(angle)),0],[0,height/2]])
M=cv2.getAffineTransform(pts1,pts2)
dst=cv2.warpAffine(im,M,(width,height))
return dst
def applyAffineTransform(src, srcTri, dstTri, size):
# Given a pair of triangles, find the affine transform.
warpMat = cv2.getAffineTransform(np.float32(srcTri), np.float32(dstTri))
# Apply the Affine Transform just found to the src image
dst = cv2.warpAffine(src, warpMat, (size[0], size[1]), None, flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101)
return dst
def warpImage(orig, features, diang, src): image = cv2.imread(src) masked_image = np.zeros(image.shape, dtype=np.uint8) for t in diang: mask = np.zeros(image.shape, dtype=np.uint8) cv2.fillPoly(mask, np.array([[features[t[0]], features[t[1]], features[t[2]]]], dtype=np.int32), (255, 255, 255)) masked_image = cv2.bitwise_or(masked_image, cv2.bitwise_and(cv2.warpAffine(image, cv2.getAffineTransform(np.float32([orig[t[0]], orig[t[1]], orig[t[2]]]), np.float32([features[t[0]], features[t[1]], features[t[2]]])), (image.shape[1],image.shape[0])), mask)) return masked_image
def render_image(q, image, dims, dst=None):
# take query bounding box (world coordinates)
qbb = q['bbox']
qbb_wpts = [[qbb[0], qbb[2]], [qbb[1], qbb[2]], [qbb[1], qbb[3]]]
#im_to_q_w = cv2.getAffineTransform(
# numpy.array(ibb_wpts, dtype='f4'),
# numpy.array(qbb_wpts, dtype='f4'))
#print("im_to_q_w {}".format(im_to_q_w))
# affine for world to tile
tpts = [[0, 0], [dims[0], 0], [dims[0], dims[1]]]
w_to_t = cv2.getAffineTransform(
numpy.array(qbb_wpts, dtype='f4'),
numpy.array(tpts, dtype='f4'))
#print("w_to_t {}".format(w_to_t))
# generate affine (2) to lay image (image) onto (world)
#im = open_tif(image['url']['0'])[::4, ::4]
im = open_image(image['url']['0'])
imshape = im.shape
ipts = [[0, 0], [imshape[1], 0], [imshape[1], imshape[0]]]
# generate affine (1) to lay image bounding box (world) onto query
ibb = image['bbox']
ibb_wpts = [
[ibb['left'], ibb['north']], [ibb['right'], ibb['north']],
[ibb['right'], ibb['south']]]
im_to_w = cv2.getAffineTransform(
numpy.array(ipts, dtype='f4'),
numpy.array(ibb_wpts, dtype='f4'))
#print("im_to_w {}".format(im_to_w))
# combine affines im_to_w -> im_to_q_w -> q_to_t
im_to_t = combine_affines(w_to_t, im_to_w)
#print("im_to_t {}".format(im_to_t))
md = 1. / max(abs(im_to_t[0, 0]), abs(im_to_t[1, 1]))
if md > 1:
s = min(int(md), im.shape[0] / 4, im.shape[1] / 4)
im_to_t[0, 0] = im_to_t[0, 0] * s
im_to_t[1, 1] = im_to_t[1, 1] * s
im = im[::s, ::s]
# convert image
# calculate original image bbox to world coordinates
# convert
#if dst is None:
# return cv2.warpAffine(im.astype('f8'), im_to_t, dims)
if dst is None:
return cv2.warpAffine(im, im_to_t, dims)
return cv2.warpAffine(
im, im_to_t, dims, dst=dst, borderMode=cv2.BORDER_TRANSPARENT)
def imgChangeUsingWarpAffine(img, topLeft1, bottomRight1, topLeft2, bottomRight2):
rows, cols, ch = img.shape
pts1 = np.float32([[topLeft1[0], topLeft1[1]], [bottomRight1[0], bottomRight1[1]], [bottomRight1[0], topLeft1[1]]])
pts2 = np.float32([[topLeft2[0], topLeft2[1]], [bottomRight2[0], bottomRight2[1]], [bottomRight2[0], topLeft2[1]]])
M = cv2.getAffineTransform(pts1, pts2)
img = cv2.warpAffine(img, M, (cols, rows))
return img
def getSingleNumber( img, cont ):
"""
single number ROI size: 60x100
"""
#step 1. get single number roi
pos1 = np.float32( [ [cont[0],0],
[cont[0]+cont[2],0],
[cont[0]+cont[2],99] ] )
pos2 = np.float32( [ [0,0],
[59,0],
[59,99] ] )
M = cv2.getAffineTransform( pos1, pos2 )
roi_sn = cv2.warpAffine( img, M, (60,100),
borderValue=cv2.cv.ScalarAll(255) )
#step 2. get 7 segments of single number
seg7 = 7*[0]
if sum( roi_sn[25][0:20] ) > 0: #f,5
seg7[5] = 1
if sum( roi_sn[25][40:60] ) > 0: #b,1
seg7[1] = 1
if sum( roi_sn[75][0:20] ) > 0: #e,4
seg7[4] = 1
if sum( roi_sn[75][40:60] ) > 0: #c,2
seg7[2] = 1
#adjust for "7." or "L"
x_ad = 0
if seg7[4]==0 and seg7[5]==0:
x_ad -= 5
if seg7[1]==0 and seg7[2]==0:
x_ad += 5
if sum([ a[30+x_ad] for a in roi_sn[0:20] ]) > 0: #a,0
seg7[0] = 1
if sum([ a[30+x_ad] for a in roi_sn[40:60] ]) > 0: #g,6
seg7[6] = 1
if sum([ a[30+x_ad] for a in roi_sn[80:100] ]) > 0: #d,3
seg7[3] = 1
#for debug
if DEBUG_MODE:
cv2.imshow( "roi_sn", roi_sn )
try:
i = number_mask.index( tuple(seg7) )
if i==10:
return "C"
elif i==11:
return "A"
elif i==12:
return "L"
elif i==13:
return "-1"
else:
return str( i )
except:
return "*"
def align(self, img, landmarks, net=False):
npLandmarks = np.float32(landmarks)
npLandmarksIndices = np.array(self.outer_eyes_and_nose)
H = cv2.getAffineTransform(npLandmarks[npLandmarksIndices],
96 * MINMAX_TEMPLATE[npLandmarksIndices])
thumbnail = cv2.warpAffine(img, H, (96, 96))
if (net):
thumbnail = self.net.forward(thumbnail).reshape(1, -1)
return thumbnail
return thumbnail
def find_table(img, display_list):
#mask_screen = find_screen(img, display_list)
## find white border
DoB = zc.get_DoB(img, 1, 31, method='Average')
zc.check_and_display('DoB',
DoB,
display_list,
resize_max=config.DISPLAY_MAX_PIXEL,
wait_time=config.DISPLAY_WAIT_TIME)
mask_white = zc.color_inrange(DoB, 'HSV', V_L=10)
mask_white = cv2.morphologyEx(mask_white,
cv2.MORPH_CLOSE,
zc.generate_kernel(7, 'circular'),
iterations=1)
zc.check_and_display_mask('mask_white_raw',
img,
mask_white,
display_list,
resize_max=config.DISPLAY_MAX_PIXEL,
wait_time=config.DISPLAY_WAIT_TIME)
## find purple table (roughly)
mask_table = zc.color_inrange(img,
'HSV',
H_L=130,
H_U=160,
S_L=60,
V_L=50,
V_U=240)
mask_table, _ = zc.get_big_blobs(mask_table, min_area=50)
mask_table = cv2.morphologyEx(mask_table,
cv2.MORPH_CLOSE,
zc.generate_kernel(7, 'circular'),
iterations=1)
mask_table, _ = zc.find_largest_CC(mask_table)
if mask_table is None:
rtn_msg = {'status': 'fail', 'message': 'Cannot find table'}
return (rtn_msg, None)
mask_table_convex, _ = zc.make_convex(mask_table.copy(), app_ratio=0.005)
mask_table = np.bitwise_or(mask_table, mask_table_convex)
mask_table_raw = mask_table.copy()
zc.check_and_display_mask('table_purple',
img,
mask_table,
display_list,
resize_max=config.DISPLAY_MAX_PIXEL,
wait_time=config.DISPLAY_WAIT_TIME)
## fine tune the purple table based on white border
#mask_white = np.bitwise_and(np.bitwise_not(mask_table), mask_white)
#mask_white = np.bitwise_and(np.bitwise_not(zc.shrink(mask_table, 3, method = "circular")), mask_white)
mask_white = np.bitwise_and(np.bitwise_not(mask_table), mask_white)
if 'mask_white' in display_list:
gray = np.float32(mask_white)
dst = cv2.cornerHarris(gray, 10, 3, 0.04)
dst = cv2.dilate(dst, None)
img_white = img.copy()
img_white[mask_white > 0, :] = [0, 255, 0]
#img_white[dst > 2.4e7] = [0, 0, 255]
zc.check_and_display('mask_white',
img_white,
display_list,
resize_max=config.DISPLAY_MAX_PIXEL,
wait_time=config.DISPLAY_WAIT_TIME)
#mask_table, _ = zc.make_convex(mask_table, app_ratio = 0.005)
for i in xrange(15):
mask_table = zc.expand(mask_table, 3)
mask_table = np.bitwise_and(np.bitwise_not(mask_white), mask_table)
if i % 4 == 3:
mask_table, _ = zc.make_convex(mask_table, app_ratio=0.01)
#img_display = img.copy()
#img_display[mask_table > 0, :] = [0, 0, 255]
#zc.display_image('table%d-b' % i, img_display, resize_max = config.DISPLAY_MAX_PIXEL, wait_time = config.DISPLAY_WAIT_TIME)
#mask_white = np.bitwise_and(np.bitwise_not(mask_table), mask_white)
mask_table = np.bitwise_and(np.bitwise_not(mask_white), mask_table)
mask_table, _ = zc.find_largest_CC(mask_table)
if mask_table is None:
rtn_msg = {'status': 'fail', 'message': 'Cannot find table'}
return (rtn_msg, None)
mask_table, hull_table = zc.make_convex(mask_table, app_ratio=0.01)
zc.check_and_display_mask('table_purple_fixed',
img,
mask_table,
display_list,
resize_max=config.DISPLAY_MAX_PIXEL,
wait_time=config.DISPLAY_WAIT_TIME)
## check if table is big enough
table_area = cv2.contourArea(hull_table)
table_area_percentage = float(table_area) / img.shape[0] / img.shape[1]
if table_area_percentage < 0.06:
rtn_msg = {
'status': 'fail',
'message': "Detected table too small: %f" % table_area_percentage
}
return (rtn_msg, None)
## find top line of table
hull_table = np.array(zc.sort_pts(hull_table[:, 0, :], order_first='y'))
ul = hull_table[0]
ur = hull_table[1]
if ul[0] > ur[0]:
t = ul
ul = ur
ur = t
i = 2
# the top two points in the hull are probably on the top line, but may not be the corners
while i < hull_table.shape[0] and hull_table[i, 1] - hull_table[0, 1] < 80:
pt_tmp = hull_table[i]
if pt_tmp[0] < ul[0] or pt_tmp[0] > ur[0]:
# computing the area of the part of triangle that lies inside the table
triangle = np.vstack([pt_tmp, ul, ur]).astype(np.int32)
mask_triangle = np.zeros_like(mask_table)
cv2.drawContours(mask_triangle, [triangle], 0, 255, -1)
pts = mask_table_raw[mask_triangle.astype(bool)]
if np.sum(pts == 255) > 10:
break
if pt_tmp[0] < ul[0]:
ul = pt_tmp
else:
ur = pt_tmp
i += 1
else:
break
ul = [int(x) for x in ul]
ur = [int(x) for x in ur]
## sanity checks about table top line detection
if zc.euc_dist(ul, ur)**2 * 2.5 < table_area:
rtn_msg = {'status': 'fail', 'message': "Table top line too short"}
return (rtn_msg, None)
if abs(zc.line_angle(ul, ur)) > 0.4:
rtn_msg = {
'status': 'fail',
'message': "Table top line tilted too much"
}
return (rtn_msg, None)
# check if two table sides form a reasonable angle
mask_table_bottom = mask_table.copy()
mask_table_bottom[:-30] = 0
p_left_most = zc.get_edge_point(mask_table_bottom, (-1, 0))
p_right_most = zc.get_edge_point(mask_table_bottom, (1, 0))
if p_left_most is None or p_right_most is None:
rtn_msg = {
'status': 'fail',
'message': "Table doesn't occupy bottom part of image"
}
return (rtn_msg, None)
left_side_angle = zc.line_angle(ul, p_left_most)
right_side_angle = zc.line_angle(ur, p_right_most)
angle_diff = zc.angle_dist(left_side_angle,
right_side_angle,
angle_range=math.pi * 2)
if abs(angle_diff) > 1.8:
rtn_msg = {
