def print_map(self):
ocmap = []
for line in self.occupancy_map:
ocmap.append([cell[0] for cell in line])
npmap = np.array(ocmap)
toimage(npmap, cmin=0.0, cmax=1.0).save('occupancy_map.jpeg')
def _data_images():
""" Make images with linear scaling, sqrt scaling, log scaling.
This might take a little bit of time. """
mag, phase = np.abs(self.rs_data), np.angle(self.rs_data)
mag *= self.blocker
# resize, then save the color and scale permutations of first_frame.
linr = mag
sqrt = np.sqrt(mag)
logd = np.log((mag-mag.min())/mag.max()*1000+1)
self.rs_image_linr = io.complex_hsv_image(linr)
self.rs_image_sqrt = io.complex_hsv_image(sqrt*np.exp(1j*phase))
self.rs_image_log = io.complex_hsv_image(logd*np.exp(1j*phase))
imgs = {'logd':smp.toimage(self.rs_image_log),
'sqrt':smp.toimage(self.rs_image_sqrt),
'linr':smp.toimage(self.rs_image_linr)}
base = './static/imaging/images/ifth_session%s_id%s_%s_%s.%s'
for key, val in imgs.items():
val.save(base%(self.session_id, self.data_id,
self.blocker_power, key, 'png'))
val.save(base%(self.session_id, self.data_id,
self.blocker_power, key, 'jpg'))
def detect_blur(f):
print "Processing %s" % f
im = Image.open("images/orig_"+f+".jpeg")
im = im.convert('F')
im = array(im)
im = filters.laplace(im)
laplacien_im = im
toimage(im).save(out_dir+"/laplacien_"+f+".png", "png")
im = morphology.white_tophat(im, (3,3))
tophat_im = im
toimage(im).save(out_dir+"/tophat_"+f+".png", "png")
im = filters.percentile_filter(im, 30, 1)
toimage(im).save(out_dir+"/final_"+f+".png", "png")
subprocess.call("/usr/local/bin/convert %s/final_%s.png %s/ppm_final_%s.ppm" % (out_dir, f, out_dir,f), shell=True)
subprocess.call("./segment/segment 0.8 100 100 %s/ppm_final_%s.ppm %s/segment_%s.ppm" % (out_dir, f, out_dir,f), shell=True)
im_seg = Image.open("%s/segment_%s.ppm" % (out_dir, f))
im_seg = array(im_seg)
print "laplacien"
laplacian_mask = build_mask(im_seg, laplacien_im)
toimage(laplacian_mask).save(out_dir+"/laplacian_mask_"+f+".png", "png")
print "tophat"
tophat_mask = build_mask(im_seg, tophat_im, 10)
toimage(tophat_mask).save(out_dir+"/tophat_mask_"+f+".png", "png")
def pairdump():
"可视化模型列"
from layerbase import DrawPatch
import cPickle
sparsedirect = cPickle.load(file('sparselinked','rb'))
drec = sparsedirect.components_.reshape((-1,1,70-25,90-0))
misc.toimage(DrawPatch(drec)).save('dictpair.jpg')
def arrayVisualizeS1(self): # Used to plot all of the arrays
for a,n in zip(self.S1.arrays,range(12)): # Plots all of the S1 arrays
array = a.arr
ph = np.empty(array.shape)
for i in range(array.shape[0]):
for j in range(array.shape[1]):
ph[i,j]= array[i,j].output
img = toimage(ph)
plt.subplot(5,3,n+1)
plt.imshow(img)
plt.title('S1_{0}'.format(n+1))
ph = np.empty(self.C0.arrays[0].arr.shape)
for i in range(self.C0.arrays[0].arr.shape[0]): # Plots the input array
for j in range(self.C0.arrays[0].arr.shape[1]):
ph[i,j]= self.C0.arrays[0].arr[i,j].output
img = toimage(ph)
plt.subplot(5,3,13)
for i in range(self.V0.arrays[0].arr.shape[0]): # Plots the inhibitory array
for j in range(self.V0.arrays[0].arr.shape[1]):
ph[i,j]= self.V0.arrays[0].arr[i,j].output
img = toimage(ph)
plt.subplot(5,3,13)
plt.imshow(img)
# plt.subplot(5,3,13)
plt.title('C0')
plt.tight_layout()
plt.draw()
def retrieve(self, request, pano_id=None, heading=0):
target_heading = get_int_value(
request, 'heading', default=heading, upper=360, strategy='modulo')
target_width = get_int_value(
request, 'width', default=750,
lower=1, upper=1600, strategy='cutoff')
target_fov = get_int_value(
request, 'fov', default=80, upper=120, strategy='cutoff')
target_width, target_fov = self._max_fov_per_width(target_width, target_fov)
target_horizon = get_float_value(
request, 'horizon', default=0.3, lower=0.0, upper=1.0)
target_aspect = get_float_value(
request, 'aspect', default=1.5, lower=1.0)
pano = get_object_or_404(Panoramas, pano_id=pano_id)
thumb = Thumbnail(pano)
thumb_img = thumb.get_image(target_width=target_width,
target_fov=target_fov,
target_horizon=target_horizon,
target_heading=target_heading,
target_aspect=target_aspect)
response = HttpResponse(content_type="image/jpeg")
misc.toimage(thumb_img).save(response, "JPEG")
return response
def rgbtiffstojpg(files, path, name):
'''
files: a list of files ordered as follow. 0: Blue Band 1: Green Band 2: Red Band
path: the path to look for the tiff files and to save the jpg
'''
import scipy.misc as sm
import gdal
import numpy as np
b2_link = gdal.Open(path+"/tiff/"+files[0])
b3_link = gdal.Open(path+"/tiff/"+files[1])
b4_link = gdal.Open(path+"/tiff/"+files[2])
# call the norm function on each band as array converted to float
def norm(band):
band_min, band_max = band.min(), band.max()
return ((band - band_min) / (band_max - band_min))
b2 = norm(b2_link.ReadAsArray().astype(np.float))
b3 = norm(b3_link.ReadAsArray().astype(np.float))
b4 = norm(b4_link.ReadAsArray().astype(np.float))
# Create RGB
rgb = np.dstack((b4, b3, b2))
del b2, b3, b4
sm.toimage(rgb, cmin=np.percentile(rgb,2), cmax=np.percentile(rgb,98)).save(path+'/images/'+name)
def overlay_predictions(image, im_softmax,image_shape, threshold, channel, seg_color=(0,255,0,172)):
"""creates a overlay using pixels with p(class) > threshold"""
segmentation = np.expand_dims(im_softmax[:,:,channel] > threshold, 2)
mask = segmentation * np.reshape(np.array(seg_color), (1,1,-1))
mask = misc.toimage(mask, mode="RGBA")
street_im = misc.toimage(image)
street_im.paste(mask, box=None, mask=mask)
return street_im
def test_jpg(w, img):
prev_i = 100
for i in range(prev_i, 0, -5):
print i,
misc.toimage(img).save("out.jpg", quality = i)
if w.extract(misc.imread("out.jpg")) is None:
return prev_i
prev_i = i
return "unbound"
def test_jpg(w, img):
prev_i = 100
for i in range(prev_i, 0, -5):
misc.toimage(img).save("out.jpg", quality = i)
try:
w.extract(misc.imread("out.jpg"))
except ReedSolomonError:
return prev_i
prev_i = i
return 5
def showtransform():
"显示神经网络转换结果"
trans = np.load('transform.npy')[:,0]
glassmodel = np.load('glassline.npy').astype('f').reshape((-1,45,90))
glassmodel /= np.max(glassmodel)
trans = np.insert(trans,2,glassmodel,axis=1)
p = trans[:,0].reshape((trans.shape[0],-1))
trans[:,0] -= ((p.max(axis=1)+p.mean(axis=1))*0.5).reshape((-1,1,1))
trans[:,0] = np.where(trans[:,0]>0, trans[:,0],0)
from layerbase import DrawPatch
misc.toimage(DrawPatch(trans,False,'bgy')).save('nntrans.png')
def reshapeAndPrint( components, fold ):
'''Takes in the vector encoding of each of the nmf components
and plots them to file'''
imHeight = 192
imWidth = 168
nComp = np.shape(components)[0]
for comp in range(nComp):
image = np.reshape(components[comp,:],(imHeight,imWidth),order='C')
savestring = './Results/images/nComp' + str(nComp) + \
'_comp' + str(comp) + '_fold' + str(fold) + '.jpg'
misc.toimage(image, cmin=0.0, cmax=...).save(savestring)
def png_buffer(array):
"""Convert an array to PNG, handling transparency in an
at-least-partially-sane manner."""
