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 affine_skew(tilt, phi, img, mask=None):
'''
affine_skew(tilt, phi, img, mask=None) -> skew_img, skew_mask, Ai
Ai - is an affine transform matrix from skew_img to img
'''
h, w = img.shape[:2]
if mask is None:
mask = np.zeros((h, w), np.uint8)
mask[:] = 255
A = np.float32([[1, 0, 0], [0, 1, 0]])
if phi != 0.0:
phi = np.deg2rad(phi)
s, c = np.sin(phi), np.cos(phi)
A = np.float32([[c,-s], [ s, c]])
corners = [[0, 0], [w, 0], [w, h], [0, h]]
tcorners = np.int32( np.dot(corners, A.T) )
x, y, w, h = cv2.boundingRect(tcorners.reshape(1,-1,2))
A = np.hstack([A, [[-x], [-y]]])
img = cv2.warpAffine(img, A, (w, h), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REPLICATE)
if tilt != 1.0:
s = 0.8*np.sqrt(tilt*tilt-1)
img = cv2.GaussianBlur(img, (0, 0), sigmaX=s, sigmaY=0.01)
img = cv2.resize(img, (0, 0), fx=1.0/tilt, fy=1.0, interpolation=cv2.INTER_NEAREST)
A[0] /= tilt
if phi != 0.0 or tilt != 1.0:
h, w = img.shape[:2]
mask = cv2.warpAffine(mask, A, (w, h), flags=cv2.INTER_NEAREST)
Ai = cv2.invertAffineTransform(A)
return img, mask, Ai
def getPano(M, img1, img2):
rows, cols = img2.shape[:2]
# This is the tricky part. For transform, the point is identified as pair
# of (col, row) instead of (row, col)
box = numpy.array([[0, 0], [cols - 1, 0], [cols - 1, rows - 1],
[0, rows - 1]], dtype=numpy.float32).reshape(-1, 1, 2)
transformed_box = cv2.transform(box, M)
min_col = min(transformed_box[:, :, 0])[0]
min_row = min(transformed_box[:, :, 1])[0]
if min_col < 0:
transformed_box[:, :, 0] -= min_col
M[0, 2] -= min_col
if min_row < 0:
transformed_box[:, :, 1] -= min_row
M[1, 2] -= min_row
max_col = max(transformed_box[:, :, 0])[0]
max_row = max(transformed_box[:, :, 1])[0]
I = numpy.array([[1, 0, 0], [0, 1, 0]], dtype=numpy.float)
transformed_img1 = cv2.warpAffine(img1, I, (max_col, max_row))
transformed_img2 = cv2.warpAffine(img2, M, (max_col, max_row))
numpy.copyto(
transformed_img1, transformed_img2, where=transformed_img1 == 0)
return transformed_img1
def fix_rotation(qr_rect, image, aux_image):
"""Fixes the rotation of the image using the qrcode rectangle. -> cv2.image"""
actual_down = np.array( [ float(qr_rect[1][0]-qr_rect[0][0]) ,
float(qr_rect[1][1]-qr_rect[0][1]) ] )
actual_down = actual_down/np.linalg.norm(actual_down)
real_down = np.array([0,1])
angle = np.arccos(np.dot(actual_down, real_down))
if np.isnan(angle):
if (actual_down == real_down).all(): angle = 0.0
else: angle = np.pi
if actual_down[0]>0: angle = 2*np.pi-angle
#calculate the size of the borders to make it squared
w, h = image.shape[::-1]
if w>h:
top, bott, left, right = (w-h)/2, (w-h)/2, 0, 0
else:
top, bott, left, right = (h-w)/2, (h-w)/2, 0, 0
#add the borders
bigger_img = cv2.copyMakeBorder(image,top,bott,left,right,cv2.BORDER_CONSTANT,value=[0,0,0])
bigger_aux = cv2.copyMakeBorder(aux_image,top,bott,left,right,cv2.BORDER_CONSTANT,value=[0,0,0])
#calculate the tramsformation
w, h = bigger_img.shape[::-1]
M = cv2.getRotationMatrix2D((w/2,h/2),180*angle/np.pi,1.0)
#TODO o not use the variable margin here, try to find the real w, h that accounts for the new transformation
#margin = doc_parameters["margin"]
return ( cv2.warpAffine(bigger_img,M,(w,h)), cv2.warpAffine(bigger_aux,M,(w,h)) )
def _apply_(self, *image):
deg = self.params['deg']
enlarge = self.params['enlarge']
res = ()
n_img = 0
for img in image:
rows, cols = img.shape[0], img.shape[1]
flags = self.SetFlag(n_img)
if enlarge:
# this part could be better adjusted
x = int(rows * (2 - 1.414213) / 1.414213)
y = int(cols * (2 - 1.414213) / 1.414213)
z = max(x, y)
big_image = self.enlarge(img, z, z)
b_rows, b_cols = big_image.shape[0], big_image.shape[1]
M = cv2.getRotationMatrix2D((b_cols / 2, b_rows / 2), deg, 1)
dst = cv2.warpAffine(big_image, M, (b_cols, b_rows), flags=flags)
res += (self.OutputType(dst[z:(z + rows), z:(z + cols)]), )
else:
M = cv2.getRotationMatrix2D((cols / 2, rows / 2), deg, 1)
sub_res = cv2.warpAffine(img, M, (cols, rows), flags=flags)
res += (self.OutputType(sub_res),)
n_img += 1
return res
def phasecorr(imlist,imlist2=None,clip=0): #cv [rowini,rowend,colini,colend]
import cv2
cx = 0.0
cy = 0.0
imlist_stb=[]
if imlist2!=None:
imlist2_stb=[]
imi=0
im_prev = imlist[0]
im_denoised_prev = np.float32(restoration.denoise_tv_chambolle(im_prev.astype('uint16'), weight=0.1, multichannel=True)) #ref
for im in imlist:
im_denoised = np.float32(restoration.denoise_tv_chambolle(im.astype('uint16'), weight=0.1, multichannel=True))
# TODO: set window around phase correlation
dp = cv2.phaseCorrelate(im_denoised_prev, im_denoised)
cx = cx - dp[0]
cy = cy - dp[1]
xform = np.float32([[1, 0, cx], [0, 1, cy]])
im_stb = cv2.warpAffine(im.astype('float32'), xform, dsize=(im_denoised.shape[1], im_denoised.shape[0]))
imlist_stb.append(imclipper(im_stb,clip))
if imlist2!=None:
im2=imlist2[imi]
im2_stb=cv2.warpAffine(im2.astype('float32'), xform, dsize=(im_denoised.shape[1], im_denoised.shape[0]))
imlist2_stb.append(imclipper(im2_stb,clip))
im_denoised_prev = im_denoised
imi+=1
if imlist2!=None:
return imlist_stb,imlist2_stb
else:
return imlist_stb
def apply_new_face(self, image, new_face, image_mask, mat, image_size, size):
base_image = numpy.copy( image )
new_image = numpy.copy( image )
cv2.warpAffine( new_face, mat, image_size, new_image, cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT )
outImage = None
if self.seamless_clone:
unitMask = numpy.clip( image_mask * 365, 0, 255 ).astype(numpy.uint8)
maxregion = numpy.argwhere(unitMask==255)
if maxregion.size > 0:
miny,minx = maxregion.min(axis=0)[:2]
maxy,maxx = maxregion.max(axis=0)[:2]
lenx = maxx - minx;
leny = maxy - miny;
masky = int(minx+(lenx//2))
maskx = int(miny+(leny//2))
outimage = cv2.seamlessClone(new_image.astype(numpy.uint8),base_image.astype(numpy.uint8),unitMask,(masky,maskx) , cv2.NORMAL_CLONE )
return outimage
foreground = cv2.multiply(image_mask, new_image.astype(float))
background = cv2.multiply(1.0 - image_mask, base_image.astype(float))
outimage = cv2.add(foreground, background)
return outimage
def get_image_mask(self, image, new_face, landmarks, mat, image_size):
face_mask = numpy.zeros(image.shape,dtype=float)
if 'rect' in self.mask_type:
face_src = numpy.ones(new_face.shape,dtype=float)
cv2.warpAffine( face_src, mat, image_size, face_mask, cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT )
hull_mask = numpy.zeros(image.shape,dtype=float)
if 'hull' in self.mask_type:
hull = cv2.convexHull( numpy.array( landmarks ).reshape((-1,2)).astype(int) ).flatten().reshape( (-1,2) )
cv2.fillConvexPoly( hull_mask,hull,(1,1,1) )
if self.mask_type == 'rect':
image_mask = face_mask
elif self.mask_type == 'facehull':
image_mask = hull_mask
else:
image_mask = ((face_mask*hull_mask))
if self.erosion_kernel is not None:
if self.erosion_kernel_size > 0:
image_mask = cv2.erode(image_mask,self.erosion_kernel,iterations = 1)
elif self.erosion_kernel_size < 0:
dilation_kernel = abs(self.erosion_kernel)
image_mask = cv2.dilate(image_mask,dilation_kernel,iterations = 1)
if self.blur_size!=0:
image_mask = cv2.blur(image_mask,(self.blur_size,self.blur_size))
return image_mask
def _transform(self, imgs):
bs = imgs.shape[0]
imgs = imgs.reshape(bs, 3, 32, 32)
imgs_ = np.zeros_like(imgs)
for i, img in enumerate(imgs):
# random flip
if np.random.randint(2):
img_ = np.copy(img[:, :, ::-1])
else:
img_ = np.copy(img)
# rotation
n = np.random.choice(np.arange(-15, 15))
M = cv2.getRotationMatrix2D((32/2, 32/2), n, 1)
dst = cv2.warpAffine(img_.transpose(1, 2, 0), M, (32, 32))
# translation
M = np.float32([[1,0,np.random.randint(-2, 2)],
[0,1,np.random.randint(-2, 2)]])
dst = cv2.warpAffine(dst, M, (32, 32))
imgs_[i] = dst.transpose(2, 0, 1)
imgs_ = imgs_.reshape(bs, 3072)
return imgs_
def generate_image(char_images, num_bg_images):
"""
Generate image
:param char_images: Dictionary of Images with all characters (char -> Image)
:param num_bg_images: Number of background image
:return: (image, number_plate_text)
"""
bg = generate_background(num_bg_images)
plate, plate_mask, code = generate_plate(FONT_HEIGHT, char_images)
while True:
M, out_of_bounds = make_affine_transform(
from_shape=plate.shape,
to_shape=bg.shape,
min_scale=0.6/2,
max_scale=0.875/2,
rotation_variation=0.2,
scale_variation=1.5,
translation_variation=2.0)
if not out_of_bounds:
break
plate = cv2.warpAffine(plate, M, (bg.shape[1], bg.shape[0]))
plate_mask = cv2.warpAffine(plate_mask, M, (bg.shape[1], bg.shape[0]))
bounding_box = bounding_box_from_mask(plate_mask)
out = plate * plate_mask + bg * (1.0 - plate_mask)
out = cv2.resize(out, (OUTPUT_SHAPE[1], OUTPUT_SHAPE[0]))
out += numpy.random.normal(scale=0.05, size=out.shape)
out = numpy.clip(out, 0., 1.)