'status': 'fail',
'message': "Angle between two side edge not right"
}
return (rtn_msg, None)
if 'table' in display_list:
img_table = img.copy()
img_table[mask_table.astype(bool), :] = [255, 0, 255]
cv2.line(img_table, tuple(ul), tuple(ur), [0, 255, 0], 3)
zc.check_and_display('table',
img_table,
display_list,
resize_max=config.DISPLAY_MAX_PIXEL,
wait_time=config.DISPLAY_WAIT_TIME)
## rotate to make opponent upright, use table edge as reference
pts1 = np.float32(
[ul, ur, [ul[0] + (ur[1] - ul[1]), ul[1] - (ur[0] - ul[0])]])
pts2 = np.float32([[0, config.O_IMG_HEIGHT],
[config.O_IMG_WIDTH, config.O_IMG_HEIGHT], [0, 0]])
M = cv2.getAffineTransform(pts1, pts2)
img[np.bitwise_not(zc.get_mask(img, rtn_type="bool", th=3)), :] = [3, 3, 3]
img_rotated = cv2.warpAffine(img, M,
(config.O_IMG_WIDTH, config.O_IMG_HEIGHT))
## sanity checks about rotated opponent image
bool_img_rotated_valid = zc.get_mask(img_rotated, rtn_type="bool")
if float(bool_img_rotated_valid.sum()
) / config.O_IMG_WIDTH / config.O_IMG_HEIGHT < 0.6:
rtn_msg = {
'status': 'fail',
'message': "Valid area too small after rotation"
}
return (rtn_msg, None)
rtn_msg = {'status': 'success'}
return (rtn_msg, (img_rotated, mask_table, M))
def AffineTransform(dst_img, img, src_point, dst_point):
rows, cols, ch = dst_img.shape
M = cv2.getAffineTransform(src_point, dst_point)
dst = cv2.warpAffine(img, M, (cols, rows))
return dst
def find_rightcounter(car_pic, color):
if type(car_pic) == type(""):
#如果输入是一个字符串，则从它指向的路径读取图片
img = imreadex(car_pic)
else:
#否则，认为输入就是一张图片
img = car_pic
oldimg = img
original = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
pic_hight, pic_width = img.shape[:2]
#如果输入图像过大，则调整图像大小
#if pic_width > 500:
# resize_rate = 500 / pic_width
# img = cv2.resize(img, (500, int(pic_hight*resize_rate)), interpolation=cv2.INTER_AREA)
#高斯模糊去噪
blur = 3
if blur > 0:
img = cv2.GaussianBlur(img, (blur, blur), 0)
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#找到图像边缘
#ret, img_thresh = cv2.threshold(img_opening, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)#大津阈值
#img_edge = cv2.Canny(img_thresh, 50, 200)#canny算子提取边缘
ret, img_edge = cv2.threshold(img, 0, 255,
cv2.THRESH_BINARY + cv2.THRESH_OTSU) #大津阈值
#img_edge = cv2.bitwise_not(img_edge)
cv2.imshow("1", img_edge)
#边缘整体化，使用开运算和闭运算让图像边缘成为一个整体
kernel = np.ones((20, 20), np.uint8)
img_edge1 = cv2.morphologyEx(img_edge, cv2.MORPH_CLOSE, kernel)
img_edge2 = cv2.morphologyEx(img_edge1, cv2.MORPH_OPEN, kernel)
#查找图像边缘整体形成的矩形区域，可能有很多，车牌就在其中一个矩形区域中
#coutours里面保存的是轮廓里面的点，cv2.CHAIN_APPROX_SIMPLE表示：只保存角点
image, contours, hierarchy = cv2.findContours(
img_edge2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) #检测物体轮廓
tmp1 = image
tmp1 = cv2.cvtColor(tmp1, cv2.COLOR_GRAY2BGR) #灰度图转化为彩色图
#参数-1表示画出所有轮廓
#参数(0, 255, 0)表示为轮廓上色的画笔颜色为绿色
#参数2表示画笔的粗细为2
cv2.drawContours(tmp1, contours, -1, (0, 255, 0), 2) # 画出分割出来的轮廓
#挑选出面积大于Min_Area的轮廓
contours = [cnt for cnt in contours if cv2.contourArea(cnt) > 500]
#print('len(contours)', len(contours))
tmp2 = image
tmp2 = cv2.cvtColor(tmp2, cv2.COLOR_GRAY2BGR) #灰度图转化为彩色图
cv2.drawContours(tmp2, contours, -1, (0, 255, 0), 2)
cv2.imshow("contours", tmp2)
##########################################################计算车牌可能出现矩形区域（开始）###############################################
car_contours = []
tmp3 = image
tmp3 = cv2.cvtColor(tmp3, cv2.COLOR_GRAY2RGB) #灰度图转化为彩色图
tmp4 = copy.deepcopy(oldimg) #import copy
tmp4 = cv2.cvtColor(tmp4, cv2.COLOR_BGR2RGB)
for cnt in contours:
#使用cv2.minAreaRect函数生成每个轮廓的最小外界矩形
#输入为表示轮廓的点集
#返回值rect中包含最小外接矩形的中心坐标，宽度高度和旋转角度（但是这里的宽度和高度不是按照其长度来定义的）
rect = cv2.minAreaRect(cnt)
area_width, area_height = rect[1]
#做一定调整，保证宽度大于高度
if area_width < area_height:
area_width, area_height = area_height, area_width
wh_ratio = area_width / area_height
#print(wh_ratio)
#要求矩形区域长宽比在2到5.5之间，2到5.5是车牌的长宽比，其余的矩形排除
if wh_ratio > 2 and wh_ratio < 5.5:
car_contours.append(rect)
box = cv2.boxPoints(rect)
box = np.int0(box)
cv2.drawContours(tmp3, [box], -1, (0, 255, 0), 2)
cv2.drawContours(tmp4, [box], -1, (0, 255, 0), 2)
######################################################将倾斜的矩形调整为不倾斜（开始）###################################################
# print("精确定位")
print(rect)
card_imgs = []
#矩形区域可能是倾斜的矩形，需要矫正，以便使用颜色定位
for rect in car_contours:
#调整角度，使得矩形框左高右低
#0度和1度之间的所有角度当作1度处理,-1度和0度之间的所有角度当作-1度处理
#这个处理是必要的，如果不做这个处理的话，后面仿射变换可能会得到一张全灰的图片
#因为如果角度接近于0,那么矩形四个角点中任意两个角点，其某一个坐标非常接近，这种
#情况下，哪个角点在最上边，哪个角点在最下边，哪个角点在最左边，哪个角点在最右边，就
#没有了很强的区分度，所以仿射变换控制点的对应关系，很可能出现错配，造成仿射变换失败
if rect[2] > -1:
angle = -1
else:
angle = rect[2]
#扩大范围，避免车牌边缘被排除
rect = (rect[0], (rect[1][0] + 5, rect[1][1] + 5), angle)
box = cv2.boxPoints(rect)
#bottom_point:矩形框4个角中最下面的点
#right_point:矩形框4个角中最右边的点
#left_point：矩形框4个角中最左边的点
#top_point:矩形框4个角中最上面的点
bottom_point = right_point = [0, 0]
left_point = top_point = [pic_width, pic_hight]
for point in box:
if left_point[0] > point[0]:
left_point = point
if top_point[1] > point[1]:
top_point = point
if bottom_point[1] < point[1]:
bottom_point = point
if right_point[0] < point[0]:
right_point = point
#这里需要注意的是：cv2.boxPoints检测矩形，返回值中角度的范围是[-90, 0]，所以该函数中并不是长度大的作为底，长度
#小的作为高，而是以从x轴逆时针旋转，最先到达的边为底，另一条边为高
#这里为了矫正图像所作的仿射变换只能处理小角度，若角度过大，畸变很严重
#在该程序里，没有对矩形做旋转，然后再仿射变换，而是直接做仿射变换，所以只有当带识别图片中车牌位于水平位置附近时，
#才能正确识别，而当车牌绕着垂直于车牌的轴有较大转动时，识别就会失败
if left_point[1] <= right_point[1]: #正角度
new_right_point = [right_point[0], bottom_point[1]]
pts2 = np.float32([left_point, bottom_point, new_right_point])
pts1 = np.float32([left_point, bottom_point, right_point])
#用3个控制点进行仿射变换
M = cv2.getAffineTransform(pts1, pts2)
dst = cv2.warpAffine(oldimg, M, (pic_width, pic_hight))
point_limit(new_right_point)
point_limit(bottom_point)
point_limit(left_point)
card_img = dst[int(left_point[1]):int(bottom_point[1]),
int(left_point[0]):int(new_right_point[0])]
card_imgs.append(card_img)
elif left_point[1] > right_point[1]: #负角度
new_left_point = [left_point[0], bottom_point[1]]
pts2 = np.float32([new_left_point, bottom_point, right_point])
pts1 = np.float32([left_point, bottom_point, right_point])
#仿射变换
M = cv2.getAffineTransform(pts1, pts2)
dst = cv2.warpAffine(oldimg, M, (pic_width, pic_hight))
point_limit(right_point)
point_limit(bottom_point)
point_limit(new_left_point)
card_img = dst[int(right_point[1]):int(bottom_point[1]),
int(new_left_point[0]):int(right_point[0])]
card_imgs.append(card_img)
######################################################将倾斜的矩形调整为不倾斜（结束）########################################
########################################################根据车牌颜色再定位，缩小边缘非车牌边界（开始）#####################################
colors = []
#enumerate是python的内置函数，对于一个可遍历的对象，enumerate将其组成一个索引序列，利用它可以同时获得索引和值
for card_index, card_img in enumerate(card_imgs):
if card_img is None:
continue
card_img_hsv = cv2.cvtColor(card_img, cv2.COLOR_BGR2HSV)
#有转换失败的可能，原因来自于上面矫正矩形出错
row_num, col_num = card_img_hsv.shape[:2]
xl, xr, yb, yt, limit1, limit2 = accurate_place(
card_img_hsv, color)
if yt == yb and xl == xr:
continue
need_accurate = False
if yt >= yb:
yt = 0
yb = row_num
need_accurate = True
if xl >= xr:
xl = 0
xr = col_num
need_accurate = True
card_imgs[card_index] = card_img[
yt:yb,
xl:xr] if color != "g" or yt < (yb - yt) // 4 else card_img[
yt - (yb - yt) // 4:yb, xl:xr]
if need_accurate: #可能x或y方向未缩小，需要再试一次
card_img = card_imgs[card_index]
card_img_hsv = cv2.cvtColor(card_img, cv2.COLOR_BGR2HSV)
xl, xr, yb, yt, limit1, limit2 = accurate_place(
card_img_hsv, color)
if yt == yb and xl == xr:
continue
if yt >= yb:
yt = 0
yb = row_num
if xl >= xr:
xl = 0
xr = col_num
card_imgs[card_index] = card_img[
yt:yb,
xl:xr] if color != "g" or yt < (yb - yt) // 4 else card_img[
yt - (yb - yt) // 4:yb, xl:xr]
cv2.imshow("ok" + str(card_index), card_imgs[card_index])
s2=src_pts_new_y[j]-src_pts_new_y[i]#先求出x2-x1和y2-y1，避免后面重复计算
for k in range(j+1,count_pts):
s4=abs(s1*(src_pts_new_y[k]-src_pts_new_y[i])-(src_pts_new_x[k]-src_pts_new_x[i])*s2)/2
if s<s4:
s=s4
idx_0 = i
idx_1 = j
idx_2 = k
src_pts_new = np.float32([[src_pts_new_x[idx_0],src_pts_new_y[idx_0]], [src_pts_new_x[idx_1],src_pts_new_y[idx_1]], [src_pts_new_x[idx_2],src_pts_new_y[idx_2]]])
dst_pts_new = np.float32([[dst_pts_new_x[idx_0],dst_pts_new_y[idx_0]], [dst_pts_new_x[idx_1],dst_pts_new_y[idx_1]], [dst_pts_new_x[idx_2],dst_pts_new_y[idx_2]]])
print("src_pts_new: ", src_pts_new)
print("dst_pts_new: ", dst_pts_new)
M_new = cv2.getAffineTransform(dst_pts_new, src_pts_new)
affined_image = cv2.warpAffine(detect_image, M_new, (detect_image.shape[1], detect_image.shape[0]))
affined_image_lb = cv2.warpAffine(label_image, M_new, (detect_image.shape[1], detect_image.shape[0]))
affined_image_bb = cv2.rectangle(affined_image, (203, 211), (1184, 432), (255,255,255), 2)
affined_image_lb_bb = cv2.rectangle(affined_image_lb, (203, 211), (1184, 432), (255,255,255), 2)
affined_image_crop = affined_image_bb[211:432, 203:1184]
affined_image_lb_crop = affined_image_lb_bb[211:432, 203:1184]
cv2.imwrite(crop_defect_path + filename+"croppic.png", affined_image_crop)
cv2.imwrite(crop_label_path + filename+"croplb.png", affined_image_lb_crop)
else:
print("Not enough matches are found - %d/%d" % (len(good), MIN_MATCH_COUNT))
def random_rotate_and_resize(X, Y=None, scale_range=1., xy = (224,224),
do_flip=True, rescale=None, unet=False):
""" augmentation by random rotation and resizing
X and Y are lists or arrays of length nimg, with dims channels x Ly x Lx (channels optional)
Parameters
----------
X: LIST of ND-arrays, float
list of image arrays of size [nchan x Ly x Lx] or [Ly x Lx]
Y: LIST of ND-arrays, float (optional, default None)
list of image labels of size [nlabels x Ly x Lx] or [Ly x Lx]. The 1st channel
of Y is always nearest-neighbor interpolated (assumed to be masks or 0-1 representation).
If Y.shape[0]==3 and not unet, then the labels are assumed to be [cell probability, Y flow, X flow].