assert array.ndim == 2
im = toimage(array)
alpha = toimage(array != 0)
im.putalpha(alpha)
# Return format is a buffer of PNG-encoded data
fp = BytesIO()
im.save(fp, format='png')
return fp.getbuffer()
def main():
image = bayer("itau02.png")
template = bayer("keyboard.png")
correlation = correlate(image, template)
c_max = max(correlation)
c_min = min(correlation)
signal = (correlation - c_min) * 255 / (c_max - c_min)
toimage(signal).save("signal.png")
winner = where(correlation == c_max, 255, 0)
toimage(winner).save("winner.png")
print searchmax(winner)
def get_phase(args):
filename = args[0]
path = args[1]
path_raw = args[2]
path_images = args[3]
mask = args[4]
coord = args[5]
file_in = os.path.join(path,filename)
file_raw = os.path.join(path_raw,'raw_'+filename)
image_phase = os.path.join(path_images,'wrapped'+filename[4:11]+'bmp')
binary_phase = os.path.join(path_raw,'wrapped'+filename[4:11]+'dat')
mod_arr = os.path.join(path_raw,'mod'+filename[4:11]+'dat')
mod_image = os.path.join(path_images,'mod'+filename[4:11]+'bmp')
qual_arr = os.path.join(path_raw,'qual'+filename[4:11]+'dat')
qual_image = os.path.join(path_images,'qual'+filename[4:11]+'bmp')
# Open meas file and grab dataset
try:
f = File(file_in, 'r')
except:
print 'Corrupt h5 file: '+filename+' ignoring'
return
sub = f.get(r'measurement0/frames/frame_full/data')
data = np.array(sub[coord[0]-1:coord[1]+1,coord[2]-1:coord[3]+1],'f')
f.close()
# Get phase
phase, modulation, intensity = calc_phase(data)
# Apply mask
phase[~mask] = 0
intensity[~mask] = 0
modulation[~mask] = 0
#phase = phase[coord[0]:coord[1],coord[2]:coord[3]]
# Save phase
toimage(phase).save(image_phase)
phase.tofile(binary_phase)
ave_mod = np.average(modulation[mask])
ave_int = np.average(intensity[mask])
'''
if ave_mod < 0.6:
print filename+' low mod:', ave_mod
else:
sys.stdout.write('.')
'''
return "%s,%f,%f\n" % (filename, ave_int, ave_mod)
def main():
# image_array = load_images('olsh.dat')
# im = Image.open("test_image_1.png")
gray = ndimage.imread("test_image_1.png", flatten=True)
# toimage(gray).show()
subsamples = get_subsamples([gray])
for i, sample in enumerate(subsamples):
pca = PCA.PCA(sample, 90)
compressed = toimage(pca.get_compressed_matrix())
filename = 'pca_images/im_ex' + str(i) + '.png'
print filename
imsave(filename, compressed)
toimage(compressed).show()
def original_color_transform(content, generated, mask=None):
generated = fromimage(toimage(generated, mode='RGB'), mode='YCbCr') # Convert to YCbCr color space
if mask is None:
generated[:, :, 1:] = content[:, :, 1:] # Generated CbCr = Content CbCr
else:
width, height, channels = generated.shape
for i in range(width):
for j in range(height):
if mask[i, j] == 1:
generated[i, j, 1:] = content[i, j, 1:]
generated = fromimage(toimage(generated, mode='YCbCr'), mode='RGB') # Convert to RGB color space
return generated
def __init__(self, abs_image, sound, frequencies):
self.abs_image = abs_image
self.image = toimage(apply_colormap(self.abs_image))
self.width, self.height = self.image.size
self.sound = sound
self.frequencies = frequencies
self.reversed_frequencies = list(reversed(frequencies))
def parse(self):
"""runs each mask(crop) across the image file to improve OCR functionality"""
image = Image.open(self.image_path)
for form_field, bounding_box in self.bounding_box_dict.items():
# the crops are scaled up and the contrast maxed out in order to enhance character
# features and increase OCR success
x1, y1, x2, y2 = bounding_box
xx = (x2-x1) << 2
yy = (y2-y1) << 2
the_crop = image.crop(bounding_box)
the_crop = the_crop.resize((xx,yy),PIL.Image.LANCZOS)
area = (xx * yy)
gray = the_crop.convert('L')
bw = np.asarray(gray).copy()
bw[bw < 200] = 0
bw[bw >= 200] = 255
the_crop = misc.toimage(bw)
# use this to check out a particular mask
#if "box_c_address_city_town_zip_postal_code" is form_field:
# the_crop.show()
if "checkbox" in form_field:
# a box is considered checked if 10% or more of it's area is black
checked = np.sum(bw) >= (0.1 * area)
self.component_contents_dict[form_field] = checked
else:
self.component_contents_dict[form_field] = self.clean_text(pytesseract.image_to_string(the_crop))
print([self.component_contents_dict['box_c_address_city_town_zip_postal_code']])
def save_merged_file(file_name, imgs, labels, imgRows=60):
"""
Saves the preprocessed images into a concatenated file. Saved file
format [numimages, [factor, minval, img], [factor, min....]...]
"""
assert(type(imgs) is list and type(imgs[0]) is np.ndarray)
with open(file_name, 'wb') as out_file:
# write the number of images
out_file.write(struct.pack('i', len(imgs)))
out_file.write(struct.pack('i', imgRows))
for ind,img in enumerate(imgs):
PIL_img = toimage(img)
# record where we need to write image size
nbytes_pos = out_file.tell()
# skip ahead
out_file.seek(4,1)
curr_file_pos = out_file.tell()
# save the image
PIL_img.save(out_file, format='png')
# jump back to write file size
next_file_pos = out_file.tell()
nbytes = next_file_pos - curr_file_pos
out_file.seek(nbytes_pos)
out_file.write(struct.pack('i', nbytes))
out_file.seek(next_file_pos)
# write labels
for l in labels[ind]:
out_file.write(struct.pack('f',l))
def hist(im_source):
arr_im_rgb = array(im_source)
arr_im_rcolor = []
arr_im_gcolor = []
arr_im_bcolor = []
i = 0
for itemL in arr_im_rgb:
arr_im_gcolor.append([])
arr_im_rcolor.append([])
arr_im_bcolor.append([])
for itemC in itemL:
arr_im_rcolor[i].append(itemC[0])
arr_im_gcolor[i].append(itemC[1])
arr_im_bcolor[i].append(itemC[2])
i = 1+i
arr_im_rcolor_hist = beautyImage(array(arr_im_rcolor))
arr_im_gcolor_hist = beautyImage(array(arr_im_gcolor))
arr_im_bcolor_hist = beautyImage(array(arr_im_bcolor))
i = 0
arr_im_hist = []
while i<len(arr_im_rcolor_hist):
ii = 0
tmp_line = []
while ii < len(arr_im_rcolor_hist[i]):
tmp_point = [arr_im_rcolor_hist[i][ii], arr_im_gcolor_hist[i][ii],arr_im_bcolor_hist[i][ii]]
tmp_line.append(tmp_point)
ii += 1
arr_im_hist.append(tmp_line)
i += 1
figure()
im_beauty = toimage(array(arr_im_hist), 255)
im_beauty.show()
im_beauty.save("../result/he/he_seperate.png")
def BuildImageFromInput(self, input_):
"""Create the initial image layer from some input.
