def get_all_faces(imgs, labels, augment=True):
'''Get a list of face images of size BOX_SIZE'''
faces = []
for img_id, rects in tqdm(labels.items(), desc='Extracting all faces'):
for face_rect in rects:
face = face_rect.extract_from_img(imgs[img_id])
ratio = face.shape[1] / face.shape[0]
if ratio < BOX_SIZE.w / BOX_SIZE.h:
scaleW = BOX_SIZE.w / face.shape[1]
face = rescale(face, scaleW, multichannel=False)
# crop top and bottom
diffH = face.shape[0] - BOX_SIZE.h
face_cropped = crop(face, ((diffH // 2, diffH - diffH//2), (0, 0)))
else:
scaleH = BOX_SIZE.h / face.shape[0]
face = rescale(face, scaleH, multichannel=False)
# crop left and right
diffW = face.shape[1] - BOX_SIZE.w
face_cropped = crop(face, ((0, 0), (diffW // 2, diffW - diffW // 2)))
faces.append(face_cropped.astype(np.float32))
if augment:
return augment_data(faces)
else:
return faces
def crop(img, top, left, width, height):
"""crop image from position top and left, width and height
Arguments:
img {numpy.ndarray} -- input image
top {int} -- start position row
left {int} -- start position column
width {int} -- crop width
height {int} -- crop height
"""
if not all([isinstance(x, int) for x in (top, left, width, height)]):
raise ValueError("params should be integer!")
if (width > img.shape[0] or height > img.shape[1]):
raise ValueError(
"the output imgage size should be small than input image!!!")
if len(img.shape) == 2:
img_height, img_width = img.shape
else:
img_height, img_width, _ = img.shape
right = img_width - (left + width)
bottom = img_height - (top + height)
if len(img.shape) == 2:
img_croped = util.crop(img, ((top, bottom), (left, right)))
else:
img_croped = util.crop(img, ((top, bottom), (left, right), (0, 0)))
return img_croped
def crop_and_flip(img, scale="small"):
h, w = np.shape(img)[0], np.shape(img)[1]
if len(np.shape(img)) > 2:
cropped_img = sutil.crop(img, ((CROP_SIZE, CROP_SIZE),
(CROP_SIZE, CROP_SIZE), (0, 0)))
if scale is "small":
# img_scale is small ,r.g height, width<640
lu = cropped_img[:256, :256, :]
ru = cropped_img[:256, w - 256:w, :]
ld = cropped_img[h - 256:h, :256, :]
rd = cropped_img[h - 256:h, w - 256:w, :]
else:
print("wait to updates!")
else:
cropped_img = sutil.crop(img, ((10, 10), (10, 10)))
if scale is "small":
lu = cropped_img[:256, :256]
ru = cropped_img[:256, w - 256:w]
ld = cropped_img[h - 256:h, :256]
rd = cropped_img[h - 256:h, w - 256:w]
else:
print("wait to updates!")
return lu, ru, ld, rd, np.fliplr(lu), np.fliplr(ru), np.fliplr(
ld), np.fliplr(rd)
def process_photo(filename, size=(8, 8)):
"""
Returns a greyscale version of an image of a given size. If the original
image has a different aspect ratio to the target size, the image will
first be cropped before being resized to prevent it getting distorted.
Parameters
----------
filename : string
name of image file (including path)
size : int tuple
size that we want input image to be rescaled to
"""
# read photo as greyscale
photo = io.imread(filename, as_grey=True)
# crop photo so it's the correct ratio
if photo.shape[0] / photo.shape[1] > size[0] / size[1]:
crop_amount = int(
0.5 * photo.shape[0] *
(photo.shape[0] / photo.shape[1] - size[0] / size[1]))
small_photo = util.crop(photo, ((crop_amount, crop_amount), (0, 0)))
else:
crop_amount = int(
0.5 * photo.shape[1] *
(photo.shape[1] / photo.shape[0] - size[1] / size[0]))
small_photo = util.crop(photo, ((0, 0), (crop_amount, crop_amount)))
# now resize the photo
small_photo = transform.resize(small_photo, size)
return small_photo
def fit_crop(self, size):
std_H = self.std.shape[0]
std_W = self.std.shape[1]
test_H = self.test.shape[0]
test_W = self.test.shape[1]
self.std = crop(self.std, ((int(
(std_H - size) / 2), int((std_H - size) / 2)), (int(
(std_W - size) / 2), int((std_W - size) / 2)), (0, 0)))
self.test = crop(self.test, ((int(
(test_H - size) / 2), int((test_H - size) / 2)), (int(
(test_W - size) / 2), int((test_W - size) / 2)), (0, 0)))
def circularcrop(img, border=200, threshold=20000, threshold1=100):
"""
This function trims the circular image by border pixels, nullifies outside borders
and crops the total img to the disk size
parameters:
img: retina image to be processed
border: width of the border that will be trimmed from the disk. This allows to get
rid of camera edge distortion
threshold: threshold for detection image shape
threshold1: threshold for detection image shape
"""
s = np.sum(img, axis=2)
cols = np.sum(s, axis=0) > threshold
rows = np.sum(s, axis=1) > threshold
height = rows.shape[0]
width = cols.shape[0]
x_min = np.argmax(cols[0:width])
x_max = width / 2 + np.argmin(cols[width / 2:width - 1])
y_min = np.argmax(rows[0:height / 2])
y_max = np.argmin(cols[height / 2:height - 1])
y_max = height / 2 + y_max if y_max > 0 else height
radius = (x_max - x_min) / 2
center_x = x_min + radius
center_y = y_min + radius # the default case (if y_min != 0)
if y_min == 0: # the upper side is cropped
if height - y_max > 0: # lower border is not 0
center_y = y_max - radius
else:
upper_line_width = np.sum(
s[0, :] > threshold1) # threshold for single line
center_y = math.sqrt(radius**2 - (upper_line_width / 2)**2)
radius1 = radius - border
mask = np.zeros(img.shape[0:2])
rr, cc = circle(center_y, center_x, radius1, img.shape)
mask[rr, cc] = 1
img[:, :, 0] *= mask
img[:, :, 1] *= mask
img[:, :, 2] *= mask