If unet, second channel is dist_to_bound.
scale_range: float (optional, default 1.0)
Range of resizing of images for augmentation. Images are resized by
(1-scale_range/2) + scale_range * np.random.rand()
xy: tuple, int (optional, default (224,224))
size of transformed images to return
do_flip: bool (optional, default True)
whether or not to flip images horizontally
rescale: array, float (optional, default None)
how much to resize images by before performing augmentations
unet: bool (optional, default False)
Returns
-------
imgi: ND-array, float
transformed images in array [nimg x nchan x xy[0] x xy[1]]
lbl: ND-array, float
transformed labels in array [nimg x nchan x xy[0] x xy[1]]
scale: array, float
amount each image was resized by
"""
scale_range = max(0, min(2, float(scale_range)))
nimg = len(X)
if X[0].ndim>2:
nchan = X[0].shape[0]
else:
nchan = 1
imgi = np.zeros((nimg, nchan, xy[0], xy[1]), np.float32)
lbl = []
if Y is not None:
if Y[0].ndim>2:
nt = Y[0].shape[0]
else:
nt = 1
lbl = np.zeros((nimg, nt, xy[0], xy[1]), np.float32)
scale = np.zeros(nimg, np.float32)
for n in range(nimg):
Ly, Lx = X[n].shape[-2:]
# generate random augmentation parameters
flip = np.random.rand()>.5
theta = np.random.rand() * np.pi * 2
scale[n] = (1-scale_range/2) + scale_range * np.random.rand()
if rescale is not None:
scale[n] *= 1. / rescale[n]
dxy = np.maximum(0, np.array([Lx*scale[n]-xy[1],Ly*scale[n]-xy[0]]))
dxy = (np.random.rand(2,) - .5) * dxy
# create affine transform
cc = np.array([Lx/2, Ly/2])
cc1 = cc - np.array([Lx-xy[1], Ly-xy[0]])/2 + dxy
pts1 = np.float32([cc,cc + np.array([1,0]), cc + np.array([0,1])])
pts2 = np.float32([cc1,
cc1 + scale[n]*np.array([np.cos(theta), np.sin(theta)]),
cc1 + scale[n]*np.array([np.cos(np.pi/2+theta), np.sin(np.pi/2+theta)])])
M = cv2.getAffineTransform(pts1,pts2)
img = X[n].copy()
if Y is not None:
labels = Y[n].copy()
if labels.ndim<3:
labels = labels[np.newaxis,:,:]
if flip and do_flip:
img = img[..., ::-1]
if Y is not None:
labels = labels[..., ::-1]
if nt > 1 and not unet:
labels[2] = -labels[2]
for k in range(nchan):
I = cv2.warpAffine(img[k], M, (xy[1],xy[0]), flags=cv2.INTER_LINEAR)
imgi[n,k] = I
if Y is not None:
for k in range(nt):
if k==0:
lbl[n,k] = cv2.warpAffine(labels[k], M, (xy[1],xy[0]), flags=cv2.INTER_NEAREST)
else:
lbl[n,k] = cv2.warpAffine(labels[k], M, (xy[1],xy[0]), flags=cv2.INTER_LINEAR)
if nt > 1 and not unet:
v1 = lbl[n,2].copy()
v2 = lbl[n,1].copy()
lbl[n,1] = (-v1 * np.sin(-theta) + v2*np.cos(-theta))
lbl[n,2] = (v1 * np.cos(-theta) + v2*np.sin(-theta))
return imgi, lbl, scale
def get_transform_mat(image_landmarks, output_size, face_type, scale=1.0):
if not isinstance(image_landmarks, np.ndarray):
image_landmarks = np.array(image_landmarks)
# estimate landmarks transform from global space to local aligned space with bounds [0..1]
mat = umeyama(
np.concatenate([image_landmarks[17:49], image_landmarks[54:55]]),
landmarks_2D_new, True)[0:2]
# get corner points in global space
g_p = transform_points(
np.float32([(0, 0), (1, 0), (1, 1), (0, 1), (0.5, 0.5)]), mat, True)
g_c = g_p[4]
# calc diagonal vectors between corners in global space
tb_diag_vec = (g_p[2] - g_p[0]).astype(np.float32)
tb_diag_vec /= npla.norm(tb_diag_vec)
bt_diag_vec = (g_p[1] - g_p[3]).astype(np.float32)
bt_diag_vec /= npla.norm(bt_diag_vec)
# calc modifier of diagonal vectors for scale and padding value
padding, remove_align = FaceType_to_padding_remove_align.get(
face_type, 0.0)
mod = (1.0 / scale) * (npla.norm(g_p[0] - g_p[2]) *
(padding * np.sqrt(2.0) + 0.5))
if face_type == FaceType.WHOLE_FACE:
# adjust center for WHOLE_FACE, 7% below in order to cover more forehead
vec = (g_p[0] - g_p[3]).astype(np.float32)
vec_len = npla.norm(vec)
vec /= vec_len
g_c += vec * vec_len * 0.07
# calc 3 points in global space to estimate 2d affine transform
if not remove_align:
l_t = np.array([
np.round(g_c - tb_diag_vec * mod),
np.round(g_c + bt_diag_vec * mod),
np.round(g_c + tb_diag_vec * mod)
])
else:
# remove_align - face will be centered in the frame but not aligned
l_t = np.array([
np.round(g_c - tb_diag_vec * mod),
np.round(g_c + bt_diag_vec * mod),
np.round(g_c + tb_diag_vec * mod),
np.round(g_c - bt_diag_vec * mod),
])
# get area of face square in global space
area = mathlib.polygon_area(l_t[:, 0], l_t[:, 1])
# calc side of square
side = np.float32(math.sqrt(area) / 2)
# calc 3 points with unrotated square
l_t = np.array([
np.round(g_c + [-side, -side]),
np.round(g_c + [side, -side]),
np.round(g_c + [side, side])
])
# calc affine transform from 3 global space points to 3 local space points size of 'output_size'
pts2 = np.float32(((0, 0), (output_size, 0), (output_size, output_size)))
mat = cv2.getAffineTransform(l_t, pts2)
return mat
def elastic_transform(image,
alpha,
sigma,
alpha_affine,
interpolation=cv2.INTER_LINEAR,
border_mode=cv2.BORDER_REFLECT_101,
random_state=None,
approximate=False):
"""Elastic deformation of images as described in [Simard2003]_ (with modifications).
Based on https://gist.github.com/erniejunior/601cdf56d2b424757de5
.. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
Convolutional Neural Networks applied to Visual Document Analysis", in
Proc. of the International Conference on Document Analysis and
Recognition, 2003.
"""
if random_state is None:
random_state = np.random.RandomState(1234)
height, width = image.shape[:2]
# Random affine
center_square = np.float32((height, width)) // 2
square_size = min((height, width)) // 3
alpha = float(alpha)
sigma = float(sigma)
alpha_affine = float(alpha_affine)
pts1 = np.float32([
center_square + square_size,
[center_square[0] + square_size, center_square[1] - square_size],
center_square - square_size
])
pts2 = pts1 + random_state.uniform(
-alpha_affine, alpha_affine, size=pts1.shape).astype(np.float32)
matrix = cv2.getAffineTransform(pts1, pts2)
image = cv2.warpAffine(image,
matrix, (width, height),
flags=interpolation,
borderMode=border_mode)
if approximate:
# Approximate computation smooth displacement map with a large enough kernel.
# On large images (512+) this is approximately 2X times faster
dx = (random_state.rand(height, width).astype(np.float32) * 2 - 1)
cv2.GaussianBlur(dx, (17, 17), sigma, dst=dx)
dx *= alpha
dy = (random_state.rand(height, width).astype(np.float32) * 2 - 1)
cv2.GaussianBlur(dy, (17, 17), sigma, dst=dy)
dy *= alpha
else:
dx = np.float32(
gaussian_filter(
(random_state.rand(height, width) * 2 - 1), sigma) * alpha)
dy = np.float32(
gaussian_filter(
(random_state.rand(height, width) * 2 - 1), sigma) * alpha)
x, y = np.meshgrid(np.arange(width), np.arange(height))
mapx = np.float32(x + dx)
mapy = np.float32(y + dy)
return cv2.remap(image, mapx, mapy, interpolation, borderMode=border_mode)
def elastic_transform3Dv2(self,
image,
alpha,
sigma,
alpha_affine,
random_state=None):
"""Elastic deformation of images as described in [Simard2003]_ (with modifications).
.. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
Convolutional Neural Networks applied to Visual Document Analysis", in
Proc. of the International Conference on Document Analysis and
Recognition, 2003.
Based on https://gist.github.com/erniejunior/601cdf56d2b424757de5
From https://www.kaggle.com/bguberfain/elastic-transform-for-data-augmentation
"""
# affine and deformation must be slice by slice and fixed for slices
if random_state is None:
random_state = np.random.RandomState(None)
shape = image.shape # image is contatenated, the first channel [:,:,:,0] is the image, the second channel
# [:,:,:,1] is the mask. The two channel are under the same tranformation.
shape_size = shape[:-1] # z y x
# Random affine
shape_size_aff = shape[1:-1] # y x
center_square = np.float32(shape_size_aff) // 2
square_size = min(shape_size_aff) // 3
pts1 = np.float32([
center_square + square_size,
[center_square[0] + square_size, center_square[1] - square_size],
center_square - square_size
])
pts2 = pts1 + random_state.uniform(
-alpha_affine, alpha_affine, size=pts1.shape).astype(np.float32)
M = cv2.getAffineTransform(pts1, pts2)
new_img = np.zeros_like(image)
for i in range(shape[0]):
new_img[i, :, :,
0] = cv2.warpAffine(image[i, :, :, 0],
M,
shape_size_aff[::-1],
borderMode=cv2.BORDER_CONSTANT,
borderValue=0.)
for j in range(1, 10):
new_img[i, :, :,
j] = cv2.warpAffine(image[i, :, :, j],
M,
shape_size_aff[::-1],
flags=cv2.INTER_NEAREST,
borderMode=cv2.BORDER_TRANSPARENT,
borderValue=0)
dx = gaussian_filter(
(random_state.rand(*shape[1:-1]) * 2 - 1), sigma) * alpha
dy = gaussian_filter(
(random_state.rand(*shape[1:-1]) * 2 - 1), sigma) * alpha
x, y = np.meshgrid(np.arange(shape_size_aff[1]),
np.arange(shape_size_aff[0]))
indices = np.reshape(y + dy, (-1, 1)), np.reshape(x + dx, (-1, 1))
new_img2 = np.zeros_like(image)
for i in range(shape[0]):
new_img2[i, :, :,
0] = map_coordinates(new_img[i, :, :, 0],
indices,
order=1,
mode='constant').reshape(shape[1:-1])
for j in range(1, 10):
new_img2[i, :, :,
j] = map_coordinates(new_img[i, :, :, j],
indices,
order=0,
mode='constant').reshape(
shape[1:-1])
return np.array(new_img2), new_img
def align(self,
imgDim,
rgbImg,
bb=None,
pad=None,
ts=None,
landmarks=None,
landmarkIndices=INNER_EYES_AND_BOTTOM_LIP,
opencv_det=False,
opencv_model="../model/opencv/cascade.xml",
only_crop=False):
r"""align(imgDim, rgbImg, bb=None, landmarks=None, landmarkIndices=INNER_EYES_AND_BOTTOM_LIP)
Transform and align a face in an image.
:param imgDim: The edge length in pixels of the square the image is resized to.
:type imgDim: int
:param rgbImg: RGB image to process. Shape: (height, width, 3)
:type rgbImg: numpy.ndarray
:param bb: Bounding box around the face to align. \
Defaults to the largest face.
:type bb: dlib.rectangle
:param pad: padding bb by left, top, right, bottom
:type pad: list
:param landmarks: Detected landmark locations. \
Landmarks found on `bb` if not provided.
:type landmarks: list of (x,y) tuples
:param landmarkIndices: The indices to transform to.
:type landmarkIndices: list of ints
:return: The aligned RGB image. Shape: (imgDim, imgDim, 3)
:rtype: numpy.ndarray
"""
assert imgDim is not None
assert rgbImg is not None
assert landmarkIndices is not None
if bb is None:
if opencv_det:
face_cascade = cv2.CascadeClassifier(opencv_model)
faces = face_cascade.detectMultiScale(rgbImg,
1.1,
2,
minSize=(30, 30))
dlib_rects = []
for (x, y, w, h) in faces:
dlib_rects.append(
dlib.rectangle(int(x), int(y), int(x + w), int(y + h)))
if len(faces) > 0:
bb = max(dlib_rects,
key=lambda rect: rect.width() * rect.height())
else:
bb = None
else:
bb = self.getLargestFaceBoundingBox(rgbImg)
if bb is None:
return
if pad is not None:
left = int(max(0, bb.left() - bb.width() * float(pad[0])))
top = int(max(0, bb.top() - bb.height() * float(pad[1])))
right = int(
min(rgbImg.shape[1],
bb.right() + bb.width() * float(pad[2])))
bottom = int(
min(rgbImg.shape[0],
bb.bottom() + bb.height() * float(pad[3])))
bb = dlib.rectangle(left, top, right, bottom)
if landmarks is None:
landmarks = self.findLandmarks(rgbImg, bb)
npLandmarks = np.float32(landmarks)
npLandmarkIndices = np.array(landmarkIndices)
dstLandmarks = imgDim * MINMAX_TEMPLATE[npLandmarkIndices]
if ts is not None:
# reserve more area of forehead on a face
dstLandmarks[(0, 1),
1] = dstLandmarks[(0, 1), 1] + imgDim * float(ts)
dstLandmarks[2, 1] = dstLandmarks[2, 1] + imgDim * float(ts) / 2
if not only_crop:
H = cv2.getAffineTransform(npLandmarks[npLandmarkIndices],
dstLandmarks)
return cv2.warpAffine(rgbImg, H, (imgDim, imgDim))
else:
return rgbImg[top:bottom,
left:right] # crop is rgbImg[y: y + h, x: x + w]
# Copyright 2017 BIG VISION LLC ALL RIGHTS RESERVED # # This code is made available to the students of # the online course titled "Computer Vision for Faces" # by Satya Mallick for personal non-commercial use. # # Sharing this code is strictly prohibited without written # permission from Big Vision LLC. # # For licensing and other inquiries, please email # [email protected] # import cv2 import numpy as np # Input triangle inp = np.float32([[50, 50], [100, 100], [200, 150]]) # Output triangle output = np.float32([[72, 51], [142, 101], [272, 136]]) # Another output triangle output2 = np.float32([[77, 76], [152, 151], [287, 236]]) # Get the tranformation matrices warpMat = cv2.getAffineTransform(inp, output) warpMat2 = cv2.getAffineTransform(inp, output2) # Display the matrices print ("Warp Matrix 1 : \n {} \n".format(warpMat)) print ("Warp Matrix 2 : \n {} \n".format(warpMat2))
maxArea, maxD = d.area, d
d = maxD
print("Detection with max area: Left: {} Top: {} Right: {} Bottom: {}".