:param input_: Input data. If array, values should lie in the range [0, 1].
:type input_: PIL.Image or 2D ndarray
:returns: image layer data.
:rtype: 2D ndarray of float
"""
resize_method = self.params.image_resize_method
if resize_method != ResizeMethod.METHOD_NONE:
resize_length = self.params.image_resize_length
resize_aspect_ratio = self.params.image_resize_aspect_ratio
# Make sure input is an image
if not isinstance(input_, Image.Image):
input_ = toimage(input_)
old_size = np.array(input_.size, np.float) # format is (width, height)
if resize_method == ResizeMethod.METHOD_SHORT_EDGE:
input_ = ScaleImage(input_, old_size / min(old_size) * resize_length)
elif resize_method == ResizeMethod.METHOD_LONG_EDGE:
input_ = ScaleImage(input_, old_size / max(old_size) * resize_length)
elif resize_method == ResizeMethod.METHOD_WIDTH:
input_ = ScaleImage(input_, old_size / old_size[0] * resize_length)
elif resize_method == ResizeMethod.METHOD_HEIGHT:
input_ = ScaleImage(input_, old_size / old_size[1] * resize_length)
elif resize_method == ResizeMethod.METHOD_SCALE_AND_CROP:
width = resize_length
height = width / resize_aspect_ratio
input_ = ScaleAndCropImage(input_, (width, height))
else:
raise ValueError("Unknown resize method: %s" % resize_method)
return ImageLayerFromInputArray(input_, self.backend)
def slide(self, value):
"""
When x or y are changed, instead of recomputing the hologram, we
use the shortcut of selection a region of a larger pre-computed hologram.
"""
source = self.sender()
# select area to display
x = round(self.lcd.value() / self.scale)
y = round(self.lcd2.value() / self.scale)
im = toimage(self.holo[256 - x : 512 - x, 256 - y : 512 - y]) # PIL image
# convert image to pixmap
# https://github.com/shuge/Enjoy-Qt-Python-Binding/blob/master/image/display_img/pil_to_qpixmap.py
if im.mode == "RGB":
pass
elif im.mode == "L":
im = im.convert("RGBA")
data = im.tostring("raw", "RGBA")
qim = QtGui.QImage(data, 256, 256, QtGui.QImage.Format_ARGB32)
pixmap = QtGui.QPixmap.fromImage(qim)
# asign to the hologram
myScaledPixmap = pixmap.scaled(QtCore.QSize(400, 400))
self.hologram.setPixmap(myScaledPixmap)
# make a sphere object size of window displayed to
# label at the bottom of the frame
sphere2 = Sphere(
n=self.lcd5.value() + 0.0001j,
r=self.lcd4.value(),
center=(self.lcd.value(), self.lcd2.value(), self.lcd3.value()),
)
self.sphObject.setText(repr(sphere2))
def test_jpg(w, img, fmt = "jpg-%d.jpg"):
prev_i = None
prev_fn = None
for i in range(100, 0, -5):
fn = fmt % (i,)
misc.toimage(img).save(fn, "JPEG", quality = i)
try:
w.extract(misc.imread(fn))
except ReedSolomonError:
os.remove(fn)
return prev_i
if prev_fn:
os.remove(prev_fn)
prev_i = i
prev_fn = fn
return "unbound"
def load_images(filename):
binary_all_images = numpy.fromfile(filename, dtype=float)
# image_edge_length = 512
# num_images = 10
all_numbers = []
array_position = 0
for k in range(0, num_images):
matrix = numpy.zeros((image_edge_length, image_edge_length), numpy.float64)
for i in range(0, image_edge_length):
for j in range(0, image_edge_length):
matrix[i][j] = binary_all_images[array_position]
array_position += 1
all_numbers.append(matrix)
toimage(all_numbers[0]).show()
return all_numbers
def delete_white_lines(image):
im = image
im = im.resize((100, 100), Image.ANTIALIAS)
pix = im.load()
res_mass = []
for x in xrange(0, im.size[0]):
new_mass = []
for y in xrange(0, im.size[1]):
pixel = pix[x, y]
new_mass.append(pixel/255)
res_mass.append(new_mass)
res_mass = np.array(res_mass)
marks = []
for i in xrange(0, 100):
if sum(res_mass[:, i]) == 100:
marks.append(i)
print marks
if marks == [] :
return image#.rotate(180)
else:
for i in reversed(marks):
res_mass = np.delete(res_mass, i, 1)
return toimage(res_mass).transpose(Image.FLIP_LEFT_RIGHT)#.rotate(90)
def appendAssay(self, assay, image=None):
"""Add statistics and image (and save it on disk) to end of the table.
Replaces both insertRow and setData.
There is issue with metadata handling and image compression.
* TIFF format leaks for standardized rich metadata container.
Although OMERO project afford necessary capabilities with OME-TIFF,
it's too much overhead to implement it by myself.
* Pillow can't compress TIFF and save it with tags without libtiff.
May be in future we switch to Bioformats or pylibtiff to overreach
this disadvantages.
"""
total_rows = self.rowCount()
self.beginInsertRows(QtCore.QModelIndex(), total_rows, total_rows)
if image is not None:
img = misc.toimage(image)
imgpath = os.path.join(
self.__datadir, assay.timestamp.strftime("%Y%m%d-%H%M%S.tif"))
tiffinfo = {
270: str(assay), # ImageDescription
305: "Immunopy", # Software
306: assay.timestamp.strftime("%Y:%m:%d %H:%M:%S")} # DateTime
img.save(
imgpath.encode(sys.getfilesystemencoding()),
format='TIFF',
compression="tiff_deflate",
tiffinfo=tiffinfo)
assay.img_path = imgpath
self.__assays.append(assay)
print("Appending assay %s" % assay)
self.endInsertRows()
return True
def domedmask():
diff = np.load('rpca_diff.npy').reshape((-1,70-25,90-0))
diffmask = np.where(diff>50,255,0)
from bigrec import bigrec
maskall = np.zeros((diff.shape[0],105,90),np.int0)
glassorig = np.load('glassorig.npy').reshape((-1,105,90))
noglass = np.zeros_like(glassorig)
for i in range(diff.shape[0]):
print i
maskall[i] = bigrec(diffmask[i], maskall[i])
medresult = maskedmedfilt(glassorig[i], maskall[i], 6,4)
noglass[i] = np.where(maskall[i], medresult, glassorig[i])
np.save('medfilt_noglass.npy',noglass)
from layerbase import DrawPatch
misc.toimage(DrawPatch(noglass.reshape((-1,1,105,90)))).save('mednoglass.png')
misc.toimage(DrawPatch(maskall.reshape((-1,1,105,90)))).save('medmask.png')
def resize(data, dims):
""" Wrapper to resize an image """
import scipy.misc as smp
import Image
tmp = smp.fromimage(smp.toimage(data, mode='F'))
tmp = tmp.resize(dims, Image.ANTIALIAS)
return tmp
def image_from_bits(self, bits, filename):
# declare pixels matrix (list of lists)
pixels = []
# turning list of bits to matrix of bits
for y in range(0,32):
i = y * 32
pixels.append(bits[i:i+32])
# convert to 0's and 255's
for y in range(len(pixels)):
row = pixels[y]
for x in range(len(row)):
val = row[x]
if val == 1:
val = 255
pixels[y][x] = val
# convert pixels to image and save
imd = numpy.array(pixels)
im = toimage(imd)
im = im.convert('RGB')
im.save(filename, 'PNG')
pass
img = sc.imread(file)
original_img = copy.deepcopy(
img) #save it for now because it will be modified soon
img = sc.imresize(img, 0.25 / 2)
if KEEP_ORIGINAL_SIZE == False:
original_img = copy.deepcopy(
img) #save it for now because it will be modified soon
brightness = Silver.brightness(img)
average_brightness = np.average(brightness)
std_brightness = np.std(brightness)
silver.avg = average_brightness
silver.std = std_brightness
print('img total size is:{}'.format(img.size))
bounding_box = get_bounding_box(img)
draw_bounding_box(img, bounding_box)
if MODE == 'show':
imshow(img)
print('that was the picture:{}\n'.format(file))
if MODE == 'write_csv':
sc.toimage(original_img, cmin=0.0,
cmax=255.0).save(str(current_file_number) + '.jpg')
if KEEP_ORIGINAL_SIZE == True:
bounding_box = tuple(
8 * x
for x in bounding_box) #Rescale back the bounding box.