x_borders = (center_x - radius1, img.shape[1] - center_x - radius1)
y_borders = (max(center_y - radius1,
0), max(img.shape[0] - center_y - radius1, 0))
imgres = util.crop(img, (y_borders, x_borders, (0, 0)))
maskT = util.crop(mask, (y_borders, x_borders))
border_pixels = np.sum(1 - maskT)
return imgres, maskT, center_x, center_y, radius
def open_and_resize(im, size):
h, w, c = im.shape
if h < w:
before_n = (w - h) / 2
after_n = w - (h + before_n)
im_cropped = crop(im, ((0, 0), (before_n, after_n), (0, 0)))
else:
before_n = (h - w) / 2
after_n = h - (w + before_n)
im_cropped = crop(im, ((before_n, after_n), (0, 0), (0, 0)))
im_cropped = resize(im_cropped, (size, size, c))
return im_cropped
def test_copy_crop():
arr = np.arange(45).reshape(9, 5)
out0 = crop(arr, 1, copy=True)
assert out0.flags.c_contiguous
out0[0, 0] = 100
assert not np.any(arr == 100)
assert not np.may_share_memory(arr, out0)
out1 = crop(arr, 1)
out1[0, 0] = 100
assert arr[1, 1] == 100
assert np.may_share_memory(arr, out1)
def open_and_resize(im, size):
h, w, c = im.shape
if h < w:
before_n = (w - h) / 2
after_n = w - (h + before_n)
im_cropped = crop(im, ((0, 0), (before_n, after_n), (0, 0)))
else:
before_n = (h - w) / 2
after_n = h - (w + before_n)
im_cropped = crop(im, ((before_n, after_n), (0, 0), (0, 0)))
im_cropped = resize(im_cropped, (size, size, c))
return im_cropped
def _mean_std(image, w):
"""Return local mean and standard deviation of each pixel using a
neighborhood defined by a rectangular window size ``w``.
The algorithm uses integral images to speedup computation. This is
used by :func:`threshold_niblack` and :func:`threshold_sauvola`.
Parameters
----------
image : ndarray
Input image.
w : int, or iterable of int
Window size specified as a single odd integer (3, 5, 7, …),
or an iterable of length ``image.ndim`` containing only odd
integers (e.g. ``(1, 5, 5)``).
Returns
-------
m : ndarray of float, same shape as ``image``
Local mean of the image.
s : ndarray of float, same shape as ``image``
Local standard deviation of the image.
References
----------
.. [1] F. Shafait, D. Keysers, and T. M. Breuel, "Efficient
implementation of local adaptive thresholding techniques
using integral images." in Document Recognition and
Retrieval XV, (San Jose, USA), Jan. 2008.
:DOI:`10.1117/12.767755`
"""
if not isinstance(w, Iterable):
w = (w, ) * image.ndim
_validate_window_size(w)
pad_width = tuple((k // 2 + 1, k // 2) for k in w)
padded = np.pad(image.astype('float'), pad_width, mode='reflect')
padded_sq = padded * padded
integral = integral_image(padded)
integral_sq = integral_image(padded_sq)
kern = np.zeros(tuple(k + 1 for k in w))
for indices in itertools.product(*([[0, -1]] * image.ndim)):
kern[indices] = (-1)**(image.ndim % 2 != np.sum(indices) % 2)
total_window_size = np.prod(w)
sum_full = ndi.correlate(integral, kern, mode='constant')
mean = crop(sum_full, pad_width) / total_window_size
sum_sq_full = ndi.correlate(integral_sq, kern, mode='constant')
ex2 = crop(sum_sq_full, pad_width) / total_window_size
stdev = np.sqrt(ex2 - mean**2)
return mean, stdev
def circularcrop(img, border=200, threshold=20000, threshold1=100):
"""
This function trims the circular image by border pixels, nullifies outside borders
and crops the total img to the disk size
parameters:
img: retina image to be processed
border: width of the border that will be trimmed from the disk. This allows to get
rid of camera edge distortion
threshold: threshold for detection image shape
threshold1: threshold for detection image shape
"""
s = np.sum(img, axis=2)
cols = np.sum(s, axis=0) > threshold
rows = np.sum(s, axis=1) > threshold
height = rows.shape[0]
width = cols.shape[0]
x_min = np.argmax(cols[0:width])
x_max = width/2 + np.argmin(cols[width/2:width-1])
y_min = np.argmax(rows[0:height/2])
y_max = np.argmin(cols[height/2:height-1])
y_max = height/2 + y_max if y_max > 0 else height
radius = (x_max - x_min)/2
center_x = x_min + radius
center_y = y_min + radius # the default case (if y_min != 0)
if y_min == 0: # the upper side is cropped
if height - y_max > 0: # lower border is not 0
center_y = y_max - radius
else:
upper_line_width = np.sum(s[0,:] > threshold1) # threshold for single line
center_y = math.sqrt( radius**2 - (upper_line_width/2)**2)
radius1 = radius - border
mask = np.zeros(img.shape[0:2])
rr, cc = circle(center_y, center_x, radius1, img.shape)
mask[rr, cc] = 1
img[:,:,0] *= mask
img[:,:,1] *= mask
img[:,:,2] *= mask
x_borders = (center_x - radius1, img.shape[1] - center_x - radius1)
y_borders = (max(center_y - radius1,0), max(img.shape[0] - center_y - radius1, 0))
imgres = util.crop(img, (y_borders, x_borders, (0,0)))
maskT = util.crop(mask, (y_borders, x_borders))
border_pixels = np.sum(1 - maskT)
return imgres, maskT, center_x, center_y, radius
def _mean_std(image, w):
"""Return local mean and standard deviation of each pixel using a
neighborhood defined by a rectangular window with size w times w.
The algorithm uses integral images to speedup computation. This is
used by threshold_niblack and threshold_sauvola.
Parameters
----------
image : ndarray
Input image.
w : int
Odd window size (e.g. 3, 5, 7, ..., 21, ...).
Returns
-------
m : 2-D array of same size of image with local mean values.
s : 2-D array of same size of image with local standard
deviation values.