return out, code, bounding_box
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 accumulate_transforms(d, A, reverse=False):
H = [None] * len(A)
ims = [None] * len(A)
a_accum = eye(3)
if reverse:
A = [inv(a) if a is not None else None for a in A]
for i, a in reversed(list(enumerate(A))):
if a is not None:
a_accum = dot(a_accum,a)
H[i] = a_accum
ims[i] = cv2.imread(d[i][2], 0)
else:
for i, a in enumerate(A):
if a is not None:
a_accum = dot(a_accum,a)
H[i] = a_accum
ims[i] = cv2.imread(d[i][2], 0)
H_valid = [h for h in H if h is not None]
if None not in H:
if reverse:
ims_reg = [cv2.warpAffine(ims[k], H[k][:2, :], ims[-1].shape[::-1]) for k in reversed(range(len(H)))]
else:
ims_reg = [cv2.warpAffine(ims[k], H[k][:2, :], ims[0].shape[::-1]) for k in range(len(H))]
else:
raise ValueError('None in H')
return H_valid, ims_reg
def imagedistort(img):
'''
distort an image
shift horizontally and vertically
rotated clockwise and anticlockwise
'''
shift_range = 0.05
rotate_range = 0.02
h, w = img.shape
size = max(w, h) *2
normal = 255 * np.ones((size, size), np.uint8)
normal[(size - h) / 2: (size + h) / 2, (size - w) / 2: (size + w) / 2] = img
# rotate
degree = 90 * random.uniform(-rotate_range, rotate_range)
M = cv2.getRotationMatrix2D((size/2, size/2), degree, 1)
rotated = cv2.warpAffine(normal, M, (size, size))
# shift
shift_value_x = size / 2 * random.uniform(-shift_range, shift_range)
shift_value_y = size / 2 * random.uniform(-shift_range, shift_range)
M = np.float32([[1, 0, shift_value_x], [0, 1, shift_value_y]])
shift = cv2.warpAffine(rotated, M, (size, size))
# crop
center = (size / 2, size / 2)
crop = cv2.getRectSubPix(shift, (w, h), center)
return crop
def show_image(idx):
global cur_img, cur_img_path, if_path, files
if idx < 0 or idx >= len(files):
return
fpath = files[idx]
new_img_path = "%s/%s" % (if_path, fpath)
if new_img_path == cur_img_path:
return
cur_img_path = new_img_path
cur_img = cv.imread(cur_img_path)
# Try to get rotation data and if set, rotate image correctly
try:
pil_img = PIL.Image.open(cur_img_path)
rot_code = pil_img._getexif()[274]
(h, w, _) = cur_img.shape
if rot_code == 3:
cur_img = cv.warpAffine(cur_img, cv.getRotationMatrix2D((w / 2.0, h / 2.0), 180, 1.0), (w, h))
elif rot_code == 6:
m = min(h, w)
cur_img = cv.warpAffine(cur_img, cv.getRotationMatrix2D((m / 2.0, m / 2.0), -90, 1.0), (h, w))
elif rot_code == 8:
cur_img = cv.warpAffine(cur_img, cv.getRotationMatrix2D((w / 2.0, w / 2.0), 90, 1.0), (h, w))
pil_img.close()
except:
pass
imshow(fpath, cur_img)
if fpath in data:
print("[%03d/%03d] Showing %s (%s)" % (idx, len(files), fpath, "GOOD" if data[fpath] else "BAD"))
else:
print("[%03d/%03d] Showing %s" % (idx, len(files), fpath))
def render(self):
"""Returns the rendered image (after transformation), and the start point of the image in global coordinates"""
if self.already_rendered:
return self.img, np.array([self.bbox[0], self.bbox[1]])
img = cv2.imread(self.img_path, cv2.IMREAD_ANYDEPTH)
adjusted_transform = self.transform_matrix[:2].copy()
adjusted_transform[0][2] -= self.bbox[0]
adjusted_transform[1][2] -= self.bbox[2]
self.img = cv2.warpAffine(img, adjusted_transform, self.shape, flags=cv2.INTER_AREA)
self.already_rendered = True
if self.compute_mask:
mask_img = np.ones(img.shape)
self.mask = cv2.warpAffine(mask_img, adjusted_transform, self.shape, flags=cv2.INTER_AREA)
self.mask[self.mask > 0] = 1
self.mask = self.mask.astype(np.uint8)
if self.compute_distances:
# The initial weights for each pixel is the minimum from the image boundary
grid = np.mgrid[0:self.height, 0:self.width]
weights_img = np.minimum(
np.minimum(grid[0], self.height - 1 - grid[0]),
np.minimum(grid[1], self.width - 1 - grid[1])
).astype(np.float32)
self.weights = cv2.warpAffine(weights_img, adjusted_transform, self.shape, flags=cv2.INTER_AREA)
# Returns the transformed image and the start point
return self.img, (self.bbox[0], self.bbox[2])
def align(im, warp_matrix):
dic = oib.image_info(im)
x = int(dic['frame_size_x'])
y = int(dic['frame_size_y'])
c = int(dic['channels'])
z = int(dic['z_steps'])
t = int(dic['time_frames'])
with bioformats.ImageReader(im, perform_init=True) as rdr:
image = np.empty([t,z,y,x,c], np.uint16)
for c in range(c):
image[:,:,:,:,c] = rdr.read(c=1, rescale=False)
image[:,:,:,:,c] = cv2.warpAffine((rdr.read(c=0, rescale=False)),
warp_matrix, (y,x), flags=cv2.INTER_LINEAR + cv2.WARP_INVERSE_MAP)
for t in range(t):
image[t,:,:,:,c] = cv2.warpAffine((rdr.read(t=t, z=0, c=0, rescale=False)),
warp_matrix, (y,x), flags=cv2.INTER_LINEAR + cv2.WARP_INVERSE_MAP)
for z in range(z):
image[:,z,:,:,c] = cv2.warpAffine((rdr.read(z=z, c=0, rescale=False)),
warp_matrix, (y,x), flags=cv2.INTER_LINEAR + cv2.WARP_INVERSE_MAP)
return image
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 mirror4(img):
"""
Create 4 mirrored images and return a merged one.
img: a gray-scaled image
"""
height, width = img.shape
# the upper left
affine = np.array([[0.5, 0.0, 0.0],
[0.0, 0.5, 0.0]])
img_tmp1 = cv2.warpAffine(img, affine, (width, height))
# the upper right
affine = np.array([[-0.5, 0.0, width-1],
[ 0.0, 0.5, 0.0]])
img_tmp2 = cv2.warpAffine(img, affine, (width, height))
# the lower right
affine = np.array([[-0.5, 0.0, width-1],
[ 0.0, -0.5, height-1]])
img_tmp3 = cv2.warpAffine(img, affine, (width, height))
# the lower left
affine = np.array([[0.5, 0.0, 0.0],
[0.0, -0.5, height-1]])
img_tmp4 = cv2.warpAffine(img, affine, (width, height))
return cv2.add(cv2.add(img_tmp1, img_tmp2), cv2.add(img_tmp3, img_tmp4))
def coarse_alignment(t_inv):
t_ide = np.identity(3, 'float32')[: 2]
coarse_src_coords = cv2.transform( \
coarse_trg_coords[:, None], t_inv)[:, 0]
# Transform target for coarse grid search
shape = tuple(int(s / d_shift) for s in src_mf.shape)
trg_mf_t = cv2.warpAffine(trg_mf, t_inv / d_shift, shape[::-1])
src_mf_t = cv2.warpAffine(src_mf, t_ide / d_shift, shape[::-1])
# Coarse grid search
t_corr_list = [libreg.affine_registration.match_template_brute( \
get_patch_at(trg_mf_t, \
src_coord / d_shift, patch_size / d_shift), \
scipy.fftpack.fft2(get_patch_at(src_mf_t, \
src_coord / d_shift, shift_space / d_shift)), \
rotation=slice(0, 1, 1) if angle_space is None \
else slice(-angle_space, +angle_space, d_angle), \
logscale_x=slice(0, 1, 1) if scale_space is None \
else slice(-scale_space, +scale_space, d_scale), \
logscale_y=slice(0, 1, 1) if scale_space is None \
else slice(-scale_space, +scale_space, d_scale), \
find_translation=libreg.affine_registration \
.cross_correlation_fft) \
for src_coord in coarse_src_coords]
dx = np.array([np.dot(t[:, :2], \
(patch_size / d_shift, patch_size / d_shift)) \
+ t[:, 2] - (shift_space / d_shift, shift_space / d_shift) \
for t, _ in t_corr_list])
coarse_src_coords += dx * d_shift
corr = np.array([corr for _, corr in t_corr_list], 'float32')
return coarse_src_coords, corr
def rotate(self):
for index, (x, y, w, h) in enumerate(self.boundary):
roi = self.img[y: y + h, x: x + w]
thresh = roi.copy()
angle = 0
smallest = 999
row, col = thresh.shape
for ang in range(-60, 61):
M = cv2.getRotationMatrix2D((col / 2, row / 2), ang, 1)
t = cv2.warpAffine(thresh.copy(), M, (col, row))
r, c = t.shape
right = 0
left = 999
for i in range(r):
for j in range(c):
if t[i][j] == 255 and left > j:
left = j
if t[i][j] == 255 and right < j:
right = j
if abs(right - left) <= smallest:
smallest = abs(right - left)
angle = ang
M = cv2.getRotationMatrix2D((col / 2, row / 2), angle, 1)
thresh = cv2.warpAffine(thresh, M, (col, row))
thresh = cv2.resize(thresh, (20, 20))
cv2.imwrite('tmp/' + str(index) + '.png', thresh)
def RegisterImageRoi(self, npRoi, moments):
npRoiReg = npRoi
# Center of mass.
if (moments['m00'] != 0.0):
xCOM = moments['m10']/moments['m00']
yCOM = moments['m01']/moments['m00']
# Rotate the fly image to 0-degrees.
angleR = self.GetResolvedAngleFiltered()
T = cv2.getRotationMatrix2D((xCOM,yCOM), -angleR*180.0/np.pi, 1.0)
npRoiReg = cv2.warpAffine(npRoi, T, (0,0))
# Correct for centering error after the transformation.
momentsRoiReg = cv2.moments(npRoiReg)
if (momentsRoiReg['m00'] != 0.0):
xCOM = momentsRoiReg['m10']/momentsRoiReg['m00']
yCOM = momentsRoiReg['m01']/momentsRoiReg['m00']