csv_file.write(
str(current_file_number) +
'.jpg,{},{},{},{}\n'.format(*bounding_box))
current_file_number += 1
if MODE == 'write_csv':
csv_file.close()
from scipy.misc import toimage, fromimage
import Image
import scipy.ndimage as snd
# opening the image and converting it to grayscale
a = Image.open('../Figures/er_image.png').convert('L')
# performing binary erosion for 5 iterations
b = snd.morphology.binary_erosion(a,iterations=25)
# converting b from an ndarray to an image
b = toimage(b)
# displaying the image
b.show()
import tensorflow as tf
import numpy as np
import scipy.io
from scipy.misc import toimage
mat = scipy.io.loadmat('W.mat')
print(mat['outputarr'].shape)
# input()
outputarr = mat['outputarr']
for i in range(len(outputarr)):
curr = outputarr[0][i]
nxt = outputarr[1][i]
toimage(curr).show()
toimage(nxt).show()
input()
def base64ToImage(base64_string):
arr = base64ToNumpyArray(base64_string)
img = misc.toimage(arr)
return img
try:
from scipy.misc import imsave, toimage
except ImportError as e:
print(
"imsave requires you to install pillow. Run `pip install pillow` and then try again."
)
sys.exit()
# Load digits dat
data, labels = datasets.load_digits().data, datasets.load_digits().target
# Create images for a custom tooltip array
tooltip_s = []
for image_data in data:
output = io.BytesIO()
img = toimage(image_data.reshape(
(8, 8))) # Data was a flat row of 64 "pixels".
img.save(output, format="PNG")
contents = output.getvalue()
img_encoded = base64.b64encode(contents)
img_tag = """<img src="data:image/png;base64,{}"> """.format(
img_encoded.decode('utf-8'))
tooltip_s.append(img_tag)
output.close()
tooltip_s = np.array(
tooltip_s) # need to make sure to feed it as a NumPy array, not a list
# Initialize to use t-SNE with 2 components (reduces data to 2 dimensions). Also note high overlap_percentage.
mapper = km.KeplerMapper(verbose=2)
# Fit and transform data
def save_img(data, i, j):
data = gaussian_filter(data,
sigma=3) #blur the image by 5 pixels gaussian width
img = smp.toimage(data) # Create a PIL image
smp.imsave("Bow_shocks/bs" + str(i) + str(j) + '.png', img)
def _fold(self):
fold_mask_root = self.fold_masks_base_dir
if not os.path.exists(fold_mask_root) or self.rebuild_mask:
os.makedirs(fold_mask_root, exist_ok=True)
fold_images = list()
fold_masks = list()
ignore_classes = self.get_classes_to_ignore()
ignore_classes2ignore_idx = {
ic: self.ignore_idx
for ic in ignore_classes
}
if self.image_set == 'train':
print(
'Ignoring classes for fold %d in split %s: ' %
(self.fold, self.image_set), ignore_classes)
else:
print('Preserving all classes for fold %d in split %s' %
(self.fold, self.image_set))
print('Preparing data for fold %d in split %s' %
(self.fold, self.image_set))
for index in tqdm(range(len(self)), position=0, leave=True):
original_mask_path = self.masks[index]
fold_mask_path = os.path.join(
fold_mask_root, os.path.basename(original_mask_path))
target = Image.open(original_mask_path)
target = np.array(target, dtype=np.int)
if self.image_set == 'train': # only filter out some classes during training
# old style, slow
# for ic in ignore_classes:
# target[target == ic] = self.ignore_idx
# new style, maybe faster
target = replace_array_ele_as_dict(
target, ignore_classes2ignore_idx)
# Images with only background and ignored will not be used
if target[target != self.ignore_idx].sum() == 0:
continue
if not os.path.exists(fold_mask_path) or self.rebuild_mask:
target = misc.toimage(target,
low=target.min(),
high=target.max())
misc.imsave(fold_mask_path, target)
fold_images.append(self.images[index])
fold_masks.append(fold_mask_path)
self.images = fold_images
self.masks = fold_masks
else:
mask_path = [
os.path.join(fold_mask_root, i)
for i in os.listdir(fold_mask_root)
]
self.masks = mask_path
new_images = []
for m_path in mask_path:
image_path = os.path.join(
self.images_base_dir,
os.path.basename(m_path).split('.')[0] + '.jpg')
assert os.path.exists(m_path), 'mask %s not exist' % m_path
assert os.path.exists(
image_path), 'image %s not exist' % image_path
new_images.append(image_path)
self.images = new_images
def xtomo_writer_f(data,
output_file=None,
x_start=0,
digits=3,
axis=0,
overwrite=False,
precision=True):
"""
Write 3-D data to a stack of tif files.
Parameters
-----------
output_file : str, optional
Name of the output file.
x_start : scalar, optional
First index of the data on first dimension
of the array.
digits : scalar, optional
Number of digits used for file indexing.
For example if 4: test_XXXX.tiff
axis : scalar, optional
Imaages is read along that axis.
overwrite: bool, optional
if overwrite=True the existing data in the
reconstruction folder will be overwritten
precision : bool, optional
Export data type precision. if True it
saves 32-bit precision. Otherwise it
uses 8-bit precision.
Notes
-----
If file exists, saves it with a modified name.
If output location is not specified, the data is
saved inside ``recon`` folder where the input data
resides. The name of the reconstructed files will
be initialized with ``recon``
Examples
--------
- Save sinogram data:
>>> import tomopy
>>>
>>> # Load data
>>> myfile = 'demo/data.h5'
>>> data, white, dark, theta = tomopy.xtomo_reader(myfile)
>>>
>>> # Save data
>>> output_file='tmp/slice_'
>>> tomopy.xtomo_writer(data, output_file, axis=1)
>>> print "Images are succesfully saved at " + output_file + '...'
- Save first 16 projections:
>>> import tomopy
>>>
>>> # Load data
>>> myfile = 'demo/data.h5'
>>> data, white, dark, theta = tomopy.xtomo_reader(myfile, projections_start=0, projections_end=16)
>>>
>>> # Save data
>>> output_file='tmp/projection_'
>>> tomopy.xtomo_writer(data, output_file, axis=0)
>>> print "Images are succesfully saved at " + output_file + '...'