References
----------
.. [1] F. Shafait, D. Keysers, and T. M. Breuel, "Efficient
implementation of local adaptive thresholding techniques
using integral images." in Document Recognition and
Retrieval XV, (San Jose, USA), Jan. 2008.
DOI:10.1117/12.767755
"""
if w == 1 or w % 2 == 0:
raise ValueError("Window size w = %s must be odd and greater than 1." %
w)
left_pad = w // 2 + 1
right_pad = w // 2
padded = np.pad(image.astype('float'), (left_pad, right_pad),
mode='reflect')
padded_sq = padded * padded
integral = integral_image(padded)
integral_sq = integral_image(padded_sq)
kern = np.zeros((w + 1, ) * image.ndim)
for indices in itertools.product(*([[0, -1]] * image.ndim)):
kern[indices] = (-1)**(image.ndim % 2 != np.sum(indices) % 2)
sum_full = ndi.correlate(integral, kern, mode='constant')
m = util.crop(sum_full, (left_pad, right_pad)) / (w**image.ndim)
sum_sq_full = ndi.correlate(integral_sq, kern, mode='constant')
g2 = util.crop(sum_sq_full, (left_pad, right_pad)) / (w**image.ndim)
s = np.sqrt(g2 - m * m)
return m, s
def test_cropped_camera_image():
image = crop(camera(), ((206, 206), (206, 206)))
assert_allclose(frangi(image), np.zeros((100, 100)), atol=1e-03)
assert_allclose(frangi(image, black_ridges=True),
np.zeros((100, 100)),
atol=1e-03)
assert_allclose(hessian(image), np.ones((100, 100)), atol=1 - 1e-07)
def contour_to_binary_mask(filename, mask_shape):
current_path = os.path.abspath(os.path.dirname(__file__))
image_path = os.path.join(current_path, filename)
image = io.imread(image_path)
image_gray = color.rgb2gray(image)
# Deleting white spaces around certain contour images
binary_image = image_gray < 0.996
y_crop = np.nonzero(binary_image)[0][0]
x_crop = np.nonzero(binary_image)[1][0]
cropped_image = crop(image, ((y_crop, y_crop), (x_crop, x_crop), (0, 0)))
cropped_image = resize(cropped_image, mask_shape,
preserve_range=True).astype(np.uint8)
cropped_gray = color.rgb2gray(cropped_image)
edge_mask = np.zeros(cropped_gray.shape)
size_y = np.shape(cropped_image)[0]
size_x = np.shape(cropped_image)[1]
# A condition for thresholding found empirically
for y in range(size_y):
for x in range(size_x):
if abs(int(cropped_image[y, x][0]) -
int(cropped_image[y, x][1])) > 10.0:
edge_mask[y, x] = 1
binary_contour = nd.morphology.binary_fill_holes(edge_mask).astype(int)
return binary_contour
def original2patch(item):
inPath = os.path.join(item.inPath, item.ID)
files = sorted(glob.glob(os.path.join(inPath, '*.tif')))
for fileName in files:
basename = os.path.splitext(fileName)[0]
basename = os.path.basename(basename)
# print(basename)
img = imread(fileName)
print(basename, img.shape)
width = img.shape[1]
height = img.shape[0]
i = 0
while i < 300:
rx = random.randint(0, width - wndSize)
ry = random.randint(0, height - wndSize)
xw = (rx, width - (rx + wndSize))
yw = (ry, height - (ry + wndSize))
zw = (0, 0)
imgC = crop(img, (yw, xw, zw))
# print(rx, ry, imgC.shape)
filename = os.path.join(item.outPath, basename + '_{0:06d}'.format(rx) + '_{0:06d}.png'.format(ry))
imsave(filename, imgC)
i += 1
def crop_pad_around(image, size):
"""
Pads or crops an image so that the image is of a given size.
Parameters
----------
image: numpy array,
3 channel numpy array to crop/pad.
size: tuple of integers,
size to achieve.
Returns
-------
A padded or croped version of image so that image
has a size of size.
"""
x, y, z = image.shape
x_pad_width = size[0] - x if size[0] > x else 0
y_pad_width = size[1] - y if size[1] > y else 0
if x_pad_width > 0 or y_pad_width > 0:
pad_width = [(x_pad_width, y_pad_width) for _ in range(2)]
pad_width +=[(0, 0)]
image = np.pad(image, pad_width, mode='constant')
x, y, z = image.shape
shapes = [x, y, z]
x_crop_width = x - size[0] if x > size[0] else 0
y_crop_width = y - size[1] if y > size[1] else 0
if x_crop_width > 0 or y_crop_width > 0:
crops = []
for i, c in enumerate([x_crop_width, y_crop_width]):
crop_v = np.random.randint(0, c) if c != 0 else 0
crops.append((crop_v, shapes[i] - size[i] - crop_v))
crops.append((0,0))
image = crop(image, crops, copy=True)
return image
def random_translation(image_array, limit):
seed = np.random.randint(limit, size=6)
choice = np.random.choice(2, 3)
rt = util.crop(copy=True,
ar=image_array,
crop_width=((seed[0], seed[1]), (seed[2], seed[3]),
(seed[4], seed[5])))
if (choice[0] == 0):
rt = util.pad(rt, ((seed[0] + seed[1], 0), (0, 0), (0, 0)), 'constant')
else:
rt = util.pad(rt, ((0, seed[0] + seed[1]), (0, 0), (0, 0)), 'constant')
if (choice[1] == 0):
rt = util.pad(rt, ((0, 0), (seed[2] + seed[3], 0), (0, 0)), 'constant')
else:
rt = util.pad(rt, ((0, 0), (0, seed[2] + seed[3]), (0, 0)), 'constant')
if (choice[2] == 0):
rt = util.pad(rt, ((0, 0), (0, 0), (seed[4] + seed[5], 0)), 'constant')