format(d.left(), d.top(), d.right(), d.bottom()))
# Get the landmarks/parts for the face in box d.
#win.clear_overlay()
#win.set_image(img)
shape = predictor(img, d)
#win.add_overlay(shape)
#print("Part 0: {}, Part 1: {} ...".format(shape.part(0),shape.part(1)))
landmarks = list(map(lambda p: (p.x, p.y), shape.parts()))
npLandmarks = np.float32(landmarks)
if MODE == 0:
npLandmarkIndices = np.array(landmarkIndices)
H = cv2.getAffineTransform(
npLandmarks[npLandmarkIndices],
imgDim * MINMAX_TEMPLATE[npLandmarkIndices] + imgDim *
(ENLARGE - 1.0) / 2)
thumbnail = cv2.warpAffine(
img, H, (int(imgDim * ENLARGE), int(imgDim * ENLARGE)))
fnSurf = '_crop.jpg'
else:
thumbnail = fronter.frontalizeImage(img, d, npLandmarks)
cut = int(thumbnail.shape[0] / 8)
thumbnail = thumbnail[cut:thumbnail.shape[0] - cut,
cut:thumbnail.shape[1] - cut, :].copy()
fnSurf = '_frontal.jpg'
#cv2.imshow('normalizedFace',thumbnail)
imPath, imName = os.path.split(f)
if FILTER_MODE == 1:
k = cv2.waitKey(-1)
if k == 49:
def ConvertMaskedFace(predictor_func, predictor_input_shape, cfg, frame_info,
img_bgr_uint8, img_bgr, img_face_landmarks):
img_size = img_bgr.shape[1], img_bgr.shape[0]
img_face_mask_a = LandmarksProcessor.get_image_hull_mask(
img_bgr.shape, img_face_landmarks)
if cfg.mode == 'original':
if cfg.export_mask_alpha:
img_bgr = np.concatenate([img_bgr, img_face_mask_a], -1)
return img_bgr, img_face_mask_a
out_img = img_bgr.copy()
out_merging_mask = None
output_size = predictor_input_shape[0]
if cfg.super_resolution_mode != 0:
output_size *= 2
face_mat = LandmarksProcessor.get_transform_mat(img_face_landmarks,
output_size,
face_type=cfg.face_type)
face_output_mat = LandmarksProcessor.get_transform_mat(
img_face_landmarks,
output_size,
face_type=cfg.face_type,
scale=1.0 + 0.01 * cfg.output_face_scale)
dst_face_bgr = cv2.warpAffine(img_bgr,
face_mat, (output_size, output_size),
flags=cv2.INTER_CUBIC)
dst_face_mask_a_0 = cv2.warpAffine(img_face_mask_a,
face_mat, (output_size, output_size),
flags=cv2.INTER_CUBIC)
predictor_input_bgr = cv2.resize(dst_face_bgr, predictor_input_shape[0:2])
predicted = predictor_func(predictor_input_bgr)
if isinstance(predicted, tuple):
#converter return bgr,mask
prd_face_bgr = np.clip(predicted[0], 0, 1.0)
prd_face_mask_a_0 = np.clip(predicted[1], 0, 1.0)
predictor_masked = True
else:
#converter return bgr only, using dst mask
prd_face_bgr = np.clip(predicted, 0, 1.0)
prd_face_mask_a_0 = cv2.resize(dst_face_mask_a_0,
predictor_input_shape[0:2])
predictor_masked = False
if cfg.super_resolution_mode:
prd_face_bgr = cfg.superres_func(cfg.super_resolution_mode,
prd_face_bgr)
if predictor_masked:
prd_face_mask_a_0 = cv2.resize(prd_face_mask_a_0,
(output_size, output_size),
cv2.INTER_CUBIC)
else:
prd_face_mask_a_0 = cv2.resize(dst_face_mask_a_0,
(output_size, output_size),
cv2.INTER_CUBIC)
if cfg.mask_mode == 2: #dst
prd_face_mask_a_0 = cv2.resize(dst_face_mask_a_0,
(output_size, output_size),
cv2.INTER_CUBIC)
elif cfg.mask_mode >= 3 and cfg.mask_mode <= 7:
if cfg.mask_mode == 3 or cfg.mask_mode == 5 or cfg.mask_mode == 6:
prd_face_fanseg_bgr = cv2.resize(prd_face_bgr,
(cfg.fanseg_input_size, ) * 2)
prd_face_fanseg_mask = cfg.fanseg_extract_func(
FaceType.FULL, prd_face_fanseg_bgr)
FAN_prd_face_mask_a_0 = cv2.resize(prd_face_fanseg_mask,
(output_size, output_size),
cv2.INTER_CUBIC)
if cfg.mask_mode >= 4 or cfg.mask_mode <= 7:
full_face_fanseg_mat = LandmarksProcessor.get_transform_mat(
img_face_landmarks,
cfg.fanseg_input_size,
face_type=FaceType.FULL)
dst_face_fanseg_bgr = cv2.warpAffine(img_bgr,
full_face_fanseg_mat,
(cfg.fanseg_input_size, ) * 2,
flags=cv2.INTER_CUBIC)
dst_face_fanseg_mask = cfg.fanseg_extract_func(
FaceType.FULL, dst_face_fanseg_bgr)
if cfg.face_type == FaceType.FULL:
FAN_dst_face_mask_a_0 = cv2.resize(dst_face_fanseg_mask,
(output_size, output_size),
cv2.INTER_CUBIC)
elif cfg.face_type == FaceType.HALF:
half_face_fanseg_mat = LandmarksProcessor.get_transform_mat(
img_face_landmarks,
cfg.fanseg_input_size,
face_type=FaceType.HALF)
fanseg_rect_corner_pts = np.array(
[[0, 0], [cfg.fanseg_input_size - 1, 0],
[0, cfg.fanseg_input_size - 1]],
dtype=np.float32)
a = LandmarksProcessor.transform_points(fanseg_rect_corner_pts,
half_face_fanseg_mat,
invert=True)
b = LandmarksProcessor.transform_points(
a, full_face_fanseg_mat)
m = cv2.getAffineTransform(b, fanseg_rect_corner_pts)
FAN_dst_face_mask_a_0 = cv2.warpAffine(
dst_face_fanseg_mask,
m, (cfg.fanseg_input_size, ) * 2,
flags=cv2.INTER_CUBIC)
FAN_dst_face_mask_a_0 = cv2.resize(FAN_dst_face_mask_a_0,
(output_size, output_size),
cv2.INTER_CUBIC)
else:
raise ValueError("cfg.face_type unsupported")
if cfg.mask_mode == 3: #FAN-prd
prd_face_mask_a_0 = FAN_prd_face_mask_a_0
elif cfg.mask_mode == 4: #FAN-dst
prd_face_mask_a_0 = FAN_dst_face_mask_a_0
elif cfg.mask_mode == 5:
prd_face_mask_a_0 = FAN_prd_face_mask_a_0 * FAN_dst_face_mask_a_0
elif cfg.mask_mode == 6:
prd_face_mask_a_0 = prd_face_mask_a_0 * FAN_prd_face_mask_a_0 * FAN_dst_face_mask_a_0
elif cfg.mask_mode == 7:
prd_face_mask_a_0 = prd_face_mask_a_0 * FAN_dst_face_mask_a_0
prd_face_mask_a_0[prd_face_mask_a_0 < 0.001] = 0.0
prd_face_mask_a = prd_face_mask_a_0[..., np.newaxis]
prd_face_mask_aaa = np.repeat(prd_face_mask_a, (3, ), axis=-1)
img_face_mask_aaa = cv2.warpAffine(prd_face_mask_aaa,
face_output_mat,
img_size,
np.zeros(img_bgr.shape,
dtype=np.float32),
flags=cv2.WARP_INVERSE_MAP
| cv2.INTER_CUBIC)
img_face_mask_aaa = np.clip(img_face_mask_aaa, 0.0, 1.0)
img_face_mask_aaa[img_face_mask_aaa <= 0.1] = 0.0 #get rid of noise
if 'raw' in cfg.mode:
face_corner_pts = np.array(
[[0, 0], [output_size - 1, 0], [output_size - 1, output_size - 1],
[0, output_size - 1]],
dtype=np.float32)
square_mask = np.zeros(img_bgr.shape, dtype=np.float32)
cv2.fillConvexPoly(square_mask, \
LandmarksProcessor.transform_points (face_corner_pts, face_output_mat, invert=True ).astype(np.int), \
(1,1,1) )
if cfg.mode == 'raw-rgb':
out_merging_mask = square_mask
if cfg.mode == 'raw-rgb' or cfg.mode == 'raw-rgb-mask':
out_img = cv2.warpAffine(prd_face_bgr, face_output_mat, img_size,
out_img,
cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC,
cv2.BORDER_TRANSPARENT)
if cfg.mode == 'raw-rgb-mask':
out_img = np.concatenate(
[out_img,
np.expand_dims(img_face_mask_aaa[:, :, 0], -1)], -1)
out_merging_mask = square_mask
elif cfg.mode == 'raw-mask-only':
out_img = img_face_mask_aaa
out_merging_mask = img_face_mask_aaa
elif cfg.mode == 'raw-predicted-only':
out_img = cv2.warpAffine(prd_face_bgr, face_output_mat, img_size,
np.zeros(img_bgr.shape, dtype=np.float32),
cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC,
cv2.BORDER_TRANSPARENT)
out_merging_mask = square_mask
out_img = np.clip(out_img, 0.0, 1.0)
else:
#averaging [lenx, leny, maskx, masky] by grayscale gradients of upscaled mask
ar = []
for i in range(1, 10):
maxregion = np.argwhere(img_face_mask_aaa > i / 10.0)
if maxregion.size != 0:
miny, minx = maxregion.min(axis=0)[:2]
maxy, maxx = maxregion.max(axis=0)[:2]
lenx = maxx - minx
leny = maxy - miny
if min(lenx, leny) >= 4:
ar += [[lenx, leny]]
if len(ar) > 0:
lenx, leny = np.mean(ar, axis=0)
lowest_len = min(lenx, leny)
if cfg.erode_mask_modifier != 0:
ero = int(lowest_len * (0.126 - lowest_len * 0.00004551365) *
0.01 * cfg.erode_mask_modifier)
if ero > 0:
img_face_mask_aaa = cv2.erode(img_face_mask_aaa,
cv2.getStructuringElement(
cv2.MORPH_ELLIPSE,
(ero, ero)),
iterations=1)
elif ero < 0:
img_face_mask_aaa = cv2.dilate(img_face_mask_aaa,
cv2.getStructuringElement(
cv2.MORPH_ELLIPSE,
(-ero, -ero)),
iterations=1)
if cfg.clip_hborder_mask_per > 0: #clip hborder before blur
prd_hborder_rect_mask_a = np.ones(prd_face_mask_a.shape,
dtype=np.float32)
prd_border_size = int(prd_hborder_rect_mask_a.shape[1] *
cfg.clip_hborder_mask_per)
prd_hborder_rect_mask_a[:, 0:prd_border_size, :] = 0
prd_hborder_rect_mask_a[:, -prd_border_size:, :] = 0
prd_hborder_rect_mask_a[-prd_border_size:, :, :] = 0
prd_hborder_rect_mask_a = np.expand_dims(
cv2.blur(prd_hborder_rect_mask_a,
(prd_border_size, prd_border_size)), -1)
img_prd_hborder_rect_mask_a = cv2.warpAffine(
prd_hborder_rect_mask_a, face_output_mat, img_size,
np.zeros(img_bgr.shape, dtype=np.float32),
cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC)
img_prd_hborder_rect_mask_a = np.expand_dims(
img_prd_hborder_rect_mask_a, -1)
img_face_mask_aaa *= img_prd_hborder_rect_mask_a
img_face_mask_aaa = np.clip(img_face_mask_aaa, 0, 1.0)
if cfg.blur_mask_modifier > 0:
blur = int(lowest_len * 0.10 * 0.01 * cfg.blur_mask_modifier)
if blur > 0:
img_face_mask_aaa = cv2.blur(img_face_mask_aaa,
(blur, blur))
img_face_mask_aaa = np.clip(img_face_mask_aaa, 0, 1.0)
if 'seamless' not in cfg.mode and cfg.color_transfer_mode != 0:
if cfg.color_transfer_mode == 1: #rct
prd_face_bgr = imagelib.reinhard_color_transfer(
np.clip((prd_face_bgr * 255).astype(np.uint8), 0, 255),
np.clip((dst_face_bgr * 255).astype(np.uint8), 0, 255),
source_mask=prd_face_mask_a,
target_mask=prd_face_mask_a)
prd_face_bgr = np.clip(
prd_face_bgr.astype(np.float32) / 255.0, 0.0, 1.0)
elif cfg.color_transfer_mode == 2: #lct
prd_face_bgr = imagelib.linear_color_transfer(
prd_face_bgr, dst_face_bgr)
prd_face_bgr = np.clip(prd_face_bgr, 0.0, 1.0)
elif cfg.color_transfer_mode == 3: #mkl
prd_face_bgr = imagelib.color_transfer_mkl(
prd_face_bgr, dst_face_bgr)
elif cfg.color_transfer_mode == 4: #mkl-m
prd_face_bgr = imagelib.color_transfer_mkl(
prd_face_bgr * prd_face_mask_a,
dst_face_bgr * prd_face_mask_a)
elif cfg.color_transfer_mode == 5: #idt
prd_face_bgr = imagelib.color_transfer_idt(
prd_face_bgr, dst_face_bgr)
elif cfg.color_transfer_mode == 6: #idt-m
prd_face_bgr = imagelib.color_transfer_idt(
prd_face_bgr * prd_face_mask_a,
dst_face_bgr * prd_face_mask_a)
elif cfg.color_transfer_mode == 7: #ebs
prd_face_bgr = cfg.ebs_ct_func(
np.clip((dst_face_bgr * 255), 0, 255).astype(np.uint8),
np.clip((prd_face_bgr * 255), 0, 255).astype(np.uint8),
) #prd_face_mask_a
prd_face_bgr = np.clip(
prd_face_bgr.astype(np.float32) / 255.0, 0.0, 1.0)
if cfg.mode == 'hist-match-bw':
prd_face_bgr = cv2.cvtColor(prd_face_bgr, cv2.COLOR_BGR2GRAY)
prd_face_bgr = np.repeat(np.expand_dims(prd_face_bgr, -1),
(3, ), -1)
if cfg.mode == 'hist-match' or cfg.mode == 'hist-match-bw':
hist_mask_a = np.ones(prd_face_bgr.shape[:2] + (1, ),
dtype=np.float32)
if cfg.masked_hist_match:
hist_mask_a *= prd_face_mask_a