- Save reconstructed slices:
>>> import tomopy
>>>
>>> # Load data
>>> myfile = 'demo/data.h5'
>>> data, white, dark, theta = tomopy.xtomo_reader(myfile)
>>>
>>> # Perform reconstruction
>>> d = tomopy.xtomo_dataset(log='error')
>>> d.dataset(data, white, dark, theta)
>>> d.center = 661.5
>>> d.gridrec()
>>>
>>> # Save data
>>> output_file='tmp/reconstruction_'
>>> tomopy.xtomo_writer(d.data_recon, output_file, axis=0)
>>> print "Images are succesfully saved at " + output_file + '...'
"""
if output_file == None:
output_file = "tmp/img_"
output_file = os.path.abspath(output_file)
dir_path = os.path.dirname(output_file)
# Remove TIFF extension if there is.
if (output_file.endswith('tif') or output_file.endswith('tiff')):
output_file = output_file.split(".")[-2]
if overwrite:
if os.path.exists(dir_path):
shutil.rmtree(dir_path)
# Create new folders.
if not os.path.exists(dir_path):
os.makedirs(dir_path)
# Select desired x from whole data.
num_x, num_y, num_z = data.shape
if axis == 0:
x_end = x_start + 1
elif axis == 1:
x_end = x_start + 1
elif axis == 2:
x_end = x_start + 1
# Write data.
file_index = ["" for x in range(digits)]
for m in range(digits):
file_index[m] = '0' * (digits - m - 1)
ind = range(x_start, x_end)
for m in range(len(ind)):
for n in range(digits):
if ind[m] < np.power(10, n + 1):
file_body = output_file + file_index[n] + str(ind[m])
file_name = file_body + '.tif'
break
if precision:
if axis == 0:
img = misc.toimage(data[m, :, :], mode='F')
elif axis == 1:
img = misc.toimage(data[:, m, :], mode='F')
elif axis == 2:
img = misc.toimage(data[:, :, m], mode='F')
else:
if axis == 0:
img = misc.toimage(data[m, :, :])
elif axis == 1:
img = misc.toimage(data[:, m, :])
elif axis == 2:
img = misc.toimage(data[:, :, m])
# check if file exists.
if os.path.isfile(file_name):
# genarate new file name.
indq = 1
FLAG_SAVE = False
while not FLAG_SAVE:
new_file_body = file_body + '-' + str(indq)
new_file_name = new_file_body + '.tif'
if not os.path.isfile(new_file_name):
img.save(new_file_name)
FLAG_SAVE = True
file_name = new_file_name
else:
indq += 1
else:
img.save(file_name)
from recompression import *
from PIL import Image
#from pylab import *
from scipy import misc
import os
im = np.array(Image.open('empire.jpg').convert('L'))
misc.toimage(im, cmin=0, cmax=255).save('gray_empire.jpg')
n, m = im.shape
new_im = np.zeros([n, m])
for i in range(n // 8):
for j in range(m // 8):
X = im[i * 8:(i + 1) * 8, j * 8:(j + 1) * 8]
X, TR = TBT(X, 1)
IP, BP_mode, res = IBP(X, 0, 7)
X = IBP_decode(IP, BP_mode, res)
X = TBT_decode(X, TR, 1)
new_im[i * 8:(i + 1) * 8, j * 8:(j + 1) * 8] = X
misc.toimage(new_im, cmin=0, cmax=255).save('new_empire.jpg')
'''
im = array(Image.open('empire.jpg').convert('L'))
# get image list
image_path = './data/'
imglist = [os.path.join(image_path,f) for f in os.listdir(image_path)]
distr = [] # document the residual distribution
for f in imglist:
print(f)
current_distr = zeros(33)
#r approximation
# for r in range(1,200):
r = 100
sigma = np.zeros((540, 540))
for i in range(r):
sigma[i][i] = s[i]
tmp = np.matmul(u, sigma)
tmp2 = np.matmul(tmp, v)
err.append(LA.norm(tmp2 - x, 'fro'))
xr = np.reshape(tmp2[2], (50, 50))
toimage(np.reshape(x[2], (50, 50))).show()
toimage(xr).show()
#isum += (np.abs(tmp - x_train[i])).sum() / (len(tmp)*len(tmp))
#err.append(isum / 540)
# plt.plot(err)
# plt.show()
# # print err[80]
detection_err = []
r = 200
#for r in range(1, 201):
import noise
import numpy as np
from scipy.misc import toimage
shape = (1024, 1024)
scale = 100.0
octaves = 6
persistence = 0.5
lacunarity = 2.0
world = np.zeros(shape)
for i in range(shape[0]):
for j in range(shape[1]):
world[i][j] = noise.pnoise2(i / scale,
j / scale,
octaves=octaves,
persistence=persistence,
lacunarity=lacunarity,
repeatx=1024,
repeaty=1024,
base=0)
toimage(world).show()
def numpyArrayToImage(arr):
return misc.toimage(arr)
]
for f in tqdm(semantic_file_list):
semantic_img = sp.imread(join(semantic_dir, f))
instance_img = sp.imread(join(instance_dir, f))
instance_img = kitti_to_cityscapes_instaces(instance_img)
out_semantic_filename = join(out_semantic_dir,
'kitti_%s_gtFine_labelIds.png' % f[:-4])
out_instance_filename = join(
out_instance_dir, 'kitti_%s_gtFine_instanceIds.png' % f[:-4])
out_polygons_filename = join(out_instance_dir,
'kitti_%s_gtFine_polygons.json' % f[:-4])
sp.toimage(semantic_img, mode='L').save(out_semantic_filename)
sp.toimage(instance_img,
high=np.max(instance_img),
low=np.min(instance_img),
mode='I').save(out_instance_filename)
# create empty json file for pseudo polygons
with open(out_polygons_filename, 'w') as out_json:
json.dump({}, out_json)
# copy and rename kitti semantics training image_2 to cityscapes format
training_img_src = join(training_dir, f)
training_img_dst = join(im_output_dir,
'kitti_%s_leftImg8bit.png' % f[:-4])
shutil.copy2(training_img_src, training_img_dst)
grayscale = Grayscale()
imageresize = ImageResize(84, 84)
sequence = Sequence(4)
#observation=sequence.process(observation)
#observation=imageresize.process(observation)
#observation=crop(observation,14,77,11,75)
#observation=crop(observation,36,190,20,144)
#imageresize=ImageResize(42,42)
#observation=grayscale.process(observation)
#observation=zeroandone(observation)
a = observation[30]
print(observation)
print(len(observation))
print(a)
print(len(a))
print(a[0])
#imageresize=ImageResize(336,336)
#observation=rgb2gray(observation)
#observation=zeroandone(observation, 105)
#observation=imageresize.process(observation)
#observation = scipy.misc.imresize(arr=observation.astype(np.uint8), size=(168, 168))
print(observation)
img = smp.toimage(observation)
smp.imsave('ong.png', img)
img.show()
def rgba(arr):
img = misc.toimage(arr)
rgba = img.convert("RGBA")
rgba_arr = numpy.array(rgba)
return rgba_arr
def convert(arr, format_str):
img = misc.toimage(arr)
converted_img = img.convert(format_str)
converted_arr = numpy.array(converted_img)
return converted_arr
#Object Recognition with Convolutional Neural Networks in the Keras Deep Learning Library
#https://machinelearningmastery.com/object-recognition-convolutional-neural-networks-keras-deep-learning-library/
#https://github.com/deep-diver/CIFAR10-img-classification-tensorflow
# Plot ad hoc CIFAR10 instances
from keras.datasets import cifar10