else:
rt = util.pad(rt, ((0, 0), (0, 0), (0, seed[4] + seed[5])), 'constant')
return rt
def extract_state(self, prev_frame, curr_frame):
diff_frame = rgb2gray(curr_frame) - rgb2gray(prev_frame)
#frame = rgb2gray(frame)
frame = crop(diff_frame, ((25, 10), (0,0)))
frame = resize(frame, (84, 84))
state = torch.from_numpy(frame).unsqueeze(0).unsqueeze(0).float()
return state
def preprocessFiles(in_dir, out_file, num_images=-1): files = os.listdir(in_dir) if num_images > 0: files = files[0:num_images] # This will store data for all images. datas = np.ndarray([len(files), DESIRED_SHAPE[0], DESIRED_SHAPE[1]], dtype=np.float16) for i, fname in enumerate(files): # Load image and convert to greyscale data = io.imread(os.path.join(in_dir, fname), as_grey=True) if data.shape[0] > data.shape[1]: # Make all images have width be the longest dimension. data = transform.rotate(data, 90, resize=True) # Scale images so that the shortest dimension matches the corresponding one in the # DESIRED_SHAPE. We do this so that later we can crop a bit of the longer axis and all # images the same dimensions without distortion. scale_factor = (float(DESIRED_SHAPE[1]) / float(data.shape[1]) if DESIRED_ASPECT_RATIO < float(data.shape[0]) / float(data.shape[1]) else float(DESIRED_SHAPE[0]) / float(data.shape[0])) data = transform.rescale(data, scale_factor) # Crop whatever is left on the long axis data = util.crop(data, [(0, data.shape[0] - DESIRED_SHAPE[0]), (0, data.shape[1] - DESIRED_SHAPE[1])]) datas[i] = data print '%s: %s, %0.5f' % (fname, data.shape, float(data.shape[0]) / float(data.shape[1])) with open(out_file, 'w') as f: f.write(datas.tobytes())
def random_augmentation(image,
blur_percent=0.5,
pepper_percent=0.5,
crop_percent=0.5,
rotation_percent=0.5):
pepper_sigma = np.random.normal(0.100, 0.010)
blur_sigma = np.random.normal(1, 0.1)
rotate_angle = np.random.normal(0, 5)
image_x = image.shape[0]
image_y = image.shape[1]
delta_x_percent = tuple(
[int(image_x * abs(np.random.normal(0, 0.03))) for _ in range(2)])
delta_y_percent = tuple(
[int(image_y * abs(np.random.normal(0, 0.03))) for _ in range(2)])
if random.random() < pepper_percent:
image = random_noise(image, var=pepper_sigma**2)
if random.random() < blur_percent:
image = gaussian(image, sigma=blur_sigma, multichannel=True)
if random.random() < rotation_percent:
image = rotate(image, angle=rotate_angle, mode='reflect')
if random.random() < crop_percent:
image = crop(image, (delta_x_percent, delta_y_percent, (0, 0)),
copy=False)
return image
def data_extend(image, angle, crop_num, prelim_size, final_size):
array_out = []
#import as 100x100 numpy array, perform distortions and resize to 32x32 to train
image = s.imprep(image, prelim_size)
image = np.reshape(image, [prelim_size, prelim_size]) #consider changing the imprep function because its so stupid
angles = np.linspace(-angle, angle, 2*angle+1)
for a in angles:
a = int(a)
image2 = rotate(image, a)
for i in range(abs(a)): #gets rid of black border from rotation, f(angle)
for j in range(prelim_size):
image2[i,j]=0.985
image2[j,i]=0.985
image2[prelim_size-1-i,j]=0.985
image2[j,prelim_size-1-i]=0.985
gradient = np.linspace(0.90, 1, 11)
for colour in gradient:
for i in range(prelim_size):
for j in range(prelim_size):
if image2[i,j]>0.5:
image2[i,j]=colour
for c in range(crop_num):
image3 = crop(image2, c)
image3 = resize(image3, (final_size, final_size))
array_out.append(image3)
array_out = np.array(array_out)
return array_out
def feature_visualization(feature, inp, steps):
"""
Images were optimized for 2560 steps in a color-decorrelated fourier-transformed space, using Adam at a learning
rate of 0.05. We used each of following transformations in the given order at each step of the optimization:
• Padding the input by 16 pixels to avoid edge artifacts
• Jittering by up to 16 pixels
• Scaling by a factor randomly selected from this list: 1, 0.975, 1.025, 0.95, 1.05
• Rotating by an angle randomly selected from this list; in degrees: -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5
• Jittering a second time by up to 8 pixels
• Cropping the padding
"""
shape = [d._value for d in inp.shape]
shape[0] = 1
x = np.random.randn(*shape) * .1
sess = K.get_session()
grad = K.gradients(feature, inp)
for i in range(steps):
g = sess.run(grad, {inp: x})[0]
g = filters.gaussian(g, sigma=1, multichannel=True, preserve_range=True)
o_size = g.shape
g = transform.rescale(g, np.random.choice([1, 0.975, 1.025, 0.95, 1.05]), multichannel=True)
g = util.crop(g, [g.shape[0] - o_size[0], g.shape[1] - o_size[1], g.shape[2] - o_size[2]])
x += g
return x
def randomZoom(image, mask, m, u=0.5):
"""
Randomly zoom the image and mask by trimming the top, bottom, left and right.