white = (1.0 - hist_mask_a) * np.ones(
prd_face_bgr.shape[:2] + (1, ), dtype=np.float32)
hist_match_1 = prd_face_bgr * hist_mask_a + white
hist_match_1[hist_match_1 > 1.0] = 1.0
hist_match_2 = dst_face_bgr * hist_mask_a + white
hist_match_2[hist_match_1 > 1.0] = 1.0
prd_face_bgr = imagelib.color_hist_match(
hist_match_1, hist_match_2, cfg.hist_match_threshold)
if cfg.mode == 'hist-match-bw':
prd_face_bgr = prd_face_bgr.astype(dtype=np.float32)
if 'seamless' in cfg.mode:
#mask used for cv2.seamlessClone
img_face_mask_a = img_face_mask_aaa[..., 0:1]
if cfg.mode == 'seamless2':
img_face_mask_a = cv2.warpAffine(
img_face_mask_a,
face_output_mat, (output_size, output_size),
flags=cv2.INTER_CUBIC)
img_face_seamless_mask_a = None
for i in range(1, 10):
a = img_face_mask_a > i / 10.0
if len(np.argwhere(a)) == 0:
continue
img_face_seamless_mask_a = img_face_mask_a.copy()
img_face_seamless_mask_a[a] = 1.0
img_face_seamless_mask_a[img_face_seamless_mask_a <= i /
10.0] = 0.0
break
if cfg.mode == 'seamless2':
face_seamless = imagelib.seamless_clone(
prd_face_bgr, dst_face_bgr, img_face_seamless_mask_a)
out_img = cv2.warpAffine(
face_seamless, face_output_mat, img_size, out_img,
cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC,
cv2.BORDER_TRANSPARENT)
else:
out_img = cv2.warpAffine(
prd_face_bgr, face_output_mat, img_size, out_img,
cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC,
cv2.BORDER_TRANSPARENT)
out_img = np.clip(out_img, 0.0, 1.0)
if 'seamless' in cfg.mode and cfg.mode != 'seamless2':
try:
#calc same bounding rect and center point as in cv2.seamlessClone to prevent jittering (not flickering)
l, t, w, h = cv2.boundingRect(
(img_face_seamless_mask_a * 255).astype(np.uint8))
s_maskx, s_masky = int(l + w / 2), int(t + h / 2)
out_img = cv2.seamlessClone(
(out_img * 255).astype(np.uint8), img_bgr_uint8,
(img_face_seamless_mask_a * 255).astype(np.uint8),
(s_maskx, s_masky), cv2.NORMAL_CLONE)
out_img = out_img.astype(dtype=np.float32) / 255.0
except Exception as e:
#seamlessClone may fail in some cases
e_str = traceback.format_exc()
if 'MemoryError' in e_str:
raise Exception(
"Seamless fail: " + e_str
) #reraise MemoryError in order to reprocess this data by other processes
else:
print("Seamless fail: " + e_str)
out_img = img_bgr * (1 - img_face_mask_aaa) + (out_img *
img_face_mask_aaa)
out_face_bgr = cv2.warpAffine(out_img, face_mat,
(output_size, output_size))
if 'seamless' in cfg.mode and cfg.color_transfer_mode != 0:
if cfg.color_transfer_mode == 1:
face_mask_aaa = cv2.warpAffine(img_face_mask_aaa, face_mat,
(output_size, output_size))
out_face_bgr = imagelib.reinhard_color_transfer(
np.clip((out_face_bgr * 255), 0, 255).astype(np.uint8),
np.clip((dst_face_bgr * 255), 0, 255).astype(np.uint8),
source_mask=face_mask_aaa,
target_mask=face_mask_aaa)
out_face_bgr = np.clip(
out_face_bgr.astype(np.float32) / 255.0, 0.0, 1.0)
elif cfg.color_transfer_mode == 2: #lct
out_face_bgr = imagelib.linear_color_transfer(
out_face_bgr, dst_face_bgr)
out_face_bgr = np.clip(out_face_bgr, 0.0, 1.0)
elif cfg.color_transfer_mode == 3: #mkl
out_face_bgr = imagelib.color_transfer_mkl(
out_face_bgr, dst_face_bgr)
elif cfg.color_transfer_mode == 4: #mkl-m
out_face_bgr = imagelib.color_transfer_mkl(
out_face_bgr * prd_face_mask_a,
dst_face_bgr * prd_face_mask_a)
elif cfg.color_transfer_mode == 5: #idt
out_face_bgr = imagelib.color_transfer_idt(
out_face_bgr, dst_face_bgr)
elif cfg.color_transfer_mode == 6: #idt-m
out_face_bgr = imagelib.color_transfer_idt(
out_face_bgr * prd_face_mask_a,
dst_face_bgr * prd_face_mask_a)
elif cfg.color_transfer_mode == 7: #ebs
out_face_bgr = cfg.ebs_ct_func(
np.clip((dst_face_bgr * 255), 0, 255).astype(np.uint8),
np.clip((out_face_bgr * 255), 0, 255).astype(np.uint8),
)
out_face_bgr = np.clip(
out_face_bgr.astype(np.float32) / 255.0, 0.0, 1.0)
if cfg.mode == 'seamless-hist-match':
out_face_bgr = imagelib.color_hist_match(
out_face_bgr, dst_face_bgr, cfg.hist_match_threshold)
cfg_mp = cfg.motion_blur_power / 100.0
if cfg_mp != 0:
k_size = int(frame_info.motion_power * cfg_mp)
if k_size >= 1:
k_size = np.clip(k_size + 1, 2, 50)
if cfg.super_resolution_mode:
k_size *= 2
out_face_bgr = imagelib.LinearMotionBlur(
out_face_bgr, k_size, frame_info.motion_deg)
if cfg.blursharpen_amount != 0:
out_face_bgr = cfg.blursharpen_func(out_face_bgr,
cfg.sharpen_mode, 3,
cfg.blursharpen_amount)
new_out = cv2.warpAffine(out_face_bgr, face_mat, img_size,
img_bgr.copy(),
cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC,
cv2.BORDER_TRANSPARENT)
out_img = np.clip(
img_bgr * (1 - img_face_mask_aaa) +
(new_out * img_face_mask_aaa), 0, 1.0)
if cfg.color_degrade_power != 0:
out_img_reduced = imagelib.reduce_colors(out_img, 256)
if cfg.color_degrade_power == 100:
out_img = out_img_reduced
else:
alpha = cfg.color_degrade_power / 100.0
out_img = (out_img * (1.0 - alpha) +
out_img_reduced * alpha)
if cfg.export_mask_alpha:
out_img = np.concatenate(
[out_img, img_face_mask_aaa[:, :, 0:1]], -1)
out_merging_mask = img_face_mask_aaa
return out_img, out_merging_mask
for image in images:
print("Working for " + image)
img = cv2.imread(base + image)
dets = detector(img, 1)
rows, cols, ch = img.shape
for k, d in enumerate(dets):
# Get the landmarks/parts for the face in box d.
shape = predictor(img, d)
sl_eyes = [0, 0]
sr_eyes = [0, 0]
s_mouth = [0, 0]
s_nose = [0, 0]
sl_eyes, sr_eyes, s_mouth, s_nose = get_params(img, d, sl_eyes,
sr_eyes, s_mouth,
s_nose)
s_mouth = [int(0.5 * x) for x in s_mouth]
pts1 = np.float32([sl_eyes, sr_eyes, s_mouth])
pts2 = np.float32([l_eyes, r_eyes, mouth])
# for i in range(68):
# # cv2.circle(img,(shape.part(i).x,shape.part(i).y),4,(0,0,255))
# cv2.circle(img,(s_mouth[0],s_mouth[1]),4,(0,0,255))
print(sl_eyes, sr_eyes, s_mouth)
print(l_eyes, r_eyes, mouth)
M = cv2.getAffineTransform(pts1, pts2)
dst = cv2.warpAffine(img, M, (cols, rows))
cv2.imwrite("Worked/" + image, dst)
def predict(self, car_pic):
if type(car_pic) == type(""):
img = imreadex(car_pic)
else:
img = car_pic
pic_hight, pic_width = img.shape[:2]
if pic_width > MAX_WIDTH:
resize_rate = MAX_WIDTH / pic_width
img = cv2.resize(img, (MAX_WIDTH, int(pic_hight * resize_rate)),
interpolation=cv2.INTER_AREA)
blur = self.cfg["blur"]
#高斯去噪
if blur > 0:
img = cv2.GaussianBlur(img, (blur, blur), 0) #图片分辨率调整
oldimg = img
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#equ = cv2.equalizeHist(img)
#img = np.hstack((img, equ))
#去掉图像中不会是车牌的区域
kernel = np.ones((20, 20), np.uint8)
img_opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)
img_opening = cv2.addWeighted(img, 1, img_opening, -1, 0)
#找到图像边缘
ret, img_thresh = cv2.threshold(img_opening, 0, 255,
cv2.THRESH_BINARY + cv2.THRESH_OTSU)
img_edge = cv2.Canny(img_thresh, 100, 200)
#使用开运算和闭运算让图像边缘成为一个整体
kernel = np.ones((self.cfg["morphologyr"], self.cfg["morphologyc"]),
np.uint8)
img_edge1 = cv2.morphologyEx(img_edge, cv2.MORPH_CLOSE, kernel)
img_edge2 = cv2.morphologyEx(img_edge1, cv2.MORPH_OPEN, kernel)
#查找图像边缘整体形成的矩形区域，可能有很多，车牌就在其中一个矩形区域中
image, contours, hierarchy = cv2.findContours(img_edge2, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
contours = [cnt for cnt in contours if cv2.contourArea(cnt) > Min_Area]
print('len(contours)', len(contours))
#一一排除不是车牌的矩形区域
car_contours = []
for cnt in contours:
rect = cv2.minAreaRect(cnt)
area_width, area_height = rect[1]
if area_width < area_height:
area_width, area_height = area_height, area_width
wh_ratio = area_width / area_height
#print(wh_ratio)
#要求矩形区域长宽比在2到5.5之间，2到5.5是车牌的长宽比，其余的矩形排除
if wh_ratio > 2 and wh_ratio < 5.5:
car_contours.append(rect)
box = cv2.boxPoints(rect)
box = np.int0(box)
#oldimg = cv2.drawContours(oldimg, [box], 0, (0, 0, 255), 2)
#cv2.imshow("edge4", oldimg)
#print(rect)
print(len(car_contours))
print("精确定位")
card_imgs = []
#矩形区域可能是倾斜的矩形，需要矫正，以便使用颜色定位
for rect in car_contours:
if rect[2] > -1 and rect[2] < 1: #创造角度，使得左、高、右、低拿到正确的值
angle = 1
else:
angle = rect[2]
rect = (rect[0], (rect[1][0] + 5, rect[1][1] + 5), angle
) #扩大范围，避免车牌边缘被排除
box = cv2.boxPoints(rect)
heigth_point = right_point = [0, 0]
left_point = low_point = [pic_width, pic_hight]
for point in box:
if left_point[0] > point[0]:
left_point = point
if low_point[1] > point[1]:
low_point = point
if heigth_point[1] < point[1]:
heigth_point = point
if right_point[0] < point[0]:
right_point = point
if left_point[1] <= right_point[1]: #正角度
new_right_point = [right_point[0], heigth_point[1]]
pts2 = np.float32([left_point, heigth_point,
new_right_point]) #字符只是高度需要改变
pts1 = np.float32([left_point, heigth_point, right_point])
M = cv2.getAffineTransform(pts1, pts2)
dst = cv2.warpAffine(oldimg, M, (pic_width, pic_hight))
point_limit(new_right_point)
point_limit(heigth_point)
point_limit(left_point)
card_img = dst[int(left_point[1]):int(heigth_point[1]),
int(left_point[0]):int(new_right_point[0])]
card_imgs.append(card_img)
#cv2.imshow("card", card_img)
#cv2.waitKey(0)
elif left_point[1] > right_point[1]: #负角度
new_left_point = [left_point[0], heigth_point[1]]
pts2 = np.float32([new_left_point, heigth_point,
right_point]) #字符只是高度需要改变
pts1 = np.float32([left_point, heigth_point, right_point])
M = cv2.getAffineTransform(pts1, pts2)
dst = cv2.warpAffine(oldimg, M, (pic_width, pic_hight))
point_limit(right_point)
point_limit(heigth_point)
point_limit(new_left_point)
card_img = dst[int(right_point[1]):int(heigth_point[1]),
int(new_left_point[0]):int(right_point[0])]
card_imgs.append(card_img)
#cv2.imshow("card", card_img)
#cv2.waitKey(0)
#开始使用颜色定位，排除不是车牌的矩形，目前只识别蓝、绿、黄车牌
colors = []
for card_index, card_img in enumerate(card_imgs):
green = yello = blue = black = white = 0
card_img_hsv = cv2.cvtColor(card_img, cv2.COLOR_BGR2HSV)
#有转换失败的可能，原因来自于上面矫正矩形出错
if card_img_hsv is None:
continue
row_num, col_num = card_img_hsv.shape[:2]
card_img_count = row_num * col_num
for i in range(row_num):
for j in range(col_num):
H = card_img_hsv.item(i, j, 0)
S = card_img_hsv.item(i, j, 1)
V = card_img_hsv.item(i, j, 2)
if 11 < H <= 34 and S > 34: #图片分辨率调整
yello += 1
elif 35 < H <= 99 and S > 34: #图片分辨率调整
green += 1
elif 99 < H <= 124 and S > 34: #图片分辨率调整
blue += 1
if 0 < H < 180 and 0 < S < 255 and 0 < V < 46:
black += 1
elif 0 < H < 180 and 0 < S < 43 and 221 < V < 225:
white += 1
color = "no"
limit1 = limit2 = 0
if yello * 2 >= card_img_count:
color = "yello"
limit1 = 11
limit2 = 34 #有的图片有色偏偏绿
elif green * 2 >= card_img_count:
color = "green"
limit1 = 35
limit2 = 99