from matplotlib import pyplot
from scipy.misc import toimage
# load data
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
# create a grid of 3x3 images
for i in range(0, 8):
pyplot.subplot(330 + 1 + i)
pyplot.imshow(toimage(X_train[i]))
# show the plot
pyplot.show()
pyplot.imshow(toimage(X_train[9]))
pyplot.imshow(toimage(X_train[12]), interpolation='nearest')
pyplot.imshow(toimage(X_train[13]), interpolation='nearest')
pyplot.imshow(toimage(X_train[14]), interpolation='nearest')
pyplot.imshow(toimage(X_train[18]), interpolation='nearest')
# Simple CNN model for CIFAR-10
import numpy
from keras.datasets import cifar10
from keras.models import Sequential
def grayscale(arr):
img = misc.toimage(arr)
grayscale = img.convert("L")
grayscale_arr = numpy.array(grayscale)
return grayscale_arr
averaged_vals = np.zeros([1,w2,3])
for i in range(w2):
r = np.mean(img[:, i * batchWidth:(i + 1) * batchWidth, 0])
b = np.mean(img[:, i * batchWidth:(i + 1) * batchWidth, 1])
g = np.mean(img[:, i * batchWidth:(i + 1) * batchWidth, 2])
averaged_vals[0][i][0] = r
averaged_vals[0][i][1] = b
averaged_vals[0][i][2] = g
averaged_vals = np.stack((averaged_vals[0],averaged_vals[0],averaged_vals[0],averaged_vals[0],averaged_vals[0],averaged_vals[0]), axis=0)
plt.imshow(averaged_vals.astype(int))
plt.show()
misc.toimage(averaged_vals, cmin=0.0, cmax=255.0).save("josh.jpg")
identifier = colorIdentifier("./josh.jpg")
print("hi")
for i in range(w2):
if(averaged_vals[0][i][0] < (identifier.getDominant()[0]+20) and averaged_vals[0][i][0] > (identifier.getDominant()[0]-20)) :
if(averaged_vals[0][i][1] < (identifier.getDominant()[1]+20) and averaged_vals[0][i][1] > (identifier.getDominant()[1]-20)) :
if(averaged_vals[0][i][2] < (identifier.getDominant()[2]+20) and averaged_vals[0][i][2] > (identifier.getDominant()[2]-20)) :
averaged_vals = averaged_vals[:,i:]
break
for i in range(averaged_vals.shape[1]-1, -1,-1):
if(averaged_vals[0][i][0] < (identifier.getDominant()[0]+20) and averaged_vals[0][i][0] > (identifier.getDominant()[0]-20)) :
if(averaged_vals[0][i][1] < (identifier.getDominant()[1]+20) and averaged_vals[0][i][1] > (identifier.getDominant()[1]-20)) :
if(averaged_vals[0][i][2] < (identifier.getDominant()[2]+20) and averaged_vals[0][i][2] > (identifier.getDominant()[2]-20)) :
reduction_rate = 0
thinness = 0
sensitivity = 0
start_time = time.time()
for file in glob.glob(test_path + "/*.png"):
count = count + 1
image = cv2.imread(file)
image = color.rgb2gray(image)
image = invert(image)
print(image.shape)
fgps = foregroundPixels(image)
print("fgps: ", fgps)
skeleton = hilditch(image)
fgpst = foregroundPixels(skeleton)
print("fgpst: ", fgpst)
reduction_rate = reduction_rate + (((fgps - fgpst) / fgps) * 100)
sensitivity = sensitivity + sensitivitycheck(skeleton, fgps)
thinnesst = thinnesscheck(skeleton)
thinnesso = thinnesscheck(image)
thinness = thinness + (1 - (thinnesst / thinnesso))
im = toimage(skeleton)
im.save(str(count) + ".png")
end_time = time.time() # Used to stop time record
seconds = end_time - start_time
print("Total time: ", seconds)
print("Average time: ", seconds / count)
print("Reduction rate: ", reduction_rate)
print("Average Reduction rate: ", reduction_rate / count)
print("sensitivity: ", sensitivity)
print("Average sensitivty: ", sensitivity / count)
print("Average thinness: ", thinness / count)
np.savetxt('labels_orl.txt', Y_orl)
np.savetxt('projections_orl.txt', Z_orl)
##########################################
############## Extra Tasks ###############
##########################################
print "\n------------------------------"
print "Eigen Faces..."
print "\nLFW Faces : Top 5 Principal Components"
for i in xrange(5):
print "Face ", (i + 1)
maxNum = np.amax(eigenLfw[:, i])
minNum = np.amin(eigenLfw[:, i])
misc.toimage(((eigenLfw[:, i] - minNum).reshape(rowLfw, colLfw)) *
(200.0 / maxNum)).save("lfw" + str(i + 1) + ".png")
print "\nORL Faces : Top 5 Principal Components"
for i in xrange(5):
print "Face ", (i + 1)
maxNum = np.amax(eigenOrl[:, i])
minNum = np.amin(eigenOrl[:, i])
misc.toimage(((eigenOrl[:, i] - minNum).reshape(rowOrl, colOrl)) *
(255.0 / maxNum)).save("orl" + str(i + 1) + ".png")
##############################################
######## Finding the accuracy ###############
##############################################
########### Helper Functions ############
imsave('gradcam.png', heatmap)
#---------------
#Guided backprop
#---------------
g = tf.get_default_graph()
with g.gradient_override_map({'Relu': 'GuidedRelu'}):
sess = tf.Session()
imgs = tf.placeholder(tf.float32, [None, 224, 224, 3])
vgg = vgg16(imgs, 'vgg16_weights.npz', sess, category)
img1 = imread(img_file, mode='RGB')
img1 = imresize(img1, (224, 224))
guided_backprop = Guided_backprop(vgg.imgs, vgg.probs, category)
ll = sess.run([vgg.probs, guided_backprop],
feed_dict={vgg.imgs: [img1]})
print ll[1].shape
guided_backprop = toimage(ll[1])
#heatmap = double2image.to_image(heatmap)
#guidedGradCam = imresize(guided_backprop, (224, 224))
imsave('guided_backprop.png', guided_backprop)
#---------------
#Guided gradcam
#---------------
guided_gradcam = ll[1]
cam = resize(ll1[1] / np.max(ll1[1]), (224, 224), preserve_range=True)
for i in range(3):
guided_gradcam[:, :, i] = guided_gradcam[:, :, i] * cam
imsave('guided_gradcam.png', toimage(guided_gradcam))
#preds = (np.argsort(prob)[::-1])[0:5]
#for p in preds:
# print p, class_names[p], prob[p]
def sample_blob_negatives(img,
roi_mask,
out_dir,
img_id,
abn,
blob_detector,
patch_size=256,
neg_cutoff=.35,
nb_bkg=100,
start_sample_nb=0,
bkg_dir='background',
verbose=False):
#命名
bkg_out = os.path.join(out_dir, bkg_dir)
basename = '_'.join([img_id, str(abn)])
#加0边距
img = add_img_margins(img, patch_size / 2)
roi_mask = add_img_margins(roi_mask, patch_size / 2)
# Get ROI bounding box.获取ROI边界
roi_mask_8u = roi_mask.astype('uint8')
ver = (cv2.__version__).split('.')
if int(ver[0]) < 3:
contours, _ = cv2.findContours(roi_mask_8u.copy(), cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
else:
_, contours, _ = cv2.findContours(roi_mask_8u.copy(), cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
cont_areas = [cv2.contourArea(cont) for cont in contours]
idx = np.argmax(cont_areas) # find the largest contour.