Args:
image: the iamge
mask: the mask
m: the maximum to trim from the top, bottom, left and right
u: the augmentation probability
"""
if random() < u:
original_shape = image.shape
c = ((randint(0,m),randint(0,m)),(randint(0,m),randint(0,m)),(0,0))
image = resize(crop(image, crop_width=c), original_shape)
mask = resize(crop(mask, crop_width=c), original_shape)
return image, mask
def _translate_one_array(one_array, seed, choice, pad_val=0):
rt = util.crop(copy=True,
ar=one_array,
crop_width=((seed[0], seed[1]), (seed[2], seed[3]),
(seed[4], seed[5])))
if (choice[0] == 0):
rt = util.pad(rt, ((seed[0] + seed[1], 0), (0, 0), (0, 0)),
'constant',
constant_values=pad_val)
else:
rt = util.pad(rt, ((0, seed[0] + seed[1]), (0, 0), (0, 0)),
'constant',
constant_values=pad_val)
if (choice[1] == 0):
rt = util.pad(rt, ((0, 0), (seed[2] + seed[3], 0), (0, 0)),
'constant',
constant_values=pad_val)
else:
rt = util.pad(rt, ((0, 0), (0, seed[2] + seed[3]), (0, 0)),
'constant',
constant_values=pad_val)
if (choice[2] == 0):
rt = util.pad(rt, ((0, 0), (0, 0), (seed[4] + seed[5], 0)),
'constant',
constant_values=pad_val)
else:
rt = util.pad(rt, ((0, 0), (0, 0), (0, seed[4] + seed[5])),
'constant',
constant_values=pad_val)
return rt
def preprocess(image): # color 210 x 160
preproc_img = color.rgb2gray(image) # gray 210 x 160
preproc_img = crop(preproc_img, ((150), (0)))
preproc_img = resize(preproc_img,
output_shape=(IMG_WIDTH, IMG_HEIGHT),
anti_aliasing=False) # 110 x 84
return preproc_img
def __call__(self, image):
pad_or_crop = np.random.randint(2)
if pad_or_crop:
# pad
margin_top, margin_bottom, margin_left, margin_right = np.random.randint(
self.max_margin + 1, size=4)
padded_image = pad(image, ((margin_top, margin_bottom),
(margin_left, margin_right)),
mode='constant',
constant_values=1.0)
resized_image = resize(padded_image,
image.shape,
mode='constant',
anti_aliasing=True)
else:
# crop
margin_top, margin_bottom, margin_left, margin_right = np.random.randint(
self.max_margin + 1, size=4)
cropped_image = crop(image, ((margin_top, margin_bottom),
(margin_left, margin_right)),
copy=True)
resized_image = resize(cropped_image,
image.shape,
mode='constant',
anti_aliasing=True)
return resized_image
def _center_crop(image):
x_start = int((image.shape[0] - IMAGE_DIMS[0]) / 2)
y_start = int((image.shape[1] - IMAGE_DIMS[1]) / 2)
x_end = image.shape[0] - (IMAGE_DIMS[0] + x_start)
y_end = image.shape[1] - (IMAGE_DIMS[1] + y_start)
return util.crop(image, ((x_start, x_end), (y_start, y_end)), copy=True)
def deconvolve(self, image, iterations):
## Code for deconvolution
psf = ai.airy_psf(self.window, self.psf_radius)
image = np.pad(image, int(psf.shape[0] / 2), mode="reflect")
deconvolved = restoration.richardson_lucy(image, psf, iterations,
False)
deconvolved = util.crop(deconvolved, int(psf.shape[0] / 2))
return deconvolved
def crop_center(image):
if not is_more_than_720(image) :
return []
height, width, _ = image.shape
if width < height: # very rare case
return []
x_margin = width - height
return skutil.crop(image,((0, 0), (x_margin/2, x_margin/2), (0, 0)))
def load_image_data(filelist, perc_tr=0.7, im_func=None):
"""
-load in the images as numpy arrays and return the following:
1. a numpy array of shape (perc_tr*len(filelist), 1, 48, 48) that contains the training image data
2. a numpy array of shape (perc_tr*len(filelist),) that contains the training emotion code for each training image
3. a numpy array of shape ((1-perc_tr)*len(filelist), 1, 48, 48) that contains the test image data
4. a numpy array of shape ((1-perc_tr)*len(filelist), ) that contains the test emotion codes for each test image
:param filelist:
:return:
"""
if perc_tr < 0.5:
print "Training subset percent too low, defaulting to 50%..."
perc_tr = 0.5
# subset the list of file names into training and testing
# note that the file names have been shuffled randomly before coming
# in to the function
tr_len = int(round(perc_tr * len(filelist)))
te_len = len(filelist) - tr_len
training = filelist[:tr_len]
test = filelist[tr_len:]
# initialize empty arrays to hold the training and testing image data
# we are cropping the images to 42 X 42
imgs_train = np.empty(shape=(tr_len, 1, 42, 42), dtype=np.float32)
emos_train = np.empty(shape=tr_len, dtype=np.int8)
imgs_test = np.empty(shape=(te_len, 1, 42, 42), dtype=np.float32)
emos_test = np.empty(shape=te_len, dtype=np.int8)
# populate the training arrays
for i in range(len(training)):
img = imread(training[i], as_grey=True)
img_crop = crop(img, crop_width=3)
img_crop = im_func(img_crop)
imgs_train[i, 0] = img_crop
emos_train[i] = get_emo_int(training[i])
# populate the test arrays
for i in range(len(test)):
img = imread(test[i], as_grey=True)
img_crop = crop(img, crop_width=3)
img_crop = im_func(img_crop)
imgs_test[i, 0] = img_crop
emos_test[i] = get_emo_int(test[i])
return imgs_train, emos_train, imgs_test, emos_test
def crop_center(image):
if not is_more_than_720(image):
return []
height, width, _ = image.shape
if width < height: # very rare case
return []
x_margin = width - height
return skutil.crop(image, ((0, 0), (x_margin / 2, x_margin / 2), (0, 0)))
def load_image_data(filelist, perc_tr=0.7, im_func=None):
"""
-load in the images as numpy arrays and return the following:
1. a numpy array of shape (perc_tr*len(filelist), 1, 48, 48) that contains the training image data
2. a numpy array of shape (perc_tr*len(filelist),) that contains the training emotion code for each training image
3. a numpy array of shape ((1-perc_tr)*len(filelist), 1, 48, 48) that contains the test image data
4. a numpy array of shape ((1-perc_tr)*len(filelist), ) that contains the test emotion codes for each test image
:param filelist:
:return:
"""
if perc_tr < 0.5:
print "Training subset percent too low, defaulting to 50%..."