# Rotate the fly image to 0-degrees.
T = np.array([[1, 0, float(self.params['tracking']['roi']['width'])/2.0-xCOM],
[0, 1, float(self.params['tracking']['roi']['height'])/2.0-yCOM]], dtype=float)
npRoiReg = cv2.warpAffine(npRoiReg, T, (0,0))
return npRoiReg
def onestep(self):
logger = logging.getLogger()
frame0 = self.rawframe = cv2.cvtColor(np.array(self.frames[self.params.skip]), cv2.COLOR_RGB2BGR)
h,w = frame0.shape[0:2]
crop = [self.params.crop[0]*w//1000,
self.params.crop[1]*w//1000,
self.params.crop[2]*h//1000,
self.params.crop[3]*h//1000]
wc = crop[1] - crop[0]
hc = crop[3] - crop[2]
out = cv2.VideoWriter(self.params.filename+".sr.m4v",cv2.VideoWriter_fourcc('m','p','4','v'), 30, (wc,hc))
cropped = frame0[crop[2]:crop[3],
crop[0]:crop[1], :]
out.write(cropped)
dx = 0
dy = 0
f = self.params.skip
while True:
newframe = cv2.cvtColor(np.array(self.frames[f]), cv2.COLOR_RGB2BGR)
f += 1
if 0 < self.params.last <= f:
return
d = pass1.motion(newframe, frame0, focus=self.params.focus, maxaccel=self.params.maxaccel, delta=(dx,dy))
if d is not None:
dx, dy = d
logger.info("{2} Delta: {0} {1}".format(dx,dy, f))
affine = np.matrix(((1.0,0.0,-dx),(0.0,1.0,-dy)))
h,w = frame0.shape[0:2]
cv2.warpAffine(newframe, affine, (w,h), newframe)
cropped = newframe[crop[2]:crop[3],
crop[0]:crop[1], :]
out.write(cropped)
yield newframe
def rotate(img, angle, crop):
h,w = img.shape[:2]
if crop:
# Calculate the rotation matrix then apply it
M = cv2.getRotationMatrix2D((w/2,h/2), angle, 1) # (center(x,y),angle,scale)
rtImg = cv2.warpAffine(img,M,(w,h))
return rtImg
else:
# Calculate the size of the canvas
r = int(math.sqrt(h*h + w*w)) + 1
imgC = addCanvas(img,r*2)
hC,wC = imgC.shape[:2]
# Calculate the rotation matrix then apply it
M = cv2.getRotationMatrix2D((wC/2,hC/2), angle, 1) # (center(x,y),angle,scale)
rtImg = cv2.warpAffine(imgC,M,(wC,hC))
relativeCorners = getVertices(h,w, math.radians(angle))
center = (wC/2,hC/2)
realCorners = [(corner[0]+center[0] , corner[1]+center[1]) for corner in relativeCorners]
print realCorners
print relativeCorners
box = surroundingBox(realCorners[0], realCorners[1], realCorners[2], realCorners[3])
# cv2.rectangle(rtImg,(box[0],box[2]), (box[1],box[3]), (0,255,0),3)
# for vertex in realCorners:
# cv2.circle(rtImg, vertex, 20, (0, 255, 0),5)
# crop the redundant canvas
rtImg = rtImg[box[2]:box[3],box[0]:box[1]]
return rtImg
def GetNormalPic(imgm):
global HEIGHT
global WIDTH
img = ReadImg(imgm)
#img1 = cv2.imread(filename)#重新读入图片不然后面移动时会出问题
img1=imgm.copy()
A = img.shape #320*240图片 rows=240 cols=320
rows = A[0]
cols = A[1]
Cx,Cy,cnt = GetHeadMeans(img) #GetMeans(img)#质心cx=278(cols) cy=127(rows)
#求取最高点和最低点
topmost = tuple(cnt[cnt[:,:,1].argmin()][0])
bottommost = tuple(cnt[cnt[:,:,1].argmax()][0])
#人的身高(detrows)
Height = bottommost[1] - topmost[1]
New_X = Cx - Height*(float(1))/3 #剪切开始的坐标x
NewWidth = Height*float(2.0)/3 #剪切的人的宽度
M = np.float32([[1,0,0],[0,1,0]])#初始化需要移动的矩阵
if(New_X+NewWidth>cols):#需要左移
delt_X = cols-(New_X + NewWidth)
M[0][2] = float(delt_X)
dst = cv2.warpAffine(img1,M,(cols,rows))
Person = dst[topmost[1]:bottommost[1],(New_X+delt_X):cols]#y,x 需要剪切的人的范围
elif(New_X<0):#需要右移
M[0][2] = float(abs(New_X))
dst = cv2.warpAffine(img1,M,(cols,rows))
Person = dst[topmost[1]:bottommost[1],0:NewWidth]
else:#不需要移动
dst = img1
Person = dst[topmost[1]:bottommost[1],New_X:(New_X+NewWidth)]
res = cv2.resize(Person,(WIDTH,HEIGHT))
#CxNew,CyNew,cntNew = GetHeadMeans(res) #GetMeans(img)#质心cx=278(cols) cy=127(rows)
return res
def translationOp(self):
# read image
im = cv2.imread(self.Image)
# NOTE: Translating (shifting) an image is given by a NumPy matrix in
# the form:
# [[1, 0, shiftX], [0, 1, shiftY]]
# You simply need to specify how many pixels you want to shift the image
# in the X and Y direction -- let's translate the image 25 pixels to the
# right and 50 pixels down
M = np.float32([[1, 0, 25], [0, 1, 50]])
shifted = cv2.warpAffine(im, M, (im.shape[1], im.shape[0]))
# show shifted image and wait for key press
cv2.imshow("Image", shifted)
cv2.waitKey(0)
# now, let's shift the image 50 pixels to the left and 90 pixels up, we
# accomplish this using negative values
M = np.float32([[1, 0, -50], [0, 1, -90]])
shifted = cv2.warpAffine(im, M, (im.shape[1], im.shape[0]))
cv2.imshow("Shifted Up and Left", shifted)
cv2.waitKey(0)
# shift down
shifted = imutils.translate(im, 0, 100)
cv2.imshow("shifted down", shifted)
cv2.waitKey(0)
return
def warp(frame,pitch=0):
if pitch == 0:
M = cv2.getRotationMatrix2D((COLS/2, ROWS/2), 1, 1)
return cv2.warpAffine(frame, M, (COLS, ROWS))
else:
M = cv2.getRotationMatrix2D((COLS/2, ROWS/2), 0, 1)
return cv2.warpAffine(frame, M, (COLS, ROWS))
def task2(img_name, sharpen_factor, h_factor, w_factor, window, threshold, step):
start_time = time.clock()
# read and split the image
img = cv2.imread(img_name)
sub_height = int(img.shape[0] / 3)
blue = img[0:sub_height,:,0]
green = img[sub_height:sub_height*2,:,1]
red = img[sub_height*2:sub_height*3,:,2]
# normalization
normalized_blue = cv2.normalize(blue, 0, 1,norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F)
normalized_green = cv2.normalize(green, 0, 1,norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F)
normalized_red = cv2.normalize(red, 0, 1,norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F)
# blue and green
rows, cols = blue.shape
b_h, b_w, g_h, g_w, best_match = get_best_match_pyramid(blue, green, sharpen_factor, h_factor, w_factor, window, threshold, step)
print "Best match for blue and green : " + str([b_h, b_w, g_h, g_w])
changed_green = cv2.warpAffine(green, np.float32([[1,0,b_w-g_w],[0,1,b_h-g_h]]), (cols, rows))
# blue and red
b_h, b_w, r_h, r_w, best_match = get_best_match_pyramid(blue, red, sharpen_factor, h_factor, w_factor, window, threshold, step)
print "Best match for blue and red : " + str([b_h, b_w, r_h, r_w])
changed_red = cv2.warpAffine(red, np.float32([[1,0,b_w-r_w],[0,1,b_h-r_h]]), (cols, rows))
# merge the images
merged_img = cv2.merge([blue, changed_green, changed_red])
end_time = time.clock()
print "Running time : " + str(end_time-start_time)
return merged_img
def stitch_horizontal(img1,img2): """ Calls functions for stitching horizontally. """ #img1 = warp.spherical_warp(img1, f) #img2 = warp.spherical_warp(img2, f) # Call matches for extracting inliers and homography H = matching.matches(img1,img2) print '----------' # Try to calculate the stitched image size h2,w2 = img2.shape[:2] tx = H[0,2] ty = H[1,2] #h = int(round(h2 + ty)*1.05)+500 #w = int(round(w2 + tx)*1.09)+500 h = int(round(h2 + 100)) w = int(round(w2*1.6)) H = np.matrix([[1,0,tx],[0,1,0],[0,0,1]]) img2_warped = cv2.warpPerspective(img2,H,(w,h)) #affine transformation matrix, the picture should align if H is ok mat = matrix([[1, 0, 0], [0, 1, 0]], dtype=float) im1w = cv2.warpAffine(img1,mat,(w,h)) im2w = cv2.warpAffine(img2_warped,mat,(w,h)) # Find a seam between the two images A = stitcher.stitch_horizontal(im1w,im2w,w2) return A
def rotate(image, image_thresh):
rows,cols = image.shape
angle = random.randint(0-rotate_range,rotate_range)
M = cv2.getRotationMatrix2D((cols/2,rows/2),angle,1)
image = cv2.warpAffine(image,M,(cols,rows), borderValue=random.randint(0,255))
image_thresh = cv2.warpAffine(image_thresh,M,(cols,rows), borderValue=255)
return image, image_thresh
def create_mask(self, shape, type=MASK_SHAPE_T, overlay_points=None,
feather_amount=11, color=1.0, scale=1.0, transform_matrix = None):
mask = numpy.zeros(self.steps["original"].shape[:2], dtype=numpy.float64)
for group in overlay_points:
self.draw_convex_hull(mask,
self.landmarks[group],
color=color)
mask = numpy.array([mask, mask, mask]).transpose((1, 2, 0))
mask = (cv2.GaussianBlur(mask, (feather_amount, feather_amount), 0) > 0) * 1.0
mask = cv2.GaussianBlur(mask, (feather_amount, feather_amount), 0)
self.steps["mask"] = mask
# self.steps["mask_rgb"] = cv2.cvtColor(mask, cv.CV_GRAY2RGB)
if transform_matrix != None:
transformed_mask = numpy.zeros(shape, dtype=mask.dtype)
cv2.warpAffine(mask,
transform_matrix[:2],
(shape[1], shape[0]),
dst=transformed_mask,
borderMode=cv2.BORDER_TRANSPARENT,
flags=cv2.WARP_INVERSE_MAP
)
self.steps["mask"] = transformed_mask
import cv2
import numpy as np
from matplotlib import pyplot as plt
import os
os.chdir('C:\\Users\\user\\Desktop\\opencv')
img = cv2.imread('go.jpg')
rows, cols = img.shape[:2]
M = np.float32([[1, 0, 150], [0, 1, 150]])
dst = cv2.warpAffine(img, M, (cols, rows))
# 좌표 150,150 이동
# 출력 이미지의 크기(너비,높이)가 세번쨰인수
plt.subplot(121), plt.imshow(img), plt.title('Original')
plt.subplot(122), plt.imshow(dst), plt.title('warpAffine')
plt.show()
def extract_image_chips(self, img, points, desired_size=256, padding=0):
"""
crop and align face
Parameters:
----------
img: numpy array, bgr order of shape (1, 3, n, m)
input image
points: numpy array, n x 10 (x1, x2 ... x5, y1, y2 ..y5)
desired_size: default 256
padding: default 0
Retures:
-------
crop_imgs: list, n
cropped and aligned faces
"""
crop_imgs = []
for p in points:
shape = []
for k in range(len(p) / 2):
shape.append(p[k])
shape.append(p[k + 5])
if padding > 0:
padding = padding
else:
padding = 0
# average positions of face points
mean_face_shape_x = [
0.224152, 0.75610125, 0.490127, 0.254149, 0.726104
]
mean_face_shape_y = [
0.2119465, 0.2119465, 0.628106, 0.780233, 0.780233
]
from_points = []
to_points = []
for i in range(len(shape) / 2):
x = (padding + mean_face_shape_x[i]) / (2 * padding +
1) * desired_size
y = (padding + mean_face_shape_y[i]) / (2 * padding +
1) * desired_size
to_points.append([x, y])
from_points.append([shape[2 * i], shape[2 * i + 1]])
# convert the points to Mat
from_mat = self.list2colmatrix(from_points)
to_mat = self.list2colmatrix(to_points)
# compute the similar transfrom
tran_m, tran_b = self.find_tfrom_between_shapes(from_mat, to_mat)
probe_vec = np.matrix([1.0, 0.0]).transpose()
probe_vec = tran_m * probe_vec
scale = np.linalg.norm(probe_vec)
angle = 180.0 / math.pi * math.atan2(probe_vec[1, 0], probe_vec[0,
0])
from_center = [(shape[0] + shape[2]) / 2.0,
(shape[1] + shape[3]) / 2.0]
to_center = [0, 0]
to_center[1] = desired_size * 0.4
to_center[0] = desired_size * 0.5
ex = to_center[0] - from_center[0]
ey = to_center[1] - from_center[1]
rot_mat = cv2.getRotationMatrix2D((from_center[0], from_center[1]),
-1 * angle, scale)
rot_mat[0][2] += ex
rot_mat[1][2] += ey