rx, ry, rw, rh = cv2.boundingRect(contours[idx])
if verbose:
M = cv2.moments(contours[idx])
cx = int(M['m10'] / M['m00'])
cy = int(M['m01'] / M['m00'])
print("ROI centroid=", (cx, cy))
sys.stdout.flush()
# Sample blob negative samples.阴性斑点样本采样
#采取特征点？
key_pts = blob_detector.detect((img / img.max() * 255).astype('uint8'))
rng = np.random.RandomState(12345)
key_pts = rng.permutation(key_pts)
sampled_bkg = 0
#循环特征点
for kp in key_pts:
#样本背景数大于等于背景总数
if sampled_bkg >= nb_bkg:
break
#获取特征点坐标
x, y = int(kp.pt[0]), int(kp.pt[1])
#如果patch与roi掩码不重叠则保存
if not overlap_patch_roi(
(x, y), patch_size, roi_mask, cutoff=neg_cutoff):
patch = img[y - patch_size / 2:y + patch_size / 2,
x - patch_size / 2:x + patch_size / 2]
patch = patch.astype('int32')
patch_img = toimage(patch,
high=patch.max(),
low=patch.min(),
mode='I')
filename = basename + "_%04d" % (start_sample_nb +
sampled_bkg) + ".png"
fullname = os.path.join(bkg_out, filename)
patch_img.save(fullname)
if verbose:
print("sampled a blob patch at (x,y) center=", (x, y))
sys.stdout.flush()
sampled_bkg += 1
return sampled_bkg
def save_image(self, image, path):
if not os.path.exists(os.path.split(path)[0]):
os.makedirs(os.path.split(path)[0])
misc.toimage(image, cmin=0, cmax=255).save(path)
def OCR(data, results, action, idname):
"""
Use pyOCR which for OCR
returns rect rois for plotting in overview
If results is None, just return the OCR content, do not add to results
"""
try:
params = action['params']
except KeyError:
params = {}
# optional parameters
ocr_options = {}
for lab in ['ocr_threshold', 'ocr_zoom', 'ocr_border']:
if lab in params:
ocr_options[lab] = int(params[lab])
inputfile = data.series_filelist[0] # give me a [filename]
rgbchannel = params.get('rgbchannel', 'B')
dcmInfile, pixelData, dicomMode = wadwrapper_lib.prepareInput(
inputfile, headers_only=False, logTag=logTag(), rgbchannel=rgbchannel)
# find probename
idname = ''
if params.get('auto_suffix', False):
if idname is None:
idname = '_' + qc.imageID(probeonly=True)
# add pluginversion to 'result' object
add_plugin_version(qc, results, 'pluginversion' + idname)
rectrois = []
error = False
msg = ''
values = {}
# solve ocr params
ocr_regions = params.get('ocr_regions', {}) # new format
regions = {}
for ocrname, ocrparams in ocr_regions.items():
regions[ocrname] = {'prefix': '', 'suffix': ''}
for key, val in ocrparams.items():
if key == 'xywh':
regions[ocrname]['xywh'] = [int(p) for p in val.split(';')]
elif key == 'prefix':
regions[ocrname]['prefix'] = val
elif key == 'suffix':
regions[ocrname]['suffix'] = val
elif key == 'type':
regions[ocrname]['type'] = val
for name, region in regions.items():
rectrois.append([(region['xywh'][0], region['xywh'][1]),
(region['xywh'][0] + region['xywh'][2],
region['xywh'][1] + region['xywh'][3])])
txt, part = ocr_lib.OCR(pixelData, region['xywh'], **ocr_options)
uname = name + str(idname)
if region['type'] == 'object':
im = toimage(part)
fn = '{}.jpg'.format(uname)
im.save(fn)
results.addObject(uname, fn)
else:
try:
value = ocr_lib.txt2type(txt, region['type'], region['prefix'],
region['suffix'])
if not results is None:
if region['type'] == 'float':
results.addFloat(uname, value)
elif region['type'] == 'string':
results.addString(uname, value)
elif region['type'] == 'bool':
results.addBool(uname, value)
else:
values[uname] = value
except:
print("error", uname, value)
error = True
msg += uname + ' '
im = toimage(part)
fn = '{}.jpg'.format(uname)
im.save(fn)
if results is None:
return values, error, msg
return rectrois, error, msg
def sample_hard_negatives(img,
roi_mask,
out_dir,
img_id,
abn,
patch_size=256,
neg_cutoff=.35,
nb_bkg=100,
start_sample_nb=0,
bkg_dir='background',
verbose=False):
#警告：hns（难类）的定义可能有问题。 已有研究表明ROI的背景对分类也很有用。
'''WARNING: the definition of hns may be problematic.
There has been study showing that the context of an ROI is also useful
for classification.
'''
#添加背景目录并命名
bkg_out = os.path.join(out_dir, bkg_dir)
basename = '_'.join([img_id, str(abn)])
#图像加0边距，掩码同样处理
img = add_img_margins(img, patch_size / 2)
roi_mask = add_img_margins(roi_mask, patch_size / 2)
#获取ROI边框（上面有讲述）
# Get ROI bounding box.
roi_mask_8u = roi_mask.astype('uint8')
ver = (cv2.__version__).split('.')
if int(ver[0]) < 3:
contours, _ = cv2.findContours(roi_mask_8u.copy(), cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
else:
_, contours, _ = cv2.findContours(roi_mask_8u.copy(), cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
cont_areas = [cv2.contourArea(cont) for cont in contours]
idx = np.argmax(cont_areas) # find the largest contour.