elif blue * 2 >= card_img_count:
color = "blue"
limit1 = 100
limit2 = 124 #有的图片有色偏偏紫
elif black + white >= card_img_count * 0.7: #TODO
color = "bw"
print(color)
colors.append(color)
print(blue, green, yello, black, white, card_img_count)
#cv2.imshow("color", card_img)
#cv2.waitKey(0)
if limit1 == 0:
continue
#以上为确定车牌颜色
#以下为根据车牌颜色再定位，缩小边缘非车牌边界
xl, xr, yh, yl = self.accurate_place(card_img_hsv, limit1, limit2,
color)
if yl == yh and xl == xr:
continue
need_accurate = False
if yl >= yh:
yl = 0
yh = row_num
need_accurate = True
if xl >= xr:
xl = 0
xr = col_num
need_accurate = True
card_imgs[card_index] = card_img[
yl:yh, xl:xr] if color != "green" or yl < (
yh - yl) // 4 else card_img[yl - (yh - yl) // 4:yh, xl:xr]
if need_accurate: #可能x或y方向未缩小，需要再试一次
card_img = card_imgs[card_index]
card_img_hsv = cv2.cvtColor(card_img, cv2.COLOR_BGR2HSV)
xl, xr, yh, yl = self.accurate_place(card_img_hsv, limit1,
limit2, color)
if yl == yh and xl == xr:
continue
if yl >= yh:
yl = 0
yh = row_num
if xl >= xr:
xl = 0
xr = col_num
card_imgs[card_index] = card_img[
yl:yh, xl:xr] if color != "green" or yl < (
yh - yl) // 4 else card_img[yl - (yh - yl) // 4:yh, xl:xr]
#以上为车牌定位
#以下为识别车牌中的字符
predict_result = []
roi = None
card_color = None
for i, color in enumerate(colors):
if color in ("blue", "yello", "green"):
card_img = card_imgs[i]
gray_img = cv2.cvtColor(card_img, cv2.COLOR_BGR2GRAY)
#黄、绿车牌字符比背景暗、与蓝车牌刚好相反，所以黄、绿车牌需要反向
if color == "green" or color == "yello":
gray_img = cv2.bitwise_not(gray_img)
ret, gray_img = cv2.threshold(
gray_img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
#查找水平直方图波峰
x_histogram = np.sum(gray_img, axis=1)
x_min = np.min(x_histogram)
x_average = np.sum(x_histogram) / x_histogram.shape[0]
x_threshold = (x_min + x_average) / 2
wave_peaks = find_waves(x_threshold, x_histogram)
if len(wave_peaks) == 0:
print("peak less 0:")
continue
#认为水平方向，最大的波峰为车牌区域
wave = max(wave_peaks, key=lambda x: x[1] - x[0])
gray_img = gray_img[wave[0]:wave[1]]
#查找垂直直方图波峰
row_num, col_num = gray_img.shape[:2]
#去掉车牌上下边缘1个像素，避免白边影响阈值判断
gray_img = gray_img[1:row_num - 1]
y_histogram = np.sum(gray_img, axis=0)
y_min = np.min(y_histogram)
y_average = np.sum(y_histogram) / y_histogram.shape[0]
y_threshold = (y_min + y_average) / 5 #U和0要求阈值偏小，否则U和0会被分成两半
wave_peaks = find_waves(y_threshold, y_histogram)
#for wave in wave_peaks:
# cv2.line(card_img, pt1=(wave[0], 5), pt2=(wave[1], 5), color=(0, 0, 255), thickness=2)
#车牌字符数应大于6
if len(wave_peaks) <= 6:
print("peak less 1:", len(wave_peaks))
continue
wave = max(wave_peaks, key=lambda x: x[1] - x[0])
max_wave_dis = wave[1] - wave[0]
#判断是否是左侧车牌边缘
if wave_peaks[0][1] - wave_peaks[0][
0] < max_wave_dis / 3 and wave_peaks[0][0] == 0:
wave_peaks.pop(0)
#组合分离汉字
cur_dis = 0
for i, wave in enumerate(wave_peaks):
if wave[1] - wave[0] + cur_dis > max_wave_dis * 0.6:
break
else:
cur_dis += wave[1] - wave[0]
if i > 0:
wave = (wave_peaks[0][0], wave_peaks[i][1])
wave_peaks = wave_peaks[i + 1:]
wave_peaks.insert(0, wave)
#去除车牌上的分隔点
point = wave_peaks[2]
if point[1] - point[0] < max_wave_dis / 3:
point_img = gray_img[:, point[0]:point[1]]
if np.mean(point_img) < 255 / 5:
wave_peaks.pop(2)
if len(wave_peaks) <= 6:
print("peak less 2:", len(wave_peaks))
continue
part_cards = seperate_card(gray_img, wave_peaks)
for i, part_card in enumerate(part_cards):
#可能是固定车牌的铆钉
if np.mean(part_card) < 255 / 5:
print("a point")
continue
part_card_old = part_card
w = abs(part_card.shape[1] - SZ) // 2
part_card = cv2.copyMakeBorder(part_card,
0,
0,
w,
w,
cv2.BORDER_CONSTANT,
value=[0, 0, 0])
part_card = cv2.resize(part_card, (SZ, SZ),
interpolation=cv2.INTER_AREA)
#part_card = deskew(part_card)
part_card = preprocess_hog([part_card])
if i == 0:
resp = self.modelchinese.predict(part_card)
charactor = provinces[int(resp[0]) - PROVINCE_START]
else:
resp = self.model.predict(part_card)
charactor = chr(resp[0])
#判断最后一个数是否是车牌边缘，假设车牌边缘被认为是1
if charactor == "1" and i == len(part_cards) - 1:
if part_card_old.shape[0] / part_card_old.shape[
1] >= 7: #1太细，认为是边缘
continue
predict_result.append(charactor)
roi = card_img
card_color = color
break
return predict_result, roi, card_color #识别到的字符、定位的车牌图像、车牌颜色
def update(val):
global pt1x, pt1y, pt2x, pt2y, pt3x, pt3y, ptAllx, ptAlly, ptAllRp, ptAllRn, loading_settings, evx, evy, imgR, imgL, img2
pt1x = spt1x.val
pt1y = spt1y.val
pt2x = spt2x.val
pt2y = spt2y.val
pt3x = spt3x.val
pt3y = spt3y.val
ptAllx = sptAllx.val
ptAlly = sptAlly.val
ptAllRp = sptAllRp.val
ptAllRn = sptAllRn.val
if (loading_settings == 0):
#print ('Rebuilding Affine')
#disparity = stereo_depth_map(rectified_pair)
#print(imgR.shape)
rows, cols, _rgb = imgR.shape
if val == 4:
ret, imgL = cap.read()
ret2, imgR = cap2.read()
if val == 2:
img2 = cv2.warpAffine(
imgR,
cv2.getAffineTransform(
np.float32([[0, 0], [rows / 2, cols / 2], [rows, 0]]),
np.float32([[0, 0], [evy, evx], [rows, 0]]),
#np.float32([[ptAlly+pt1y,ptAllx+pt1x],[ptAlly+pt2y,ptAllx+pt2x+cols],[ptAlly+pt3y+rows,ptAllx+pt3x]])
),
(cols, rows))
else: #if val==3:
imgLwl = imgL.copy()
cv2.line(imgLwl, (0, 0), (cols, rows), (0, 255, 0), 1)
cv2.line(imgLwl, (0, rows), (cols, 0), (0, 255, 0), 1)
imgLwl = imgLwl[:, :, ::-1]
img1Object.set_data(imgLwl)
figL.canvas.draw()
#
x1 = 0.0
y1 = 0.0
x2 = float(cols)
y2 = 0.0
x3 = 0.0
y3 = float(rows)
x0 = float((x2 + x3) / 2)
y0 = float((y3 + y2) / 2)
print((x1 - x0) * cos(radians(ptAllRp)))
print((y1 - y0) * sin(radians(ptAllRp)))
nx1 = (x1 - x0) * cos(radians(ptAllRp)) - (y1 - y0) * sin(
radians(ptAllRp)) + x0
ny1 = (x1 - x0) * sin(radians(ptAllRp)) + (y1 - y0) * cos(
radians(ptAllRp)) + y0
nx2 = (x2 - x0) * cos(radians(ptAllRp)) - (y2 - y0) * sin(
radians(ptAllRp)) + x0
ny2 = (x2 - x0) * sin(radians(ptAllRp)) + (y2 - y0) * cos(
radians(ptAllRp)) + y0
nx3 = (x3 - x0) * cos(radians(ptAllRp)) - (y3 - y0) * sin(
radians(ptAllRp)) + x0
ny3 = (x3 - x0) * sin(radians(ptAllRp)) + (y3 - y0) * cos(
radians(ptAllRp)) + y0
#print(cols,rows)
print(x1, y1, x2, y2, x3, y3, cos(radians(ptAllRp)),
sin(radians(ptAllRp)), x0, y0)
x1 = nx1 + pt1x + ptAllx
y1 = ny1 + pt1y + ptAlly
x2 = nx2 + pt2x + ptAllx
y2 = ny2 + pt2y + ptAlly
x3 = nx3 + pt3x + ptAllx
y3 = ny3 + pt3y + ptAlly
img2 = cv2.warpAffine(
imgR,
cv2.getAffineTransform(
np.float32([[0, 0], [cols, 0], [0, rows]]),
np.float32([[x1, y1], [x2, y2], [x3, y3]])), (cols, rows))
#else:
# img2 = cv2.warpAffine(imgR,cv2.getAffineTransform(
# np.float32([[0,0],[0,cols],[rows,0]]),
# np.float32([[ptAlly+pt1y,ptAllx+pt1x],[ptAlly+pt2y,ptAllx+pt2x+cols],[ptAlly+pt3y+rows,ptAllx+pt3x]])
# ),(cols,rows))
img2wl = img2.copy()
cv2.line(img2wl, (0, 0), (cols, rows), (0, 255, 0), 1)
cv2.line(img2wl, (0, rows), (cols, 0), (0, 255, 0), 1)
img2wl = img2wl[:, :, ::-1]
img2Object.set_data(img2wl)
#cv2.imshow("in2", img2)
#cv2.waitKey()
#cv2.destroyAllWindows()
#print ('Redraw Affine')
fig1.canvas.draw()
if key == 32:
break
print('three points of image 1 ')
qoordinates1 = []
points('MRIF.png', qoordinates1)
#making array of points with float32 type
qoordinates1 = np.array(qoordinates1, dtype='float32')
print('three points of image 2')
qoordinates2 = []
points('MRIS.png', qoordinates2)
#making array of points with float32 type
qoordinates2 = np.array(qoordinates2, dtype='float32')
#calculate transform function
Affine = cv.getAffineTransform(qoordinates2, qoordinates1)
print(Affine)
#Apply the transform function on the image
img = cv.imread('MRIS.png')
affine_transform_image = cv.warpAffine(img, Affine, (1000, 1000))
cv.imshow('new image', affine_transform_image)
cv.waitKey(0)
cv.destroyAllWindows()
# In[ ]:
# In[ ]:
def getImageFromWord(word):
word = changeCase(word)
height, width = 512, 512
img = np.zeros((height, width, 3), np.uint8)
img[:, :, :] = 255
font = random.choice([0, 1, 2, 3, 4]) | 16 if random.randint(0, 1) else 0
bottomLeftCornerOfText = (30, 150)
fontScale = 5
fontColor = (0, 0, 0)
lineType = random.randint(2, 4)
thickness = 6
lineType = 8
while True:
textsize = cv2.getTextSize(word, font, fontScale, lineType)[0]
if textsize[0] < width - 20:
break
else:
fontScale -= 1
# print textsize
# get coords based on boundary
textX = (img.shape[1] - textsize[0]) / 2
textY = (img.shape[0] + textsize[1]) / 2
# add text centered on image
cv2.putText(img, word, (textX, textY), font, fontScale, fontColor,
lineType)
rotateFlag = random.randint(0, 1)
if rotateFlag:
rotateAngle = random.randint(-10, 10)
M = cv2.getRotationMatrix2D((width / 2, height / 2), rotateAngle, 1)
img = cv2.warpAffine(img,
M, (width, height),
borderValue=(255, 255, 255))
affineFlag = random.randint(0, 1)
if affineFlag:
pts1 = np.float32([[10, 10], [200, 50], [50, 200]])
pts2 = np.float32(
[[10 + random.randint(-20, 20), 30 + random.randint(-20, 20)],
[200, 50],
[50 + random.randint(-20, 20), 200 + random.randint(-20, 20)]])
M = cv2.getAffineTransform(pts1, pts2)
img = cv2.warpAffine(img,
M, (width, height),
borderValue=(255, 255, 255))
img = cv2.resize(img, (227, 227))
bg_image = get_random_crop()
bg_image = merge_background_text(img, bg_image)
# print(bg_image.shape)
# img = np.add(img,bg_image)
# plt.imshow(img)
# print img.shape
return bg_image
cv2.BORDER_REPLICATE)
src = np.array([
(cols * (20.0 / 512.0) + wexp, rows * (200.0 / 512.0) + hexp),
(cols * (256.0 / 512.0) + wexp, rows * (495.0 / 512.0) + hexp),
(cols * (492.0 / 512.0) + wexp, rows * (200.0 / 512.0) + hexp)
])
#src = np.array([(20, 200), (256, 495), (492, 200)])
src = np.uint8(src)
src = np.float32(src)
dest = np.array([(shapes.part(0).x - box.left() + wexp,
shapes.part(0).y - box.top() + hexp),
(shapes.part(8).x - box.left() + wexp,
shapes.part(8).y - box.top() + hexp),
(shapes.part(16).x - box.left() + wexp,
shapes.part(16).y - box.top() + hexp)])
dest = np.float32(dest)
rows, cols, d = emoji.shape
trans = cv2.getAffineTransform(src, dest)
emoji = cv2.warpAffine(emoji, trans, (cols, rows))
#print(happy)
for c in range(0, 3):
img[box.top() - hexp:box.bottom() + hexp,
box.left() - wexp:box.right() + wexp,
c] = emoji[:, :, c] * (emoji[:, :, 3] / 255.0) + img[
box.top() - hexp:box.bottom() + hexp,
box.left() - wexp:box.right() + wexp,
c] * (1.0 - emoji[:, :, 3] / 255.0)
plt.imsave('s.jpg', img)
def rotate(img, angle, fixed=True, point=[-1, -1], scale = 1.0):
"""
Inspired from SimpleCV
**PARAMETERS**
* *angle* - angle in degrees positive is clockwise, negative is counter clockwise
* *point* - the point about which we want to rotate, if none is defined we use the center.