chips = cv2.warpAffine(img, rot_mat, (desired_size, desired_size))
crop_imgs.append(chips)
return crop_imgs
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)
# 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
try:
contours, hierarchy = cv2.findContours(img_edge2, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
except ValueError:
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
# 以下为识别车牌中的字符
predict_result = []
roi = None
card_color = None
color = "blue"
for card_img in card_imgs:
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(int(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 warp_and_crop_face(src_img,
facial_pts,
reference_pts=None,
crop_size=(96, 112),
align_type='smilarity'):
"""
Function:
----------
apply affine transform 'trans' to uv
Parameters:
----------
@src_img: 3x3 np.array
input image
@facial_pts: could be
1)a list of K coordinates (x,y)
or
2) Kx2 or 2xK np.array
each row or col is a pair of coordinates (x, y)
@reference_pts: could be
1) a list of K coordinates (x,y)
or
2) Kx2 or 2xK np.array
each row or col is a pair of coordinates (x, y)
or
3) None
if None, use default reference facial points
@crop_size: (w, h)
output face image size
@align_type: transform type, could be one of
1) 'similarity': use similarity transform
2) 'cv2_affine': use the first 3 points to do affine transform,
by calling cv2.getAffineTransform()
3) 'affine': use all points to do affine transform
Returns:
----------
@face_img: output face image with size (w, h) = @crop_size
"""
if reference_pts is None:
if crop_size[0] == 96 and crop_size[1] == 112:
reference_pts = REFERENCE_FACIAL_POINTS
else:
default_square = False
inner_padding_factor = 0
outer_padding = (0, 0)
output_size = crop_size
reference_pts = get_reference_facial_points(
output_size, inner_padding_factor, outer_padding,
default_square)
ref_pts = np.float32(reference_pts)
ref_pts_shp = ref_pts.shape
if max(ref_pts_shp) < 3 or min(ref_pts_shp) != 2:
raise FaceWarpException(
'reference_pts.shape must be (K,2) or (2,K) and K>2')
if ref_pts_shp[0] == 2:
ref_pts = ref_pts.T
src_pts = np.float32(facial_pts)
src_pts_shp = src_pts.shape
if max(src_pts_shp) < 3 or min(src_pts_shp) != 2:
raise FaceWarpException(
'facial_pts.shape must be (K,2) or (2,K) and K>2')
if src_pts_shp[0] == 2:
src_pts = src_pts.T
# #print('--->src_pts:\n', src_pts
# #print('--->ref_pts\n', ref_pts
if src_pts.shape != ref_pts.shape:
raise FaceWarpException(
'facial_pts and reference_pts must have the same shape')
if align_type is 'cv2_affine':
tfm = cv2.getAffineTransform(src_pts[0:3], ref_pts[0:3])
# #print(('cv2.getAffineTransform() returns tfm=\n' + str(tfm))
elif align_type is 'affine':
tfm = get_affine_transform_matrix(src_pts, ref_pts)
# #print(('get_affine_transform_matrix() returns tfm=\n' + str(tfm))
else:
tfm = get_similarity_transform_for_cv2(src_pts, ref_pts)
# #print(('get_similarity_transform_for_cv2() returns tfm=\n' + str(tfm))
# #print('--->Transform matrix: '
# #print(('type(tfm):' + str(type(tfm)))
# #print(('tfm.dtype:' + str(tfm.dtype))
# #print( tfm
face_img = cv2.warpAffine(src_img, tfm, (crop_size[0], crop_size[1]))
return face_img
def translate(image, x, y):
M = np.float32([[1, 0, x], [0, 1, y]])
shifted = cv2.warpAffine(image, M, (image.shape[1], image.shape[0]))
return shifted
img_move[0:height, 0:n] = im[0:height, -n:]
#图片镜像
img_mirr = np.zeros([height * 2, width, deep], np.uint8)
for i in range(height):
for j in range(width):
img_mirr[i, j] = im[i, j]
img_mirr[height * 2 - i - 1, j] = im[i, j]
for i in range(width):
img_mirr[height, i] = (0, 0, 255)
#图片缩放
reheight = int(height * 1.2)
rewidth = int(width * 1.1)
img_resize = cv2.resize(im, (reheight, rewidth))
#图片旋转
matRotate = cv2.getRotationMatrix2D((height * 0.5, width * 0.6), 45,
1) # mat rotate 1 center 2 angle 3 缩放系数
img_cir = cv2.warpAffine(im, matRotate, (height, width))
# 展示不同的图片
titles = ['img', 'move', 'mirror', 'resize', 'circle']
imgs = [im, img_move, img_mirr, img_resize, img_cir]
for i in range(5):
plt.subplot(2, 3, i + 1) #注意，这和matlab中类似，没有0，数组下标从1开始
plt.imshow(imgs[i])
plt.title(titles[i])
plt.show()
def load_data(self, is_train, repeat, mirror=None):
# 判断是否需要对其进行镜像处理的图像增强操作
if (mirror is not None):
# 读取镜像后坐标点排列的文件，只有一行数
with open(mirror, 'r') as f:
lines = f.readlines()
assert len(lines) == 1
mirror_idx = lines[0].strip().split(',')
# 保存landmarks镜像后坐标点的序列
mirror_idx = list(map(int, mirror_idx))
# 求原始坐标的最小值（原始最左下角）
xy = np.min(self.landmark, axis=0).astype(np.int32)
# 求原始坐标的最大值（原始最右上角）
zz = np.max(self.landmark, axis=0).astype(np.int32)
# 求landmark的宽度和高度
wh = zz - xy + 1
# 求landmark的中心点(center_x,center_y)
center = (xy + wh / 2).astype(np.int32)
img = cv2.imread(self.path)
# 扩大ROI 0.2倍(以最长的边 * 1.2作为boxsize)
boxsize = int(np.max(wh) * 1.2)
# 求取扩展后的最左上点
xy = center - boxsize // 2
# 扩展后最左上点
x1, y1 = xy
# 扩展后最右下点
x2, y2 = xy + boxsize
# 获取图像长宽
height, width, _ = img.shape
# 判断是否超出原始图像
dx = max(0, -x1)
dy = max(0, -y1)
# 避免坐标出现负值
x1 = max(0, x1)
y1 = max(0, y1)
# 判断是否超出原始图像
edx = max(0, x2 - width)
edy = max(0, y2 - height)
# 限制bbox不超过原图
x2 = min(width, x2)
y2 = min(height, y2)
# 不会超出原图（截取bbox的图像数据）
imgT = img[y1:y2, x1:x2]
# 若超出原始图像就需要对bbox截取的图片进行填充，cv2.BORDER_CONSTANT，扩展区域填充0
if (dx > 0 or dy > 0 or edx > 0 or edy > 0):
imgT = cv2.copyMakeBorder(imgT, dy, edy, dx, edx,
cv2.BORDER_CONSTANT, 0)
# 特殊情况：landmark的跨度，宽度为0，或者长度为0的情况（观察）：
if imgT.shape[0] == 0 or imgT.shape[1] == 0:
imgTT = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
for x, y in (self.landmark + 0.5).astype(np.int32):
cv2.circle(imgTT, (x, y), 1, (0, 0, 255))
cv2.imshow('0', imgTT)
if cv2.waitKey(0) == 27:
exit()
# 将图片resize到image_size=112，默认112，形状一定要正方形，这个跟landmark有关
imgT = cv2.resize(imgT, (self.image_size, self.image_size))
# 将关键点坐标归一化，bbox为正方形
landmark = (self.landmark - xy) / boxsize
# 检查坐标点数据是否满足0<= x,y <= 1
assert (landmark >= 0).all(), str(landmark) + str([dx, dy])
assert (landmark <= 1).all(), str(landmark) + str([dx, dy])
# 添加到图片数据集
self.imgs.append(imgT)
# 添加到关键点数据集
self.landmarks.append(landmark)
if is_train:
while len(self.imgs) < repeat:
# 绕随机中心点做随机旋转(-30,30)
angle = np.random.randint(-30, 30)
# 上面已经计算了人脸框的中心点
cx, cy = center
# 将中心点随机偏移，作为图像的旋转中心
cx = cx + int(np.random.randint(-boxsize * 0.1, boxsize * 0.1))
cy = cy + int(np.random.randint(-boxsize * 0.1, boxsize * 0.1))
# 计算变换矩阵和返回变换后的landmarks
M, landmark = rotate(angle, (cx, cy), self.landmark)
# 将图片按照变换矩阵进行仿射变换 输入图像,M: 变换矩阵,dsize:输出图像的大小
imgT = cv2.warpAffine(
img, M, (int(img.shape[1] * 1.1), int(img.shape[0] * 1.1)))
# np.ptp(axis=0)是同一列中不同列的最大值和最小值的差值，求得新宽度和高度
wh = np.ptp(landmark, axis=0).astype(np.int32) + 1
# np.ceil向上取整, 运算称为Ceiling，扩展1.25倍，然后随机选取
size = np.random.randint(int(np.min(wh)),
np.ceil(np.max(wh) * 1.25))
# 计算新的左上角坐标
xy = np.asarray((cx - size // 2, cy - size // 2),
dtype=np.int32)
# 归一化坐标点
landmark = (landmark - xy) / size
# 检查比例情况，因为这里有随机过程，如果比例不对就重新算
if (landmark < 0).any() or (landmark > 1).any():
continue
x1, y1 = xy
x2, y2 = xy + size
height, width, _ = imgT.shape
dx = max(0, -x1)
dy = max(0, -y1)
x1 = max(0, x1)
y1 = max(0, y1)
edx = max(0, x2 - width)
edy = max(0, y2 - height)
x2 = min(width, x2)
y2 = min(height, y2)
imgT = imgT[y1:y2, x1:x2]
if (dx > 0 or dy > 0 or edx > 0 or edy > 0):
imgT = cv2.copyMakeBorder(imgT, dy, edy, dx, edx,
cv2.BORDER_CONSTANT, 0)
# resize 112 X 112
imgT = cv2.resize(imgT, (self.image_size, self.image_size))
# 随机水平翻转
if mirror is not None and np.random.choice((True, False)):
# 关键点坐标水平翻转
landmark[:, 0] = 1 - landmark[:, 0]
# mirror_idx 镜像后的坐标排列，用来表示镜像后的坐标
landmark = landmark[mirror_idx]
# 图像水平翻转
imgT = cv2.flip(imgT, 1)
# 添加到图片数据集
self.imgs.append(imgT)
# 添加到关键点坐标集
self.landmarks.append(landmark)
def preprocess_tsa_data():
# OPTION 1: get a list of all subjects for which there are labels
df = pd.read_csv(STAGE1_LABELS)
df['Subject'], df['Zone'] = df['Id'].str.split('_',1).str
SUBJECT_LIST = df['Subject'].unique()
# OPTION 2: get a list of all subjects for whom there is data
# SUBJECT_LIST = [os.path.splitext(subject)[0] for subject in os.listdir(INPUT_FOLDER)]
#print(len(SUBJECT_LIST))
#print(SUBJECT_LIST)
# OPTION 3: get a list of subjects for small bore test purposes
#SUBJECT_LIST = ['00360f79fd6e02781457eda48f85da90','0043db5e8c819bffc15261b1f1ac5e42',
# '0050492f92e22eed3474ae3a6fc907fa','006ec59fa59dd80a64c85347eef810c7',
# '0097503ee9fa0606559c56458b281a08','011516ab0eca7cad7f5257672ddde70e',
# '47e2a4a8e13ec7100f6af8cd839d1bb3','e087226320cc189142228b5fb93ed58f']
# intialize tracking and saving items
batch_num = 1
count = 0
threat_zone_examples = []
start_time = timer()
print(len(SUBJECT_LIST))
for subject in SUBJECT_LIST:
count += 1
# read in the images
print('--------------------------------------------------------------')
print('t+> {:5.3f} |Reading images for subject #: {}'.format(timer()-start_time,
subject))
print('--------------------------------------------------------------')
images = tsa.read_data(INPUT_FOLDER + '/' + subject + '.aps')
# transpose so that the slice is the first dimension shape(16, 620, 512)
images = images.transpose()
# for each threat zone, loop through each image, mask off the zone and then crop it
for tz_num, threat_zone_x_crop_dims in enumerate(zip(tsa.zone_slice_list,
tsa.zone_crop_list)):
threat_zone = threat_zone_x_crop_dims[0]
crop_dims = threat_zone_x_crop_dims[1]
# get label
label = np.array(tsa.get_subject_zone_label(tz_num,
tsa.get_subject_labels(STAGE1_LABELS, subject)))
# print(STAGE1_LABELS, subject)
for img_num, img in enumerate(images):
print('Threat Zone:Image -> {}:{}'.format(tz_num, img_num))
print('Threat Zone Label -> {}'.format(label))
if label[0] == 0:
print('threat is present')
if threat_zone[img_num] is not None:
# correct the orientation of the image
print('-> reorienting base image')
base_img = np.flipud(img)
print('-> shape {}|mean={}'.format(base_img.shape,
base_img.mean()))
# convert to grayscale
print('-> converting to grayscale')
rescaled_img = tsa.convert_to_grayscale(base_img)
print('-> shape {}|mean={}'.format(rescaled_img.shape,
rescaled_img.mean()))
# spread the spectrum to improve contrast
print('-> spreading spectrum')
high_contrast_img = tsa.spread_spectrum(rescaled_img)
print('-> shape {}|mean={}'.format(high_contrast_img.shape,
high_contrast_img.mean()))
# get the masked image
print('-> masking image')
masked_img = tsa.roi(high_contrast_img, threat_zone[img_num])