rx, ry, rw, rh = cv2.boundingRect(contours[idx])
if verbose:
M = cv2.moments(contours[idx])
cx = int(M['m10'] / M['m00'])
cy = int(M['m01'] / M['m00'])
print("ROI centroid=", (cx, cy))
sys.stdout.flush()
rng = np.random.RandomState(12345)
# Sample hard negative samples.难类样本采样
#初始化样本背景
sampled_bkg = start_sample_nb
#当样本背景小于初始样本数加背景数时进行循环
while sampled_bkg < start_sample_nb + nb_bkg:
#扩大感兴趣区域，取不小于patch_size/2,不超过img.shape - patch_size/2的x,y的随机值
x1, x2 = (rx - patch_size / 2, rx + rw + patch_size / 2)
y1, y2 = (ry - patch_size / 2, ry + rh + patch_size / 2)
x1 = crop_val(x1, patch_size / 2, img.shape[1] - patch_size / 2)
x2 = crop_val(x2, patch_size / 2, img.shape[1] - patch_size / 2)
y1 = crop_val(y1, patch_size / 2, img.shape[0] - patch_size / 2)
y2 = crop_val(y2, patch_size / 2, img.shape[0] - patch_size / 2)
x = rng.randint(x1, x2)
y = rng.randint(y1, y2)
#如果patch与掩码不重叠则执行
if not overlap_patch_roi(
(x, y), patch_size, roi_mask, cutoff=neg_cutoff):
#选取图像中patch
patch = img[y - patch_size / 2:y + patch_size / 2,
x - patch_size / 2:x + patch_size / 2]
patch = patch.astype('int32')
#patch由数组转为图像，并保存至背景
patch_img = toimage(patch,
high=patch.max(),
low=patch.min(),
mode='I')
filename = basename + "_%04d" % (sampled_bkg) + ".png"
fullname = os.path.join(bkg_out, filename)
patch_img.save(fullname)
sampled_bkg += 1
if verbose:
print("sampled a hns patch at (x,y) center=", (x, y))
sys.stdout.flush()
for i in range(iter_n):
g, score = sess.run([t_grad, t_score], {t_input: img0})
g /= g.std() + 1e-8
img0 += g * step
img_noise += g * step
print("Step ", i, ", Loss value:", score)
## Display
score = sess.run(T('output2'), feed_dict={t_input: img0})
print(" -- After fooling CNN --")
top1 = print_prob(score[0])
cv2.putText(img0, str(top1), (70, 220), 0, 0.5, (255, 0, 0), 2)
toimage(img_noise).show()
toimage(img0).show()
#gibbon: 185, panda: 169, bulldog: 82
img0 = PIL.Image.open(os.path.expanduser("~/Downloads/dog.jpg"))
img0 = img0.resize((224, 224), PIL.Image.NEAREST)
img0 = np.float32(img0)
img0 = np.array(
img0[:, :, :3]) # crop image in OS X create a 4-dim depth image
img = img0.copy()
score = sess.run(T('output2'), feed_dict={t_input: img0})
top1 = print_prob(score[0])
cv2.putText(img, str(top1), (70, 220), 0, 0.5, (255, 0, 0), 2)
toimage(img).show()
fool_CNN(T("softmax2_pre_activation")[:, 169], img0)
def sample_patches(img,
roi_mask,
out_dir,
img_id,
abn,
pos,
patch_size=256,
pos_cutoff=.75,
neg_cutoff=.35,
nb_bkg=100,
nb_abn=100,
start_sample_nb=0,
itype='calc',
bkg_dir='background',
calc_pos_dir='calc_mal',
calc_neg_dir='calc_ben',
mass_pos_dir='mass_mal',
mass_neg_dir='mass_ben',
verbose=False):
#图像如果为阳性
if pos:
#是钙化点
if itype == 'calc':
#图像放入输出目录中钙化阳性目录
roi_out = os.path.join(out_dir, calc_pos_dir)
else:
#否则放入输出目录中肿块阳性目录
roi_out = os.path.join(out_dir, mass_pos_dir)
#图像如果为阴性
else:
#是钙化点
if itype == 'calc':
#图像放入输出目录中钙化阴性目录
roi_out = os.path.join(out_dir, calc_neg_dir)
else:
#否则放入输出目录中肿块阴性目录
roi_out = os.path.join(out_dir, mass_neg_dir)
#背景放到输出目录中背景目录下
bkg_out = os.path.join(out_dir, bkg_dir)
#命名
basename = '_'.join([img_id, str(abn)])
#图像增加边距
img = add_img_margins(img, patch_size / 2)
#掩码增加边距
roi_mask = add_img_margins(roi_mask, patch_size / 2)
# Get ROI bounding box.
#获取感兴趣区域边界
#掩码转为8位无符号整型
roi_mask_8u = roi_mask.astype('uint8')
#获取轮廓contours
ver = (cv2.__version__).split('.')
if int(ver[0]) < 3:
contours, _ = cv2.findContours(roi_mask_8u.copy(), cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
else:
_, contours, _ = cv2.findContours(roi_mask_8u.copy(), cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
#获取轮廓面积
cont_areas = [cv2.contourArea(cont) for cont in contours]
#返回轮廓面积的索引值
idx = np.argmax(cont_areas) # find the largest contour.
#获取轮廓左上角点的坐标以及宽高
rx, ry, rw, rh = cv2.boundingRect(contours[idx])
#显示详细信息为true
if verbose:
#将计算得到的矩以一个字典的形式返回至M
M = cv2.moments(contours[idx])
try:
cx = int(M['m10'] / M['m00'])
cy = int(M['m01'] / M['m00'])
print("ROI centroid=", (cx, cy))
sys.stdout.flush()
except ZeroDivisionError:
cx = rx + int(rw / 2)
cy = ry + int(rh / 2)
print("ROI centroid=Unknown, use b-box center=", (cx, cy))
sys.stdout.flush()
#实现的随机数生成通常为伪随机数生成器，为了使得具备随机性的代码最终的结果可复现，需要设置相同的种子值
rng = np.random.RandomState(12345)
# Sample abnormality first.采样异常图片先进行
sampled_abn = 0
nb_try = 0
while sampled_abn < nb_abn:
if nb_abn > 1: #randint用于产生基质的均匀分布的随机整数
x = rng.randint(rx, rx + rw)
y = rng.randint(ry, ry + rh)
nb_try += 1
if nb_try >= 1000:
# 试验的Nb达到最大值，重叠截止以0.05幅度降低
print(
"Nb of trials reached maximum, decrease overlap cutoff by 0.05"
)
sys.stdout.flush()
pos_cutoff -= .05
nb_try = 0
if pos_cutoff <= .0:
# 重叠截止值达到了非阳性界限，检查感性区域掩码输入值
raise Exception("overlap cutoff becomes non-positive, "
"check roi mask input.")
else:
x = cx
y = cy
# import pdb; pdb.set_trace()
if nb_abn == 1 or overlap_patch_roi(
(x, y), patch_size, roi_mask, cutoff=pos_cutoff):
patch = img[y - patch_size / 2:y + patch_size / 2,
x - patch_size / 2:x + patch_size / 2]
patch = patch.astype('int32')
#max返回最大值，最小值
patch_img = toimage(patch,
high=patch.max(),
low=patch.min(),
mode='I')
# patch = patch.reshape((patch.shape[0], patch.shape[1], 1))
filename = basename + "_%04d" % (sampled_abn) + ".png"
fullname = os.path.join(roi_out, filename)
# import pdb; pdb.set_trace()
patch_img.save(fullname)
sampled_abn += 1 #异常样本数量记录
nb_try = 0
if verbose:
#将异常样本的块坐标显示
print("sampled an abn patch at (x,y) center=", (x, y))
sys.stdout.flush()
# Sample background.
sampled_bkg = start_sample_nb
while sampled_bkg < start_sample_nb + nb_bkg:
x = rng.randint(patch_size / 2, img.shape[1] - patch_size / 2)
y = rng.randint(patch_size / 2, img.shape[0] - patch_size / 2)
if not overlap_patch_roi(
(x, y), patch_size, roi_mask, cutoff=neg_cutoff):
patch = img[y - patch_size / 2:y + patch_size / 2,
x - patch_size / 2:x + patch_size / 2]
patch = patch.astype('int32')
patch_img = toimage(patch,
high=patch.max(),
low=patch.min(),
mode='I')
filename = basename + "_%04d" % (sampled_bkg) + ".png"
fullname = os.path.join(bkg_out, filename)
patch_img.save(fullname)
sampled_bkg += 1
if verbose:
#将样本背景中心块坐标显示
print("sampled a bkg patch at (x,y) center=", (x, y))
sys.stdout.flush()
Shelf(current_order[0], WIDTH_TYPE[shelf_type], shelf_type))
print("The following shelfs have been created: \n")
# print the created shelfs in terminal
for x in shelf_list:
print("**********************\n")
print(x.state)
print("\n")
# try to add every shelf to a plate, if no available plate is found, create new plate with own height and type
for current_shelf in shelf_list:
for current_plate in plate_list:
if current_plate.add_shelf(current_shelf):
break
if current_plate == plate_list[-1]:
plate_type = current_shelf.category
plate_list.append(
Plate(HEIGHT_TYPE[plate_type], WIDTH_TYPE[plate_type],
plate_type))
print("\n This resulted in the following way of filling the plates:\n")
# print the created plates in terminal
for x in plate_list:
print("**********************\n")
print(x.state)
print("\n")
toimage(plate_list[0].state).show()
def imwrite(filename, matrix):
toimage(matrix, cmin=0, cmax=256).save(filename)