perc_tr = 0.5
# subset the list of file names into training and testing
# note that the file names have been shuffled randomly before coming
# in to the function
tr_len = int(round(perc_tr * len(filelist)))
te_len = len(filelist) - tr_len
training = filelist[:tr_len]
test = filelist[tr_len:]
# initialize empty arrays to hold the training and testing image data
# we are cropping the images to 42 X 42
imgs_train = np.empty(shape=(tr_len, 1, 42, 42), dtype=np.float32)
emos_train = np.empty(shape=tr_len, dtype=np.int8)
imgs_test = np.empty(shape=(te_len, 1, 42, 42), dtype=np.float32)
emos_test = np.empty(shape=te_len, dtype=np.int8)
# populate the training arrays
for i in range(len(training)):
img = imread(training[i], as_grey=True)
img_crop = crop(img, crop_width=3)
img_crop = im_func(img_crop)
imgs_train[i, 0] = img_crop
emos_train[i] = get_emo_int(training[i])
# populate the test arrays
for i in range(len(test)):
img = imread(test[i], as_grey=True)
img_crop = crop(img, crop_width=3)
img_crop = im_func(img_crop)
imgs_test[i, 0] = img_crop
emos_test[i] = get_emo_int(test[i])
return imgs_train, emos_train, imgs_test, emos_test
def image_crop(img, img_size_ori=101, img_size_target=img_size_t):
if img_size_ori == img_size_target:
return img
crop_width = ((int(np.floor((img_size_t-img_size_ori)/2)),
int(np.ceil((img_size_t-img_size_ori)/2))),
(int(np.floor((img_size_t-img_size_ori)/2)),
int(np.ceil((img_size_t-img_size_ori)/2))))
out_image = util.crop(img, crop_width)
return out_image
def split_letters(image, num_letters=4, debug=False):
"""
split full captcha image into `num_letters` lettersself.
return list of letters binary image (0: white, 255: black)
"""
# move left
left = crop(image, ((0, 0), (0, image.shape[1] - SHIFT_PIXEL)), copy=True)
image[:, :-SHIFT_PIXEL] = image[:, SHIFT_PIXEL:]
image[:, -SHIFT_PIXEL:] = left
# binarization
binary = image > BINARY_THRESH
# find contours
# contours = find_contours(binary, 0.5)
# contours = [[
# [int(floor(min(contour[:, 1]))), int(floor(min(contour[:, 0])))], # top-left point
# [int(ceil(max(contour[:, 1]))), int(ceil(max(contour[:, 0])))] # down-right point
# ] for contour in contours]
# # keep letters order
# contours = sorted(contours, key=lambda contour: contour[0][0])
# # find letters box
# letter_boxs = []
letter_boxs = [[[0, 7], [11, 24]], [[13, 5], [30, 30]], [[30, 5], [45,
29]],
[[47, 4], [61, 28]]]
# for contour in contours:
# if len(letter_boxs) > 0 and contour[0][0] < letter_boxs[-1][1][0] - 5:
# # skip inner contour
# continue
# # extract letter boxs by contour
# boxs = get_letter_boxs(binary, contour)
# for box in boxs:
# letter_boxs.append(box)
# check letter outer boxs number
if len(letter_boxs) != num_letters:
print('ERROR: number of letters is NOT valid', len(letter_boxs))
# debug
if debug:
print(letter_boxs)
plt.imshow(binary, interpolation='nearest', cmap=plt.cm.gray)
for [x_min, y_min], [x_max, y_max] in letter_boxs:
plt.plot([x_min, x_max, x_max, x_min, x_min],
[y_min, y_min, y_max, y_max, y_min],
linewidth=2)
plt.xticks([])
plt.yticks([])
plt.show()
return None
# normalize size (40x40)
letters = []
for [x_min, y_min], [x_max, y_max] in letter_boxs:
letter = resize(image[y_min:y_max, x_min:x_max], LETTER_SIZE)
letter = img_as_ubyte(letter < 0.6)
letters.append(letter)
return letters
def detect_face(data):
img = read_image(data)
dets, scores, idx = detector.run(img, 1)
scores = list(scores)
best = dets[scores.index(max(scores))]
x,y,c = img.shape
c_img = util.crop(img, ((best.top(), y - best.bottom()), (best.left(), x - best.right()), (0,0)))
s = write_image(c_img)
return s
def slightRotation(img, degrees):
height = img.shape[0]
width = img.shape[1]
channels = img.shape[2]
rotation = trans.rotate(img, degrees)
margin = np.ceil(np.abs((img.shape[0] * np.pi * degrees) / 360)) / 2
rotation = crop(rotation, ((margin, margin), (margin, margin), (0, 0)))
rotation = trans.resize(rotation, (height, width, channels))
return rotation
def crop_manual(img):
'''
Set the "threshold" & crop based on when we have reached it
'''
threshold = 20000
s = np.sum(img, axis=2)
cols = np.sum(s, axis=0) > threshold
rows = np.sum(s, axis=1) > threshold
left_border = np.argmax(cols[0:len(cols)/2])
right_border = np.argmax(cols[len(cols)-1:len(cols)/2:-1])
upper_border = np.argmax(rows[0:len(rows)/2])
lower_border = np.argmax(rows[len(rows)-1:len(rows)/2:-1])
return crop(img, ((upper_border, lower_border),(left_border, right_border), (0,0)))
def fill_holes_with_contour_filling(gray_mask,inverse=False):
'''
Input: Grayscale image
Returns: Image with holes filled by contour mapping
'''
filled = gray_mask.copy()
filled = pad(filled,((5,5),(0,0)),'constant',constant_values=255)
if inverse:
filled = cv2.bitwise_not(filled)
image, contour, _ = cv2.findContours(filled, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
for cnt in contour:
cv2.drawContours(filled,[cnt], 0, 255, -1)
if inverse:
filled = cv2.bitwise_not(filled)
filled = crop(filled,((5,5),(0,0)))
return filled
def crop_ronc(self, ronc):
"""
Crop the input Ronchigram by the specified ammount using the specified method.
Parameters
----------
ronc : numpy.array
Input image to be cropped.