* *scale* - and optional floating point scale parameter.
"""
if( point[0] == -1 or point[1] == -1 ):
point[0] = (img.shape[1]-1)/2
point[1] = (img.shape[0]-1)/2
# first we create what we thing the rotation matrix should be
rotMat = cv2.getRotationMatrix2D((float(point[0]), float(point[1])), float(angle), float(scale))
if fixed:
return cv2.warpAffine(img, M=rotMat, dsize=img.shape)
A = np.array([0, 0, 1])
B = np.array([img.shape[1], 0, 1])
C = np.array([img.shape[1], img.shape[0], 1])
D = np.array([0, img.shape[0], 1])
#So we have defined our image ABC in homogenous coordinates
#and apply the rotation so we can figure out the image size
a = np.dot(rotMat, A)
b = np.dot(rotMat, B)
c = np.dot(rotMat, C)
d = np.dot(rotMat, D)
#I am not sure about this but I think the a/b/c/d are transposed
#now we calculate the extents of the rotated components.
minY = min(a[1], b[1], c[1], d[1])
minX = min(a[0], b[0], c[0], d[0])
maxY = max(a[1], b[1], c[1], d[1])
maxX = max(a[0], b[0], c[0], d[0])
#from the extents we calculate the new size
newWidth = np.ceil(maxX-minX)
newHeight = np.ceil(maxY-minY)
#now we calculate a new translation
tX = 0
tY = 0
#calculate the translation that will get us centered in the new image
if( minX < 0 ):
tX = -1.0*minX
elif(maxX > newWidth-1 ):
tX = -1.0*(maxX-newWidth)
if( minY < 0 ):
tY = -1.0*minY
elif(maxY > newHeight-1 ):
tY = -1.0*(maxY-newHeight)
#now we construct an affine map that will the rotation and scaling we want with the
#the corners all lined up nicely with the output image.
src = np.float32([(A[0], A[1]), (B[0], B[1]), (C[0], C[1])])
dst = np.float32([(a[0]+tX, a[1]+tY), (b[0]+tX, b[1]+tY), (c[0]+tX, c[1]+tY)])
rotMat = cv2.getAffineTransform(src, dst)
#calculate the translation of the corners to center the image
#use these new corner positions as the input to cvGetAffineTransform
retVal = cv2.warpAffine(img, rotMat, (img.shape[1], img.shape[0]))
return retVal
import cv2
import numpy as np
img = cv2.imread('images/input.jpg')
rows, cols = img.shape[:2]
src_points = np.float32([[0,0], [cols-1,0], [0,rows-1]])
dst_points = np.float32([[0,0], [int(0.6*(cols-1)),0], [int(0.4*(cols-1)),rows-1]])
affine_matrix = cv2.getAffineTransform(src_points, dst_points)
img_output = cv2.warpAffine(img, affine_matrix, (cols,rows))
cv2.imshow('Input', img)
cv2.imshow('Output', img_output)
cv2.waitKey()
def imgafin(imagen, r_altura, r_ancho):
inicio = np.array([pto1, pto2, pto3], dtype=np.float32)
destino = np.array([(0, r_altura), (0, 0), (r_ancho, 0)], dtype=np.float32)
matriz = cv2.getAffineTransform(inicio, destino)
result = cv2.warpAffine(imagen, matriz, (r_ancho, r_altura))
return result
en la imagen de salida.
"""
# Creamos una copia de la imagen para mostrar sus puntos usados para la transformacion afin.
image_points = image.copy()
cv2.circle(image_points, (100, 45), 5, (255, 0, 255), -1)
cv2.circle(image_points, (385, 45), 5, (255, 0, 255), -1)
cv2.circle(image_points, (275, 230), 5, (255, 0, 255), -1)
show_with_matplotlib(image_points, 'Antes de la transformacion afin')
# Creamos las matrices con los 3 puntos usados anteriormente y otras matrices
# para las posiciones deseadas de la imagen de salida.
pts_1 = np.float32([[100, 45], [385, 45], [275, 230]])
pts_2 = np.float32([[50, 45], [385, 45], [290, 230]])
# Creamos la matrix de 2x3 basandonos en los puntos anteriores.
M = cv2.getAffineTransform(pts_1, pts_2)
dst_image = cv2.warpAffine(image_points, M, (width, height))
show_with_matplotlib(dst_image, 'Transformacion Afin')
"""
5-. Transformacion de perspectiva.
- Usamos cv2.getPerspectiveTransform por que necesitamos 4 pares de
puntos (coordenadas de un cuadrangulo en la imagen de origen y salida).
"""
# Cramos una copia de la imagen donde se mostraran los puntos.
image_points = image.copy()
cv2.circle(image_points, (450, 65), 5, (255, 0, 255), -1)
cv2.circle(image_points, (517, 65), 5, (255, 0, 255), -1)
cv2.circle(image_points, (431, 164), 5, (255, 0, 255), -1)
cv2.circle(image_points, (552, 164), 5, (255, 0, 255), -1)
eyes_centers, eyes_means[0], eyes_stds * 1.96)
most_possible_right_eye, valid_right_eye = valid_object_exist(
eyes_centers, eyes_means[1], eyes_stds * 1.96)
most_possible_nose, valid_nose = valid_object_exist(
noses_centers, nose_means, nose_stds * 1.96)
# if the facial objects are all valid transfer it and try save / identify the person
if valid_nose & valid_right_eye & valid_left_eye:
current_pts = np.float32(
np.vstack((most_possible_left_eye, most_possible_right_eye,
most_possible_nose)))
# set up the affine transformation
affine_transformation = cv2.getAffineTransform(
current_pts, ideal_pts)
# transform the face
transformed_face = cv2.warpAffine(face, affine_transformation,
(64, 64))
# if the program is ran with an extra parameter specify the name of the person
# then save some images of the person
if len(sys.argv) > 1:
f = 'img/' + str(sys.argv[1]) + '_' + str(int(
time.time())) + '.png'
cv2.imwrite(f, transformed_face)
# extract features from the transformed face image using the tuned extractor loaded
feature = extractor.describe(transformed_face)
def ImgAffine(img, pts1, pts2):
rows, cols = img.shape
Affinematrix = cv2.getAffineTransform(pts1, pts2)
affine = cv2.warpAffine(img, Affinematrix, (cols, rows))
return affine
def img_Transform(car_rect, image): #传入填充掩膜后的最小矩形，原图
img_h, img_w = image.shape[:2]
rect_w, rect_h = car_rect[1][0], car_rect[1][1]
angle = car_rect[2]
return_flag = False
if car_rect[2] == 0: #旋转角度为0
return_flag = True
if car_rect[2] == -90 and rect_w < rect_h: #旋转角度=-90并且矩形的宽<高
rect_w, rect_h = rect_h, rect_w
return_flag = True
if return_flag:
car_img = image[int(car_rect[0][1] - rect_h / 2):int(car_rect[0][1] +
rect_h / 2),
int(car_rect[0][0] - rect_w / 2):int(car_rect[0][0] +
rect_w / 2)]
return car_img
car_rect = (car_rect[0], (rect_w, rect_h), angle)
box = cv2.boxPoints(car_rect) #获取矩形的四个顶点坐标
heigth_point = right_point = [0, 0]
left_point = low_point = [car_rect[0][0], car_rect[0][1]] #矩形中心点坐标(x,y)
for point in box:
if left_point[0] > point[0]:
left_point = point
if low_point[1] > point[1]:
low_point = point
if heigth_point[1] < point[1]:
heigth_point = point
if right_point[0] < point[0]:
right_point = point
if left_point[1] <= right_point[1]: # 正角度
new_right_point = [right_point[0], heigth_point[1]]
pts1 = np.float32([left_point, heigth_point, right_point])
pts2 = np.float32([left_point, heigth_point,
new_right_point]) # 字符只是高度需要改变
M = cv2.getAffineTransform(pts1, pts2)
print('Mat1', M)
print('pts1_1', pts1)
print('pts1_2', pts2)
'''
仿射变换，其实是将图形在2D平面内做变换，变换前后图片中原来平行的线仍会保持平行，可以想象是将长方形变换为平行四边形
M=cv2.getAffineTransform(pos1,pos2),其中两个位置就是变换前后的对应位置关系。输出的就是仿射矩阵M,shape为[2,3]
cv.getAffineTransform将创建一个2x3矩阵，该矩阵将传递给cv.warpAffine。
'''
dst = cv2.warpAffine(image, M, (round(img_w * 2), round(img_h * 2)))
'''
cv2.warpAffine(src, M, dsize[, dst[, flags[, borderMode[, borderValue]]]]) → dst
dsize为输出图像的大小;
flags表示插值方式，默认为 flags=cv2.INTER_LINEAR，表示线性插值，此外还有：cv2.INTER_NEAREST(最近邻插值)、cv2.INTER_AREA(区域插值)、cv2.INTER_CUBIC(三次样条插值)、cv2.INTER_LANCZOS4(Lanczos插值)
borderMode - 边界像素模式
borderValue - 边界填充值; 默认情况下，它为0
round() 方法返回浮点数x的四舍五入值。round(x,n) 返回浮点数x的四舍五入的小数点后的n位数值
'''
car_img = dst[int(left_point[1]):int(heigth_point[1]),
int(left_point[0]):int(new_right_point[0])]
elif left_point[1] > right_point[1]: # 负角度
new_left_point = [left_point[0], heigth_point[1]]
pts1 = np.float32([left_point, heigth_point, right_point])
pts2 = np.float32([new_left_point, heigth_point,
right_point]) # 字符只是高度需要改变
print('pts2_1', pts1)
print('pts2_2', pts2)
M = cv2.getAffineTransform(pts1, pts2)
print('Mat2', M)
dst = cv2.warpAffine(image, M, (round(img_w * 2), round(img_h * 2)))
car_img = dst[int(right_point[1]):int(heigth_point[1]),
int(new_left_point[0]):int(right_point[0])]
return car_img
import cv2 as cv
import numpy as np
import os
image_dir = "../622data/test/businessRuleTask/"
name = os.listdir(image_dir)[0]
image_path = image_dir + name
src = cv.imread(image_path)
srcTri = np.array([[0, 0], [src.shape[1] - 1, 0],
[0, src.shape[0] - 1]]).astype(np.float32)
dstTri = np.array([[0, src.shape[1] * 0.33],
[src.shape[1] * 0.85, src.shape[0] * 0.25],
[src.shape[1] * 0.15,
src.shape[0] * 0.7]]).astype(np.float32)
warp_mat = cv.getAffineTransform(srcTri, dstTri)
warp_dst = cv.warpAffine(src, warp_mat, (src.shape[1], src.shape[0]))
# Rotating the image_mat after Warp
center = (warp_dst.shape[1] // 2, warp_dst.shape[0] // 2)
angle = -50
scale = 0.6
rot_mat = cv.getRotationMatrix2D(center, angle, scale)
warp_rotate_dst = cv.warpAffine(warp_dst, rot_mat,
(warp_dst.shape[1], warp_dst.shape[0]))
cv.imshow('Source image_mat', src)
cv.imshow('Warp', warp_dst)
cv.imshow('Warp + Rotate', warp_rotate_dst)
cv.waitKey()
# 图片的仿射
import cv2
import numpy as np
img = cv2.imread("girl.jpg", 1)
# cv2.imshow("img", img)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
print(imgInfo)
# src 3->dst 3 原图上(左上角 左下角 右上角) 转换到新图片上三个点
# 原图三个点
matSrc = np.float32([[0, 0], [0, height - 1], [width - 1, 0]])
# 新图上的位置
matDst = np.float32([[50, 50], [200, height - 100], [width - 200, 100]])
matAffine = cv2.getAffineTransform(matSrc, matDst)
dst = cv2.warpAffine(img, matAffine, (width, height))
cv2.imshow("affine", dst)
cv2.waitKey(0)
cv2.destroyAllWindows()
def align(img_path):
'''
Aligns user image with painting image using manual alignment
and facial affine transforms.