print('-> shape {}|mean={}'.format(masked_img.shape,
masked_img.mean()))
# crop the image
print('-> cropping image')
cropped_img = tsa.crop(masked_img, crop_dims[img_num])
print('-> shape {}|mean={}'.format(cropped_img.shape,
cropped_img.mean()))
# normalize the image
print('-> normalizing image')
normalized_img = tsa.normalize(cropped_img)
print('-> shape {}|mean={}'.format(normalized_img.shape,
normalized_img.mean()))
# zero center the image
print('-> zero centering')
zero_centered_img = tsa.zero_center(normalized_img)
print('-> shape {}|mean={}'.format(zero_centered_img.shape,
zero_centered_img.mean()))
# append the features and labels to this threat zone's example array
print ('-> appending example to threat zone {}'.format(tz_num))
threat_zone_examples.append([[tz_num], zero_centered_img, label])
center = (125,125)
M = cv2.getRotationMatrix2D(center, 5, 1.0)
rotated = cv2.warpAffine(zero_centered_img, M, (250, 250))
# print('rotated image shape {} | mean= {}'.format(zero_centered_img.shape,
# zero_centered_img.mean()))
# cv2.imwrite("thumbnail.png", cropped)
# cv2.imwrite("rotated.jpg", rotated)
# cv2.imshow("original.jpg", zero_centered_img)
# cv2.waitKey(0)
# cv2.imshow("rotated.jpg", rotated)
# cv2.waitKey(0)
threat_zone_examples.append([[tz_num], rotated, label])
M = cv2.getRotationMatrix2D(center, 10, 1.0)
rotated1 = cv2.warpAffine(zero_centered_img, M, (250, 250))
threat_zone_examples.append([[tz_num], rotated1, label])
# cv2.imshow("rotated1.jpg", rotated1)
# cv2.waitKey(0)
M = cv2.getRotationMatrix2D(center, 15, 1.0)
rotated2 = cv2.warpAffine(zero_centered_img, M, (250, 250))
threat_zone_examples.append([[tz_num], rotated2, label])
# cv2.imshow("rotated2.jpg", rotated2)
# cv2.waitKey(0)
# M = cv2.getRotationMatrix2D(center, 20, 1.0)
rotated3 = cv2.warpAffine(zero_centered_img, M, (250, 250))
threat_zone_examples.append([[tz_num], rotated3, label])
# cv2.imshow("rotated3.jpg", rotated3)
# cv2.waitKey(0)
print ('-> shape {:d}:{:d}:{:d}:{:d}:{:d}:{:d}'.format(
len(threat_zone_examples),
len(threat_zone_examples[0]),
len(threat_zone_examples[0][0]),
len(threat_zone_examples[0][1][0]),
len(threat_zone_examples[0][1][1]),
len(threat_zone_examples[0][2])))
else:
print('-> No view of tz:{} in img:{}. Skipping to next...'.format(
tz_num, img_num))
print('------------------------------------------------')
else:
print('threat not present and label is', label[0])
if count >= 0:
# count = 0
print('IN LOOP')
if threat_zone[img_num] is not None:
# correct the orientation of the image
print('-> reorienting base image')
base_img = np.flipud(img)
print('-> shape {}|mean={}'.format(base_img.shape,
base_img.mean()))
# convert to grayscale
print('-> converting to grayscale')
rescaled_img = tsa.convert_to_grayscale(base_img)
print('-> shape {}|mean={}'.format(rescaled_img.shape,
rescaled_img.mean()))
# spread the spectrum to improve contrast
print('-> spreading spectrum')
high_contrast_img = tsa.spread_spectrum(rescaled_img)
print('-> shape {}|mean={}'.format(high_contrast_img.shape,
high_contrast_img.mean()))
# get the masked image
print('-> masking image')
masked_img = tsa.roi(high_contrast_img, threat_zone[img_num])
print('-> shape {}|mean={}'.format(masked_img.shape,
masked_img.mean()))
# crop the image
print('-> cropping image')
cropped_img = tsa.crop(masked_img, crop_dims[img_num])
print('-> shape {}|mean={}'.format(cropped_img.shape,
cropped_img.mean()))
# normalize the image
print('-> normalizing image')
normalized_img = tsa.normalize(cropped_img)
print('-> shape {}|mean={}'.format(normalized_img.shape,
normalized_img.mean()))
# zero center the image
print('-> zero centering')
zero_centered_img = tsa.zero_center(normalized_img)
print('-> shape {}|mean={}'.format(zero_centered_img.shape,
zero_centered_img.mean()))
# append the features and labels to this threat zone's example array
print ('-> appending example to threat zone {}'.format(tz_num))
threat_zone_examples.append([[tz_num], zero_centered_img, label])
print ('-> shape {:d}:{:d}:{:d}:{:d}:{:d}:{:d}'.format(
len(threat_zone_examples),
len(threat_zone_examples[0]),
len(threat_zone_examples[0][0]),
len(threat_zone_examples[0][1][0]),
len(threat_zone_examples[0][1][1]),
len(threat_zone_examples[0][2])))
# count = 0
# each subject gets EXAMPLES_PER_SUBJECT number of examples (182 to be exact,
# so this section just writes out the the data once there is a full minibatch
# complete.
if ((len(threat_zone_examples) % (BATCH_SIZE * EXAMPLES_PER_SUBJECT)) == 0):
for tz_num, tz in enumerate(tsa.zone_slice_list):
tz_examples_to_save = []
# write out the batch and reset
print(' -> writing: ' + PREPROCESSED_DATA_FOLDER +
'preprocessed_TSA_scans-tz{}-{}-{}-b{}.npy'.format(
tz_num+1,
len(threat_zone_examples[0][1][0]),
len(threat_zone_examples[0][1][1]),
batch_num))
# get this tz's examples
tz_examples = [example for example in threat_zone_examples if example[0] ==
[tz_num]]
# drop unused columns
tz_examples_to_save.append([[features_label[1], features_label[2]]
for features_label in tz_examples])
# save batch. Note that the trainer looks for tz{} where {} is a
# tz_num 1 based in the minibatch file to select which batches to
# use for training a given threat zone
np.save(PREPROCESSED_DATA_FOLDER +
'preprocessed_TSA_scans-tz{}-{}-{}-b{}.npy'.format(tz_num+1,
len(threat_zone_examples[0][1][0]),
len(threat_zone_examples[0][1][1]),
batch_num),
tz_examples_to_save)
del tz_examples_to_save
#reset for next batch
del threat_zone_examples
threat_zone_examples = []
batch_num += 1
# we may run out of subjects before we finish a batch, so we write out
# the last batch stub
if (len(threat_zone_examples) > 0):
for tz_num, tz in enumerate(tsa.zone_slice_list):
tz_examples_to_save = []
# write out the batch and reset
print(' -> writing: ' + PREPROCESSED_DATA_FOLDER
+ 'preprocessed_TSA_scans-tz{}-{}-{}-b{}.npy'.format(tz_num+1,
len(threat_zone_examples[0][1][0]),
len(threat_zone_examples[0][1][1]),
batch_num))
# get this tz's examples
tz_examples = [example for example in threat_zone_examples if example[0] ==
[tz_num]]
# drop unused columns
tz_examples_to_save.append([[features_label[1], features_label[2]]
for features_label in tz_examples])
#save batch
np.save(PREPROCESSED_DATA_FOLDER +
'preprocessed_TSA_scans-tz{}-{}-{}-b{}.npy'.format(tz_num+1,
len(threat_zone_examples[0][1][0]),
len(threat_zone_examples[0][1][1]),
batch_num),
tz_examples_to_save)
import cv2
import numpy as np
import os
for i in os.listdir("img1/"):
mask = cv2.imread('mask.jpg', 0)
mask.reshape(320, 240, 1)
mask = np.expand_dims(mask, axis=2)
print(i)
img = cv2.imread("img1/" + i)
img = cv2.resize(img, dsize=(320, 240), interpolation=cv2.INTER_AREA)
dst = cv2.inpaint(img, mask, 3, cv2.INPAINT_TELEA)
src = dst
height, width, channel = src.shape
matrix = cv2.getRotationMatrix2D((width / 2, height / 2), 180, 1)
ak = cv2.warpAffine(src, matrix, (width, height))
cv2.waitKey(0)
cv2.imwrite('asdf/' + i, ak)
for img_i in os.listdir('./source/'):
if os.path.splitext(img_i)[-1] == '.jpg': # skip processing when the extension is not 'jpg'.
facenum = 0
src = cv2.imread('./source/' + img_i)
srcwidth, srcheight = src.shape[:2]
imwidth = int(math.hypot(srcwidth, srcheight)) + 2
imheight = imwidth
# rotate image by 5 degrees and detect
for angle_i in range(-20, 25, 5):
tM = np.float32([[1, 0, (imheight - srcheight) / 2], [0, 1, (imwidth - srcwidth) / 2]])
img_moved = cv2.warpAffine(src, tM, (imwidth, imheight)) # move image to center
tM = cv2.getRotationMatrix2D((imwidth * 0.5, imheight * 0.5), angle_i, 1.0)
img_rotated = cv2.warpAffine(img_moved, tM, (imwidth, imheight)) # rotate image
src_gray = cv2.cvtColor(img_rotated, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(src_gray, scaleFactor=factor)
if len(faces) >= 1:
for x, y, w, h in faces:
face = img_rotated[y: y + h, x: x + w]
cv2.imwrite('./faces/' + os.path.splitext(os.path.basename(img_i))[0] + '_' + str(angle_i).zfill(3) + '_' + str(facenum) + '.jpg', face) # save face image
cv2.rectangle(img_rotated, (x, y), (x + w, y + h), (255, 0, 0), 2) # draw frame at detected area of source image
facenum += 1
def createSamplesAndSave(imagePath, dirPath, basename, infoDat, increaseFlag = False):
# 画像ファイルを読み込み
img = cv2.imread(imagePath)
# 画像ファイルを読み込めたか確認
if img is None:
print(imagePath + " cant read")
return
# img内から顔を検出して顔下半分のみの画像にする
face = cutUnderFaceFromImage(img)
# faceから口を切り取る
mouth = cutMouthFromImage(face)
# 口周りのひげやしわを切り取る
cleanMouth = cutMouthExtraPart(mouth)
# DirPathに新たなファイルを作成
os.makedirs(dirPath, exist_ok=True)
# DirPathとBaseNameを結合
basePath = os.path.join(dirPath, basename)
# 画像の水増しをしない
if not increaseFlag:
height, width = cleanMouth.shape[:2]
cv2.imwrite(basePath + ".jpg", cleanMouth)
infoDat.write(basename + ".jpg 1 0 0 " + str(width) + " " + str(height)+"\n")
# 画像の水増しをする
else:
# imgを左右反転
flipCleanMouth = cv2.flip(cleanMouth, 1)
# 水増しのベースとなる画像リストに格納
baseImgs = []
baseImgs.append(cleanMouth)
baseImgs.append(flipCleanMouth)
# リサイズの倍率
resizeVector = (0.9, 1.0, 1.2)
# 回転角
angles = (350, 355, 0, 5, 10)
# コントラストの倍率
contrast = (1.2, 1.5, 1.7)
# このiは水増しによって生成された画像の数を表す
i = 0
for baseImg in baseImgs:
height, width = baseImg.shape[:2]
# ベース画像をリサイズする
for vx, vy in list(itertools.product(resizeVector, repeat=2)):
newImg = cv2.resize(baseImg, (int(width*vx), int(height*vy)))
newHeight, newWidth = newImg.shape[:2]
# 回転の中心を指定
center = (newWidth//2, newHeight//2)
# 画像を回転させる
for angle in angles:
trans = cv2.getRotationMatrix2D(center, angle, 1.0)
newImgRotated = cv2.warpAffine(newImg, trans, (newWidth, newHeight))
newRotatedHeight, newRotatedWidth = newImgRotated.shape[:2]
# 回転させた画像を保存
cv2.imwrite('{}{}.{}'.format(basePath, str(i), 'jpg'), newImgRotated)
# info.datにvecファイルを生成するのに必要なデータを書きこむ
infoDat.write(basename + str(i)+ ".jpg 1 0 0 " + str(newRotatedWidth) + " " + str(newRotatedHeight)+"\n")
i += 1
# コントラストを調整する
for k in contrast:
newImgContrast = adjust(newImg, contrast=k)
newContrastHeight, newContrastWidth = newImgContrast.shape[:2]
# コントラスト調整した画像を保存
cv2.imwrite('{}{}.{}'.format(basePath, str(i), 'jpg'), newImgContrast)
# info.txtにdatファイルを生成するのに必要なデータを書きこむ
infoDat.write(basename + str(i)+ ".jpg 1 0 0 " + str(newContrastWidth) + " " + str(newContrastHeight)+"\n")
i += 1
filename = p.split("\\")[-2] + p.split("\\")[-1]
all_bb=np.load("./align_data/bb/"+filename+".npy")