Returns
-------
cropped_ronc : numpy.array
Cropped image
"""
if self.crop_ammount is None:
return ronc
crop_ammount = self.crop_ammount
crop_method = self.crop_method
if crop_method == 'percent':
crop_ammount = np.round(np.atleast_2d(crop_ammount) / 100.0 * ronc.shape)
crop_ammount = tuple([tuple(row) for row in crop_ammount.astype(np.uint32)])
elif crop_method == 'absolute':
if isinstance(crop_ammount, int):
crop_ammount = ((crop_ammount,), (crop_ammount,))
elif len(crop_ammount) == 2:
crop_ammount = ((crop_ammount[0],), (crop_ammount[1],))
elif len(crop_ammount) == 4:
crop_ammount = ((crop_ammount[0], crop_ammount[1]), (crop_ammount[2], crop_ammount[3]))
else:
raise ValueError('The crop_ammount should be an integer or list of 2 or 4 integers.')
else:
raise ValueError('Allowed values of crop_method are percent and absolute.')
crop_ammount = ((0,), (0,)) + crop_ammount
cropped_ronc = crop(ronc, crop_ammount)
if any([dim == 0 for dim in cropped_ronc.shape]):
warn("Requested crop ammount is greater than the image size. No cropping will be done.")
return ronc
return cropped_ronc
def mkbox( mask_seg ):
''' Crop the non_zeros pixels of an image to a new image
and pad with zeros to make double the new image size
to avoid periodic artifacts from the FFT at correlation lengths.
'''
pxlst = np.where(mask_seg.ravel())[0]
dims = mask_seg.shape
imgwidthy = dims[1] #dimension in y, but in plot being x
imgwidthx = dims[0] #dimension in x, but in plot being y
#x and y are flipped???
#matrix notation!!!
pixely = pxlst%imgwidthy
pixelx = pxlst//imgwidthy
minpixelx = np.min(pixelx)
minpixely = np.min(pixely)
maxpixelx = np.max(pixelx)
maxpixely = np.max(pixely)
img_crop = crop( mask_seg, ((minpixelx, imgwidthx - maxpixelx -1 ),
(minpixely, imgwidthy - maxpixely -1 )) )
widthx = maxpixelx - minpixelx + 1
widthy = maxpixely - minpixely + 1
oldimg = np.zeros(dims)
oldimg.ravel()[pxlst] = 1
#img_crop = np.copy(oldimg[minpixelx:maxpixelx+1,minpixely:maxpixely+1])
#subpxlst = np.where(img_crop.ravel() != 0)[0]
#print("min/maxpixelx: {},{};min/maxpixely: {},{}".format(minpixelx,maxpixelx,minpixely,maxpixely))
#padx = 2**np.int( np.ceil( log(widthx +20 )/log(2) ) ) - widthx
#pady = 2**np.int( np.ceil( log(widthy +20)/log(2) ) ) -widthy
padx = 2**np.int( np.ceil( log(widthx*3)/log(2) ) ) - widthx
pady = 2**np.int( np.ceil( log(widthy*3)/log(2) ) ) -widthy
img_pad = pad( img_crop, ((0, padx),(0, pady ) ) ,'constant', constant_values=(0,0))
subpxlst = np.where(img_pad.ravel() != 0)[0]
return np.array(subpxlst,dtype = np.int32), np.array(img_pad,dtype = np.int32)
def crop_to_fit(image, shape):
"""
Return a copy of image resized and cropped to precisely fill a shape.
To resize a colored 2D image, pass in a shape with two entries. When
``len(shape) < image.ndim``, higher dimensions are ignored.
Parameters
----------
image : array
shape : tuple
e.g., ``(height, width)`` but any length <= ``image.ndim`` is allowed
Returns
-------
cropped_image : array
"""
# Resize smallest dimension (width or height) to fit.
d = np.argmin(np.array(image.shape)[:2] / np.array(shape))
enlarged_shape = (
tuple(np.ceil(np.array(image.shape[: len(shape)]) * shape[d] / image.shape[d])) + image.shape[len(shape) :]
)
resized = resize(image, enlarged_shape)
# Now the image is as large or larger than the shape along all dimensions.
# Crop any overhang in the other dimension.
crop_width = []
for actual, target in zip(resized.shape, shape):
overflow = actual - target
# Center the image and crop, biasing left if overflow is odd.
left_margin = np.floor(overflow / 2)
right_margin = np.ceil(overflow / 2)
crop_width.append((left_margin, right_margin))
# Do not crop any additional dimensions beyond those given in shape.
for _ in range(resized.ndim - len(shape)):
crop_width.append((0, 0))
cropped = crop(resized, crop_width)
return cropped
def get_our_images(imdir):
"""
-resize our images to 48X48 which is what the model is expecting
:param imdir:
:return:
"""
jacob = imdir + "HJACOB.png"
alex = imdir + "HALEX.png"
david = imdir + "HDAVID.png"
yannet = imdir + "HYANNET.png"
james = imdir + "HJAMES.png"
paul = imdir + "DPAUL.png"
# load us in
X_jacob_orig = io.imread(jacob, as_grey=True)
X_alex_orig = io.imread(alex, as_grey=True)
X_david_orig = io.imread(david, as_grey=True)
X_yannet_orig = io.imread(yannet, as_grey=True)
X_james_orig = io.imread(james, as_grey=True)
X_paul_orig = io.imread(paul, as_grey=True)
# resize to 48 X 48
X_jacob_orig = transform.resize(X_jacob_orig, [48, 48])
X_alex_orig = transform.resize(X_alex_orig, [48, 48])
X_david_orig = transform.resize(X_david_orig, [48, 48])
X_yannet_orig = transform.resize(X_yannet_orig, [48, 48])
X_james_orig = transform.resize(X_james_orig, [48, 48])
X_paul_orig = transform.resize(X_paul_orig, [48, 48])
# smooth the image a tiny bit
X_jacob_orig = ndimage.median_filter(X_jacob_orig, 1.5)
X_alex_orig = ndimage.median_filter(X_alex_orig, 1.5)
X_david_orig = ndimage.median_filter(X_david_orig, 1.5)