params: img_path - path to style image
'''
filename1 = img_path
predictor_path = "shape_predictor_68_face_landmarks.dat"
global detector, predictor
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(predictor_path)
img1 = cv2.imread(filename1)
box = findBiggestFace(img1)
points, shape = findLandmarks(img1, box)
img_cropped = cv2.resize(
img1[box.top() - 50:box.bottom() + 50,
box.left() - 50:box.right() + 50], (500, 500))
#img_cropped = cv2.resize(img1, (500,500))
box1 = findBiggestFace(img_cropped)
points1, shape1 = findLandmarks(img_cropped, box1)
TEMPLATE = np.float32(shape1)
TEMPLATE = TEMPLATE
facialPoints = [41, 8, 47]
imgDim = 500
cap = cv2.VideoCapture(0)
i = 0
while (True):
text_box = 255 * np.ones((80, 400, 3))
ret, frame = cap.read()
try:
Img = frame
if i == 0:
box_v = findBiggestFace(Img)
i = 1
points_v, shape_v = findLandmarks(Img, box_v)
lol = cv2.resize(
Img[box_v.top() - 100:box_v.bottom() + 100,
box_v.left() - 100:box_v.right() + 100], (500, 500))
img_cropped_v = cv2.resize(
Img[box_v.top() - 50:box_v.bottom() + 50,
box_v.left() - 50:box_v.right() + 50], (500, 500))
img_cropped_v = cv2.resize(Img, (500, 500))
box_v1 = findBiggestFace(img_cropped_v)
points1_v, shape1_v = findLandmarks(img_cropped_v, box_v1)
###PAINTING NOSE###
for k in range(27, 36):
cv2.circle(img_cropped_v,
(int(shape1[k][0]), int(shape1[k][1])), 2,
(255, 0, 0), -1)
##################################################
############CONDITIONS FOR MOVEMENT ##############
##################################################
font = cv2.FONT_HERSHEY_SIMPLEX
############################
####HORIZONTAL MOVEMENT#####
############################
if shape1_v[27][0] - shape1[27][0] > 2 and shape1_v[28][
0] - shape1[28][0] > 2 and shape1_v[29][0] - shape1[29][
0] > 2 and shape1_v[30][0] - shape1[30][0] > 2:
font = cv2.FONT_HERSHEY_SIMPLEX
#### SUBJECTS NOSE###
for k in range(27, 31):
cv2.circle(img_cropped_v,
(int(shape1_v[k][0]), int(shape1_v[k][1])), 2,
(0, 0, 255), -1)
flag1 = 1
elif shape1_v[27][0] - shape1[27][0] < -2 and shape1_v[28][
0] - shape1[28][0] < -2 and shape1_v[29][0] - shape1[29][
0] < -2 and shape1_v[30][0] - shape1[30][0] < -2:
#### SUBJECTS NOSE###
for k in range(27, 31):
cv2.circle(img_cropped_v,
(int(shape1_v[k][0]), int(shape1_v[k][1])), 2,
(0, 0, 255), -1)
flag1 = 2
else:
#### SUBJECTS NOSE###
for k in range(27, 31):
cv2.circle(img_cropped_v,
(int(shape1_v[k][0]), int(shape1_v[k][1])), 2,
(0, 255, 0), -1)
flag1 = 0
###########################################
##########VERTICAL MOVEMENT ###############
###########################################
if shape1_v[31][1] - shape1[31][1] > 2 and shape1_v[32][
1] - shape1[32][1] > 2 and shape1_v[33][1] - shape1[33][
1] > 2 and shape1_v[34][1] - shape1[34][
1] > 2 and shape1_v[35][1] - shape1[35][1] > 2:
font = cv2.FONT_HERSHEY_SIMPLEX
#### SUBJECTS NOSE###
for k in range(31, 36):
cv2.circle(img_cropped_v,
(int(shape1_v[k][0]), int(shape1_v[k][1])), 2,
(0, 0, 255), -1)
flag2 = 1
elif shape1_v[31][1] - shape1[31][1] < -2 and shape1_v[32][
1] - shape1[32][1] < -2 and shape1_v[33][1] - shape1[33][
1] < -2 and shape1_v[34][1] - shape1[34][
1] < -2 and shape1_v[35][1] - shape1[35][1] < -2:
#### SUBJECTS NOSE###
for k in range(31, 36):
cv2.circle(img_cropped_v,
(int(shape1_v[k][0]), int(shape1_v[k][1])), 2,
(0, 0, 255), -1)
flag2 = 2
else:
#### SUBJECTS NOSE###
for k in range(31, 36):
cv2.circle(img_cropped_v,
(int(shape1_v[k][0]), int(shape1_v[k][1])), 2,
(0, 255, 0), -1)
flag2 = 0
if flag1 == 1 and flag2 == 1:
cv2.putText(text_box, "Look right and up", (5, 40), font, 1,
(0, 0, 255), 1, cv2.LINE_AA)
elif flag1 == 1 and flag2 == 2:
cv2.putText(text_box, "Look right and down", (5, 40), font, 1,
(0, 0, 255), 1, cv2.LINE_AA)
elif flag1 == 1 and flag2 == 0:
cv2.putText(text_box, "Look right", (5, 40), font, 1,
(0, 0, 255), 1, cv2.LINE_AA)
elif flag1 == 2 and flag2 == 1:
cv2.putText(text_box, "Look left and up", (5, 40), font, 1,
(0, 0, 255), 1, cv2.LINE_AA)
elif flag1 == 2 and flag2 == 2:
cv2.putText(text_box, "Look left and down", (5, 40), font, 1,
(0, 0, 255), 1, cv2.LINE_AA)
elif flag1 == 2 and flag2 == 0:
cv2.putText(text_box, "Look left", (5, 40), font, 1,
(0, 0, 255), 1, cv2.LINE_AA)
elif flag1 == 0 and flag2 == 1:
cv2.putText(text_box, "Look up", (5, 40), font, 1, (0, 0, 255),
1, cv2.LINE_AA)
elif flag1 == 0 and flag2 == 2:
cv2.putText(text_box, "Look down", (5, 40), font, 1,
(0, 0, 255), 1, cv2.LINE_AA)
elif flag1 == 0 and flag2 == 0:
cv2.putText(text_box, "Hold pose", (5, 40), font, 1,
(0, 255, 0), 1, cv2.LINE_AA)
cv2.imshow('1', img_cropped_v)
cv2.imshow('text', text_box)
cv2.imshow('2', img_cropped)
except Exception as e:
pass
if cv2.waitKey(1) & 0xFF == ord('q'):
cv2.imwrite('img.jpg', lol)
#cv2.imwrite('unaligned.jpg', img_cropped_v)
break
cap.release()
cv2.destroyAllWindows()
############################################
############# WARPING IMAGE ################
############################################
facialPoints = [0, 8, 16]
facialPoints2 = [36, 45, 57]
img1 = cv2.resize(cv2.imread(filename1), (500, 500))
#img1 = cv2.resize(cv2.imread('img.jpg'), (500,500))
box = findBiggestFace(img1)
_, TEMPLATE = findLandmarks(img1, box)
TEMPLATE = np.float32(TEMPLATE)
TEMPLATE = TEMPLATE / 500
filename2 = 'img.jpg'
imgDim = 500
Img = cv2.resize(cv2.imread(filename2), (500, 500))
box = findBiggestFace(Img)
_, landmarks = findLandmarks(Img, box)
npLandmarks = np.float32(landmarks)
npfacialPoints = np.array(facialPoints)
H = cv2.getAffineTransform(npLandmarks[npfacialPoints],
500 * TEMPLATE[npfacialPoints])
thumbnail = cv2.warpAffine(Img, H, (imgDim, imgDim))
#######################
### Second warping ####
#######################
Img = thumbnail
box = findBiggestFace(Img)
_, landmarks = findLandmarks(Img, box)
npLandmarks = np.float32(landmarks)
npfacialPoints = np.array(facialPoints2)
H1 = cv2.getAffineTransform(npLandmarks[npfacialPoints],
500 * TEMPLATE[npfacialPoints])
thumbnail2 = cv2.warpAffine(Img, H1, (imgDim, imgDim))
return img1, lol, thumbnail2
rect2 = cv2.boundingRect(triangle2)
(x, y, w, h) = rect2
cropped_tr2_mask = np.zeros((h, w), np.uint8)
points2 = np.array([[tr2_pt1[0] - x, tr2_pt1[1] - y],
[tr2_pt2[0] - x, tr2_pt2[1] - y],
[tr2_pt3[0] - x, tr2_pt3[1] - y]], np.int32)
cv2.fillConvexPoly(cropped_tr2_mask, points2, 255)
# Warp triangles
points = np.float32(points)
points2 = np.float32(points2)
M = cv2.getAffineTransform(points, points2)
warped_triangle = cv2.warpAffine(cropped_triangle, M, (w, h))
warped_triangle = cv2.bitwise_and(warped_triangle,
warped_triangle,
mask=cropped_tr2_mask)
# Reconstructing destination face
img2_new_face_rect_area = img2_new_face[y:y + h, x:x + w]
img2_new_face_rect_area_gray = cv2.cvtColor(img2_new_face_rect_area,
cv2.COLOR_BGR2GRAY)
_, mask_triangles_designed = cv2.threshold(
img2_new_face_rect_area_gray, 1, 255, cv2.THRESH_BINARY_INV)
warped_triangle = cv2.bitwise_and(warped_triangle,
warped_triangle,
mask=mask_triangles_designed)
def elastic_transform(im, alpha=0.5, sigma=0.2, affine_sigma=1.):
"""
Based on https://gist.github.com/erniejunior/601cdf56d2b424757de5
elastic deformation of images as described in [Simard2003]
"""
# fixme : not implemented for multi channel !
import cv2
islist = isinstance(im, (tuple, list))
ima = im[0] if islist else im
# image shape
shape = ima.shape
shape_size = shape[:2]
# Random affine transform
center_square = np.float32(shape_size) // 2
square_size = min(shape_size) // 3
pts1 = np.float32([
center_square + square_size,
[center_square[0] + square_size, center_square[1] - square_size],
center_square - square_size
])
pts2 = pts1 + np.random.uniform(
-affine_sigma, affine_sigma, size=pts1.shape).astype(np.float32)
M = cv2.getAffineTransform(pts1, pts2)
if islist:
res = []
for i, ima in enumerate(im):
if i == 0:
res.append(
cv2.warpAffine(ima,
M,
shape_size[::-1],
borderMode=cv2.BORDER_REFLECT_101))
else:
res.append(cv2.warpAffine(ima, M, shape_size[::-1]))
im = res
else:
ima = cv2.warpAffine(ima,
M,
shape_size[::-1],
borderMode=cv2.BORDER_REFLECT_101)
# ima = cv2.warpAffine(ima, M, shape_size[::-1])
# fast gaussian filter
blur_size = int(4 * sigma) | 1
dx = cv2.GaussianBlur((np.random.rand(*shape) * 2 - 1),
ksize=(blur_size, blur_size),
sigmaX=sigma) * alpha
dy = cv2.GaussianBlur((np.random.rand(*shape) * 2 - 1),
ksize=(blur_size, blur_size),
sigmaX=sigma) * alpha
# remap
x, y = np.meshgrid(np.arange(shape[1]), np.arange(shape[0]))
map_x, map_y = (y + dy).astype('float32'), (x + dx).astype('float32')
def remap(data):
r = cv2.remap(data,
map_y,
map_x,
interpolation=cv2.INTER_LINEAR,
borderMode=cv2.BORDER_REFLECT_101)
return r[..., np.newaxis]
if islist:
return tuple([remap(ima) for ima in im])
else:
return remap(ima)