all_landmark = np.load("./align_data/landmark/" + filename + ".npy")
new_path=p.split("\\")[-2] +"/"+ p.split("\\")[-1]+"/"
i=0
for path in all_image:
bgr = cv2.imread(path)
bb=all_bb[i,:]
npLandmarks = np.float32(all_landmark[i,:,:])
face=bgr[int(bb[1]):int(bb[3]),int(bb[0]):int(bb[2])]
H = cv2.estimateRigidTransform(npLandmarks[ALIGN_POINTS], dstLandmarks[ALIGN_POINTS],0)
align_face=cv2.warpAffine(bgr, H, (256, 256))
if not os.path.exists("./align_face/"+new_path):
os.makedirs("./align_face/"+new_path)
if not os.path.exists("./face/"+new_path):
os.makedirs("./face/"+new_path)
cv2.imwrite("./face/"+new_path+path.split("\\")[-1],face)
cv2.imwrite("./align_face/" + new_path + path.split("\\")[-1], align_face)
i+=1
print(p+' done')
def random_perspective(img,
targets=(),
degrees=10,
translate=.1,
scale=.1,
shear=10,
perspective=0.0,
border=(0, 0)):
# torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
# targets = [cls, xyxy]
height = img.shape[0] + border[0] * 2 # shape(h,w,c)
width = img.shape[1] + border[1] * 2
# Center
C = np.eye(3)
C[0, 2] = -img.shape[1] / 2 # x translation (pixels)
C[1, 2] = -img.shape[0] / 2 # y translation (pixels)
# Perspective
P = np.eye(3)
P[2, 0] = random.uniform(-perspective,
perspective) # x perspective (about y)
P[2, 1] = random.uniform(-perspective,
perspective) # y perspective (about x)
# Rotation and Scale
R = np.eye(3)
a = random.uniform(-degrees, degrees)
# a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations
s = random.uniform(1 - scale, 1 + scale)
# s = 2 ** random.uniform(-scale, scale)
R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
# Shear
S = np.eye(3)
S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi /
180) # x shear (deg)
S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi /
180) # y shear (deg)
# Translation
T = np.eye(3)
T[0, 2] = random.uniform(0.5 - translate,
0.5 + translate) * width # x translation (pixels)
T[1, 2] = random.uniform(
0.5 - translate, 0.5 + translate) * height # y translation (pixels)
# Combined rotation matrix
M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT
if (border[0] != 0) or (border[1] !=
0) or (M != np.eye(3)).any(): # image changed
if perspective:
img = cv2.warpPerspective(img,
M,
dsize=(width, height),
borderValue=(114, 114, 114))
else: # affine
img = cv2.warpAffine(img,
M[:2],
dsize=(width, height),
borderValue=(114, 114, 114))
# Visualize
# import matplotlib.pyplot as plt
# ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
# ax[0].imshow(img[:, :, ::-1]) # base
# ax[1].imshow(img2[:, :, ::-1]) # warped
# Transform label coordinates
n = len(targets)
if n:
# warp points
xy = np.ones((n * 4, 3))
xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(
n * 4, 2) # x1y1, x2y2, x1y2, x2y1
xy = xy @ M.T # transform
if perspective:
xy = (xy[:, :2] / xy[:, 2:3]).reshape(n, 8) # rescale
else: # affine
xy = xy[:, :2].reshape(n, 8)
# create new boxes
x = xy[:, [0, 2, 4, 6]]
y = xy[:, [1, 3, 5, 7]]
xy = np.concatenate(
(x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
# # apply angle-based reduction of bounding boxes
# radians = a * math.pi / 180
# reduction = max(abs(math.sin(radians)), abs(math.cos(radians))) ** 0.5
# x = (xy[:, 2] + xy[:, 0]) / 2
# y = (xy[:, 3] + xy[:, 1]) / 2
# w = (xy[:, 2] - xy[:, 0]) * reduction
# h = (xy[:, 3] - xy[:, 1]) * reduction
# xy = np.concatenate((x - w / 2, y - h / 2, x + w / 2, y + h / 2)).reshape(4, n).T
# clip boxes
xy[:, [0, 2]] = xy[:, [0, 2]].clip(0, width)
xy[:, [1, 3]] = xy[:, [1, 3]].clip(0, height)
# filter candidates
i = box_candidates(box1=targets[:, 1:5].T * s, box2=xy.T)
targets = targets[i]
targets[:, 1:5] = xy[i]
return img, targets
def rotate(image, angle):
height, width = image.shape[:2]
rot_mat = cv.getRotationMatrix2D((width / 2, height / 2), angle, 1)
rotated_img = cv.warpAffine(image, rot_mat, (width, height))
return rotated_img
def detection():
print('Starting detection')
# Initialising variable
counter = 0
marker = 1
positions = []
headings = []
centres = []
height_of_target = []
square = 2
# if Static_Test:
# cap = cv2.VideoCapture("TestData2.mp4") # video use
cap = cv2.VideoCapture(0)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 800) #800
cap.set(3, 800) #800
cap.set(4, 800) #800
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 800)
cap.set(cv2.CAP_PROP_FPS, 60)
time.sleep(2) # allows the camera to start-up
print('Camera on')
# Run detection when camera is turn on
while (cap.isOpened()): # for video use
# while True:
# the camera will keep running even after the if statement so it can detect multiple ground marker
if counter == 0 or start - end < 5:
if Static_Test:
distance = input("Distance it was taken")
# start - end < 5
if not Static_Test:
distance = 1
ret, frame = cap.read()
# Gathering data from Pixhawk
if GPS:
position = vehicle.location.global_relative_frame
heading = vehicle.heading
# end if
# starting the timer for the length of time it hasn't found a target
start = time.time()
# applying image processing
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # converts to gray
blurred = cv2.GaussianBlur(
gray, (5, 5),
0) # blur the gray image for better edge detection
edged = cv2.Canny(
blurred, 14,
10) # the lower the value the more detailed it would be
# find contours in the thresholded image and initialize the
(contours,
_) = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE) # grabs contours
# outer square
for c in contours:
peri = cv2.arcLength(
c, True
) # grabs the contours of each points to complete a shape
# get the approx. points of the actual edges of the corners
approx = cv2.approxPolyDP(c, 0.01 * peri, True)
if 4 <= len(approx) <= 6:
(x, y, w, h) = cv2.boundingRect(
approx
) # gets the (x,y) of the top left of the square and the (w,h)
aspectRatio = w / float(
h) # gets the aspect ratio of the width to height
area = cv2.contourArea(
c) # grabs the area of the completed square
hullArea = cv2.contourArea(cv2.convexHull(c))
solidity = area / float(hullArea)
keepDims = w > 25 and h > 25
keepSolidity = solidity > 0.9 # to check if it's near to be an area of a square
keepAspectRatio = 0.6 <= aspectRatio <= 1.4
if keepDims and keepSolidity and keepAspectRatio: # checks if the values are true
# captures the region of interest with a 5 pixel lesser in all 2D directions
roi = frame[y:y + h, x:x + w]
height, width, numchannels = frame.shape
centre_region = (x + w / 2, y + h / 2)
if GPS:
centre_target = (y + h / 2, x + w / 2)
# grabs the angle for rotation to make the square level
angle = cv2.minAreaRect(approx)[
-1] # -1 is the angle the rectangle is at
if 0 == angle:
angle = angle
elif -45 > angle > 90:
angle = -(90 + angle)
elif -45 > angle:
angle = 90 + angle
else:
angle = angle
rotated = cv2.getRotationMatrix2D(
tuple(centre_region), angle, 1.0)
imgRotated = cv2.warpAffine(
frame, rotated,
(width, height)) # width and height was changed
imgCropped = cv2.getRectSubPix(imgRotated, (w, h),
tuple(centre_region))
HSVCropp = cv2.cvtColor(imgCropped, cv2.COLOR_BGR2HSV)
if square == 2:
color = imgCropped[int((h / 2) -
(h / 4)):int((h / 2) +
(h / 4)),
int((w / 2) -
(w / 4)):int((w / 2) +
(w / 4))]
else:
color = imgCropped
if Step_detection:
cv2.imshow("crop", imgCropped)
cv2.imshow("okay", color)
print(HSVCropp[int((h / 2) - (h * (6 / 10))),
int((w / 2) - (w * (6 / 10)))])
# # Convert the image to grayscale and turn to outline of the letter
# g_rotated = cv2.cvtColor(imgCropped, cv2.COLOR_BGR2GRAY)
# b_rotated = cv2.GaussianBlur(g_rotated, (5, 5), 0)
# e_rotated = cv2.Canny(b_rotated, 70, 20)
#
# # uses the outline to detect the corners for the cropping of the image
# (contours, _) = cv2.findContours(e_rotated.copy(), cv2.RETR_LIST,
# cv2.CHAIN_APPROX_SIMPLE)
#
# # inner square detection
# for cny in contours:
# perin = cv2.arcLength(cny, True)
# approxny = cv2.approxPolyDP(cny, 0.01 * perin, True)
# if 4 <= len(approxny) <= 6:
# (xx, yy), (ww, hh), angle = cv2.minAreaRect(approxny)
# aspectRatio = ww / float(hh)
# keepAspectRatio = 0.7 <= aspectRatio <= 1.3
# angle = cv2.minAreaRect(approxny)[-1]
# keep_angle = angle == 0, 90, 180, 270, 360
# if keepAspectRatio and keep_angle:
# (xxx, yyy, www, hhh) = cv2.boundingRect(approxny)
# color = imgCropped[yyy:yyy + hhh, xxx:xxx + www]
# appends the data of the image to the list
if GPS:
positions.append(
[position.lat, position.lon, position.alt])
headings.append(heading)
centres.append(centre_target)
height_of_target.append(h)
# time that the target has been last seen
end = time.time()
time.sleep(0.5)
# keep count of number of saved images
counter = counter + 1
cv2.imwrite("colour%d.png" % counter, color)
cv2.imwrite(
'C:/Users/kevin/Desktop/2018-2019/method A/results/{0}_{1}.png'
.format(marker, counter), color)
print("Detected and saved a target")
if Static_Test:
# testing purposes
if not os.path.exists(distance):
os.makedirs(distance)
cv2.imwrite(
'C:/Users/kevin/Desktop/2018-2019/method A/{0}/results{1}_{2}.png'
.format(distance, marker, counter), color)
cv2.imwrite(
'C:/Users/kevin/Desktop/2018-2019/method A/{0}/captured{1}_{2}.png'
.format(distance, marker, counter), roi)
cv2.imwrite(
'C:/Users/kevin/Desktop/2018-2019/method A/{0}/orginal{1}_{2}.png'
.format(distance, marker, counter), frame)
else:
distance = 0
if Step_detection:
cv2.imshow("roi", roi)
cv2.imshow("cropped", imgCropped)
cv2.waitKey(0)
# end if
if counter == 7:
counter, marker = solution(counter, marker,
distance)
else:
counter, marker = solution(counter, marker, distance)
if Step_camera:
cv2.imshow('frame', frame)
cv2.imshow('edge', edged)
k = cv2.waitKey(5) & 0xFF
if k == 27:
break
# end if
cap.release()
cv2.destroyAllWindows()
print('INTER_CUBIC zoom cost {}'.format(time.time() - start_time))
# 組合 + 顯示圖片
img_zoom = np.hstack((img_area_scale, img_cubic_scale))
while True:
cv2.imshow('zoom image', img_zoom)
k = cv2.waitKey(0)
if k == 27:
cv2.destroyAllWindows()
break
# %%
'''
## 平移幾何轉換
'''
# %%
# 設定 translation transformation matrix
# x 平移 50 pixel; y 平移 100 pixel
M = np.array([[1, 0, 50], [0, 1, 100]], dtype=np.float32)
shift_img = cv2.warpAffine(img, M, (img.shape[1], img.shape[0]))
# 組合 + 顯示圖片
img_shift = np.hstack((img, shift_img))
while True:
cv2.imshow('shift image', img_shift)
k = cv2.waitKey(0)
if k == 27:
cv2.destroyAllWindows()
break
def calculate_slice_stats(s):
# Read in image and skeleton
image = cv.imread(s.image, -1)
label = cv.imread(s.skel, -1)