X_yannet_orig = ndimage.median_filter(X_yannet_orig, 1.5)
X_james_orig = ndimage.median_filter(X_james_orig, 1.5)
X_paul_orig = ndimage.median_filter(X_paul_orig, 1.5)
# crop the image down to 42 X 42
X_jacob_orig = crop(X_jacob_orig, crop_width=3)
X_alex_orig = crop(X_alex_orig, crop_width=3)
X_david_orig = crop(X_david_orig, crop_width=3)
X_yannet_orig = crop(X_yannet_orig, crop_width=3)
X_james_orig = crop(X_james_orig, crop_width=3)
X_paul_orig = crop(X_paul_orig, crop_width=3)
# rescale the image to [0, 1] scale
X_jacob = rescale_image(X_jacob_orig)
X_alex = rescale_image(X_alex_orig)
X_david = rescale_image(X_david_orig)
X_yannet = rescale_image(X_yannet_orig)
X_james = rescale_image(X_james_orig)
X_paul = rescale_image(X_paul_orig)
originals = np.empty(shape=(6, 1, 42, 42))
our_test = np.empty(shape=(6, 1, 42, 42), dtype=np.float32)
our_emos = np.empty(shape=6, dtype=np.int8)
# original images resized
originals[0, 0] = X_jacob_orig
# io.imshow(originals[0,0])
# io.show()
originals[1, 0] = X_alex_orig
originals[2, 0] = X_david_orig
originals[3, 0] = X_yannet_orig
originals[4, 0] = X_james_orig
originals[5, 0] = X_paul_orig
# inputs
our_test[0, 0] = X_jacob
our_test[1, 0] = X_alex
our_test[2, 0] = X_david
our_test[3, 0] = X_yannet
our_test[4, 0] = X_james
our_test[5, 0] = X_paul
# input classifications
our_emos[0,] = 0
our_emos[1,] = 0
our_emos[2,] = 0
our_emos[3,] = 1
our_emos[4,] = 0
our_emos[5,] = 1
return originals, our_test, our_emos
def _gen_batches():
num_samples = len(self.X)
idx = np.random.permutation(num_samples)
batches = range(0, num_samples - self.batchsize + 1, self.batchsize)
for batch in batches:
X_batch = self.X[idx[batch:batch + self.batchsize]]
y_batch = self.y[idx[batch:batch + self.batchsize]]
# set empty copy to hold augmented images so that we don't overwrite
X_batch_aug = np.empty(shape = (X_batch.shape[0], 3, cropPIXELS, cropPIXELS), dtype = 'float32')
# random rotations betweein -8 and 8 degrees
dorotate = randint(-30,30)
# random translations
trans_1 = randint(-12,12)
trans_2 = randint(-12,12)
crop_amt = ((12 - trans_1, 12 + trans_1), (12 - trans_2, 12 + trans_2), (0,0))
# random zooms
zoom = uniform(1, 1.3)
# shearing
shear_deg = uniform(-10, 10)
# set the transform parameters for skimage.transform.warp
# have to shift to center and then shift back after transformation otherwise
# rotations will make image go out of frame
center_shift = np.array((PIXELS, PIXELS)) / 2. - 0.5
tform_center = transform.SimilarityTransform(translation=-center_shift)
tform_uncenter = transform.SimilarityTransform(translation=center_shift)
tform_aug = transform.AffineTransform(rotation = np.deg2rad(dorotate),
#translation = (trans_1, trans_2),
shear = np.deg2rad(shear_deg),
scale = (1/zoom, 1/zoom))
tform = tform_center + tform_aug + tform_uncenter
r_intensity = randint(0,1)
g_intensity = randint(0,1)
b_intensity = randint(0,1)
intensity_scaler = uniform(-0.25, 0.25)
# images in the batch do the augmentation
for j in range(X_batch.shape[0]):
img = X_batch[j]
img = img.transpose(1, 2, 0)
img_aug = np.zeros((PIXELS, PIXELS, 3))
for k in range(0,3):
img_aug[:, :, k] = fast_warp(img[:, :, k], tform, output_shape = (PIXELS, PIXELS))
img_aug = crop(img_aug, crop_amt)
if r_intensity == 1:
img_aug[:, :, 0] = img_aug[:, :, 0] + (np.std(img_aug[:, :, 0]) * intensity_scaler)
if g_intensity == 1:
img_aug[:, :, 1] = img_aug[:, :, 1] + (np.std(img_aug[:, :, 1]) * intensity_scaler)
if b_intensity == 1:
img_aug[:, :, 2] = img_aug[:, :, 2] + (np.std(img_aug[:, :, 2]) * intensity_scaler)
X_batch_aug[j] = img_aug.transpose(2, 0, 1)
# Flip half of the images in this batch at random:
bs = X_batch.shape[0]
indices = np.random.choice(bs, bs / 2, replace=False)
X_batch_aug[indices] = X_batch_aug[indices, :, :, ::-1]
yield [X_batch_aug, y_batch]
def test_multi_crop():
arr = np.arange(45).reshape(9, 5)
out = crop(arr, ((1, 2), (2, 1)))
assert_array_equal(out[0], [7, 8])
assert_array_equal(out[-1], [32, 33])
assert_equal(out.shape, (6, 2))
def test_int_crop():
arr = np.arange(45).reshape(9, 5)
out = crop(arr, 1)
assert_array_equal(out[0], [6, 7, 8])
assert_array_equal(out[-1], [36, 37, 38])
assert_equal(out.shape, (7, 3))
def test_pair_crop():
arr = np.arange(45).reshape(9, 5)
out = crop(arr, (1, 2))
assert_array_equal(out[0], [6, 7])
assert_array_equal(out[-1], [31, 32])
assert_equal(out.shape, (6, 2))
def crop_images(images, shape):
left, top, right, bottom = shape
crop_width = ((0, 0),(top, bottom),(left, right),(0, 0))
return util.crop(images.reshape((1, images.shape[0], images.shape[1], images.shape[2])), crop_width)
def test_cropped_camera_image():
image = crop(camera(), ((206, 206), (206, 206)))
assert_allclose(frangi(image), np.zeros((100, 100)), atol=1e-03)
assert_allclose(frangi(image, black_ridges=True),
np.zeros((100, 100)), atol=1e-03)
assert_allclose(hessian(image), np.ones((100, 100)), atol=1-1e-07)
def test_zero_crop():
arr = np.arange(45).reshape(9, 5)
out = crop(arr, 0)
assert out.shape == (9, 5)