# Use the scipy curve_fit() method to fit the quadratic function to the data.
params, params_covariance = optimize.curve_fit(quad, s.greys, s.densities)
# Find the center point
a, b = s.points
c = center_point(a, b)
# Find the vector from the firs point to the second.
vector = [b[1] - a[1], b[0] - a[0]]
# Find angle between the line drawn by the two points and the x axis
angle_r = np.arctan2(*vector)
angle_d = np.degrees(angle_r)
# Rotate the image around the central point
matrix = cv.getRotationMatrix2D(center=tuple(c), angle=angle_d, scale=1)
rotated_image = cv.warpAffine(src=image, M=matrix, dsize=image.shape[::-1])
rotated_label = cv.warpAffine(src=label, M=matrix, dsize=label.shape[::-1])
# Rotate the two points too.
a_r, b_r = rotate([a, b], origin=c, angle=-angle_r)
# Define how wide the rectangular area should be.
box_width = s.box_width // 2
# Crop the image and the label to the rectangular area.
cropped_image = rotated_image[int(c[1] - box_width):int(c[1] + box_width):,
int(a_r[0]):int(b_r[0])]
cropped_label = rotated_label[int(c[1] - box_width):int(c[1] + box_width):,
int(a_r[0]):int(b_r[0])]
_, cropped_label = cv.threshold(cropped_label, 50, 255, cv.THRESH_BINARY)
# Find any very small boundaries that should be removed.
processed = morphology.remove_small_objects(cropped_label.astype(bool),
min_size=6,
connectivity=2).astype(int)
# black out pixels
cropped_label[np.where(processed == 0)] = 0
c_image_densities = np.zeros(cropped_image.shape)
for y in range(cropped_image.shape[0]):
for x in range(cropped_image.shape[1]):
c_image_densities[y, x] = quad(cropped_image[y, x], *params)
# Calculate the density error. Do so by finding a mean density for each horizontal
# line of pixels and then find the standard deviation of these means.
density_means = np.zeros(box_width * 2)
for y in range(box_width * 2):
split = c_image_densities[y, :]
density_means[y] = np.mean(split)
density_error = np.std(density_means)
mean_density = np.mean(density_means)
# Use the OpenCV connectedComponents() method to label the individual boundaries.
num_labels, labels_image = cv.connectedComponents(cropped_label)
for i in range(num_labels):
if np.sum(labels_image == i) < 15:
cropped_label[labels_image == i] = 0
labels_image[labels_image == i] = 0
labels = []
yearly_labels = []
# Sort the boundaries with the growth surface first.
for x in range(labels_image.shape[1] - 1, -1, -1):
for y in range(labels_image.shape[0]):
label = labels_image[y, x]
if cropped_label[y, x] != 0 and label not in labels:
labels.append(label)
for i in range(len(labels)):
if i % 2 == 0:
yearly_labels.append(labels[i])
boundaries = {}
for i in range(len(yearly_labels)):
boundaries[i] = []
# Save the boundary pixels into a boundaries dictionary.
for y in range(labels_image.shape[0]):
for x in range(labels_image.shape[1]):
label = labels_image[y, x]
if label in yearly_labels:
index = yearly_labels.index(label)
boundaries[index].append((y, x))
euclidean_image = euclidean(cropped_label.shape, boundaries)
raw_distances = []
# Find the average distance in pixels between each of the boundaries.
averages = np.zeros(len(yearly_labels) - 1)
for i in range(len(yearly_labels) - 1):
for j in boundaries[i]:
averages[i] += +euclidean_image[j]
raw_distances.append(euclidean_image[j])
averages[i] /= len(boundaries[i])
# Calculate the extension rate standard error
extension_error = (np.std(raw_distances) /
np.sqrt(len(raw_distances))) * s.voxel_size
# print(raw_distances)
# Calculate the linear extension rate and the calcification rate.
linear_extension_mm = np.mean(averages) * s.voxel_size
calcification = (linear_extension_mm / 10) * mean_density
# Calculate the calcification rate standard error
calcification_error = np.sqrt((density_error / mean_density)**2 +
(extension_error / linear_extension_mm)**2)
return (mean_density, density_error, linear_extension_mm, extension_error,
calcification, calcification_error, density_means, raw_distances)
print('dx2', dx2)
print('dy2', dy2)
# by 김주희_두 기울기의 평균으로 회전하기_201019
ave = math.atan(dy2/dx2)
# ave = (math.atan(dy/dx) + math.atan(dy2/dx2)) / 2
# math.atan(dy2/dx2)
print('atan', ave)
# by 김주희_ 각도 구하기 _201012
angle = ave * (180.0 / math.pi)
h, w = closing.shape[:2]
# M1 = cv.getRotationMatrix2D((w/2, h/2), 10, 1)
M = cv.getRotationMatrix2D((w/2, h/2), angle, 1)
rotation = cv.warpAffine(closing, M,(w, h))
# by 김주희_ 현재 closing에 contour 실시함 -> 사각형 그려져 있음._201012
# by 김주희_ 거리를 계산하여 주변에 점이 많으면 같이 없앰.
# 넓이들중에 최빈 값을 구한다.
cnt = Counter(compare_area)
# print(cnt.most_common(3))
# print(cnt)
#
# print(closing.shape)
ro_center = []
center_x = []
center_y = []
contours, _ = cv.findContours(rotation[:, :, 0], cv.RETR_TREE, cv.CHAIN_APPROX_NONE)
for i in range(len(contours)):
cnt = contours[i]
#기울어진 빗변을 내렸을 때의 비율을 구해서 나중에 wapaffine
scale = aligned_eye_distance / eye_distance
#eyes의 중간점을 찾아서 이를 기준으로 돌리기 위해 좌표 필요
eyes_center = ((left_eye_center[0, 0] + right_eye_center[0, 0]) // 2,
(left_eye_center[0, 1] + right_eye_center[0, 1]) // 2)
cv2.circle(image, eyes_center, 5, (255, 0, 0), -1)
#기준점인 eye_center를 가지고 앞에서 계산한 degree만큼 scale을 해서 돌리기 위해 필요한 matrix
metrix = cv2.getRotationMatrix2D(eyes_center, degree, scale)
cv2.putText(image, "{:.5f}".format(degree),
(right_eye_center[0, 0], right_eye_center[0, 1] + 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
warped = cv2.warpAffine(image_origin,
metrix, (image_width, image_height),
flags=cv2.INTER_CUBIC)
cv2.imshow("warpAffine", warped)
(startX, endX, startY, endY) = getCropDimension(rect, eyes_center)
croped = warped[startY:endY, startX:endX]
output = cv2.resize(croped, OUTPUT_SIZE)
cv2.imshow("output", output)
for (i, point) in enumerate(show_parts):
x = point[0, 0]
y = point[0, 1]
cv2.circle(image, (x, y), 1, (0, 255, 255), -1)
cv2.imshow("Face Alignment", image)
cv2.waitKey(0)
for k in range(0, len(intrseccon)):
p.append([intrseccon[k][0][0][1], intrseccon[k][0][0][0]])
#### rotating the intersection points and scaling them by the factor a/b
im_test = np.zeros(ROI.shape)
rows, cols = ROI.shape
for k in range(0, len(p)):
im_test[p[k][0], p[k][1]] = 255
alpha = -THETA[m_er] * 180 / (np.pi)
M = cv2.getRotationMatrix2D((int(CENTER[m_er][1]), int(CENTER[m_er][0])),
alpha, 1)
im_test = cv2.warpAffine(im_test, M, (cols, rows))
for i in range(0, im_test.shape[0]):
for j in range(0, im_test.shape[1]):
if im_test[i, j] != 0:
im_test[i, j] = 255
im_res = cv2.resize(im_test, (0, 0), fx=1, fy=float(a) / float(b))
im_res = np.array(im_res, dtype=np.uint8)
p_prime = np.zeros((len(p), 2))
_, testcon, _ = cv2.findContours(im_res.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
for k in range(0, len(testcon)):
def __call__(self, img, bbox, name):
output_w = self._width // self._scale_factor
output_h = self._height // self._scale_factor
if self._augmentation:
# random color jittering
distortion = np.random.choice([False, True], p=[0.7, 0.3])
if distortion:
img = image_random_color_distort(img)
# random expansion with prob 0.5
expansion = np.random.choice([False, True], p=[0.7, 0.3])
if expansion:
# Random expand original image with borders, this is identical to placing the original image on a larger canvas.
img, expand = random_expand(img, max_ratio=4, fill=[m * 255 for m in [0.485, 0.456, 0.406]],
keep_ratio=True)
bbox = box_translate(bbox, x_offset=expand[0], y_offset=expand[1], shape=img.shape[:-1])
# random cropping
h, w, _ = img.shape
bbox, crop = box_random_crop_with_constraints(bbox, (w, h),
min_scale=0.1,
max_scale=1,
max_aspect_ratio=2,
constraints=None,
max_trial=30)
x0, y0, w, h = crop
img = mx.image.fixed_crop(img, x0, y0, w, h)
# random horizontal flip with probability of 0.5
h, w, _ = img.shape
img, flips = random_flip(img, px=0.5)
bbox = box_flip(bbox, (w, h), flip_x=flips[0])
# random vertical flip with probability of 0.5
img, flips = random_flip(img, py=0.5)
bbox = box_flip(bbox, (w, h), flip_y=flips[1])
# random translation
translation = np.random.choice([False, True], p=[0.5, 0.5])
if translation:
img[:, :, (0, 1, 2)] = img[:, :, (2, 1, 0)]
img = img.asnumpy()
x_offset = np.random.randint(-7, high=7)
y_offset = np.random.randint(-7, high=7)
M = np.float32([[1, 0, x_offset], [0, 1, y_offset]]) # +일 경우, (오른쪽, 아래)
img = cv2.warpAffine(img, M, (w, h), borderValue=[m * 255 for m in [0.406, 0.456, 0.485]])
bbox = box_translate(bbox, x_offset=x_offset, y_offset=y_offset, shape=(h, w))
img[:, :, (0, 1, 2)] = img[:, :, (2, 1, 0)]
img = mx.nd.array(img)
# resize with random interpolation
h, w, _ = img.shape
interp = np.random.randint(0, 5)
img = mx.image.imresize(img, self._width, self._height, interp=interp)
bbox = box_resize(bbox, (w, h), (output_w, output_h))
else:
h, w, _ = img.shape
img = mx.image.imresize(img, self._width, self._height, interp=1)
bbox = box_resize(bbox, (w, h), (output_w, output_h))
# heatmap 기반이기 때문에 제한 해줘야 한다.
bbox[:, 0] = np.clip(bbox[:, 0], 0, output_w)
bbox[:, 1] = np.clip(bbox[:, 1], 0, output_h)
bbox[:, 2] = np.clip(bbox[:, 2], 0, output_w)
bbox[:, 3] = np.clip(bbox[:, 3], 0, output_h)
img = mx.nd.image.to_tensor(img) # 0 ~ 1 로 바꾸기
img = mx.nd.image.normalize(img, mean=self._mean, std=self._std)
if self._make_target:
bbox = bbox[np.newaxis, :, :]
bbox = mx.nd.array(bbox)
heatmap, offset_target, wh_target, mask_target = self._target_generator(bbox[:, :, :4], bbox[:, :, 4:5],
output_w, output_h, img.context)
return img, bbox[0], heatmap[0], offset_target[0], wh_target[0], mask_target[0], name
else:
return img, bbox, name
import cv2
import numpy as np
import argparse
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True)
args = vars(ap.parse_args())
image = cv2.imread(args["image"])
cv2.imshow("original", image)
cv2.waitKey(0)
M = np.float32([[1, 0, 25], [0, 1, 50]])
shifted = cv2.warpAffine(image, M, (image.shape[1], image.shape[0]))
cv2.imshow("Shifted Down and right", shifted)
cv2.waitKey(0)
M = np.float32([[1, 0, -50], [0, 1, -90]])
shifted = cv2.warpAffine(image, M, (image.shape[1], image.shape[0]))
cv2.imshow("Shifted up and left", shifted)
cv2.waitKey(0)
def Rotate(path, pickle_name, factor=1):
"""Augment dataset
Parameters
----------
path : str
The file location of the .pickle
factor: int
How many augmented variations will be created for each frame
"""
dataset = pd.read_pickle(path)
logging.info('[DataManipulator] dataset shape: ' + str(dataset.shape))
h, w, c = DataManipulator.GetSizeDataFromDataFrame(dataset)
sizes = DataManipulator.CreateSizeDataFrame(h, w, c)
x_set = dataset['x'].values
y_set = dataset['y'].values
z_set = dataset['z'].values
t_set = dataset['t'].values
x_set = np.vstack(x_set[:])
x_set = np.reshape(x_set, (-1, h, w, c))
np.random.seed()
x_augset = []
y_augset = []
z_augset = []
t_augset = []
r_augset = []
max_angle = int(factor / 2)
center = (w / 2, h / 2)
scale = 1.0
for i in range(len(x_set)):
y = y_set[i]
z = z_set[i]
t = t_set[i]
x = x_set[i]
x = np.reshape(x, (h, w)).astype("uint8")
for r in range(factor):
#rot_angle = np.random.randint(-max_angle, max_angle)
rot_angle = r - max_angle
M = cv2.getRotationMatrix2D(center, rot_angle, scale)
img = cv2.warpAffine(x, M, (h, w))
x_augset.append(img)
y_augset.append(y)
z_augset.append(z)
t_augset.append(t)
r_augset.append(rot_angle)
data = pd.DataFrame(
data={
'x': x_augset,
'y': y_augset,
'z': z_augset,
't': t_augset,
'r': r_augset
})
df2 = pd.concat([data, sizes], axis=1)
df2.to_pickle(pickle_name)
def funcRotate(degree=0):
degree = cv2.getTrackbarPos('degree', 'Frame')
rotation_matrix = cv2.getRotationMatrix2D((width / 2, height / 2), degree,
1)
rotated_image = cv2.warpAffine(original, rotation_matrix, (width, height))
cv2.imshow('Rotate', rotated_image)
def extractPlate(imgOriginal, listOfMatchingChars):
possiblePlate = PossiblePlate.PossiblePlate(
) # this will be the return value
listOfMatchingChars.sort(
key=lambda matchingChar: matchingChar.intCenterX
) # sort chars from left to right based on x position
# calculate the center point of the plate
fltPlateCenterX = (
listOfMatchingChars[0].intCenterX +
listOfMatchingChars[len(listOfMatchingChars) - 1].intCenterX) / 2.0
fltPlateCenterY = (
listOfMatchingChars[0].intCenterY +
listOfMatchingChars[len(listOfMatchingChars) - 1].intCenterY) / 2.0
ptPlateCenter = fltPlateCenterX, fltPlateCenterY
# calculate plate width and height
intPlateWidth = int(
(listOfMatchingChars[len(listOfMatchingChars) - 1].intBoundingRectX +
listOfMatchingChars[len(listOfMatchingChars) - 1].intBoundingRectWidth
- listOfMatchingChars[0].intBoundingRectX) *
PLATE_WIDTH_PADDING_FACTOR)
intTotalOfCharHeights = 0
for matchingChar in listOfMatchingChars:
intTotalOfCharHeights = intTotalOfCharHeights + matchingChar.intBoundingRectHeight
# end for
fltAverageCharHeight = intTotalOfCharHeights / len(listOfMatchingChars)
intPlateHeight = int(fltAverageCharHeight * PLATE_HEIGHT_PADDING_FACTOR)
# calculate correction angle of plate region
fltOpposite = listOfMatchingChars[
len(listOfMatchingChars) -
1].intCenterY - listOfMatchingChars[0].intCenterY
fltHypotenuse = DetectChars.distanceBetweenChars(
listOfMatchingChars[0],
listOfMatchingChars[len(listOfMatchingChars) - 1])
fltCorrectionAngleInRad = math.asin(fltOpposite / fltHypotenuse)
fltCorrectionAngleInDeg = fltCorrectionAngleInRad * (180.0 / math.pi)
# pack plate region center point, width and height, and correction angle into rotated rect member variable of plate
possiblePlate.rrLocationOfPlateInScene = (tuple(ptPlateCenter),
(intPlateWidth, intPlateHeight),
fltCorrectionAngleInDeg)
# final steps are to perform the actual rotation
# get the rotation matrix for our calculated correction angle
rotationMatrix = cv2.getRotationMatrix2D(tuple(ptPlateCenter),
fltCorrectionAngleInDeg, 1.0)
height, width, numChannels = imgOriginal.shape # unpack original image width and height
imgRotated = cv2.warpAffine(imgOriginal, rotationMatrix,
(width, height)) # rotate the entire image
imgCropped = cv2.getRectSubPix(imgRotated, (intPlateWidth, intPlateHeight),
tuple(ptPlateCenter))
possiblePlate.imgPlate = imgCropped # copy the cropped plate image into the applicable member variable of the possible plate
return possiblePlate
grey_img = cv2.cvtColor(src_img, cv2.COLOR_BGR2GRAY)
print("Applying Adaptive Threshold with kernel :- 21 X 21")
bin_img = cv2.adaptiveThreshold(grey_img,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY_INV,21,20)
coords = np.column_stack(np.where(bin_img > 0))
angle = cv2.minAreaRect(coords)[-1]
if angle < -45:
angle = -(90 + angle)
else:
angle = -angle
h = bin_img.shape[0]
w = bin_img.shape[1]
center = (w//2,h//2)
angle = 0
M = cv2.getRotationMatrix2D(center,angle,1.0)
bin_img = cv2.warpAffine(bin_img,M,(w,h),
flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
bin_img1 = bin_img.copy()
bin_img2 = bin_img.copy()
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))
kernel1 = np.array([[1,0,1],[0,1,0],[1,0,1]], dtype = np.uint8)
# final_thr = cv2.morphologyEx(bin_img, cv2.MORPH_OPEN, kernel)
# final_thr = cv2.dilate(bin_img,kernel1,iterations = 1)
print("Noise Removal From Image.........")
final_thr = cv2.morphologyEx(bin_img, cv2.MORPH_CLOSE, kernel)
contr_retrival = final_thr.copy()
print("Beginning Character Segmentation..............")
count_x = np.zeros(shape= (height))
def compute_features(img):
scalenum = 2
feat = []
# make a copy of the image
im_original = img.copy()
# scale the images twice
for itr_scale in range(scalenum):
im = im_original.copy()
# normalize the image
im = im / 255.0
# calculating MSCN coefficients
mu = cv2.GaussianBlur(im, (7, 7), 1.166)
mu_sq = mu * mu
sigma = cv2.GaussianBlur(im * im, (7, 7), 1.166)
sigma = (sigma - mu_sq) ** 0.5
# structdis is the MSCN image
structdis = im - mu
structdis /= (sigma + 1.0 / 255)
# calculate best fitted parameters from MSCN image
best_fit_params = AGGDfit(structdis)
# unwrap the best fit parameters
lsigma_best = best_fit_params[0]
rsigma_best = best_fit_params[1]
gamma_best = best_fit_params[2]
# append the best fit parameters for MSCN image
feat.append(gamma_best)
feat.append((lsigma_best * lsigma_best + rsigma_best * rsigma_best) / 2)
# shifting indices for creating pair-wise products
shifts = [[0, 1], [1, 0], [1, 1], [-1, 1]] # H V D1 D2
for itr_shift in range(1, len(shifts) + 1):
OrigArr = structdis
reqshift = shifts[itr_shift - 1] # shifting index
# create transformation matrix for warpAffine function
M = np.float32([[1, 0, reqshift[1]], [0, 1, reqshift[0]]])
ShiftArr = cv2.warpAffine(OrigArr, M, (structdis.shape[1], structdis.shape[0]))
Shifted_new_structdis = ShiftArr
Shifted_new_structdis = Shifted_new_structdis * structdis
# shifted_new_structdis is the pairwise product
# best fit the pairwise product
best_fit_params = AGGDfit(Shifted_new_structdis)
lsigma_best = best_fit_params[0]
rsigma_best = best_fit_params[1]
gamma_best = best_fit_params[2]
constant = m.pow(tgamma(1 / gamma_best), 0.5) / m.pow(tgamma(3 / gamma_best), 0.5)
meanparam = (rsigma_best - lsigma_best) * (tgamma(2 / gamma_best) / tgamma(1 / gamma_best)) * constant
# append the best fit calculated parameters
feat.append(gamma_best) # gamma best
feat.append(meanparam) # mean shape
feat.append(m.pow(lsigma_best, 2)) # left variance square
feat.append(m.pow(rsigma_best, 2)) # right variance square
# resize the image on next iteration
im_original = cv2.resize(im_original, (0, 0), fx=0.5, fy=0.5, interpolation=cv2.INTER_CUBIC)
return feat
def rotate_image(image, angle):
(h, w) = image.shape[:2]
center = (w / 2, h / 2)
rotation_matrix = cv2.getRotationMatrix2D(center, angle, 1.0)
return cv2.warpAffine(image, rotation_matrix, (w, h))
for (i, c) in enumerate(contours):
(x, y, w, h) = cv2.boundingRect(c)
if (w > 500):
return (True, contours[i])
return (False, 0)
#get edge by Sobel kernel
frame = getPict(VIDEO_PATH, FRAME_INDEX)
img_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
img_line = cv2.filter2D(img_gray, -1, Sobel_kernel)
img_cont = cv2.filter2D(img_gray, -1, my_kernel)
rows, cols = img_gray.shape
M = cv2.getRotationMatrix2D(((cols - 1) / 2.0, (rows - 1) / 2.0), 90, 1)
img_line = cv2.warpAffine(img_line, M, (cols, rows))
img_cont = cv2.warpAffine(img_cont, M, (cols, rows))
frame = cv2.warpAffine(frame, M, (cols, rows))
line_flag, line = lineDetect(img_line)
if (line_flag):
x1 = line[0]
y1 = line[1]
x2 = line[2]
y2 = line[3]
print('Line: ', np.sqrt(np.power(x1 - x2, 2) + np.power(y1 - y2, 2)),
', k = ' + str((y2 - y1) / (x2 - x1)))
cv2.line(frame, (x1, y1), (x2, y2), (0, 0, 255), 2)
else:
print('No Line')
def rotate_image(image, angle):
"""
Rotates an OpenCV 2 / NumPy image about it's centre by the given angle
(in degrees). The returned image will be large enough to hold the entire
new image, with a black background
"""
# Get the image size
# No that's not an error - NumPy stores image matricies backwards
image_size = (image.shape[1], image.shape[0])
image_center = tuple(np.array(image_size) / 2)
# Convert the OpenCV 3x2 rotation matrix to 3x3
rot_mat = np.vstack(
[cv2.getRotationMatrix2D(image_center, angle, 1.0), [0, 0, 1]]
)
rot_mat_notranslate = np.matrix(rot_mat[0:2, 0:2])
# Shorthand for below calcs
image_w2 = image_size[0] * 0.5
image_h2 = image_size[1] * 0.5
# Obtain the rotated coordinates of the image corners
rotated_coords = [
(np.array([-image_w2, image_h2]) * rot_mat_notranslate).A[0],
(np.array([ image_w2, image_h2]) * rot_mat_notranslate).A[0],
(np.array([-image_w2, -image_h2]) * rot_mat_notranslate).A[0],
(np.array([ image_w2, -image_h2]) * rot_mat_notranslate).A[0]
]
# Find the size of the new image
x_coords = [pt[0] for pt in rotated_coords]
x_pos = [x for x in x_coords if x > 0]
x_neg = [x for x in x_coords if x < 0]
y_coords = [pt[1] for pt in rotated_coords]
y_pos = [y for y in y_coords if y > 0]
y_neg = [y for y in y_coords if y < 0]
right_bound = max(x_pos)
left_bound = min(x_neg)
top_bound = max(y_pos)
bot_bound = min(y_neg)
new_w = int(abs(right_bound - left_bound))
new_h = int(abs(top_bound - bot_bound))
# We require a translation matrix to keep the image centred
trans_mat = np.matrix([
[1, 0, int(new_w * 0.5 - image_w2)],
[0, 1, int(new_h * 0.5 - image_h2)],
[0, 0, 1]
])
# Compute the tranform for the combined rotation and translation
affine_mat = (np.matrix(trans_mat) * np.matrix(rot_mat))[0:2, :]
# Apply the transform
result = cv2.warpAffine(
image,
affine_mat,
(new_w, new_h),
flags=cv2.INTER_LINEAR
)
return result
